From 113f42a9054ee47002d9ac22ab5d9887157eb09c Mon Sep 17 00:00:00 2001 From: Grimspen66 Date: Sun, 6 Apr 2025 20:16:23 -0500 Subject: [PATCH 1/3] Add files via upload --- app.py | 1086 ++++---- keypoint_classification.ipynb | 2668 +++++++++++------- keypoint_classification_EN.ipynb | 2228 +++++++-------- point_history_classification.ipynb | 4149 ++++++++++++++-------------- 4 files changed, 5369 insertions(+), 4762 deletions(-) diff --git a/app.py b/app.py index c77a35d1..df953dab 100644 --- a/app.py +++ b/app.py @@ -1,543 +1,543 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -import csv -import copy -import argparse -import itertools -from collections import Counter -from collections import deque - -import cv2 as cv -import numpy as np -import mediapipe as mp - -from utils import CvFpsCalc -from model import KeyPointClassifier -from model import PointHistoryClassifier - - -def get_args(): - parser = argparse.ArgumentParser() - - parser.add_argument("--device", type=int, default=0) - parser.add_argument("--width", help='cap width', type=int, default=960) - parser.add_argument("--height", help='cap height', type=int, default=540) - - parser.add_argument('--use_static_image_mode', action='store_true') - parser.add_argument("--min_detection_confidence", - help='min_detection_confidence', - type=float, - default=0.7) - parser.add_argument("--min_tracking_confidence", - help='min_tracking_confidence', - type=int, - default=0.5) - - args = parser.parse_args() - - return args - - -def main(): - # Argument parsing ################################################################# - args = get_args() - - cap_device = args.device - cap_width = args.width - cap_height = args.height - - use_static_image_mode = args.use_static_image_mode - min_detection_confidence = args.min_detection_confidence - min_tracking_confidence = args.min_tracking_confidence - - use_brect = True - - # Camera preparation ############################################################### - cap = cv.VideoCapture(cap_device) - cap.set(cv.CAP_PROP_FRAME_WIDTH, cap_width) - cap.set(cv.CAP_PROP_FRAME_HEIGHT, cap_height) - - # Model load ############################################################# - mp_hands = mp.solutions.hands - hands = mp_hands.Hands( - static_image_mode=use_static_image_mode, - max_num_hands=1, - min_detection_confidence=min_detection_confidence, - min_tracking_confidence=min_tracking_confidence, - ) - - keypoint_classifier = KeyPointClassifier() - - point_history_classifier = PointHistoryClassifier() - - # Read labels ########################################################### - with open('model/keypoint_classifier/keypoint_classifier_label.csv', - encoding='utf-8-sig') as f: - keypoint_classifier_labels = csv.reader(f) - keypoint_classifier_labels = [ - row[0] for row in keypoint_classifier_labels - ] - with open( - 'model/point_history_classifier/point_history_classifier_label.csv', - encoding='utf-8-sig') as f: - point_history_classifier_labels = csv.reader(f) - point_history_classifier_labels = [ - row[0] for row in point_history_classifier_labels - ] - - # FPS Measurement ######################################################## - cvFpsCalc = CvFpsCalc(buffer_len=10) - - # Coordinate history ################################################################# - history_length = 16 - point_history = deque(maxlen=history_length) - - # Finger gesture history ################################################ - finger_gesture_history = deque(maxlen=history_length) - - # ######################################################################## - mode = 0 - - while True: - fps = cvFpsCalc.get() - - # Process Key (ESC: end) ################################################# - key = cv.waitKey(10) - if key == 27: # ESC - break - number, mode = select_mode(key, mode) - - # Camera capture ##################################################### - ret, image = cap.read() - if not ret: - break - image = cv.flip(image, 1) # Mirror display - debug_image = copy.deepcopy(image) - - # Detection implementation ############################################################# - image = cv.cvtColor(image, cv.COLOR_BGR2RGB) - - image.flags.writeable = False - results = hands.process(image) - image.flags.writeable = True - - # #################################################################### - if results.multi_hand_landmarks is not None: - for hand_landmarks, handedness in zip(results.multi_hand_landmarks, - results.multi_handedness): - # Bounding box calculation - brect = calc_bounding_rect(debug_image, hand_landmarks) - # Landmark calculation - landmark_list = calc_landmark_list(debug_image, hand_landmarks) - - # Conversion to relative coordinates / normalized coordinates - pre_processed_landmark_list = pre_process_landmark( - landmark_list) - pre_processed_point_history_list = pre_process_point_history( - debug_image, point_history) - # Write to the dataset file - logging_csv(number, mode, pre_processed_landmark_list, - pre_processed_point_history_list) - - # Hand sign classification - hand_sign_id = keypoint_classifier(pre_processed_landmark_list) - if hand_sign_id == 2: # Point gesture - point_history.append(landmark_list[8]) - else: - point_history.append([0, 0]) - - # Finger gesture classification - finger_gesture_id = 0 - point_history_len = len(pre_processed_point_history_list) - if point_history_len == (history_length * 2): - finger_gesture_id = point_history_classifier( - pre_processed_point_history_list) - - # Calculates the gesture IDs in the latest detection - finger_gesture_history.append(finger_gesture_id) - most_common_fg_id = Counter( - finger_gesture_history).most_common() - - # Drawing part - debug_image = draw_bounding_rect(use_brect, debug_image, brect) - debug_image = draw_landmarks(debug_image, landmark_list) - debug_image = draw_info_text( - debug_image, - brect, - handedness, - keypoint_classifier_labels[hand_sign_id], - point_history_classifier_labels[most_common_fg_id[0][0]], - ) - else: - point_history.append([0, 0]) - - debug_image = draw_point_history(debug_image, point_history) - debug_image = draw_info(debug_image, fps, mode, number) - - # Screen reflection ############################################################# - cv.imshow('Hand Gesture Recognition', debug_image) - - cap.release() - cv.destroyAllWindows() - - -def select_mode(key, mode): - number = -1 - if 48 <= key <= 57: # 0 ~ 9 - number = key - 48 - if key == 110: # n - mode = 0 - if key == 107: # k - mode = 1 - if key == 104: # h - mode = 2 - return number, mode - - -def calc_bounding_rect(image, landmarks): - image_width, image_height = image.shape[1], image.shape[0] - - landmark_array = np.empty((0, 2), int) - - for _, landmark in enumerate(landmarks.landmark): - landmark_x = min(int(landmark.x * image_width), image_width - 1) - landmark_y = min(int(landmark.y * image_height), image_height - 1) - - landmark_point = [np.array((landmark_x, landmark_y))] - - landmark_array = np.append(landmark_array, landmark_point, axis=0) - - x, y, w, h = cv.boundingRect(landmark_array) - - return [x, y, x + w, y + h] - - -def calc_landmark_list(image, landmarks): - image_width, image_height = image.shape[1], image.shape[0] - - landmark_point = [] - - # Keypoint - for _, landmark in enumerate(landmarks.landmark): - landmark_x = min(int(landmark.x * image_width), image_width - 1) - landmark_y = min(int(landmark.y * image_height), image_height - 1) - # landmark_z = landmark.z - - landmark_point.append([landmark_x, landmark_y]) - - return landmark_point - - -def pre_process_landmark(landmark_list): - temp_landmark_list = copy.deepcopy(landmark_list) - - # Convert to relative coordinates - base_x, base_y = 0, 0 - for index, landmark_point in enumerate(temp_landmark_list): - if index == 0: - base_x, base_y = landmark_point[0], landmark_point[1] - - temp_landmark_list[index][0] = temp_landmark_list[index][0] - base_x - temp_landmark_list[index][1] = temp_landmark_list[index][1] - base_y - - # Convert to a one-dimensional list - temp_landmark_list = list( - itertools.chain.from_iterable(temp_landmark_list)) - - # Normalization - max_value = max(list(map(abs, temp_landmark_list))) - - def normalize_(n): - return n / max_value - - temp_landmark_list = list(map(normalize_, temp_landmark_list)) - - return temp_landmark_list - - -def pre_process_point_history(image, point_history): - image_width, image_height = image.shape[1], image.shape[0] - - temp_point_history = copy.deepcopy(point_history) - - # Convert to relative coordinates - base_x, base_y = 0, 0 - for index, point in enumerate(temp_point_history): - if index == 0: - base_x, base_y = point[0], point[1] - - temp_point_history[index][0] = (temp_point_history[index][0] - - base_x) / image_width - temp_point_history[index][1] = (temp_point_history[index][1] - - base_y) / image_height - - # Convert to a one-dimensional list - temp_point_history = list( - itertools.chain.from_iterable(temp_point_history)) - - return temp_point_history - - -def logging_csv(number, mode, landmark_list, point_history_list): - if mode == 0: - pass - if mode == 1 and (0 <= number <= 9): - csv_path = 'model/keypoint_classifier/keypoint.csv' - with open(csv_path, 'a', newline="") as f: - writer = csv.writer(f) - writer.writerow([number, *landmark_list]) - if mode == 2 and (0 <= number <= 9): - csv_path = 'model/point_history_classifier/point_history.csv' - with open(csv_path, 'a', newline="") as f: - writer = csv.writer(f) - writer.writerow([number, *point_history_list]) - return - - -def draw_landmarks(image, landmark_point): - if len(landmark_point) > 0: - # Thumb - cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]), - (255, 255, 255), 2) - - # Index finger - cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]), - (255, 255, 255), 2) - - # Middle finger - cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]), - (255, 255, 255), 2) - - # Ring finger - cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]), - (255, 255, 255), 2) - - # Little finger - cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]), - (255, 255, 255), 2) - - # Palm - cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]), - (255, 255, 255), 2) - cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]), - (0, 0, 0), 6) - cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]), - (255, 255, 255), 2) - - # Key Points - for index, landmark in enumerate(landmark_point): - if index == 0: # 手首1 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 1: # 手首2 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 2: # 親指：付け根 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 3: # 親指：第1関節 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 4: # 親指：指先 - cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) - if index == 5: # 人差指：付け根 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 6: # 人差指：第2関節 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 7: # 人差指：第1関節 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 8: # 人差指：指先 - cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) - if index == 9: # 中指：付け根 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 10: # 中指：第2関節 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 11: # 中指：第1関節 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 12: # 中指：指先 - cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) - if index == 13: # 薬指：付け根 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 14: # 薬指：第2関節 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 15: # 薬指：第1関節 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 16: # 薬指：指先 - cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) - if index == 17: # 小指：付け根 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 18: # 小指：第2関節 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 19: # 小指：第1関節 - cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) - if index == 20: # 小指：指先 - cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), - -1) - cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) - - return image - - -def draw_bounding_rect(use_brect, image, brect): - if use_brect: - # Outer rectangle - cv.rectangle(image, (brect[0], brect[1]), (brect[2], brect[3]), - (0, 0, 0), 1) - - return image - - -def draw_info_text(image, brect, handedness, hand_sign_text, - finger_gesture_text): - cv.rectangle(image, (brect[0], brect[1]), (brect[2], brect[1] - 22), - (0, 0, 0), -1) - - info_text = handedness.classification[0].label[0:] - if hand_sign_text != "": - info_text = info_text + ':' + hand_sign_text - cv.putText(image, info_text, (brect[0] + 5, brect[1] - 4), - cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1, cv.LINE_AA) - - if finger_gesture_text != "": - cv.putText(image, "Finger Gesture:" + finger_gesture_text, (10, 60), - cv.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), 4, cv.LINE_AA) - cv.putText(image, "Finger Gesture:" + finger_gesture_text, (10, 60), - cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), 2, - cv.LINE_AA) - - return image - - -def draw_point_history(image, point_history): - for index, point in enumerate(point_history): - if point[0] != 0 and point[1] != 0: - cv.circle(image, (point[0], point[1]), 1 + int(index / 2), - (152, 251, 152), 2) - - return image - - -def draw_info(image, fps, mode, number): - cv.putText(image, "FPS:" + str(fps), (10, 30), cv.FONT_HERSHEY_SIMPLEX, - 1.0, (0, 0, 0), 4, cv.LINE_AA) - cv.putText(image, "FPS:" + str(fps), (10, 30), cv.FONT_HERSHEY_SIMPLEX, - 1.0, (255, 255, 255), 2, cv.LINE_AA) - - mode_string = ['Logging Key Point', 'Logging Point History'] - if 1 <= mode <= 2: - cv.putText(image, "MODE:" + mode_string[mode - 1], (10, 90), - cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1, - cv.LINE_AA) - if 0 <= number <= 9: - cv.putText(image, "NUM:" + str(number), (10, 110), - cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1, - cv.LINE_AA) - return image - - -if __name__ == '__main__': - main() +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import csv +import copy +import argparse +import itertools +from collections import Counter +from collections import deque + +import cv2 as cv +import numpy as np +import mediapipe as mp + +from utils import CvFpsCalc +from model import KeyPointClassifier +from model import PointHistoryClassifier + + +def get_args(): + parser = argparse.ArgumentParser() + + parser.add_argument("--device", type=int, default=0) + parser.add_argument("--width", help='cap width', type=int, default=960) + parser.add_argument("--height", help='cap height', type=int, default=540) + + parser.add_argument('--use_static_image_mode', action='store_true') + parser.add_argument("--min_detection_confidence", + help='min_detection_confidence', + type=float, + default=0.7) + parser.add_argument("--min_tracking_confidence", + help='min_tracking_confidence', + type=int, + default=0.5) + + args = parser.parse_args() + + return args + + +def main(): + # Argument parsing ################################################################# + args = get_args() + + cap_device = args.device + cap_width = args.width + cap_height = args.height + + use_static_image_mode = args.use_static_image_mode + min_detection_confidence = args.min_detection_confidence + min_tracking_confidence = args.min_tracking_confidence + + use_brect = True + + # Camera preparation ############################################################### + cap = cv.VideoCapture(cap_device) + cap.set(cv.CAP_PROP_FRAME_WIDTH, cap_width) + cap.set(cv.CAP_PROP_FRAME_HEIGHT, cap_height) + + # Model load ############################################################# + mp_hands = mp.solutions.hands + hands = mp_hands.Hands( + static_image_mode=use_static_image_mode, + max_num_hands=2, + min_detection_confidence=min_detection_confidence, + min_tracking_confidence=min_tracking_confidence, + ) + + keypoint_classifier = KeyPointClassifier() + + point_history_classifier = PointHistoryClassifier() + + # Read labels ########################################################### + with open('model/keypoint_classifier/keypoint_classifier_label.csv', + encoding='utf-8-sig') as f: + keypoint_classifier_labels = csv.reader(f) + keypoint_classifier_labels = [ + row[0] for row in keypoint_classifier_labels + ] + with open( + 'model/point_history_classifier/point_history_classifier_label.csv', + encoding='utf-8-sig') as f: + point_history_classifier_labels = csv.reader(f) + point_history_classifier_labels = [ + row[0] for row in point_history_classifier_labels + ] + + # FPS Measurement ######################################################## + cvFpsCalc = CvFpsCalc(buffer_len=10) + + # Coordinate history ################################################################# + history_length = 16 + point_history = deque(maxlen=history_length) + + # Finger gesture history ################################################ + finger_gesture_history = deque(maxlen=history_length) + + # ######################################################################## + mode = 0 + + while True: + fps = cvFpsCalc.get() + + # Process Key (ESC: end) ################################################# + key = cv.waitKey(10) + if key == 27: # ESC + break + number, mode = select_mode(key, mode) + + # Camera capture ##################################################### + ret, image = cap.read() + if not ret: + break + image = cv.flip(image, 1) # Mirror display + debug_image = copy.deepcopy(image) + + # Detection implementation ############################################################# + image = cv.cvtColor(image, cv.COLOR_BGR2RGB) + + image.flags.writeable = False + results = hands.process(image) + image.flags.writeable = True + + # #################################################################### + if results.multi_hand_landmarks is not None: + for hand_landmarks, handedness in zip(results.multi_hand_landmarks, + results.multi_handedness): + # Bounding box calculation + brect = calc_bounding_rect(debug_image, hand_landmarks) + # Landmark calculation + landmark_list = calc_landmark_list(debug_image, hand_landmarks) + + # Conversion to relative coordinates / normalized coordinates + pre_processed_landmark_list = pre_process_landmark( + landmark_list) + pre_processed_point_history_list = pre_process_point_history( + debug_image, point_history) + # Write to the dataset file + logging_csv(number, mode, pre_processed_landmark_list, + pre_processed_point_history_list) + + # Hand sign classification + hand_sign_id = keypoint_classifier(pre_processed_landmark_list) + if hand_sign_id == 2: # Point gesture + point_history.append(landmark_list[8]) + else: + point_history.append([0, 0]) + + # Finger gesture classification + finger_gesture_id = 0 + point_history_len = len(pre_processed_point_history_list) + if point_history_len == (history_length * 2): + finger_gesture_id = point_history_classifier( + pre_processed_point_history_list) + + # Calculates the gesture IDs in the latest detection + finger_gesture_history.append(finger_gesture_id) + most_common_fg_id = Counter( + finger_gesture_history).most_common() + + # Drawing part + debug_image = draw_bounding_rect(use_brect, debug_image, brect) + debug_image = draw_landmarks(debug_image, landmark_list) + debug_image = draw_info_text( + debug_image, + brect, + handedness, + keypoint_classifier_labels[hand_sign_id], + point_history_classifier_labels[most_common_fg_id[0][0]], + ) + else: + point_history.append([0, 0]) + + debug_image = draw_point_history(debug_image, point_history) + debug_image = draw_info(debug_image, fps, mode, number) + + # Screen reflection ############################################################# + cv.imshow('Hand Gesture Recognition', debug_image) + + cap.release() + cv.destroyAllWindows() + + +def select_mode(key, mode): + number = -1 + if 48 <= key <= 57: # 0 ~ 9 + number = key - 48 + if key == 110: # n + mode = 0 + if key == 107: # k + mode = 1 + if key == 104: # h + mode = 2 + return number, mode + + +def calc_bounding_rect(image, landmarks): + image_width, image_height = image.shape[1], image.shape[0] + + landmark_array = np.empty((0, 2), int) + + for _, landmark in enumerate(landmarks.landmark): + landmark_x = min(int(landmark.x * image_width), image_width - 1) + landmark_y = min(int(landmark.y * image_height), image_height - 1) + + landmark_point = [np.array((landmark_x, landmark_y))] + + landmark_array = np.append(landmark_array, landmark_point, axis=0) + + x, y, w, h = cv.boundingRect(landmark_array) + + return [x, y, x + w, y + h] + + +def calc_landmark_list(image, landmarks): + image_width, image_height = image.shape[1], image.shape[0] + + landmark_point = [] + + # Keypoint + for _, landmark in enumerate(landmarks.landmark): + landmark_x = min(int(landmark.x * image_width), image_width - 1) + landmark_y = min(int(landmark.y * image_height), image_height - 1) + # landmark_z = landmark.z + + landmark_point.append([landmark_x, landmark_y]) + + return landmark_point + + +def pre_process_landmark(landmark_list): + temp_landmark_list = copy.deepcopy(landmark_list) + + # Convert to relative coordinates + base_x, base_y = 0, 0 + for index, landmark_point in enumerate(temp_landmark_list): + if index == 0: + base_x, base_y = landmark_point[0], landmark_point[1] + + temp_landmark_list[index][0] = temp_landmark_list[index][0] - base_x + temp_landmark_list[index][1] = temp_landmark_list[index][1] - base_y + + # Convert to a one-dimensional list + temp_landmark_list = list( + itertools.chain.from_iterable(temp_landmark_list)) + + # Normalization + max_value = max(list(map(abs, temp_landmark_list))) + + def normalize_(n): + return n / max_value + + temp_landmark_list = list(map(normalize_, temp_landmark_list)) + + return temp_landmark_list + + +def pre_process_point_history(image, point_history): + image_width, image_height = image.shape[1], image.shape[0] + + temp_point_history = copy.deepcopy(point_history) + + # Convert to relative coordinates + base_x, base_y = 0, 0 + for index, point in enumerate(temp_point_history): + if index == 0: + base_x, base_y = point[0], point[1] + + temp_point_history[index][0] = (temp_point_history[index][0] - + base_x) / image_width + temp_point_history[index][1] = (temp_point_history[index][1] - + base_y) / image_height + + # Convert to a one-dimensional list + temp_point_history = list( + itertools.chain.from_iterable(temp_point_history)) + + return temp_point_history + + +def logging_csv(number, mode, landmark_list, point_history_list): + if mode == 0: + pass + if mode == 1 and (0 <= number <= 9): + csv_path = 'model/keypoint_classifier/keypoint.csv' + with open(csv_path, 'a', newline="") as f: + writer = csv.writer(f) + writer.writerow([number, *landmark_list]) + if mode == 2 and (0 <= number <= 9): + csv_path = 'model/point_history_classifier/point_history.csv' + with open(csv_path, 'a', newline="") as f: + writer = csv.writer(f) + writer.writerow([number, *point_history_list]) + return + + +def draw_landmarks(image, landmark_point): + if len(landmark_point) > 0: + # Thumb + cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]), + (255, 255, 255), 2) + + # Index finger + cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]), + (255, 255, 255), 2) + + # Middle finger + cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]), + (255, 255, 255), 2) + + # Ring finger + cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]), + (255, 255, 255), 2) + + # Little finger + cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]), + (255, 255, 255), 2) + + # Palm + cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]), + (255, 255, 255), 2) + cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]), + (0, 0, 0), 6) + cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]), + (255, 255, 255), 2) + + # Key Points + for index, landmark in enumerate(landmark_point): + if index == 0: # 手首1 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 1: # 手首2 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 2: # 親指：付け根 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 3: # 親指：第1関節 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 4: # 親指：指先 + cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) + if index == 5: # 人差指：付け根 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 6: # 人差指：第2関節 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 7: # 人差指：第1関節 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 8: # 人差指：指先 + cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) + if index == 9: # 中指：付け根 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 10: # 中指：第2関節 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 11: # 中指：第1関節 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 12: # 中指：指先 + cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) + if index == 13: # 薬指：付け根 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 14: # 薬指：第2関節 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 15: # 薬指：第1関節 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 16: # 薬指：指先 + cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) + if index == 17: # 小指：付け根 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 18: # 小指：第2関節 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 19: # 小指：第1関節 + cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) + if index == 20: # 小指：指先 + cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), + -1) + cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) + + return image + + +def draw_bounding_rect(use_brect, image, brect): + if use_brect: + # Outer rectangle + cv.rectangle(image, (brect[0], brect[1]), (brect[2], brect[3]), + (0, 0, 0), 1) + + return image + + +def draw_info_text(image, brect, handedness, hand_sign_text, + finger_gesture_text): + cv.rectangle(image, (brect[0], brect[1]), (brect[2], brect[1] - 22), + (0, 0, 0), -1) + + info_text = handedness.classification[0].label[0:] + if hand_sign_text != "": + info_text = info_text + ':' + hand_sign_text + cv.putText(image, info_text, (brect[0] + 5, brect[1] - 4), + cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1, cv.LINE_AA) + + if finger_gesture_text != "": + cv.putText(image, "Finger Gesture:" + finger_gesture_text, (10, 60), + cv.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), 4, cv.LINE_AA) + cv.putText(image, "Finger Gesture:" + finger_gesture_text, (10, 60), + cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), 2, + cv.LINE_AA) + + return image + + +def draw_point_history(image, point_history): + for index, point in enumerate(point_history): + if point[0] != 0 and point[1] != 0: + cv.circle(image, (point[0], point[1]), 1 + int(index / 2), + (152, 251, 152), 2) + + return image + + +def draw_info(image, fps, mode, number): + cv.putText(image, "FPS:" + str(fps), (10, 30), cv.FONT_HERSHEY_SIMPLEX, + 1.0, (0, 0, 0), 4, cv.LINE_AA) + cv.putText(image, "FPS:" + str(fps), (10, 30), cv.FONT_HERSHEY_SIMPLEX, + 1.0, (255, 255, 255), 2, cv.LINE_AA) + + mode_string = ['Logging Key Point', 'Logging Point History'] + if 1 <= mode <= 2: + cv.putText(image, "MODE:" + mode_string[mode - 1], (10, 90), + cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1, + cv.LINE_AA) + if 0 <= number <= 9: + cv.putText(image, "NUM:" + str(number), (10, 110), + cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1, + cv.LINE_AA) + return image + + +if __name__ == '__main__': + main() diff --git a/keypoint_classification.ipynb b/keypoint_classification.ipynb index 563c0df1..1b1f4500 100644 --- a/keypoint_classification.ipynb +++ b/keypoint_classification.ipynb @@ -1,1019 +1,1649 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import csv\n", - "\n", - "import numpy as np\n", - "import tensorflow as tf\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "RANDOM_SEED = 42" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 各パス指定" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = 'model/keypoint_classifier/keypoint.csv'\n", - "model_save_path = 'model/keypoint_classifier/keypoint_classifier.hdf5'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 分類数設定" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "NUM_CLASSES = 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 学習データ読み込み" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "X_dataset = np.loadtxt(dataset, delimiter=',', dtype='float32', usecols=list(range(1, (21 * 2) + 1)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "y_dataset = np.loadtxt(dataset, delimiter=',', dtype='int32', usecols=(0))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, train_size=0.75, random_state=RANDOM_SEED)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# モデル構築" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "model = tf.keras.models.Sequential([\n", - " tf.keras.layers.Input((21 * 2, )),\n", - " tf.keras.layers.Dropout(0.2),\n", - " tf.keras.layers.Dense(20, activation='relu'),\n", - " tf.keras.layers.Dropout(0.4),\n", - " tf.keras.layers.Dense(10, activation='relu'),\n", - " tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dropout (Dropout) (None, 42) 0 \n", - "_________________________________________________________________\n", - "dense (Dense) (None, 20) 860 \n", - "_________________________________________________________________\n", - "dropout_1 (Dropout) (None, 20) 0 \n", - "_________________________________________________________________\n", - "dense_1 (Dense) (None, 10) 210 \n", - "_________________________________________________________________\n", - "dense_2 (Dense) (None, 3) 33 \n", - "=================================================================\n", - "Total params: 1,103\n", - "Trainable params: 1,103\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "model.summary() # tf.keras.utils.plot_model(model, show_shapes=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# モデルチェックポイントのコールバック\n", - "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", - " model_save_path, verbose=1, save_weights_only=False)\n", - "# 早期打ち切り用コールバック\n", - "es_callback = tf.keras.callbacks.EarlyStopping(patience=20, verbose=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# モデルコンパイル\n", - "model.compile(\n", - " optimizer='adam',\n", - " loss='sparse_categorical_crossentropy',\n", - " metrics=['accuracy']\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# モデル訓練" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 1.1295 - accuracy: 0.3203\n", - "Epoch 00001: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 11ms/step - loss: 1.1004 - accuracy: 0.3602 - val_loss: 1.0431 - val_accuracy: 0.5220\n", - "Epoch 2/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 1.0440 - accuracy: 0.4844\n", - "Epoch 00002: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 1.0503 - accuracy: 0.4297 - val_loss: 0.9953 - val_accuracy: 0.6397\n", - "Epoch 3/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 1.0043 - accuracy: 0.5312\n", - "Epoch 00003: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 4ms/step - loss: 1.0210 - accuracy: 0.4582 - val_loss: 0.9545 - val_accuracy: 0.6523\n", - "Epoch 4/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.9503 - accuracy: 0.5625\n", - "Epoch 00004: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 4ms/step - loss: 0.9906 - accuracy: 0.5022 - val_loss: 0.9168 - val_accuracy: 0.6721\n", - "Epoch 5/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.9562 - accuracy: 0.5469\n", - "Epoch 00005: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.9654 - accuracy: 0.5340 - val_loss: 0.8791 - val_accuracy: 0.7017\n", - "Epoch 6/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.9184 - accuracy: 0.5938\n", - "Epoch 00006: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.9256 - accuracy: 0.5577 - val_loss: 0.8344 - val_accuracy: 0.7269\n", - "Epoch 7/1000\n", - "27/27 [==============================] - ETA: 0s - loss: 0.9050 - accuracy: 0.5715\n", - "Epoch 00007: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 4ms/step - loss: 0.9050 - accuracy: 0.5715 - val_loss: 0.7887 - val_accuracy: 0.7646\n", - "Epoch 8/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.9135 - accuracy: 0.5547\n", - "Epoch 00008: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.8642 - accuracy: 0.5993 - val_loss: 0.7414 - val_accuracy: 0.7996\n", - "Epoch 9/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.8002 - accuracy: 0.6172\n", - "Epoch 00009: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.8258 - accuracy: 0.6263 - val_loss: 0.6881 - val_accuracy: 0.8149\n", - "Epoch 10/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.8056 - accuracy: 0.6328\n", - "Epoch 00010: saving model to 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saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.6903 - accuracy: 0.6928 - val_loss: 0.4781 - val_accuracy: 0.8805\n", - "Epoch 17/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.7165 - accuracy: 0.6875\n", - "Epoch 00017: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.6919 - accuracy: 0.6973 - val_loss: 0.4696 - val_accuracy: 0.8895\n", - "Epoch 18/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.6268 - accuracy: 0.7422\n", - "Epoch 00018: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.6498 - accuracy: 0.7303 - val_loss: 0.4440 - val_accuracy: 0.8967\n", - "Epoch 19/1000\n", - "27/27 [==============================] - ETA: 0s - loss: 0.6499 - accuracy: 0.7261\n", - "Epoch 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"Epoch 00022: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.6154 - accuracy: 0.7480 - val_loss: 0.3907 - val_accuracy: 0.9218\n", - "Epoch 23/1000\n", - "25/27 [==========================>...] - ETA: 0s - loss: 0.5967 - accuracy: 0.7588\n", - "Epoch 00023: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 4ms/step - loss: 0.5971 - accuracy: 0.7582 - val_loss: 0.3763 - val_accuracy: 0.9227\n", - "Epoch 24/1000\n", - "26/27 [===========================>..] - ETA: 0s - loss: 0.6064 - accuracy: 0.7569\n", - "Epoch 00024: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 4ms/step - loss: 0.6066 - accuracy: 0.7567 - val_loss: 0.3714 - val_accuracy: 0.9254\n", - "Epoch 25/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.6211 - accuracy: 0.7422\n", - "Epoch 00025: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 7ms/step - loss: 0.5954 - accuracy: 0.7579 - val_loss: 0.3611 - val_accuracy: 0.9353\n", - "Epoch 26/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.5621 - accuracy: 0.7812\n", - "Epoch 00026: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.5818 - accuracy: 0.7737 - val_loss: 0.3498 - val_accuracy: 0.9380\n", - "Epoch 27/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.6431 - accuracy: 0.7500\n", - "Epoch 00027: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.5882 - accuracy: 0.7648 - val_loss: 0.3355 - val_accuracy: 0.9416\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 28/1000\n", - " 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- " 1/27 [>.............................] - ETA: 0s - loss: 0.5643 - accuracy: 0.7578\n", - "Epoch 00031: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.5450 - accuracy: 0.7773 - val_loss: 0.3111 - val_accuracy: 0.9443\n", - "Epoch 32/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.5507 - accuracy: 0.7812\n", - "Epoch 00032: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 6ms/step - loss: 0.5574 - accuracy: 0.7860 - val_loss: 0.3017 - val_accuracy: 0.9434\n", - "Epoch 33/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.5302 - accuracy: 0.8125\n", - "Epoch 00033: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.5444 - accuracy: 0.7905 - val_loss: 0.2917 - val_accuracy: 0.9479\n", - "Epoch 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accuracy: 0.8235 - val_loss: 0.2218 - val_accuracy: 0.9596\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 55/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3760 - accuracy: 0.8672\n", - "Epoch 00055: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4630 - accuracy: 0.8241 - val_loss: 0.2242 - val_accuracy: 0.9578\n", - "Epoch 56/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4607 - accuracy: 0.7734\n", - "Epoch 00056: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4546 - accuracy: 0.8277 - val_loss: 0.2168 - val_accuracy: 0.9605\n", - "Epoch 57/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4946 - accuracy: 0.7969\n", - "Epoch 00057: saving model to 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0.8438\n", - "Epoch 00072: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4581 - accuracy: 0.8292 - val_loss: 0.2059 - val_accuracy: 0.9623\n", - "Epoch 73/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4450 - accuracy: 0.8750\n", - "Epoch 00073: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4508 - accuracy: 0.8403 - val_loss: 0.2083 - val_accuracy: 0.9614\n", - "Epoch 74/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3858 - accuracy: 0.8906\n", - "Epoch 00074: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4472 - accuracy: 0.8361 - val_loss: 0.2043 - val_accuracy: 0.9650\n", - "Epoch 75/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4439 - accuracy: 0.8359\n", - "Epoch 00075: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4515 - accuracy: 0.8325 - val_loss: 0.2138 - val_accuracy: 0.9632\n", - "Epoch 76/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3983 - accuracy: 0.8203\n", - "Epoch 00076: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4462 - accuracy: 0.8334 - val_loss: 0.2065 - val_accuracy: 0.9623\n", - "Epoch 77/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.5020 - accuracy: 0.8047\n", - "Epoch 00077: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4325 - accuracy: 0.8388 - val_loss: 0.2061 - val_accuracy: 0.9605\n", - "Epoch 78/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3215 - accuracy: 0.8672\n", - "Epoch 00078: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4394 - accuracy: 0.8391 - val_loss: 0.2054 - val_accuracy: 0.9578\n", - "Epoch 79/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4025 - accuracy: 0.8359\n", - "Epoch 00079: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4370 - accuracy: 0.8310 - val_loss: 0.2031 - val_accuracy: 0.9605\n", - "Epoch 80/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4505 - accuracy: 0.8125\n", - "Epoch 00080: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4419 - accuracy: 0.8340 - val_loss: 0.2010 - val_accuracy: 0.9596\n", - "Epoch 81/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.5287 - accuracy: 0.7891\n", - "Epoch 00081: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4369 - accuracy: 0.8304 - val_loss: 0.2081 - val_accuracy: 0.9578\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 82/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.5132 - accuracy: 0.8047\n", - "Epoch 00082: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4360 - accuracy: 0.8460 - val_loss: 0.2045 - val_accuracy: 0.9605\n", - "Epoch 83/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4239 - accuracy: 0.8125\n", - "Epoch 00083: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4511 - accuracy: 0.8313 - val_loss: 0.1984 - val_accuracy: 0.9605\n", - "Epoch 84/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4853 - accuracy: 0.8203\n", - "Epoch 00084: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4357 - accuracy: 0.8304 - val_loss: 0.2024 - val_accuracy: 0.9623\n", - "Epoch 85/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4782 - accuracy: 0.8125\n", - "Epoch 00085: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4320 - accuracy: 0.8424 - val_loss: 0.2015 - val_accuracy: 0.9587\n", - "Epoch 86/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3985 - accuracy: 0.8828\n", - "Epoch 00086: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4422 - accuracy: 0.8349 - val_loss: 0.2087 - val_accuracy: 0.9587\n", - "Epoch 87/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4810 - accuracy: 0.8359\n", - "Epoch 00087: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4393 - accuracy: 0.8316 - val_loss: 0.2105 - val_accuracy: 0.9605\n", - "Epoch 88/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4819 - accuracy: 0.8125\n", - "Epoch 00088: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4381 - accuracy: 0.8400 - val_loss: 0.2070 - val_accuracy: 0.9623\n", - "Epoch 89/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.5002 - accuracy: 0.8281\n", - "Epoch 00089: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4428 - accuracy: 0.8343 - val_loss: 0.2044 - 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0.1999 - val_accuracy: 0.9623\n", - "Epoch 93/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4155 - accuracy: 0.8516\n", - "Epoch 00093: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4166 - accuracy: 0.8412 - val_loss: 0.1947 - val_accuracy: 0.9614\n", - "Epoch 94/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3642 - accuracy: 0.8750\n", - "Epoch 00094: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4204 - accuracy: 0.8418 - val_loss: 0.2008 - val_accuracy: 0.9569\n", - "Epoch 95/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3773 - accuracy: 0.8594\n", - "Epoch 00095: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 4ms/step - loss: 0.4171 - accuracy: 0.8421 - 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0.8400 - val_loss: 0.1950 - val_accuracy: 0.9596\n", - "Epoch 99/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3967 - accuracy: 0.8438\n", - "Epoch 00099: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4260 - accuracy: 0.8418 - val_loss: 0.2044 - val_accuracy: 0.9551\n", - "Epoch 100/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4173 - accuracy: 0.8516\n", - "Epoch 00100: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4200 - accuracy: 0.8442 - val_loss: 0.2066 - val_accuracy: 0.9560\n", - "Epoch 101/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3892 - accuracy: 0.8438\n", - "Epoch 00101: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4245 - 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00119: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.3797 - accuracy: 0.8622 - val_loss: 0.1900 - val_accuracy: 0.9677\n", - "Epoch 120/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3636 - accuracy: 0.8594\n", - "Epoch 00120: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4017 - accuracy: 0.8460 - val_loss: 0.1908 - val_accuracy: 0.9659\n", - "Epoch 121/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4521 - accuracy: 0.8359\n", - "Epoch 00121: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4023 - accuracy: 0.8538 - val_loss: 0.1935 - val_accuracy: 0.9659\n", - "Epoch 122/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4849 - accuracy: 0.8203\n", - "Epoch 00122: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4186 - accuracy: 0.8457 - val_loss: 0.1937 - val_accuracy: 0.9659\n", - "Epoch 123/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4021 - accuracy: 0.8516\n", - "Epoch 00123: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4156 - accuracy: 0.8478 - val_loss: 0.1907 - val_accuracy: 0.9632\n", - "Epoch 124/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3601 - accuracy: 0.8906\n", - "Epoch 00124: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.3948 - accuracy: 0.8550 - val_loss: 0.1862 - val_accuracy: 0.9605\n", - "Epoch 125/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.4446 - accuracy: 0.7891\n", - "Epoch 00125: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.4152 - accuracy: 0.8520 - val_loss: 0.1888 - val_accuracy: 0.9623\n", - "Epoch 126/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3733 - accuracy: 0.8438\n", - "Epoch 00126: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.3913 - accuracy: 0.8550 - val_loss: 0.1937 - val_accuracy: 0.9632\n", - "Epoch 127/1000\n", - " 1/27 [>.............................] - ETA: 0s - loss: 0.3000 - accuracy: 0.8828\n", - "Epoch 00127: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n", - "27/27 [==============================] - 0s 3ms/step - loss: 0.3820 - accuracy: 0.8583 - val_loss: 0.1867 - val_accuracy: 0.9632\n", - "Epoch 00127: early stopping\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit(\n", - " X_train,\n", - " y_train,\n", - " epochs=1000,\n", - " batch_size=128,\n", - " validation_data=(X_test, y_test),\n", - " callbacks=[cp_callback, es_callback]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9/9 [==============================] - 0s 1ms/step - loss: 0.1867 - accuracy: 0.9632\n" - ] - } - ], - "source": [ - "# モデル評価\n", - "val_loss, val_acc = model.evaluate(X_test, y_test, batch_size=128)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# 保存したモデルのロード\n", - "model = tf.keras.models.load_model(model_save_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.77297777 0.1697358 0.05728642]\n", - "0\n" - ] - } - ], - "source": [ - "# 推論テスト\n", - "predict_result = model.predict(np.array([X_test[0]]))\n", - "print(np.squeeze(predict_result))\n", - "print(np.argmax(np.squeeze(predict_result)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 混同行列" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Classification Report\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00 0.99 0.99 410\n", - " 1 0.98 0.92 0.95 385\n", - " 2 0.91 0.99 0.95 318\n", - "\n", - " accuracy 0.96 1113\n", - " macro avg 0.96 0.96 0.96 1113\n", - "weighted avg 0.96 0.96 0.96 1113\n", - "\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import confusion_matrix, classification_report\n", - "\n", - "def print_confusion_matrix(y_true, y_pred, report=True):\n", - " labels = sorted(list(set(y_true)))\n", - " cmx_data = confusion_matrix(y_true, y_pred, labels=labels)\n", - " \n", - " df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)\n", - " \n", - " fig, ax = plt.subplots(figsize=(7, 6))\n", - " sns.heatmap(df_cmx, annot=True, fmt='g' ,square=False)\n", - " ax.set_ylim(len(set(y_true)), 0)\n", - " plt.show()\n", - " \n", - " if report:\n", - " print('Classification Report')\n", - " print(classification_report(y_test, y_pred))\n", - "\n", - "Y_pred = model.predict(X_test)\n", - "y_pred = np.argmax(Y_pred, axis=1)\n", - "\n", - "print_confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tensorflow-Lite用のモデルへ変換" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# 推論専用のモデルとして保存\n", - "model.save(model_save_path, include_optimizer=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From d:\\00.envs\\20201208_mediapipe\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", - "WARNING:tensorflow:From d:\\00.envs\\20201208_mediapipe\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", - "INFO:tensorflow:Assets written to: C:\\Users\\sihit\\AppData\\Local\\Temp\\tmpy2l6ipxu\\assets\n" - ] - }, - { - "data": { - "text/plain": [ - "6224" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# モデルを変換(量子化)\n", - "tflite_save_path = 'model/keypoint_classifier/keypoint_classifier.tflite'\n", - "\n", - "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n", - "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n", - "tflite_quantized_model = converter.convert()\n", - "\n", - "open(tflite_save_path, 'wb').write(tflite_quantized_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 推論テスト" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "interpreter = tf.lite.Interpreter(model_path=tflite_save_path)\n", - "interpreter.allocate_tensors()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# 入出力テンソルを取得\n", - "input_details = interpreter.get_input_details()\n", - "output_details = interpreter.get_output_details()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "interpreter.set_tensor(input_details[0]['index'], np.array([X_test[0]]))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wall time: 0 ns\n" - ] - } - ], - "source": [ - "%%time\n", - "# 推論実施\n", - "interpreter.invoke()\n", - "tflite_results = interpreter.get_tensor(output_details[0]['index'])" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.7729778 0.16973573 0.05728643]\n", - "0\n" - ] - } - ], - "source": [ - "print(np.squeeze(tflite_results))\n", - "print(np.argmax(np.squeeze(tflite_results)))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} +{ + "cells": [ + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:09:19.924935Z", + "start_time": "2025-04-06T15:09:19.920937Z" + } + }, + "source": [ + "import csv\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "RANDOM_SEED = 42" + ], + "outputs": [], + "execution_count": 23 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 各パス指定" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:09:19.940212Z", + "start_time": "2025-04-06T15:09:19.927609Z" + } + }, + "source": [ + "dataset = 'model/keypoint_classifier/keypoint.csv'\n", + "\n", + "# Define the path where the model checkpoint will be saved (with proper path format)\n", + "model_save_path = r'grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras'\n", + "\n", + "# モデルチェックポイントのコールバック\n", + "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", + " model_save_path, verbose=1, save_weights_only=False)\n", + "\n", + "# 早期打ち切り用コールバック\n", + "es_callback = tf.keras.callbacks.EarlyStopping(patience=20, verbose=1)\n", + "\n", + "# You can now use these callbacks during model training:\n", + "# model.fit(X_train, y_train, epochs=100, callbacks=[cp_callback, es_callback])\n" + ], + "outputs": [], + "execution_count": 24 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 分類数設定" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:09:19.972212Z", + "start_time": "2025-04-06T15:09:19.957213Z" + } + }, + "source": "NUM_CLASSES = 6", + "outputs": [], + "execution_count": 25 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 学習データ読み込み" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:09:20.034998Z", + "start_time": "2025-04-06T15:09:19.989213Z" + } + }, + "source": [ + "X_dataset = np.loadtxt(dataset, delimiter=',', dtype='float32', usecols=list(range(1, (21 * 2) + 1)))" + ], + "outputs": [], + "execution_count": 26 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:09:20.066452Z", + "start_time": "2025-04-06T15:09:20.051453Z" + } + }, + "source": "y_dataset = np.loadtxt(dataset, delimiter=',', dtype='int32', usecols=(0))", + "outputs": [], + "execution_count": 27 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:09:20.097742Z", + "start_time": "2025-04-06T15:09:20.082910Z" + } + }, + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, train_size=0.75, random_state=RANDOM_SEED)" + ], + "outputs": [], + "execution_count": 28 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# モデル構築" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:09:20.144382Z", + "start_time": "2025-04-06T15:09:20.114353Z" + } + }, + "source": [ + "model = tf.keras.models.Sequential([\n", + " tf.keras.layers.Input((21 * 2, )),\n", + " tf.keras.layers.Dropout(0.2),\n", + " tf.keras.layers.Dense(20, activation='relu'),\n", + " tf.keras.layers.Dropout(0.4),\n", + " tf.keras.layers.Dense(10, activation='relu'),\n", + " tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n", + "])" + ], + "outputs": [], + "execution_count": 29 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:09:20.175513Z", + "start_time": "2025-04-06T15:09:20.160513Z" + } + }, + "source": [ + "model.summary() # tf.keras.utils.plot_model(model, show_shapes=True)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001B[1mModel: \"sequential_1\"\u001B[0m\n" + ], + "text/html": [ + "
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\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 30 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:09:20.205753Z", + "start_time": "2025-04-06T15:09:20.191513Z" + } + }, + "source": [ + "# モデルチェックポイントのコールバック\n", + "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", + " model_save_path, verbose=1, save_weights_only=False)\n", + "# 早期打ち切り用コールバック\n", + "es_callback = tf.keras.callbacks.EarlyStopping(patience=20, verbose=1)" + ], + "outputs": [], + "execution_count": 31 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:09:20.236662Z", + "start_time": "2025-04-06T15:09:20.221662Z" + } + }, + "source": [ + "# モデルコンパイル\n", + "model.compile(\n", + " optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy']\n", + ")" + ], + "outputs": [], + "execution_count": 32 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# モデル訓練" + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-04-06T15:09:41.367793Z", + "start_time": "2025-04-06T15:09:20.252911Z" + } + }, + "source": [ + "model.fit(\n", + " X_train,\n", + " y_train,\n", + " epochs=1000,\n", + " batch_size=128,\n", + " validation_data=(X_test, y_test),\n", + " callbacks=[cp_callback, es_callback]\n", + ")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m9s\u001B[0m 349ms/step - accuracy: 0.0625 - loss: 2.0222\n", + "Epoch 1: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.0773 - loss: 1.9791 - val_accuracy: 0.2572 - val_loss: 1.7389\n", + "Epoch 2/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.2109 - loss: 1.7578\n", + "Epoch 2: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.2342 - loss: 1.7574 - val_accuracy: 0.4045 - val_loss: 1.6213\n", + "Epoch 3/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3438 - loss: 1.6719\n", + "Epoch 3: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.3428 - loss: 1.6372 - val_accuracy: 0.4080 - val_loss: 1.5256\n", + "Epoch 4/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.2734 - loss: 1.5504\n", + "Epoch 4: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.3527 - loss: 1.5440 - val_accuracy: 0.4568 - val_loss: 1.4422\n", + "Epoch 5/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4219 - loss: 1.5022\n", + "Epoch 5: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4197 - loss: 1.4810 - val_accuracy: 0.5283 - val_loss: 1.3709\n", + "Epoch 6/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4062 - loss: 1.4479\n", + "Epoch 6: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4316 - loss: 1.4221 - val_accuracy: 0.5388 - val_loss: 1.3062\n", + "Epoch 7/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.3906 - loss: 1.4662\n", + "Epoch 7: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4453 - loss: 1.3788 - val_accuracy: 0.5632 - val_loss: 1.2367\n", + "Epoch 8/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.4688 - loss: 1.3573\n", + "Epoch 8: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4599 - loss: 1.3226 - val_accuracy: 0.5859 - val_loss: 1.1669\n", + "Epoch 9/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4609 - loss: 1.2706\n", + "Epoch 9: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4964 - loss: 1.2402 - val_accuracy: 0.6085 - val_loss: 1.0906\n", + "Epoch 10/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.5234 - loss: 1.2300\n", + "Epoch 10: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5111 - loss: 1.1958 - val_accuracy: 0.6173 - val_loss: 1.0209\n", + "Epoch 11/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5391 - loss: 1.1013\n", + "Epoch 11: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5180 - loss: 1.1655 - val_accuracy: 0.6460 - val_loss: 0.9645\n", + "Epoch 12/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5625 - loss: 1.1091\n", + "Epoch 12: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5249 - loss: 1.1306 - val_accuracy: 0.6879 - val_loss: 0.9138\n", + "Epoch 13/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5469 - loss: 1.0677\n", + "Epoch 13: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5443 - loss: 1.1049 - val_accuracy: 0.7306 - val_loss: 0.8627\n", + "Epoch 14/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4766 - loss: 1.1436\n", + "Epoch 14: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5409 - loss: 1.0798 - val_accuracy: 0.7384 - val_loss: 0.8244\n", + "Epoch 15/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6328 - loss: 0.9176\n", + "Epoch 15: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5931 - loss: 1.0027 - val_accuracy: 0.7515 - val_loss: 0.7856\n", + "Epoch 16/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.5547 - loss: 1.0661\n", + "Epoch 16: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5589 - loss: 1.0331 - val_accuracy: 0.7454 - val_loss: 0.7571\n", + "Epoch 17/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6797 - loss: 0.9360\n", + "Epoch 17: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5960 - loss: 0.9712 - val_accuracy: 0.7733 - val_loss: 0.7259\n", + "Epoch 18/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6406 - loss: 0.9400\n", + "Epoch 18: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5959 - loss: 0.9798 - val_accuracy: 0.7951 - val_loss: 0.6978\n", + "Epoch 19/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.5547 - loss: 1.0904\n", + "Epoch 19: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6070 - loss: 0.9680 - val_accuracy: 0.8073 - val_loss: 0.6693\n", + "Epoch 20/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6641 - loss: 0.8054\n", + "Epoch 20: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6246 - loss: 0.9202 - val_accuracy: 0.8152 - val_loss: 0.6566\n", + "Epoch 21/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6328 - loss: 0.8654\n", + "Epoch 21: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6358 - loss: 0.8990 - val_accuracy: 0.8204 - val_loss: 0.6292\n", + "Epoch 22/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6484 - loss: 0.8508\n", + "Epoch 22: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6250 - loss: 0.9057 - val_accuracy: 0.8387 - val_loss: 0.6121\n", + "Epoch 23/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7031 - loss: 0.8454\n", + "Epoch 23: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6503 - loss: 0.8875 - val_accuracy: 0.8370 - val_loss: 0.5931\n", + "Epoch 24/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6328 - loss: 0.9711\n", + "Epoch 24: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6464 - loss: 0.8882 - val_accuracy: 0.8439 - val_loss: 0.5758\n", + "Epoch 25/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.5938 - loss: 1.0014\n", + "Epoch 25: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6365 - loss: 0.9033 - val_accuracy: 0.8457 - val_loss: 0.5629\n", + "Epoch 26/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6641 - loss: 0.8698\n", + "Epoch 26: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6581 - loss: 0.8625 - val_accuracy: 0.8527 - val_loss: 0.5412\n", + "Epoch 27/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6719 - loss: 0.7902\n", + "Epoch 27: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6539 - loss: 0.8377 - val_accuracy: 0.8596 - val_loss: 0.5299\n", + "Epoch 28/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6250 - loss: 0.8936\n", + "Epoch 28: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6500 - loss: 0.8674 - val_accuracy: 0.8666 - val_loss: 0.5235\n", + "Epoch 29/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7109 - loss: 0.7589\n", + "Epoch 29: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6793 - loss: 0.8141 - val_accuracy: 0.8684 - val_loss: 0.5123\n", + "Epoch 30/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6250 - loss: 0.8867\n", + "Epoch 30: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6680 - loss: 0.8343 - val_accuracy: 0.8710 - val_loss: 0.4999\n", + "Epoch 31/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6641 - loss: 0.9109\n", + "Epoch 31: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6822 - loss: 0.8166 - val_accuracy: 0.8718 - val_loss: 0.4865\n", + "Epoch 32/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 19ms/step - accuracy: 0.7266 - loss: 0.7984\n", + "Epoch 32: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6785 - loss: 0.8232 - val_accuracy: 0.8666 - val_loss: 0.4868\n", + "Epoch 33/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7109 - loss: 0.7727\n", + "Epoch 33: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6978 - loss: 0.7922 - val_accuracy: 0.8701 - val_loss: 0.4778\n", + "Epoch 34/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.7798\n", + "Epoch 34: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7039 - loss: 0.7837 - val_accuracy: 0.8745 - val_loss: 0.4668\n", + "Epoch 35/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6172 - loss: 0.8736\n", + "Epoch 35: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6690 - loss: 0.8212 - val_accuracy: 0.8771 - val_loss: 0.4574\n", + "Epoch 36/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m2s\u001B[0m 78ms/step - accuracy: 0.6953 - loss: 0.6885\n", + "Epoch 36: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6829 - loss: 0.7829 - val_accuracy: 0.8858 - val_loss: 0.4441\n", + "Epoch 37/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6406 - loss: 0.9419\n", + "Epoch 37: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6929 - loss: 0.8005 - val_accuracy: 0.8788 - val_loss: 0.4418\n", + "Epoch 38/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6905\n", + "Epoch 38: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7031 - loss: 0.7612 - val_accuracy: 0.8788 - val_loss: 0.4334\n", + "Epoch 39/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6797 - loss: 0.8182\n", + "Epoch 39: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6940 - loss: 0.7918 - val_accuracy: 0.9024 - val_loss: 0.4231\n", + "Epoch 40/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6406 - loss: 0.8019\n", + "Epoch 40: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6839 - loss: 0.7694 - val_accuracy: 0.9032 - val_loss: 0.4128\n", + "Epoch 41/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6641 - loss: 0.9634\n", + "Epoch 41: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6943 - loss: 0.7897 - val_accuracy: 0.8858 - val_loss: 0.4184\n", + "Epoch 42/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8047 - loss: 0.5583\n", + "Epoch 42: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7236 - loss: 0.7259 - val_accuracy: 0.8963 - val_loss: 0.4082\n", + "Epoch 43/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6077\n", + "Epoch 43: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7190 - loss: 0.7192 - val_accuracy: 0.8971 - val_loss: 0.4047\n", + "Epoch 44/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7500 - loss: 0.6481\n", + "Epoch 44: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7204 - loss: 0.7467 - val_accuracy: 0.8875 - val_loss: 0.3997\n", + "Epoch 45/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6998\n", + "Epoch 45: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7163 - loss: 0.7389 - val_accuracy: 0.8945 - val_loss: 0.3954\n", + "Epoch 46/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.7393\n", + "Epoch 46: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7241 - loss: 0.7325 - val_accuracy: 0.9058 - val_loss: 0.3874\n", + "Epoch 47/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6875 - loss: 0.7247\n", + "Epoch 47: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7255 - loss: 0.7239 - val_accuracy: 0.9058 - val_loss: 0.3832\n", + "Epoch 48/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7031 - loss: 0.8094\n", + "Epoch 48: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7219 - loss: 0.7399 - val_accuracy: 0.8989 - val_loss: 0.3788\n", + "Epoch 49/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7422 - loss: 0.7648\n", + "Epoch 49: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7165 - loss: 0.7276 - val_accuracy: 0.9067 - val_loss: 0.3711\n", + "Epoch 50/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6797 - loss: 0.7365\n", + "Epoch 50: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7138 - loss: 0.7280 - val_accuracy: 0.9102 - val_loss: 0.3631\n", + "Epoch 51/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7422 - loss: 0.6953\n", + "Epoch 51: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7216 - loss: 0.7328 - val_accuracy: 0.9224 - val_loss: 0.3581\n", + "Epoch 52/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7109 - loss: 0.8351\n", + "Epoch 52: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7119 - loss: 0.7598 - val_accuracy: 0.9198 - val_loss: 0.3601\n", + "Epoch 53/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 32ms/step - accuracy: 0.6172 - loss: 0.8963\n", + "Epoch 53: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7344 - loss: 0.7193 - val_accuracy: 0.9128 - val_loss: 0.3577\n", + "Epoch 54/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6406 - loss: 0.9208\n", + "Epoch 54: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7072 - loss: 0.7303 - val_accuracy: 0.9180 - val_loss: 0.3552\n", + "Epoch 55/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.6616\n", + "Epoch 55: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7326 - loss: 0.6984 - val_accuracy: 0.9250 - val_loss: 0.3499\n", + "Epoch 56/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6953 - loss: 0.7095\n", + "Epoch 56: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7316 - loss: 0.7040 - val_accuracy: 0.9085 - val_loss: 0.3502\n", + "Epoch 57/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7031 - loss: 0.6687\n", + "Epoch 57: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7242 - loss: 0.7037 - val_accuracy: 0.9276 - val_loss: 0.3347\n", + "Epoch 58/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5950\n", + "Epoch 58: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7249 - loss: 0.7049 - val_accuracy: 0.9224 - val_loss: 0.3354\n", + "Epoch 59/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6875 - loss: 0.7105\n", + "Epoch 59: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7245 - loss: 0.6898 - val_accuracy: 0.9076 - val_loss: 0.3374\n", + "Epoch 60/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.7135\n", + "Epoch 60: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7400 - loss: 0.6977 - val_accuracy: 0.9224 - val_loss: 0.3325\n", + "Epoch 61/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6953 - loss: 0.7222\n", + "Epoch 61: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7408 - loss: 0.6716 - val_accuracy: 0.9303 - val_loss: 0.3204\n", + "Epoch 62/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6953 - loss: 0.7629\n", + "Epoch 62: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7210 - loss: 0.7136 - val_accuracy: 0.9215 - val_loss: 0.3329\n", + "Epoch 63/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6797 - loss: 0.7956\n", + "Epoch 63: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7465 - loss: 0.6779 - val_accuracy: 0.9346 - val_loss: 0.3217\n", + "Epoch 64/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.6471\n", + "Epoch 64: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7539 - loss: 0.6725 - val_accuracy: 0.9381 - val_loss: 0.3256\n", + "Epoch 65/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7500 - loss: 0.7264\n", + "Epoch 65: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7441 - loss: 0.7074 - val_accuracy: 0.9364 - val_loss: 0.3285\n", + "Epoch 66/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.7449\n", + "Epoch 66: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7395 - loss: 0.6798 - val_accuracy: 0.9364 - val_loss: 0.3282\n", + "Epoch 67/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7109 - loss: 0.7946\n", + "Epoch 67: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7326 - loss: 0.6987 - val_accuracy: 0.9268 - val_loss: 0.3246\n", + "Epoch 68/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.7638\n", + "Epoch 68: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7447 - loss: 0.6932 - val_accuracy: 0.9381 - val_loss: 0.3191\n", + "Epoch 69/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6797 - loss: 0.7415\n", + "Epoch 69: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7433 - loss: 0.6551 - val_accuracy: 0.9390 - val_loss: 0.3144\n", + "Epoch 70/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.6711\n", + "Epoch 70: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7574 - loss: 0.6524 - val_accuracy: 0.9329 - val_loss: 0.3120\n", + "Epoch 71/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.6528\n", + "Epoch 71: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7535 - loss: 0.6642 - val_accuracy: 0.9355 - val_loss: 0.3092\n", + "Epoch 72/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.7630\n", + "Epoch 72: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7539 - loss: 0.6652 - val_accuracy: 0.9416 - val_loss: 0.3094\n", + "Epoch 73/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7422 - loss: 0.6481\n", + "Epoch 73: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7373 - loss: 0.6876 - val_accuracy: 0.9337 - val_loss: 0.3103\n", + "Epoch 74/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7266 - loss: 0.7056\n", + "Epoch 74: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7349 - loss: 0.6759 - val_accuracy: 0.9425 - val_loss: 0.3064\n", + "Epoch 75/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.6441\n", + "Epoch 75: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7595 - loss: 0.6500 - val_accuracy: 0.9547 - val_loss: 0.2963\n", + "Epoch 76/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5595\n", + "Epoch 76: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7595 - loss: 0.6330 - val_accuracy: 0.9538 - val_loss: 0.2915\n", + "Epoch 77/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.5877\n", + "Epoch 77: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7503 - loss: 0.6423 - val_accuracy: 0.9529 - val_loss: 0.2879\n", + "Epoch 78/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6448\n", + "Epoch 78: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7581 - loss: 0.6498 - val_accuracy: 0.9582 - val_loss: 0.2861\n", + "Epoch 79/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.7513\n", + "Epoch 79: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7631 - loss: 0.6580 - val_accuracy: 0.9529 - val_loss: 0.2934\n", + "Epoch 80/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 20ms/step - accuracy: 0.7422 - loss: 0.6293\n", + "Epoch 80: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7506 - loss: 0.6667 - val_accuracy: 0.9390 - val_loss: 0.2949\n", + "Epoch 81/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6252\n", + "Epoch 81: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7688 - loss: 0.6295 - val_accuracy: 0.9538 - val_loss: 0.2898\n", + "Epoch 82/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7188 - loss: 0.6518\n", + "Epoch 82: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7460 - loss: 0.6422 - val_accuracy: 0.9573 - val_loss: 0.2781\n", + "Epoch 83/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7266 - loss: 0.6937\n", + "Epoch 83: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7439 - loss: 0.6609 - val_accuracy: 0.9503 - val_loss: 0.2829\n", + "Epoch 84/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5661\n", + "Epoch 84: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7619 - loss: 0.6163 - val_accuracy: 0.9660 - val_loss: 0.2658\n", + "Epoch 85/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.5787\n", + "Epoch 85: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7615 - loss: 0.6401 - val_accuracy: 0.9660 - val_loss: 0.2697\n", + "Epoch 86/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.5988\n", + "Epoch 86: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7484 - loss: 0.6443 - val_accuracy: 0.9590 - val_loss: 0.2783\n", + "Epoch 87/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7500 - loss: 0.6345\n", + "Epoch 87: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7585 - loss: 0.6592 - val_accuracy: 0.9538 - val_loss: 0.2806\n", + "Epoch 88/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.7078\n", + "Epoch 88: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7586 - loss: 0.6501 - val_accuracy: 0.9582 - val_loss: 0.2738\n", + "Epoch 89/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6031\n", + "Epoch 89: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7550 - loss: 0.6698 - val_accuracy: 0.9564 - val_loss: 0.2767\n", + "Epoch 90/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7344 - loss: 0.6736\n", + "Epoch 90: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7487 - loss: 0.6666 - val_accuracy: 0.9564 - val_loss: 0.2798\n", + "Epoch 91/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7344 - loss: 0.6264\n", + "Epoch 91: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7578 - loss: 0.6019 - val_accuracy: 0.9520 - val_loss: 0.2776\n", + "Epoch 92/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.6732\n", + "Epoch 92: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7549 - loss: 0.6583 - val_accuracy: 0.9643 - val_loss: 0.2647\n", + "Epoch 93/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.5483\n", + "Epoch 93: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7742 - loss: 0.6153 - val_accuracy: 0.9547 - val_loss: 0.2792\n", + "Epoch 94/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7188 - loss: 0.7038\n", + "Epoch 94: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7667 - loss: 0.6522 - val_accuracy: 0.9538 - val_loss: 0.2709\n", + "Epoch 95/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6660\n", + "Epoch 95: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7662 - loss: 0.6430 - val_accuracy: 0.9582 - val_loss: 0.2694\n", + "Epoch 96/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5784\n", + "Epoch 96: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7763 - loss: 0.6094 - val_accuracy: 0.9625 - val_loss: 0.2579\n", + "Epoch 97/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7109 - loss: 0.7181\n", + "Epoch 97: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7669 - loss: 0.6159 - val_accuracy: 0.9564 - val_loss: 0.2700\n", + "Epoch 98/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5916\n", + "Epoch 98: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7673 - loss: 0.6355 - val_accuracy: 0.9590 - val_loss: 0.2620\n", + "Epoch 99/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8125 - loss: 0.5287\n", + "Epoch 99: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7845 - loss: 0.6140 - val_accuracy: 0.9608 - val_loss: 0.2598\n", + "Epoch 100/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6493\n", + "Epoch 100: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7731 - loss: 0.6279 - val_accuracy: 0.9582 - val_loss: 0.2605\n", + "Epoch 101/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7109 - loss: 0.8103\n", + "Epoch 101: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7497 - loss: 0.6695 - val_accuracy: 0.9582 - val_loss: 0.2614\n", + "Epoch 102/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8047 - loss: 0.6126\n", + "Epoch 102: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7838 - loss: 0.6041 - val_accuracy: 0.9608 - val_loss: 0.2557\n", + "Epoch 103/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7422 - loss: 0.7509\n", + "Epoch 103: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7654 - loss: 0.6345 - val_accuracy: 0.9625 - val_loss: 0.2576\n", + "Epoch 104/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.5592\n", + "Epoch 104: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7920 - loss: 0.5897 - val_accuracy: 0.9634 - val_loss: 0.2491\n", + "Epoch 105/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8281 - loss: 0.4830\n", + "Epoch 105: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7775 - loss: 0.6139 - val_accuracy: 0.9599 - val_loss: 0.2533\n", + "Epoch 106/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.7242\n", + "Epoch 106: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7698 - loss: 0.6416 - val_accuracy: 0.9625 - val_loss: 0.2566\n", + "Epoch 107/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5654\n", + "Epoch 107: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7763 - loss: 0.6012 - val_accuracy: 0.9634 - val_loss: 0.2449\n", + "Epoch 108/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.6409\n", + "Epoch 108: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7752 - loss: 0.6369 - val_accuracy: 0.9651 - val_loss: 0.2491\n", + "Epoch 109/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8438 - loss: 0.4575\n", + "Epoch 109: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7760 - loss: 0.6142 - val_accuracy: 0.9704 - val_loss: 0.2486\n", + "Epoch 110/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8203 - loss: 0.5242\n", + "Epoch 110: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7705 - loss: 0.6172 - val_accuracy: 0.9686 - val_loss: 0.2460\n", + "Epoch 111/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8281 - loss: 0.5177\n", + "Epoch 111: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7850 - loss: 0.5857 - val_accuracy: 0.9669 - val_loss: 0.2394\n", + "Epoch 112/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5381\n", + "Epoch 112: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7873 - loss: 0.5639 - val_accuracy: 0.9721 - val_loss: 0.2352\n", + "Epoch 113/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.5871\n", + "Epoch 113: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7702 - loss: 0.6352 - val_accuracy: 0.9634 - val_loss: 0.2433\n", + "Epoch 114/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5980\n", + "Epoch 114: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7894 - loss: 0.5816 - val_accuracy: 0.9669 - val_loss: 0.2301\n", + "Epoch 115/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.5700\n", + "Epoch 115: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7765 - loss: 0.5956 - val_accuracy: 0.9721 - val_loss: 0.2284\n", + "Epoch 116/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5322\n", + "Epoch 116: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7893 - loss: 0.5923 - val_accuracy: 0.9616 - val_loss: 0.2418\n", + "Epoch 117/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.6899\n", + "Epoch 117: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7638 - loss: 0.6227 - val_accuracy: 0.9555 - val_loss: 0.2467\n", + "Epoch 118/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7812 - loss: 0.5865\n", + "Epoch 118: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7773 - loss: 0.5944 - val_accuracy: 0.9677 - val_loss: 0.2354\n", + "Epoch 119/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5463\n", + "Epoch 119: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7695 - loss: 0.6114 - val_accuracy: 0.9677 - val_loss: 0.2298\n", + "Epoch 120/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.6110\n", + "Epoch 120: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7693 - loss: 0.6375 - val_accuracy: 0.9686 - val_loss: 0.2340\n", + "Epoch 121/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7891 - loss: 0.5587\n", + "Epoch 121: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7871 - loss: 0.5946 - val_accuracy: 0.9643 - val_loss: 0.2286\n", + "Epoch 122/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7188 - loss: 0.7024\n", + "Epoch 122: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7688 - loss: 0.6131 - val_accuracy: 0.9677 - val_loss: 0.2359\n", + "Epoch 123/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5494\n", + "Epoch 123: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7797 - loss: 0.5854 - val_accuracy: 0.9651 - val_loss: 0.2450\n", + "Epoch 124/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5866\n", + "Epoch 124: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7832 - loss: 0.5969 - val_accuracy: 0.9651 - val_loss: 0.2334\n", + "Epoch 125/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8359 - loss: 0.4527\n", + "Epoch 125: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7991 - loss: 0.5792 - val_accuracy: 0.9704 - val_loss: 0.2312\n", + "Epoch 126/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7188 - loss: 0.6996\n", + "Epoch 126: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7721 - loss: 0.6165 - val_accuracy: 0.9669 - val_loss: 0.2352\n", + "Epoch 127/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.6257\n", + "Epoch 127: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7860 - loss: 0.5916 - val_accuracy: 0.9695 - val_loss: 0.2305\n", + "Epoch 128/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.7016\n", + "Epoch 128: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7817 - loss: 0.5928 - val_accuracy: 0.9643 - val_loss: 0.2326\n", + "Epoch 129/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6301\n", + "Epoch 129: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7720 - loss: 0.6227 - val_accuracy: 0.9634 - val_loss: 0.2300\n", + "Epoch 130/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5741\n", + "Epoch 130: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7778 - loss: 0.6205 - val_accuracy: 0.9643 - val_loss: 0.2281\n", + "Epoch 131/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5895\n", + "Epoch 131: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7834 - loss: 0.5788 - val_accuracy: 0.9686 - val_loss: 0.2241\n", + "Epoch 132/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.6158\n", + "Epoch 132: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7946 - loss: 0.5877 - val_accuracy: 0.9686 - val_loss: 0.2217\n", + "Epoch 133/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5522\n", + "Epoch 133: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7835 - loss: 0.6043 - val_accuracy: 0.9669 - val_loss: 0.2245\n", + "Epoch 134/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.6516\n", + "Epoch 134: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7921 - loss: 0.5971 - val_accuracy: 0.9677 - val_loss: 0.2298\n", + "Epoch 135/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5581\n", + "Epoch 135: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7910 - loss: 0.5781 - val_accuracy: 0.9686 - val_loss: 0.2268\n", + "Epoch 136/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5913\n", + "Epoch 136: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7888 - loss: 0.5802 - val_accuracy: 0.9651 - val_loss: 0.2260\n", + "Epoch 137/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5728\n", + "Epoch 137: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7920 - loss: 0.5850 - val_accuracy: 0.9712 - val_loss: 0.2189\n", + "Epoch 138/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.6518\n", + "Epoch 138: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7768 - loss: 0.6069 - val_accuracy: 0.9616 - val_loss: 0.2280\n", + "Epoch 139/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.6347\n", + "Epoch 139: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7782 - loss: 0.6206 - val_accuracy: 0.9677 - val_loss: 0.2307\n", + "Epoch 140/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5122\n", + "Epoch 140: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7862 - loss: 0.5758 - val_accuracy: 0.9721 - val_loss: 0.2253\n", + "Epoch 141/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.5543\n", + "Epoch 141: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7889 - loss: 0.5646 - val_accuracy: 0.9686 - val_loss: 0.2281\n", + "Epoch 142/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5501\n", + "Epoch 142: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7812 - loss: 0.5992 - val_accuracy: 0.9721 - val_loss: 0.2231\n", + "Epoch 143/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5760\n", + "Epoch 143: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7969 - loss: 0.5820 - val_accuracy: 0.9634 - val_loss: 0.2256\n", + "Epoch 144/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7812 - loss: 0.6266\n", + "Epoch 144: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7913 - loss: 0.5928 - val_accuracy: 0.9669 - val_loss: 0.2318\n", + "Epoch 145/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7891 - loss: 0.5853\n", + "Epoch 145: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7865 - loss: 0.5908 - val_accuracy: 0.9677 - val_loss: 0.2295\n", + "Epoch 146/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5663\n", + "Epoch 146: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7903 - loss: 0.5989 - val_accuracy: 0.9677 - val_loss: 0.2338\n", + "Epoch 147/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6402\n", + "Epoch 147: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8003 - loss: 0.5763 - val_accuracy: 0.9677 - val_loss: 0.2298\n", + "Epoch 148/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7891 - loss: 0.5296\n", + "Epoch 148: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7952 - loss: 0.5684 - val_accuracy: 0.9686 - val_loss: 0.2288\n", + "Epoch 149/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.5079\n", + "Epoch 149: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8098 - loss: 0.5733 - val_accuracy: 0.9660 - val_loss: 0.2221\n", + "Epoch 150/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5778\n", + "Epoch 150: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7880 - loss: 0.5866 - val_accuracy: 0.9712 - val_loss: 0.2232\n", + "Epoch 151/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5207\n", + "Epoch 151: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7791 - loss: 0.5876 - val_accuracy: 0.9712 - val_loss: 0.2222\n", + "Epoch 152/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.5847\n", + "Epoch 152: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7905 - loss: 0.5643 - val_accuracy: 0.9721 - val_loss: 0.2134\n", + "Epoch 153/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.5806\n", + "Epoch 153: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7878 - loss: 0.5723 - val_accuracy: 0.9730 - val_loss: 0.2110\n", + "Epoch 154/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.4988\n", + "Epoch 154: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7963 - loss: 0.5629 - val_accuracy: 0.9704 - val_loss: 0.2186\n", + "Epoch 155/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.5500\n", + "Epoch 155: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7978 - loss: 0.5776 - val_accuracy: 0.9730 - val_loss: 0.2155\n", + "Epoch 156/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8203 - loss: 0.4869\n", + "Epoch 156: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7828 - loss: 0.6050 - val_accuracy: 0.9695 - val_loss: 0.2208\n", + "Epoch 157/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7578 - loss: 0.6877\n", + "Epoch 157: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7981 - loss: 0.5727 - val_accuracy: 0.9695 - val_loss: 0.2170\n", + "Epoch 158/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5425\n", + "Epoch 158: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7884 - loss: 0.5843 - val_accuracy: 0.9704 - val_loss: 0.2182\n", + "Epoch 159/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8047 - loss: 0.5415\n", + "Epoch 159: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7896 - loss: 0.5767 - val_accuracy: 0.9721 - val_loss: 0.2196\n", + "Epoch 160/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7266 - loss: 0.6685\n", + "Epoch 160: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7913 - loss: 0.5859 - val_accuracy: 0.9738 - val_loss: 0.2158\n", + "Epoch 161/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5529\n", + "Epoch 161: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7805 - loss: 0.5922 - val_accuracy: 0.9695 - val_loss: 0.2194\n", + "Epoch 162/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8203 - loss: 0.4853\n", + "Epoch 162: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7974 - loss: 0.5612 - val_accuracy: 0.9712 - val_loss: 0.2151\n", + "Epoch 163/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.5796\n", + "Epoch 163: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8022 - loss: 0.5668 - val_accuracy: 0.9677 - val_loss: 0.2146\n", + "Epoch 164/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.6269\n", + "Epoch 164: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7851 - loss: 0.5766 - val_accuracy: 0.9704 - val_loss: 0.2156\n", + "Epoch 165/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.6097\n", + "Epoch 165: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7952 - loss: 0.5745 - val_accuracy: 0.9721 - val_loss: 0.2215\n", + "Epoch 166/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8516 - loss: 0.4536\n", + "Epoch 166: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8142 - loss: 0.5531 - val_accuracy: 0.9686 - val_loss: 0.2156\n", + "Epoch 167/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8438 - loss: 0.5172\n", + "Epoch 167: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8020 - loss: 0.5605 - val_accuracy: 0.9730 - val_loss: 0.2079\n", + "Epoch 168/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.8203 - loss: 0.5927\n", + "Epoch 168: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8056 - loss: 0.5605 - val_accuracy: 0.9695 - val_loss: 0.2079\n", + "Epoch 169/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 20ms/step - accuracy: 0.8047 - loss: 0.6494\n", + "Epoch 169: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7937 - loss: 0.5838 - val_accuracy: 0.9695 - val_loss: 0.2152\n", + "Epoch 170/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5463\n", + "Epoch 170: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8001 - loss: 0.5745 - val_accuracy: 0.9669 - val_loss: 0.2130\n", + "Epoch 171/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8203 - loss: 0.5899\n", + "Epoch 171: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7972 - loss: 0.5781 - val_accuracy: 0.9677 - val_loss: 0.2113\n", + "Epoch 172/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8438 - loss: 0.4425\n", + "Epoch 172: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7996 - loss: 0.5530 - val_accuracy: 0.9695 - val_loss: 0.2084\n", + "Epoch 173/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5852\n", + "Epoch 173: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7948 - loss: 0.5861 - val_accuracy: 0.9721 - val_loss: 0.2094\n", + "Epoch 174/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.6026\n", + "Epoch 174: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7899 - loss: 0.5752 - val_accuracy: 0.9730 - val_loss: 0.2062\n", + "Epoch 175/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.8203 - loss: 0.4570\n", + "Epoch 175: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8049 - loss: 0.5561 - val_accuracy: 0.9730 - val_loss: 0.2061\n", + "Epoch 176/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8438 - loss: 0.4919\n", + "Epoch 176: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8105 - loss: 0.5427 - val_accuracy: 0.9747 - val_loss: 0.1993\n", + "Epoch 177/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.4823\n", + "Epoch 177: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7987 - loss: 0.5707 - val_accuracy: 0.9721 - val_loss: 0.2064\n", + "Epoch 178/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.4889\n", + "Epoch 178: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7993 - loss: 0.5592 - val_accuracy: 0.9747 - val_loss: 0.2077\n", + "Epoch 179/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5535\n", + "Epoch 179: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7976 - loss: 0.5620 - val_accuracy: 0.9738 - val_loss: 0.2032\n", + "Epoch 180/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5824\n", + "Epoch 180: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7998 - loss: 0.5565 - val_accuracy: 0.9704 - val_loss: 0.2099\n", + "Epoch 181/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.4362\n", + "Epoch 181: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8115 - loss: 0.5441 - val_accuracy: 0.9730 - val_loss: 0.2129\n", + "Epoch 182/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8672 - loss: 0.4783\n", + "Epoch 182: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8001 - loss: 0.5569 - val_accuracy: 0.9721 - val_loss: 0.2149\n", + "Epoch 183/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5520\n", + "Epoch 183: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7988 - loss: 0.5608 - val_accuracy: 0.9712 - val_loss: 0.2111\n", + "Epoch 184/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8516 - loss: 0.4611\n", + "Epoch 184: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8002 - loss: 0.5642 - val_accuracy: 0.9712 - val_loss: 0.2075\n", + "Epoch 185/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5170\n", + "Epoch 185: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7906 - loss: 0.5788 - val_accuracy: 0.9686 - val_loss: 0.2076\n", + "Epoch 186/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.6221\n", + "Epoch 186: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7828 - loss: 0.5824 - val_accuracy: 0.9738 - val_loss: 0.2102\n", + "Epoch 187/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8125 - loss: 0.5062\n", + "Epoch 187: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7874 - loss: 0.5760 - val_accuracy: 0.9738 - val_loss: 0.2071\n", + "Epoch 188/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.7137\n", + "Epoch 188: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7996 - loss: 0.5999 - val_accuracy: 0.9765 - val_loss: 0.1995\n", + "Epoch 189/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7188 - loss: 0.6855\n", + "Epoch 189: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7843 - loss: 0.5840 - val_accuracy: 0.9738 - val_loss: 0.2051\n", + "Epoch 190/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8594 - loss: 0.4604\n", + "Epoch 190: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8124 - loss: 0.5375 - val_accuracy: 0.9730 - val_loss: 0.2084\n", + "Epoch 191/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.8359 - loss: 0.5506\n", + "Epoch 191: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8139 - loss: 0.5482 - val_accuracy: 0.9730 - val_loss: 0.2033\n", + "Epoch 192/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8125 - loss: 0.5925\n", + "Epoch 192: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8040 - loss: 0.5540 - val_accuracy: 0.9738 - val_loss: 0.2073\n", + "Epoch 193/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.5939\n", + "Epoch 193: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7946 - loss: 0.5501 - val_accuracy: 0.9730 - val_loss: 0.2028\n", + "Epoch 194/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8047 - loss: 0.5613\n", + "Epoch 194: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8188 - loss: 0.5278 - val_accuracy: 0.9747 - val_loss: 0.1980\n", + "Epoch 195/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.4923\n", + "Epoch 195: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7967 - loss: 0.5564 - val_accuracy: 0.9704 - val_loss: 0.2058\n", + "Epoch 196/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.4770\n", + "Epoch 196: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8013 - loss: 0.5379 - val_accuracy: 0.9738 - val_loss: 0.2090\n", + "Epoch 197/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5925\n", + "Epoch 197: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8057 - loss: 0.5529 - val_accuracy: 0.9738 - val_loss: 0.2016\n", + "Epoch 198/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5831\n", + "Epoch 198: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8131 - loss: 0.5349 - val_accuracy: 0.9738 - val_loss: 0.1982\n", + "Epoch 199/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.4914\n", + "Epoch 199: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8042 - loss: 0.5477 - val_accuracy: 0.9747 - val_loss: 0.2021\n", + "Epoch 200/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.4765\n", + "Epoch 200: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8212 - loss: 0.5181 - val_accuracy: 0.9756 - val_loss: 0.1932\n", + "Epoch 201/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.4399\n", + "Epoch 201: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7886 - loss: 0.5582 - val_accuracy: 0.9686 - val_loss: 0.2054\n", + "Epoch 202/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8438 - loss: 0.5077\n", + "Epoch 202: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7797 - loss: 0.6027 - val_accuracy: 0.9712 - val_loss: 0.2037\n", + "Epoch 203/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.5925\n", + "Epoch 203: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7958 - loss: 0.5549 - val_accuracy: 0.9721 - val_loss: 0.2013\n", + "Epoch 204/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.5804\n", + "Epoch 204: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8199 - loss: 0.5386 - val_accuracy: 0.9712 - val_loss: 0.2010\n", + "Epoch 205/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5225\n", + "Epoch 205: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7965 - loss: 0.5663 - val_accuracy: 0.9721 - val_loss: 0.2024\n", + "Epoch 206/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5693\n", + "Epoch 206: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8049 - loss: 0.5511 - val_accuracy: 0.9704 - val_loss: 0.2048\n", + "Epoch 207/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.4656\n", + "Epoch 207: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8151 - loss: 0.5216 - val_accuracy: 0.9756 - val_loss: 0.1941\n", + "Epoch 208/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8281 - loss: 0.4725\n", + "Epoch 208: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8062 - loss: 0.5431 - val_accuracy: 0.9782 - val_loss: 0.1922\n", + "Epoch 209/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5688\n", + "Epoch 209: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7842 - loss: 0.5888 - val_accuracy: 0.9686 - val_loss: 0.2042\n", + "Epoch 210/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8672 - loss: 0.3775\n", + "Epoch 210: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8099 - loss: 0.5346 - val_accuracy: 0.9712 - val_loss: 0.2106\n", + "Epoch 211/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5993\n", + "Epoch 211: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8200 - loss: 0.5330 - val_accuracy: 0.9669 - val_loss: 0.2067\n", + "Epoch 212/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8828 - loss: 0.3879\n", + "Epoch 212: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8207 - loss: 0.5247 - val_accuracy: 0.9686 - val_loss: 0.2023\n", + "Epoch 213/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.5587\n", + "Epoch 213: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8088 - loss: 0.5496 - val_accuracy: 0.9712 - val_loss: 0.2021\n", + "Epoch 214/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8359 - loss: 0.4606\n", + "Epoch 214: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8054 - loss: 0.5312 - val_accuracy: 0.9756 - val_loss: 0.1850\n", + "Epoch 215/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6467\n", + "Epoch 215: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7851 - loss: 0.5892 - val_accuracy: 0.9730 - val_loss: 0.1966\n", + "Epoch 216/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7109 - loss: 0.7488\n", + "Epoch 216: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7757 - loss: 0.6108 - val_accuracy: 0.9669 - val_loss: 0.2035\n", + "Epoch 217/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 15ms/step - accuracy: 0.8125 - loss: 0.5911\n", + "Epoch 217: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8046 - loss: 0.5562 - val_accuracy: 0.9704 - val_loss: 0.1994\n", + "Epoch 218/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8750 - loss: 0.4520\n", + "Epoch 218: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8013 - loss: 0.5472 - val_accuracy: 0.9712 - val_loss: 0.2020\n", + "Epoch 219/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5736\n", + "Epoch 219: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8099 - loss: 0.5462 - val_accuracy: 0.9686 - val_loss: 0.2029\n", + "Epoch 220/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.4038\n", + "Epoch 220: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8003 - loss: 0.5340 - val_accuracy: 0.9773 - val_loss: 0.1938\n", + "Epoch 221/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8359 - loss: 0.5431\n", + "Epoch 221: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7956 - loss: 0.5731 - val_accuracy: 0.9695 - val_loss: 0.2046\n", + "Epoch 222/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5285\n", + "Epoch 222: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7972 - loss: 0.5681 - val_accuracy: 0.9669 - val_loss: 0.2058\n", + "Epoch 223/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.4823\n", + "Epoch 223: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8122 - loss: 0.5155 - val_accuracy: 0.9712 - val_loss: 0.1965\n", + "Epoch 224/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.5541\n", + "Epoch 224: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8056 - loss: 0.5680 - val_accuracy: 0.9677 - val_loss: 0.1991\n", + "Epoch 225/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8125 - loss: 0.5274\n", + "Epoch 225: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8088 - loss: 0.5392 - val_accuracy: 0.9747 - val_loss: 0.1946\n", + "Epoch 226/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7812 - loss: 0.6112\n", + "Epoch 226: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7956 - loss: 0.5639 - val_accuracy: 0.9695 - val_loss: 0.2017\n", + "Epoch 227/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5353\n", + "Epoch 227: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7965 - loss: 0.5624 - val_accuracy: 0.9660 - val_loss: 0.2044\n", + "Epoch 228/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6194\n", + "Epoch 228: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8034 - loss: 0.5514 - val_accuracy: 0.9730 - val_loss: 0.1976\n", + "Epoch 229/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8750 - loss: 0.4060\n", + "Epoch 229: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8203 - loss: 0.5072 - val_accuracy: 0.9695 - val_loss: 0.2029\n", + "Epoch 230/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.5064\n", + "Epoch 230: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8003 - loss: 0.5466 - val_accuracy: 0.9738 - val_loss: 0.1990\n", + "Epoch 231/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5423\n", + "Epoch 231: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7937 - loss: 0.5568 - val_accuracy: 0.9712 - val_loss: 0.1984\n", + "Epoch 232/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6099\n", + "Epoch 232: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8091 - loss: 0.5300 - val_accuracy: 0.9747 - val_loss: 0.1860\n", + "Epoch 233/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.6119\n", + "Epoch 233: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8058 - loss: 0.5446 - val_accuracy: 0.9730 - val_loss: 0.1926\n", + "Epoch 234/1000\n", + "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8438 - loss: 0.5648\n", + "Epoch 234: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n", + "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8114 - loss: 0.5478 - val_accuracy: 0.9677 - val_loss: 0.2020\n", + "Epoch 234: early stopping\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 33 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:10:26.710697Z", + "start_time": "2025-04-06T15:10:26.649490Z" + } + }, + "source": [ + "# モデル評価\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, batch_size=128)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1m9/9\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.9692 - loss: 0.2051 \n" + ] + } + ], + "execution_count": 34 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:10:26.772708Z", + "start_time": "2025-04-06T15:10:26.726968Z" + } + }, + "source": [ + "# 保存したモデルのロード\n", + "model = tf.keras.models.load_model(model_save_path)" + ], + "outputs": [], + "execution_count": 35 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:10:26.850307Z", + "start_time": "2025-04-06T15:10:26.789232Z" + } + }, + "source": [ + "# 推論テスト\n", + "predict_result = model.predict(np.array([X_test[0]]))\n", + "print(np.squeeze(predict_result))\n", + "print(np.argmax(np.squeeze(predict_result)))" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step\n", + "[4.1954637e-02 7.1722388e-02 8.7705678e-01 8.6475220e-03 5.7122717e-04\n", + " 4.7421348e-05]\n", + "2\n" + ] + } + ], + "execution_count": 36 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 混同行列" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:10:27.038162Z", + "start_time": "2025-04-06T15:10:26.866818Z" + } + }, + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import confusion_matrix, classification_report\n", + "\n", + "def print_confusion_matrix(y_true, y_pred, report=True):\n", + " labels = sorted(list(set(y_true)))\n", + " cmx_data = confusion_matrix(y_true, y_pred, labels=labels)\n", + " \n", + " df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)\n", + " \n", + " fig, ax = plt.subplots(figsize=(7, 6))\n", + " sns.heatmap(df_cmx, annot=True, fmt='g' ,square=False)\n", + " ax.set_ylim(len(set(y_true)), 0)\n", + " plt.show()\n", + " \n", + " if report:\n", + " print('Classification Report')\n", + " print(classification_report(y_test, y_pred))\n", + "\n", + "Y_pred = model.predict(X_test)\n", + "y_pred = np.argmax(Y_pred, axis=1)\n", + "\n", + "print_confusion_matrix(y_test, y_pred)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1m36/36\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 680us/step\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification Report\n", + " precision recall f1-score support\n", + "\n", + " 0 0.99 0.99 0.99 408\n", + " 1 1.00 0.89 0.94 237\n", + " 2 0.91 0.99 0.95 329\n", + " 3 1.00 1.00 1.00 70\n", + " 4 1.00 0.95 0.98 64\n", + " 5 1.00 0.97 0.99 39\n", + "\n", + " accuracy 0.97 1147\n", + " macro avg 0.98 0.97 0.97 1147\n", + "weighted avg 0.97 0.97 0.97 1147\n", + "\n" + ] + } + ], + "execution_count": 37 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tensorflow-Lite用のモデルへ変換" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:10:27.242924Z", + "start_time": "2025-04-06T15:10:27.054441Z" + } + }, + "source": [ + "# モデルを変換(量子化)\n", + "tflite_save_path = 'model/keypoint_classifier/keypoint_classifier.tflite'\n", + "\n", + "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n", + "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n", + "tflite_quantized_model = converter.convert()\n", + "\n", + "open(tflite_save_path, 'wb').write(tflite_quantized_model)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: C:\\Users\\grimm\\AppData\\Local\\Temp\\tmpx0n8ggq1\\assets\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: C:\\Users\\grimm\\AppData\\Local\\Temp\\tmpx0n8ggq1\\assets\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved artifact at 'C:\\Users\\grimm\\AppData\\Local\\Temp\\tmpx0n8ggq1'. The following endpoints are available:\n", + "\n", + "* Endpoint 'serve'\n", + " args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 42), dtype=tf.float32, name='input_layer_1')\n", + "Output Type:\n", + " TensorSpec(shape=(None, 6), dtype=tf.float32, name=None)\n", + "Captures:\n", + " 2614704504176: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", + " 2614869353648: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", + " 2614704496960: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", + " 2614869345904: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", + " 2614869344496: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", + " 2614871934928: TensorSpec(shape=(), dtype=tf.resource, name=None)\n" + ] + }, + { + "data": { + "text/plain": [ + "6644" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 38 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 推論テスト" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:10:27.274132Z", + "start_time": "2025-04-06T15:10:27.259133Z" + } + }, + "source": [ + "interpreter = tf.lite.Interpreter(model_path=tflite_save_path)\n", + "interpreter.allocate_tensors()" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\.venv\\lib\\site-packages\\tensorflow\\lite\\python\\interpreter.py:457: UserWarning: Warning: tf.lite.Interpreter is deprecated and is scheduled for deletion in\n", + " TF 2.20. Please use the LiteRT interpreter from the ai_edge_litert package.\n", + " See the [migration guide](https://ai.google.dev/edge/litert/migration)\n", + " for details.\n", + " \n", + " warnings.warn(_INTERPRETER_DELETION_WARNING)\n" + ] + } + ], + "execution_count": 39 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:10:27.306131Z", + "start_time": "2025-04-06T15:10:27.291134Z" + } + }, + "source": [ + "# 入出力テンソルを取得\n", + "input_details = interpreter.get_input_details()\n", + "output_details = interpreter.get_output_details()" + ], + "outputs": [], + "execution_count": 40 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:10:27.337021Z", + "start_time": "2025-04-06T15:10:27.322449Z" + } + }, + "source": [ + "interpreter.set_tensor(input_details[0]['index'], np.array([X_test[0]]))" + ], + "outputs": [], + "execution_count": 41 + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-04-06T15:10:27.368247Z", + "start_time": "2025-04-06T15:10:27.354023Z" + } + }, + "source": [ + "%%time\n", + "# 推論実施\n", + "interpreter.invoke()\n", + "tflite_results = interpreter.get_tensor(output_details[0]['index'])" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: total: 0 ns\n", + "Wall time: 0 ns\n" + ] + } + ], + "execution_count": 42 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:10:27.415239Z", + "start_time": "2025-04-06T15:10:27.384549Z" + } + }, + "cell_type": "code", + "source": [ + "# 推論専用のモデルとして保存\n", + "model.save(model_save_path, include_optimizer=False)" + ], + "outputs": [], + "execution_count": 43 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T15:10:27.445830Z", + "start_time": "2025-04-06T15:10:27.431554Z" + } + }, + "cell_type": "code", + "source": [ + "print(np.squeeze(tflite_results))\n", + "print(np.argmax(np.squeeze(tflite_results)))" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.1954637e-02 7.1722366e-02 8.7705678e-01 8.6475220e-03 5.7122711e-04\n", + " 4.7421348e-05]\n", + "2\n" + ] + } + ], + "execution_count": 44 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/keypoint_classification_EN.ipynb b/keypoint_classification_EN.ipynb index 6da6b90e..69ab6c06 100644 --- a/keypoint_classification_EN.ipynb +++ b/keypoint_classification_EN.ipynb @@ -1,1194 +1,1034 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "colab": { - "name": "keypoint_classification_EN.ipynb", - "provenance": [], - "collapsed_sections": [], - "toc_visible": true - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "code", - "metadata": { - "id": "igMyGnjE9hEp" - }, - "source": [ - "import csv\n", - "\n", - "import numpy as np\n", - "import tensorflow as tf\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "RANDOM_SEED = 42" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "t2HDvhIu9hEr" - }, - "source": [ - "# Specify each path" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "9NvZP2Zn9hEy" - }, - "source": [ - "dataset = 'model/keypoint_classifier/keypoint.csv'\n", - "model_save_path = 'model/keypoint_classifier/keypoint_classifier.hdf5'\n", - "tflite_save_path = 'model/keypoint_classifier/keypoint_classifier.tflite'" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s5oMH7x19hEz" - }, - "source": [ - "# Set number of classes" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "du4kodXL9hEz" - }, - "source": [ - "NUM_CLASSES = 4" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XjnL0uso9hEz" - }, - "source": [ - "# Dataset reading" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "QT5ZqtEz9hE0" - }, - "source": [ - "X_dataset = np.loadtxt(dataset, delimiter=',', dtype='float32', usecols=list(range(1, (21 * 2) + 1)))" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "QmoKFsp49hE0" - }, - "source": [ - "y_dataset = np.loadtxt(dataset, delimiter=',', dtype='int32', usecols=(0))" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "xQU7JTZ_9hE0" - }, - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, train_size=0.75, random_state=RANDOM_SEED)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mxK_lETT9hE0" - }, - "source": [ - "# Model building" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "vHBmUf1t9hE1" - }, - "source": [ - "model = tf.keras.models.Sequential([\n", - " tf.keras.layers.Input((21 * 2, )),\n", - " tf.keras.layers.Dropout(0.2),\n", - " tf.keras.layers.Dense(20, activation='relu'),\n", - " tf.keras.layers.Dropout(0.4),\n", - " tf.keras.layers.Dense(10, activation='relu'),\n", - " tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n", - "])" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "ypqky9tc9hE1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "5db082bb-30e3-4110-bf63-a1ee777ecd46" - }, - "source": [ - "model.summary() # tf.keras.utils.plot_model(model, show_shapes=True)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dropout (Dropout) (None, 42) 0 \n", - "_________________________________________________________________\n", - "dense (Dense) (None, 20) 860 \n", - "_________________________________________________________________\n", - "dropout_1 (Dropout) (None, 20) 0 \n", - "_________________________________________________________________\n", - "dense_1 (Dense) (None, 10) 210 \n", - "_________________________________________________________________\n", - "dense_2 (Dense) (None, 4) 44 \n", - "=================================================================\n", - "Total params: 1,114\n", - "Trainable params: 1,114\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "MbMjOflQ9hE1" - }, - "source": [ - "# Model checkpoint callback\n", - "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", - " model_save_path, verbose=1, save_weights_only=False)\n", - "# Callback for early stopping\n", - "es_callback = tf.keras.callbacks.EarlyStopping(patience=20, verbose=1)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "c3Dac0M_9hE2" - }, - "source": [ - "# Model compilation\n", - "model.compile(\n", - " optimizer='adam',\n", - " loss='sparse_categorical_crossentropy',\n", - " metrics=['accuracy']\n", - ")" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7XI0j1Iu9hE2" - }, - "source": [ - "# Model training" - ] - }, - { - "cell_type": "code", - "metadata": { - "scrolled": true, - "id": "WirBl-JE9hE3", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "71b30ca2-8294-4d9d-8aa2-800d90d399de" - }, - "source": [ - "model.fit(\n", - " X_train,\n", - " y_train,\n", - " epochs=1000,\n", - " batch_size=128,\n", - " validation_data=(X_test, y_test),\n", - " callbacks=[cp_callback, es_callback]\n", - ")" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Epoch 1/1000\n", - "29/29 [==============================] - 2s 15ms/step - loss: 1.3853 - accuracy: 0.3360 - val_loss: 1.2779 - val_accuracy: 0.4244\n", - "\n", - "Epoch 00001: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 2/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 1.2943 - accuracy: 0.3780 - val_loss: 1.2151 - val_accuracy: 0.4703\n", - "\n", - "Epoch 00002: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 3/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 1.2524 - accuracy: 0.3749 - val_loss: 1.1472 - val_accuracy: 0.5572\n", - "\n", - "Epoch 00003: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 4/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 1.1989 - accuracy: 0.4251 - val_loss: 1.0682 - val_accuracy: 0.6374\n", - "\n", - "Epoch 00004: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 5/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 1.1363 - accuracy: 0.4733 - val_loss: 1.0027 - val_accuracy: 0.6608\n", - "\n", - "Epoch 00005: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 6/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 1.0938 - accuracy: 0.5107 - val_loss: 0.9416 - val_accuracy: 0.6717\n", - "\n", - "Epoch 00006: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 7/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 1.0426 - accuracy: 0.5351 - val_loss: 0.8775 - val_accuracy: 0.7043\n", - "\n", - "Epoch 00007: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 8/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 1.0024 - accuracy: 0.5597 - val_loss: 0.8238 - val_accuracy: 0.7243\n", - "\n", - "Epoch 00008: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 9/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.9845 - accuracy: 0.5475 - val_loss: 0.7726 - val_accuracy: 0.7444\n", - "\n", - "Epoch 00009: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 10/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.9527 - accuracy: 0.5661 - val_loss: 0.7256 - val_accuracy: 0.7602\n", - "\n", - "Epoch 00010: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 11/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.9066 - accuracy: 0.5915 - val_loss: 0.6922 - val_accuracy: 0.7886\n", - "\n", - "Epoch 00011: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 12/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.8825 - accuracy: 0.6094 - val_loss: 0.6512 - val_accuracy: 0.8087\n", - "\n", - "Epoch 00012: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 13/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.8901 - accuracy: 0.6124 - val_loss: 0.6228 - val_accuracy: 0.8246\n", - "\n", - "Epoch 00013: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 14/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.8616 - accuracy: 0.6238 - val_loss: 0.5969 - val_accuracy: 0.8688\n", - "\n", - "Epoch 00014: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 15/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.8060 - accuracy: 0.6412 - val_loss: 0.5634 - val_accuracy: 0.8780\n", - "\n", - "Epoch 00015: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 16/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.8036 - accuracy: 0.6573 - val_loss: 0.5445 - val_accuracy: 0.8897\n", - "\n", - "Epoch 00016: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 17/1000\n", - "29/29 [==============================] - 0s 6ms/step - loss: 0.7658 - accuracy: 0.6752 - val_loss: 0.5247 - val_accuracy: 0.9056\n", - "\n", - "Epoch 00017: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 18/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.7816 - accuracy: 0.6566 - val_loss: 0.5061 - val_accuracy: 0.9073\n", - "\n", - "Epoch 00018: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 19/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.7564 - accuracy: 0.6842 - val_loss: 0.4910 - val_accuracy: 0.9039\n", - "\n", - "Epoch 00019: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 20/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.7531 - accuracy: 0.6800 - val_loss: 0.4742 - val_accuracy: 0.9098\n", - "\n", - "Epoch 00020: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 21/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.7271 - accuracy: 0.6952 - val_loss: 0.4619 - val_accuracy: 0.9190\n", - "\n", - "Epoch 00021: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 22/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.7392 - accuracy: 0.6969 - val_loss: 0.4552 - val_accuracy: 0.9240\n", - "\n", - "Epoch 00022: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 23/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.7169 - accuracy: 0.6980 - val_loss: 0.4407 - val_accuracy: 0.9190\n", - "\n", - "Epoch 00023: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 24/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.7228 - accuracy: 0.6844 - val_loss: 0.4299 - val_accuracy: 0.9348\n", - "\n", - "Epoch 00024: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 25/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.7076 - accuracy: 0.6995 - val_loss: 0.4208 - val_accuracy: 0.9273\n", - "\n", - "Epoch 00025: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 26/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.7026 - accuracy: 0.7094 - val_loss: 0.4089 - val_accuracy: 0.9340\n", - "\n", - "Epoch 00026: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 27/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6647 - accuracy: 0.7284 - val_loss: 0.4033 - val_accuracy: 0.9307\n", - "\n", - "Epoch 00027: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 28/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.7012 - accuracy: 0.7009 - val_loss: 0.4048 - val_accuracy: 0.9282\n", - "\n", - "Epoch 00028: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 29/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6833 - accuracy: 0.7196 - val_loss: 0.3877 - val_accuracy: 0.9390\n", - "\n", - "Epoch 00029: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 30/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6934 - accuracy: 0.7080 - val_loss: 0.3753 - val_accuracy: 0.9465\n", - "\n", - "Epoch 00030: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 31/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6482 - accuracy: 0.7330 - val_loss: 0.3744 - val_accuracy: 0.9407\n", - "\n", - "Epoch 00031: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 32/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6765 - accuracy: 0.7150 - val_loss: 0.3752 - val_accuracy: 0.9465\n", - "\n", - "Epoch 00032: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 33/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6690 - accuracy: 0.7229 - val_loss: 0.3627 - val_accuracy: 0.9490\n", - "\n", - "Epoch 00033: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 34/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6609 - accuracy: 0.7177 - val_loss: 0.3601 - val_accuracy: 0.9415\n", - "\n", - "Epoch 00034: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 35/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.6472 - accuracy: 0.7369 - val_loss: 0.3538 - val_accuracy: 0.9357\n", - "\n", - "Epoch 00035: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 36/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6448 - accuracy: 0.7398 - val_loss: 0.3439 - val_accuracy: 0.9482\n", - "\n", - "Epoch 00036: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 37/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.6417 - accuracy: 0.7341 - val_loss: 0.3454 - val_accuracy: 0.9482\n", - "\n", - "Epoch 00037: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 38/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6605 - accuracy: 0.7270 - val_loss: 0.3479 - val_accuracy: 0.9507\n", - "\n", - "Epoch 00038: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 39/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6306 - accuracy: 0.7349 - val_loss: 0.3439 - val_accuracy: 0.9499\n", - "\n", - "Epoch 00039: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 40/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6719 - accuracy: 0.7174 - val_loss: 0.3491 - val_accuracy: 0.9490\n", - "\n", - "Epoch 00040: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 41/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6213 - accuracy: 0.7391 - val_loss: 0.3326 - val_accuracy: 0.9465\n", - "\n", - "Epoch 00041: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 42/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6397 - accuracy: 0.7397 - val_loss: 0.3294 - val_accuracy: 0.9499\n", - "\n", - "Epoch 00042: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 43/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6255 - accuracy: 0.7534 - val_loss: 0.3269 - val_accuracy: 0.9515\n", - "\n", - "Epoch 00043: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 44/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.6403 - accuracy: 0.7363 - val_loss: 0.3300 - val_accuracy: 0.9507\n", - "\n", - "Epoch 00044: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 45/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.6105 - accuracy: 0.7541 - val_loss: 0.3156 - val_accuracy: 0.9574\n", - "\n", - "Epoch 00045: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 46/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.6065 - accuracy: 0.7611 - val_loss: 0.3083 - val_accuracy: 0.9582\n", - "\n", - "Epoch 00046: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 47/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5970 - accuracy: 0.7595 - val_loss: 0.3147 - val_accuracy: 0.9432\n", - "\n", - "Epoch 00047: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 48/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5949 - accuracy: 0.7490 - val_loss: 0.3119 - val_accuracy: 0.9524\n", - "\n", - "Epoch 00048: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 49/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.6136 - accuracy: 0.7447 - val_loss: 0.3049 - val_accuracy: 0.9591\n", - "\n", - "Epoch 00049: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 50/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.6021 - accuracy: 0.7487 - val_loss: 0.3109 - val_accuracy: 0.9591\n", - "\n", - "Epoch 00050: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 51/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5964 - accuracy: 0.7519 - val_loss: 0.3073 - val_accuracy: 0.9607\n", - "\n", - "Epoch 00051: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 52/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.6201 - accuracy: 0.7530 - val_loss: 0.3005 - val_accuracy: 0.9657\n", - "\n", - "Epoch 00052: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 53/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5830 - accuracy: 0.7675 - val_loss: 0.2952 - val_accuracy: 0.9641\n", - "\n", - "Epoch 00053: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 54/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5810 - accuracy: 0.7717 - val_loss: 0.2938 - val_accuracy: 0.9624\n", - "\n", - "Epoch 00054: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 55/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5772 - accuracy: 0.7629 - val_loss: 0.2909 - val_accuracy: 0.9616\n", - "\n", - "Epoch 00055: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 56/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5697 - accuracy: 0.7671 - val_loss: 0.2858 - val_accuracy: 0.9616\n", - "\n", - "Epoch 00056: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 57/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.6045 - accuracy: 0.7662 - val_loss: 0.2897 - val_accuracy: 0.9607\n", - "\n", - "Epoch 00057: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 58/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5758 - accuracy: 0.7605 - val_loss: 0.2866 - val_accuracy: 0.9649\n", - "\n", - "Epoch 00058: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 59/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5680 - accuracy: 0.7674 - val_loss: 0.2823 - val_accuracy: 0.9666\n", - "\n", - "Epoch 00059: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 60/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.6012 - accuracy: 0.7609 - val_loss: 0.2797 - val_accuracy: 0.9632\n", - "\n", - "Epoch 00060: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 61/1000\n", - "29/29 [==============================] - 0s 7ms/step - loss: 0.5754 - accuracy: 0.7716 - val_loss: 0.2738 - val_accuracy: 0.9657\n", - "\n", - "Epoch 00061: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 62/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5867 - accuracy: 0.7662 - val_loss: 0.2681 - val_accuracy: 0.9641\n", - "\n", - "Epoch 00062: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 63/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5659 - accuracy: 0.7775 - val_loss: 0.2679 - val_accuracy: 0.9624\n", - "\n", - "Epoch 00063: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 64/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5809 - accuracy: 0.7579 - val_loss: 0.2676 - val_accuracy: 0.9616\n", - "\n", - "Epoch 00064: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 65/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5411 - accuracy: 0.7946 - val_loss: 0.2597 - val_accuracy: 0.9657\n", - "\n", - "Epoch 00065: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 66/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5708 - accuracy: 0.7580 - val_loss: 0.2605 - val_accuracy: 0.9683\n", - "\n", - "Epoch 00066: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 67/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5758 - accuracy: 0.7743 - val_loss: 0.2610 - val_accuracy: 0.9666\n", - "\n", - "Epoch 00067: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 68/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5817 - accuracy: 0.7755 - val_loss: 0.2627 - val_accuracy: 0.9649\n", - "\n", - "Epoch 00068: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 69/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5433 - accuracy: 0.7961 - val_loss: 0.2506 - val_accuracy: 0.9691\n", - "\n", - "Epoch 00069: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 70/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5277 - accuracy: 0.7969 - val_loss: 0.2515 - val_accuracy: 0.9674\n", - "\n", - "Epoch 00070: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 71/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5326 - accuracy: 0.7916 - val_loss: 0.2523 - val_accuracy: 0.9657\n", - "\n", - "Epoch 00071: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 72/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5366 - accuracy: 0.7858 - val_loss: 0.2503 - val_accuracy: 0.9632\n", - "\n", - "Epoch 00072: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 73/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5365 - accuracy: 0.7923 - val_loss: 0.2454 - val_accuracy: 0.9674\n", - "\n", - "Epoch 00073: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 74/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5308 - accuracy: 0.7907 - val_loss: 0.2477 - val_accuracy: 0.9666\n", - "\n", - "Epoch 00074: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 75/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5484 - accuracy: 0.7893 - val_loss: 0.2423 - val_accuracy: 0.9666\n", - "\n", - "Epoch 00075: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 76/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5486 - accuracy: 0.7919 - val_loss: 0.2460 - val_accuracy: 0.9657\n", - "\n", - "Epoch 00076: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 77/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5641 - accuracy: 0.7737 - val_loss: 0.2504 - val_accuracy: 0.9674\n", - "\n", - "Epoch 00077: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 78/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5293 - accuracy: 0.7988 - val_loss: 0.2358 - val_accuracy: 0.9691\n", - "\n", - "Epoch 00078: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 79/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5279 - accuracy: 0.8004 - val_loss: 0.2314 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00079: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 80/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5228 - accuracy: 0.7923 - val_loss: 0.2357 - val_accuracy: 0.9708\n", - "\n", - "Epoch 00080: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 81/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5105 - accuracy: 0.8039 - val_loss: 0.2433 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00081: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 82/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5105 - accuracy: 0.7994 - val_loss: 0.2415 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00082: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 83/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5323 - accuracy: 0.7907 - val_loss: 0.2395 - val_accuracy: 0.9674\n", - "\n", - "Epoch 00083: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 84/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5423 - accuracy: 0.7904 - val_loss: 0.2396 - val_accuracy: 0.9691\n", - "\n", - "Epoch 00084: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 85/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5419 - accuracy: 0.7913 - val_loss: 0.2335 - val_accuracy: 0.9683\n", - "\n", - "Epoch 00085: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 86/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5259 - accuracy: 0.8041 - val_loss: 0.2351 - val_accuracy: 0.9666\n", - "\n", - "Epoch 00086: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 87/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5282 - accuracy: 0.7899 - val_loss: 0.2285 - val_accuracy: 0.9708\n", - "\n", - "Epoch 00087: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 88/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5117 - accuracy: 0.7977 - val_loss: 0.2334 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00088: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 89/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5065 - accuracy: 0.8034 - val_loss: 0.2389 - val_accuracy: 0.9657\n", - "\n", - "Epoch 00089: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 90/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5209 - accuracy: 0.7974 - val_loss: 0.2391 - val_accuracy: 0.9632\n", - "\n", - "Epoch 00090: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 91/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5023 - accuracy: 0.8022 - val_loss: 0.2242 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00091: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 92/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5345 - accuracy: 0.7989 - val_loss: 0.2257 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00092: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 93/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5404 - accuracy: 0.7961 - val_loss: 0.2314 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00093: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 94/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5065 - accuracy: 0.8004 - val_loss: 0.2324 - val_accuracy: 0.9691\n", - "\n", - "Epoch 00094: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 95/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5292 - accuracy: 0.8023 - val_loss: 0.2263 - val_accuracy: 0.9733\n", - "\n", - "Epoch 00095: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 96/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5031 - accuracy: 0.8069 - val_loss: 0.2243 - val_accuracy: 0.9724\n", - "\n", - "Epoch 00096: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 97/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4866 - accuracy: 0.8238 - val_loss: 0.2188 - val_accuracy: 0.9691\n", - "\n", - "Epoch 00097: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 98/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5235 - accuracy: 0.8082 - val_loss: 0.2216 - val_accuracy: 0.9741\n", - "\n", - "Epoch 00098: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 99/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5103 - accuracy: 0.8040 - val_loss: 0.2309 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00099: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 100/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4986 - accuracy: 0.8049 - val_loss: 0.2237 - val_accuracy: 0.9733\n", - "\n", - "Epoch 00100: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 101/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4859 - accuracy: 0.8137 - val_loss: 0.2149 - val_accuracy: 0.9724\n", - "\n", - "Epoch 00101: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 102/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5011 - accuracy: 0.8068 - val_loss: 0.2196 - val_accuracy: 0.9724\n", - "\n", - "Epoch 00102: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 103/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4784 - accuracy: 0.8216 - val_loss: 0.2194 - val_accuracy: 0.9733\n", - "\n", - "Epoch 00103: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 104/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4757 - accuracy: 0.8298 - val_loss: 0.2195 - val_accuracy: 0.9749\n", - "\n", - "Epoch 00104: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 105/1000\n", - "29/29 [==============================] - 0s 6ms/step - loss: 0.5101 - accuracy: 0.7992 - val_loss: 0.2342 - val_accuracy: 0.9632\n", - "\n", - "Epoch 00105: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 106/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4907 - accuracy: 0.8168 - val_loss: 0.2276 - val_accuracy: 0.9641\n", - "\n", - "Epoch 00106: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 107/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5035 - accuracy: 0.8050 - val_loss: 0.2187 - val_accuracy: 0.9691\n", - "\n", - "Epoch 00107: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 108/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5085 - accuracy: 0.7979 - val_loss: 0.2091 - val_accuracy: 0.9724\n", - "\n", - "Epoch 00108: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 109/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4948 - accuracy: 0.8134 - val_loss: 0.2169 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00109: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 110/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4943 - accuracy: 0.8085 - val_loss: 0.2115 - val_accuracy: 0.9657\n", - "\n", - "Epoch 00110: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 111/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5165 - accuracy: 0.8106 - val_loss: 0.2226 - val_accuracy: 0.9657\n", - "\n", - "Epoch 00111: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 112/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4962 - accuracy: 0.8136 - val_loss: 0.2235 - val_accuracy: 0.9666\n", - "\n", - "Epoch 00112: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 113/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5203 - accuracy: 0.8059 - val_loss: 0.2215 - val_accuracy: 0.9691\n", - "\n", - "Epoch 00113: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 114/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5095 - accuracy: 0.8011 - val_loss: 0.2207 - val_accuracy: 0.9724\n", - "\n", - "Epoch 00114: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 115/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4947 - accuracy: 0.8178 - val_loss: 0.2101 - val_accuracy: 0.9683\n", - "\n", - "Epoch 00115: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 116/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5079 - accuracy: 0.8116 - val_loss: 0.2172 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00116: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 117/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5056 - accuracy: 0.8056 - val_loss: 0.2236 - val_accuracy: 0.9691\n", - "\n", - "Epoch 00117: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 118/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5451 - accuracy: 0.7969 - val_loss: 0.2210 - val_accuracy: 0.9649\n", - "\n", - "Epoch 00118: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 119/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5005 - accuracy: 0.8192 - val_loss: 0.2220 - val_accuracy: 0.9733\n", - "\n", - "Epoch 00119: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 120/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.5130 - accuracy: 0.8043 - val_loss: 0.2170 - val_accuracy: 0.9716\n", - "\n", - "Epoch 00120: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 121/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4874 - accuracy: 0.8132 - val_loss: 0.2177 - val_accuracy: 0.9724\n", - "\n", - "Epoch 00121: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 122/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4863 - accuracy: 0.8106 - val_loss: 0.2247 - val_accuracy: 0.9716\n", - "\n", - "Epoch 00122: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 123/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4867 - accuracy: 0.8101 - val_loss: 0.2138 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00123: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 124/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4550 - accuracy: 0.8279 - val_loss: 0.2110 - val_accuracy: 0.9724\n", - "\n", - "Epoch 00124: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 125/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4908 - accuracy: 0.8152 - val_loss: 0.2088 - val_accuracy: 0.9716\n", - "\n", - "Epoch 00125: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 126/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5095 - accuracy: 0.8085 - val_loss: 0.2052 - val_accuracy: 0.9749\n", - "\n", - "Epoch 00126: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 127/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4814 - accuracy: 0.8096 - val_loss: 0.2055 - val_accuracy: 0.9733\n", - "\n", - "Epoch 00127: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 128/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4960 - accuracy: 0.8126 - val_loss: 0.2153 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00128: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 129/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4922 - accuracy: 0.8236 - val_loss: 0.2169 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00129: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 130/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4516 - accuracy: 0.8311 - val_loss: 0.2098 - val_accuracy: 0.9666\n", - "\n", - "Epoch 00130: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 131/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4596 - accuracy: 0.8147 - val_loss: 0.2010 - val_accuracy: 0.9758\n", - "\n", - "Epoch 00131: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 132/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4641 - accuracy: 0.8284 - val_loss: 0.1976 - val_accuracy: 0.9733\n", - "\n", - "Epoch 00132: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 133/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4831 - accuracy: 0.8071 - val_loss: 0.2037 - val_accuracy: 0.9766\n", - "\n", - "Epoch 00133: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 134/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4643 - accuracy: 0.8149 - val_loss: 0.2157 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00134: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 135/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4682 - accuracy: 0.8230 - val_loss: 0.2119 - val_accuracy: 0.9699\n", - "\n", - "Epoch 00135: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 136/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4986 - accuracy: 0.8085 - val_loss: 0.2093 - val_accuracy: 0.9724\n", - "\n", - "Epoch 00136: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 137/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4729 - accuracy: 0.8226 - val_loss: 0.1992 - val_accuracy: 0.9741\n", - "\n", - "Epoch 00137: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 138/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.5082 - accuracy: 0.8100 - val_loss: 0.2034 - val_accuracy: 0.9724\n", - "\n", - "Epoch 00138: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 139/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4770 - accuracy: 0.8159 - val_loss: 0.2011 - val_accuracy: 0.9691\n", - "\n", - "Epoch 00139: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 140/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4842 - accuracy: 0.8167 - val_loss: 0.2040 - val_accuracy: 0.9733\n", - "\n", - "Epoch 00140: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 141/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4895 - accuracy: 0.8208 - val_loss: 0.2113 - val_accuracy: 0.9657\n", - "\n", - "Epoch 00141: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 142/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4821 - accuracy: 0.8175 - val_loss: 0.2193 - val_accuracy: 0.9649\n", - "\n", - "Epoch 00142: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 143/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4909 - accuracy: 0.8237 - val_loss: 0.2194 - val_accuracy: 0.9607\n", - "\n", - "Epoch 00143: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 144/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4814 - accuracy: 0.8163 - val_loss: 0.2222 - val_accuracy: 0.9591\n", - "\n", - "Epoch 00144: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 145/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4776 - accuracy: 0.8226 - val_loss: 0.2159 - val_accuracy: 0.9657\n", - "\n", - "Epoch 00145: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 146/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4810 - accuracy: 0.8290 - val_loss: 0.2111 - val_accuracy: 0.9632\n", - "\n", - "Epoch 00146: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 147/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4892 - accuracy: 0.8163 - val_loss: 0.2065 - val_accuracy: 0.9666\n", - "\n", - "Epoch 00147: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 148/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4642 - accuracy: 0.8298 - val_loss: 0.2058 - val_accuracy: 0.9716\n", - "\n", - "Epoch 00148: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 149/1000\n", - "29/29 [==============================] - 0s 7ms/step - loss: 0.4666 - accuracy: 0.8255 - val_loss: 0.2084 - val_accuracy: 0.9733\n", - "\n", - "Epoch 00149: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 150/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4687 - accuracy: 0.8264 - val_loss: 0.1983 - val_accuracy: 0.9749\n", - "\n", - "Epoch 00150: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 151/1000\n", - "29/29 [==============================] - 0s 4ms/step - loss: 0.4801 - accuracy: 0.8201 - val_loss: 0.2018 - val_accuracy: 0.9724\n", - "\n", - "Epoch 00151: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 152/1000\n", - "29/29 [==============================] - 0s 3ms/step - loss: 0.4387 - accuracy: 0.8379 - val_loss: 0.2064 - val_accuracy: 0.9724\n", - "\n", - "Epoch 00152: saving model to model/keypoint_classifier/keypoint_classifier.hdf5\n", - "Epoch 00152: early stopping\n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 11 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "pxvb2Y299hE3", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "59eb3185-2e37-4b9e-bc9d-ab1b8ac29b7f" - }, - "source": [ - "# Model evaluation\n", - "val_loss, val_acc = model.evaluate(X_test, y_test, batch_size=128)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "10/10 [==============================] - 0s 2ms/step - loss: 0.2064 - accuracy: 0.9724\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "RBkmDeUW9hE4" - }, - "source": [ - "# Loading the saved model\n", - "model = tf.keras.models.load_model(model_save_path)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "tFz9Tb0I9hE4", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "1c3b3528-54ae-4ee2-ab04-77429211cbef" - }, - "source": [ - "# Inference test\n", - "predict_result = model.predict(np.array([X_test[0]]))\n", - "print(np.squeeze(predict_result))\n", - "print(np.argmax(np.squeeze(predict_result)))" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[9.8105639e-01 1.8674158e-02 2.2328236e-04 4.6191799e-05]\n", - "0\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "S3U4yNWx9hE4" - }, - "source": [ - "# Confusion matrix" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "AP1V6SCk9hE5", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 582 - }, - "outputId": "08e41a80-7a4a-4619-8125-ecc371368d19" - }, - "source": [ - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import confusion_matrix, classification_report\n", - "\n", - "def print_confusion_matrix(y_true, y_pred, report=True):\n", - " labels = sorted(list(set(y_true)))\n", - " cmx_data = confusion_matrix(y_true, y_pred, labels=labels)\n", - " \n", - " df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)\n", - " \n", - " fig, ax = plt.subplots(figsize=(7, 6))\n", - " sns.heatmap(df_cmx, annot=True, fmt='g' ,square=False)\n", - " ax.set_ylim(len(set(y_true)), 0)\n", - " plt.show()\n", - " \n", - " if report:\n", - " print('Classification Report')\n", - " print(classification_report(y_test, y_pred))\n", - "\n", - "Y_pred = model.predict(X_test)\n", - "y_pred = np.argmax(Y_pred, axis=1)\n", - "\n", - "print_confusion_matrix(y_test, y_pred)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - }, - { - "output_type": "stream", - "text": [ - "Classification Report\n", - " precision recall f1-score support\n", - "\n", - " 0 0.99 1.00 0.99 402\n", - " 1 0.98 0.94 0.96 366\n", - " 2 0.94 0.98 0.96 343\n", - " 3 1.00 0.99 0.99 86\n", - "\n", - " accuracy 0.97 1197\n", - " macro avg 0.98 0.98 0.98 1197\n", - "weighted avg 0.97 0.97 0.97 1197\n", - "\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FNP6aqzc9hE5" - }, - "source": [ - "# Convert to model for Tensorflow-Lite" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "ODjnYyld9hE6" - }, - "source": [ - "# Save as a model dedicated to inference\n", - "model.save(model_save_path, include_optimizer=False)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "zRfuK8Y59hE6", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a4ca585c-b5d5-4244-8291-8674063209bb" - }, - "source": [ - "# Transform model (quantization)\n", - "\n", - "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n", - "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n", - "tflite_quantized_model = converter.convert()\n", - "\n", - "open(tflite_save_path, 'wb').write(tflite_quantized_model)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmpe5yx255p/assets\n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "6352" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 17 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CHBPBXdx9hE6" - }, - "source": [ - "# Inference test" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "mGAzLocO9hE7" - }, - "source": [ - "interpreter = tf.lite.Interpreter(model_path=tflite_save_path)\n", - "interpreter.allocate_tensors()" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "oQuDK8YS9hE7" - }, - "source": [ - "# Get I / O tensor\n", - "input_details = interpreter.get_input_details()\n", - "output_details = interpreter.get_output_details()" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "2_ixAf_l9hE7" - }, - "source": [ - "interpreter.set_tensor(input_details[0]['index'], np.array([X_test[0]]))" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "scrolled": true, - "id": "s4FoAnuc9hE7", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "91f18257-8d8b-4ef3-c558-e9b5f94fabbf" - }, - "source": [ - "%%time\n", - "# Inference implementation\n", - "interpreter.invoke()\n", - "tflite_results = interpreter.get_tensor(output_details[0]['index'])" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "CPU times: user 131 µs, sys: 17 µs, total: 148 µs\n", - "Wall time: 679 µs\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "vONjp19J9hE8", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "77205e24-fd00-42c4-f7b6-e06e527c2cba" - }, - "source": [ - "print(np.squeeze(tflite_results))\n", - "print(np.argmax(np.squeeze(tflite_results)))" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[9.8105639e-01 1.8674169e-02 2.2328216e-04 4.6191799e-05]\n", - "0\n" - ], - "name": "stdout" - } - ] - } - ] -} \ No newline at end of file +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "colab": { + "name": "keypoint_classification_EN.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "igMyGnjE9hEp", + "ExecuteTime": { + "end_time": "2025-04-06T14:45:24.152533Z", + "start_time": "2025-04-06T14:45:24.142416Z" + } + }, + "source": [ + "import csv\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "RANDOM_SEED = 42" + ], + "outputs": [], + "execution_count": 23 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "t2HDvhIu9hEr" + }, + "source": [ + "# Specify each path" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9NvZP2Zn9hEy", + "ExecuteTime": { + "end_time": "2025-04-06T14:45:24.168038Z", + "start_time": "2025-04-06T14:45:24.155536Z" + } + }, + "source": [ + "dataset = 'model/keypoint_classifier/keypoint.csv'\n", + "model_save_path = 'model/keypoint_classifier/keypoint_classifier.keras'\n", + "tflite_save_path = 'model/keypoint_classifier/keypoint_classifier.tflite'" + ], + "outputs": [], + "execution_count": 24 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s5oMH7x19hEz" + }, + "source": [ + "# Set number of classes" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "du4kodXL9hEz", + "ExecuteTime": { + "end_time": "2025-04-06T14:45:24.199254Z", + "start_time": "2025-04-06T14:45:24.184255Z" + } + }, + "source": "NUM_CLASSES = 5", + "outputs": [], + "execution_count": 25 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XjnL0uso9hEz" + }, + "source": [ + "# Dataset reading" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QT5ZqtEz9hE0", + "ExecuteTime": { + "end_time": "2025-04-06T14:45:24.262267Z", + "start_time": "2025-04-06T14:45:24.216254Z" + } + }, + "source": [ + "X_dataset = np.loadtxt(dataset, delimiter=',', dtype='float32', usecols=list(range(1, (21 * 2) + 1)))" + ], + "outputs": [], + "execution_count": 26 + }, + { + "cell_type": "code", + "metadata": { + "id": "QmoKFsp49hE0", + "ExecuteTime": { + "end_time": "2025-04-06T14:45:24.309269Z", + "start_time": "2025-04-06T14:45:24.277928Z" + } + }, + "source": [ + "y_dataset = np.loadtxt(dataset, delimiter=',', dtype='int32', usecols=(0))" + ], + "outputs": [], + "execution_count": 27 + }, + { + "cell_type": "code", + "metadata": { + "id": "xQU7JTZ_9hE0", + "ExecuteTime": { + "end_time": "2025-04-06T14:45:24.340387Z", + "start_time": "2025-04-06T14:45:24.326059Z" + } + }, + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, train_size=0.75, random_state=RANDOM_SEED)" + ], + "outputs": [], + "execution_count": 28 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mxK_lETT9hE0" + }, + "source": [ + "# Model building" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vHBmUf1t9hE1", + "ExecuteTime": { + "end_time": "2025-04-06T14:45:24.387152Z", + "start_time": "2025-04-06T14:45:24.356636Z" + } + }, + "source": [ + "model = tf.keras.models.Sequential([\n", + " tf.keras.layers.Input((21 * 2, )),\n", + " tf.keras.layers.Dropout(0.2),\n", + " tf.keras.layers.Dense(20, activation='relu'),\n", + " tf.keras.layers.Dropout(0.4),\n", + " tf.keras.layers.Dense(10, activation='relu'),\n", + " tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n", + "])" + ], + "outputs": [], + "execution_count": 29 + }, + { + "cell_type": "code", + "metadata": { + "id": "ypqky9tc9hE1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5db082bb-30e3-4110-bf63-a1ee777ecd46", + "ExecuteTime": { + "end_time": "2025-04-06T14:45:24.418388Z", + "start_time": "2025-04-06T14:45:24.403879Z" + } + }, + "source": [ + "model.summary() # tf.keras.utils.plot_model(model, show_shapes=True)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001B[1mModel: \"sequential_1\"\u001B[0m\n" + ], + "text/html": [ + "
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accuracy: 0.1953 - loss: 1.6084\n", + "Epoch 1: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.2250 - loss: 1.5929 - val_accuracy: 0.3814 - val_loss: 1.5218\n", + "Epoch 2/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.3047 - loss: 1.5177\n", + "Epoch 2: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.3112 - loss: 1.5054 - val_accuracy: 0.4586 - val_loss: 1.4139\n", + "Epoch 3/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3281 - loss: 1.4603\n", + "Epoch 3: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.3643 - loss: 1.4192 - val_accuracy: 0.4451 - val_loss: 1.3149\n", + "Epoch 4/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4688 - loss: 1.2876\n", + "Epoch 4: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.4201 - loss: 1.3231 - val_accuracy: 0.4896 - val_loss: 1.2231\n", + "Epoch 5/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4609 - loss: 1.2559\n", + "Epoch 5: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4451 - loss: 1.2600 - val_accuracy: 0.5932 - val_loss: 1.1387\n", + "Epoch 6/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4609 - loss: 1.1967\n", + "Epoch 6: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.4665 - loss: 1.2117 - val_accuracy: 0.6473 - val_loss: 1.0768\n", + "Epoch 7/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5156 - loss: 1.1476\n", + "Epoch 7: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.5104 - loss: 1.1613 - val_accuracy: 0.6863 - val_loss: 1.0110\n", + "Epoch 8/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.5625 - loss: 1.0796\n", + "Epoch 8: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5219 - loss: 1.1159 - val_accuracy: 0.6967 - val_loss: 0.9488\n", + "Epoch 9/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5312 - loss: 1.1139\n", + "Epoch 9: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.5371 - loss: 1.0842 - val_accuracy: 0.7102 - val_loss: 0.8944\n", + "Epoch 10/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5078 - loss: 1.0926\n", + "Epoch 10: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5419 - loss: 1.0645 - val_accuracy: 0.7396 - val_loss: 0.8457\n", + "Epoch 11/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5703 - loss: 0.9679\n", + "Epoch 11: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5770 - loss: 1.0044 - val_accuracy: 0.7420 - val_loss: 0.7995\n", + "Epoch 12/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5703 - loss: 1.0771\n", + "Epoch 12: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5787 - loss: 1.0050 - val_accuracy: 0.7659 - val_loss: 0.7561\n", + "Epoch 13/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6406 - loss: 0.8542\n", + "Epoch 13: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.5961 - loss: 0.9563 - val_accuracy: 0.8089 - val_loss: 0.7225\n", + "Epoch 14/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6406 - loss: 0.8939\n", + "Epoch 14: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.6199 - loss: 0.9169 - val_accuracy: 0.8073 - val_loss: 0.6949\n", + "Epoch 15/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5938 - loss: 0.9604\n", + "Epoch 15: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6071 - loss: 0.9359 - val_accuracy: 0.8161 - val_loss: 0.6721\n", + "Epoch 16/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5938 - loss: 0.9904\n", + "Epoch 16: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6136 - loss: 0.9143 - val_accuracy: 0.8193 - val_loss: 0.6504\n", + "Epoch 17/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6406 - loss: 0.8992\n", + "Epoch 17: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6218 - loss: 0.9049 - val_accuracy: 0.8177 - val_loss: 0.6292\n", + "Epoch 18/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6562 - loss: 0.8734\n", + "Epoch 18: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6356 - loss: 0.8709 - val_accuracy: 0.8280 - val_loss: 0.6049\n", + "Epoch 19/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6484 - loss: 0.8783\n", + "Epoch 19: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6448 - loss: 0.8715 - val_accuracy: 0.8400 - val_loss: 0.5920\n", + "Epoch 20/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6250 - loss: 0.8917\n", + "Epoch 20: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6444 - loss: 0.8544 - val_accuracy: 0.8479 - val_loss: 0.5746\n", + "Epoch 21/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6406 - loss: 0.8452\n", + "Epoch 21: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6532 - loss: 0.8258 - val_accuracy: 0.8567 - val_loss: 0.5542\n", + "Epoch 22/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7109 - loss: 0.7396\n", + "Epoch 22: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6710 - loss: 0.8069 - val_accuracy: 0.8559 - val_loss: 0.5424\n", + "Epoch 23/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7188 - loss: 0.7342\n", + "Epoch 23: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6735 - loss: 0.7803 - val_accuracy: 0.8678 - val_loss: 0.5294\n", + "Epoch 24/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6328 - loss: 0.9532\n", + "Epoch 24: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6628 - loss: 0.8159 - val_accuracy: 0.8623 - val_loss: 0.5198\n", + "Epoch 25/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6562 - loss: 0.8667\n", + "Epoch 25: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6748 - loss: 0.8030 - val_accuracy: 0.8686 - val_loss: 0.5088\n", + "Epoch 26/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6250 - loss: 0.7683\n", + "Epoch 26: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6848 - loss: 0.7822 - val_accuracy: 0.8646 - val_loss: 0.5015\n", + "Epoch 27/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6328 - loss: 0.9834\n", + "Epoch 27: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6820 - loss: 0.8063 - val_accuracy: 0.8718 - val_loss: 0.5012\n", + "Epoch 28/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7109 - loss: 0.7102\n", + "Epoch 28: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.6876 - loss: 0.7574 - val_accuracy: 0.8734 - val_loss: 0.4913\n", + "Epoch 29/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6797 - loss: 0.7794\n", + "Epoch 29: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6802 - loss: 0.7801 - val_accuracy: 0.8702 - val_loss: 0.4805\n", + "Epoch 30/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6641 - loss: 0.8337\n", + "Epoch 30: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6880 - loss: 0.7710 - val_accuracy: 0.8758 - val_loss: 0.4702\n", + "Epoch 31/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.6962\n", + "Epoch 31: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7054 - loss: 0.7254 - val_accuracy: 0.8710 - val_loss: 0.4637\n", + "Epoch 32/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6719 - loss: 0.8108\n", + "Epoch 32: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6712 - loss: 0.7703 - val_accuracy: 0.8798 - val_loss: 0.4561\n", + "Epoch 33/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7031 - loss: 0.7438\n", + "Epoch 33: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6900 - loss: 0.7467 - val_accuracy: 0.8814 - val_loss: 0.4527\n", + "Epoch 34/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6641 - loss: 0.7874\n", + "Epoch 34: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6893 - loss: 0.7567 - val_accuracy: 0.8838 - val_loss: 0.4389\n", + "Epoch 35/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7031 - loss: 0.6894\n", + "Epoch 35: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.7073 - loss: 0.7134 - val_accuracy: 0.8774 - val_loss: 0.4399\n", + "Epoch 36/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.5961\n", + "Epoch 36: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.7120 - loss: 0.6963 - val_accuracy: 0.8854 - val_loss: 0.4290\n", + "Epoch 37/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.6530\n", + "Epoch 37: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7089 - loss: 0.7233 - val_accuracy: 0.8838 - val_loss: 0.4288\n", + "Epoch 38/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6953 - loss: 0.7137\n", + "Epoch 38: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7096 - loss: 0.7194 - val_accuracy: 0.8869 - val_loss: 0.4218\n", + "Epoch 39/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6719 - loss: 0.7741\n", + "Epoch 39: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7013 - loss: 0.7169 - val_accuracy: 0.8838 - val_loss: 0.4200\n", + "Epoch 40/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6797 - loss: 0.7328\n", + "Epoch 40: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7036 - loss: 0.7143 - val_accuracy: 0.8877 - val_loss: 0.4118\n", + "Epoch 41/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7188 - loss: 0.6635\n", + "Epoch 41: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7087 - loss: 0.7039 - val_accuracy: 0.8901 - val_loss: 0.4091\n", + "Epoch 42/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6797 - loss: 0.7643\n", + "Epoch 42: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7097 - loss: 0.7095 - val_accuracy: 0.8893 - val_loss: 0.4064\n", + "Epoch 43/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6797 - loss: 0.6689\n", + "Epoch 43: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7024 - loss: 0.7039 - val_accuracy: 0.8877 - val_loss: 0.3990\n", + "Epoch 44/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7188 - loss: 0.7303\n", + "Epoch 44: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7352 - loss: 0.6769 - val_accuracy: 0.8869 - val_loss: 0.3973\n", + "Epoch 45/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7266 - loss: 0.6801\n", + "Epoch 45: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7095 - loss: 0.7069 - val_accuracy: 0.8885 - val_loss: 0.3942\n", + "Epoch 46/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6953 - loss: 0.7340\n", + "Epoch 46: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6962 - loss: 0.7316 - val_accuracy: 0.8949 - val_loss: 0.3938\n", + "Epoch 47/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6875 - loss: 0.7919\n", + "Epoch 47: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7055 - loss: 0.7090 - val_accuracy: 0.8925 - val_loss: 0.3915\n", + "Epoch 48/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7500 - loss: 0.6764\n", + "Epoch 48: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7318 - loss: 0.6735 - val_accuracy: 0.8941 - val_loss: 0.3857\n", + "Epoch 49/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7734 - loss: 0.5898\n", + "Epoch 49: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7451 - loss: 0.6481 - val_accuracy: 0.8909 - val_loss: 0.3789\n", + "Epoch 50/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7031 - loss: 0.7234\n", + "Epoch 50: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7266 - loss: 0.6766 - val_accuracy: 0.9100 - val_loss: 0.3837\n", + "Epoch 51/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.6571\n", + "Epoch 51: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7379 - loss: 0.6709 - val_accuracy: 0.9068 - val_loss: 0.3770\n", + "Epoch 52/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7031 - loss: 0.7096\n", + "Epoch 52: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7205 - loss: 0.6914 - val_accuracy: 0.9068 - val_loss: 0.3794\n", + "Epoch 53/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.6484\n", + "Epoch 53: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7356 - loss: 0.6619 - val_accuracy: 0.9124 - val_loss: 0.3734\n", + "Epoch 54/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6953 - loss: 0.7277\n", + "Epoch 54: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7288 - loss: 0.6591 - val_accuracy: 0.9204 - val_loss: 0.3661\n", + "Epoch 55/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.6324\n", + "Epoch 55: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7452 - loss: 0.6431 - val_accuracy: 0.9180 - val_loss: 0.3635\n", + "Epoch 56/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6875 - loss: 0.7121\n", + "Epoch 56: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.7250 - loss: 0.6594 - val_accuracy: 0.9148 - val_loss: 0.3637\n", + "Epoch 57/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6562 - loss: 0.7921\n", + "Epoch 57: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7322 - loss: 0.6624 - val_accuracy: 0.9148 - val_loss: 0.3610\n", + "Epoch 58/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6875 - loss: 0.7217\n", + "Epoch 58: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - accuracy: 0.7354 - loss: 0.6633 - val_accuracy: 0.9315 - val_loss: 0.3510\n", + "Epoch 59/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 20ms/step - accuracy: 0.6797 - loss: 0.7286\n", + "Epoch 59: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7248 - loss: 0.6512 - val_accuracy: 0.9379 - val_loss: 0.3492\n", + "Epoch 60/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7891 - loss: 0.5594\n", + "Epoch 60: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7479 - loss: 0.6388 - val_accuracy: 0.9331 - val_loss: 0.3451\n", + "Epoch 61/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.8906 - loss: 0.4979\n", + "Epoch 61: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7643 - loss: 0.6194 - val_accuracy: 0.9355 - val_loss: 0.3395\n", + "Epoch 62/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6549\n", + "Epoch 62: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7414 - loss: 0.6411 - val_accuracy: 0.9275 - val_loss: 0.3461\n", + "Epoch 63/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.6774\n", + "Epoch 63: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7465 - loss: 0.6484 - val_accuracy: 0.9347 - val_loss: 0.3392\n", + "Epoch 64/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5636\n", + "Epoch 64: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7546 - loss: 0.6185 - val_accuracy: 0.9371 - val_loss: 0.3372\n", + "Epoch 65/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.6386\n", + "Epoch 65: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7490 - loss: 0.6555 - val_accuracy: 0.9379 - val_loss: 0.3383\n", + "Epoch 66/1000\n", + "\u001B[1m 1/30\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7422 - loss: 0.7631\n", + "Epoch 66: saving model to model/keypoint_classifier/keypoint_classifier.keras\n", + "\u001B[1m30/30\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7357 - loss: 0.6574 - val_accuracy: 0.9419 - val_loss: 0.3427\n", + "Epoch 67/1000\n" + ] + } + ], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "id": "pxvb2Y299hE3", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "59eb3185-2e37-4b9e-bc9d-ab1b8ac29b7f", + "ExecuteTime": { + "end_time": "2025-04-06T14:42:46.020954Z", + "start_time": "2025-04-06T14:42:45.957934Z" + } + }, + "source": [ + "# Model evaluation\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, batch_size=128)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1m10/10\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.9746 - loss: 0.1850 \n" + ] + } + ], + "execution_count": 12 + }, + { + "cell_type": "code", + "metadata": { + "id": "RBkmDeUW9hE4", + "ExecuteTime": { + "end_time": "2025-04-06T14:42:46.131030Z", + "start_time": "2025-04-06T14:42:46.068957Z" + } + }, + "source": [ + "# Loading the saved model\n", + "model = tf.keras.models.load_model(model_save_path)" + ], + "outputs": [], + "execution_count": 13 + }, + { + "cell_type": "code", + "metadata": { + "id": "tFz9Tb0I9hE4", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1c3b3528-54ae-4ee2-ab04-77429211cbef", + "ExecuteTime": { + "end_time": "2025-04-06T14:42:46.194982Z", + "start_time": "2025-04-06T14:42:46.134983Z" + } + }, + "source": [ + "# Inference test\n", + "predict_result = model.predict(np.array([X_test[0]]))\n", + "print(np.squeeze(predict_result))\n", + "print(np.argmax(np.squeeze(predict_result)))" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step\n", + "[1.6097246e-02 5.2194584e-02 9.3088049e-01 7.2499871e-04 1.0261367e-04]\n", + "2\n" + ] + } + ], + "execution_count": 14 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S3U4yNWx9hE4" + }, + "source": [ + "# Confusion matrix" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "AP1V6SCk9hE5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 582 + }, + "outputId": "08e41a80-7a4a-4619-8125-ecc371368d19", + "ExecuteTime": { + "end_time": "2025-04-06T14:42:46.479204Z", + "start_time": "2025-04-06T14:42:46.242995Z" + } + }, + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import confusion_matrix, classification_report\n", + "\n", + "def print_confusion_matrix(y_true, y_pred, report=True):\n", + " labels = sorted(list(set(y_true)))\n", + " cmx_data = confusion_matrix(y_true, y_pred, labels=labels)\n", + " \n", + " df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)\n", + " \n", + " fig, ax = plt.subplots(figsize=(7, 6))\n", + " sns.heatmap(df_cmx, annot=True, fmt='g' ,square=False)\n", + " ax.set_ylim(len(set(y_true)), 0)\n", + " plt.show()\n", + " \n", + " if report:\n", + " print('Classification Report')\n", + " print(classification_report(y_test, y_pred))\n", + "\n", + "Y_pred = model.predict(X_test)\n", + "y_pred = np.argmax(Y_pred, axis=1)\n", + "\n", + "print_confusion_matrix(y_test, y_pred)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1m39/39\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 767us/step\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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tNTVVp06dSrc5+y5UuHBhjRo1yhQvTnLiFC5r1qxRdHS0Nm3apGrVqpniJU1UVJQ2btxovneO165d23csLCxM1atX9x33BwUMAADwmTRpkik2zt+cfX/mjjvuUJs2bcxYmKZNmyohIcEUOOcLDw/XkSNHzPf/67g/6EICAMA23qzrQoqJiVGHDh3S7QsJCfnTx4wePdp0KTndSU53UFJS0kWPcW6nJTn/67g/KGAAAEC6QuJ/FSwXqlmzpvmakpKiXr16qVWrVqZIOZ9TnOTMmdN8HxoaelGx4tzOly+f369JFxIAALbxZN0YGH85icvixYvT7atQoYLOnDmjQoUKmeMX3j+t26hIkSKXPO48zl8UMAAAIMMOHDhgpkbHxcX59m3ZssVMiXbGzWzdulXJycm+Y84gX2fNF4fz1bmdxklrtm3b5jvuDwoYAAAs4/V4smzLSLeRM3Oob9++2rVrl5YuXaqhQ4fq6aefNjORihUrpj59+mjnzp2aPHmyNm/erIceesg81uliWr9+vdnvHHfuV7JkSb+nUDsoYAAAQIZlz55d48ePN1OgH3nkEfXr109t27Y1q+qmHXNmGzmL1X366acaN26cihcvbh7rFCtjxowx68I4Rc3x48fN8WzZsvn9+tm8GVn2LguF5izldhOC1oklg9xuQlAKb9LP7SYErZSzZ9xuApBhZ1MPXrHX+u+b7bLsuXP3my4bMAsJAADbeN1fiddtdCEBAADrkMAAAGAbT0CM/nAVCQwAALAOCQwAALbxMAaGBAYAAFiHBAYAANt4GANDAgMAAKxDAgMAgG28jIGhgAEAwDYeupDoQgIAANYhgQEAwDJeplGTwAAAAPuQwAAAYBsPY2BIYDIgJCREb496Q0cO/6B9ses1YMALbjfJGvvijurp4TNV95mBatprlN7/coXv2OAPvlJkxwHpttlLVl/0HP9as80cg//v1zVr/qkGDer69pUpU1KLFs1UfMI2rV33te68s4GrbbRZaGioJk8apsT4bdofu17dn4txu0lBg3MLf5DAZMCI4f3VsGF9Nb+vrfLmza0Z08dp374DmjJllttNC2gej1ddR81W9euLa27/TqaYeXHSAhUukFfN6tbUnkMJerbVHWpx642+x+TOGZruOU6eTjaFDvz/AHjv/bdVrXrldPvnzn1HW7f+qAa33qf77muq2XMm6aZajXXgwCHX2mqrwYNeUlRUpJrc9bBKlymp96aOUuy+A1qw4HO3m2Y9zq0fPCQwJDB+KlAgv9q3/7ue6fyC1q7dqG++Wa5Rb0/WzTfXcrtpAe+Xk6dUuXQRvdS2mcoUCVeDGyoquur12rBzvzm+53CiqpYppojr8vi2sNAc6Z5j5Idfq2ShAi79BHapUqWC/rN0ocpdXybd/ttvr6fry5VWt259tWPHbg0bNl6rV61Xu8cfdq2ttsqVK0xPdGytHj1e0YaNW/TJJ19p2PAJ6vJMe7ebZj3OLfxFAeOn+rfcrBMnftV3333v2+d8AMTE9HK1XTYolD+vhj7zkHKHhcrr9WrDzn1a/1Osalcuo1NJKYo/9qvKFA3/w8ev3bFXa3+M1VPNb72i7bbVrQ3q6tulK9Wo0YPp9t8cXUsbN27R6dNJvn0rVq5VneibXGil3SJvqK4cOXKY85dm+fLVio6upWzZsrnaNttxbjOwkJ03izZL0IXkp+uvL63Y2AN69NFWeqF3V4WE5NC06fM0aNBo86EM/9zTe7QO/3JCt0VWVOPaVbX150NyfidNWfSdlv2wS/lz51LbpnV1f/1Ic//UM2c14P3P1eexe5Tj2uxuN98KU96Zecn9RYsW1uHD8en2xccnqniJoleoZcGjaLHCSkw8qjNnzvj2xcUnKCwsTOHhBcwxXB7OrZ88fO5QwPgpd57cqlChrJ588lE91amn+TAYN3aQkk4nma4k+Gd4578p8cQpvTnjCw2d/S9VK1tM2ZRNZYtGqPWd0Vq7I1YDpi0yY2DujKqiyZ99q6pliuqWGuW15se9bjff+mg+NSU13b7UlBSFhoa41iabz2XKBecy7bYz/giXj3OLLClg1qxZ4/d9b775ZgWTs2fP6rrr8unxx7tp376DZl/pUiUUE9OOAiYDnIG8jtSzZ9Vn8kKtePgF3R5ZSdflCTP7K5Uqoti4X/Thf9aqdJGCmr90vT4a8LTLrQ4OyckpKlgwV7p9IaGhpghHxs/lhYVf2u3zu+iQcZxb/3hJYDJWwAwYMEC7du0y3/9Zt4nTT7l9+3YFkyNH4pWUlOwrXhw//bRbJUv+9oGMP/bLiVPatPuA7ripim9fuWKFdObsOf03OVUF8qb/UC1XLEKrt+/VknXbdfK/Sbr3xTG+2UwOZyr2y+2a6956Na/wT2K3Q4eOqGrVSun2FSlSSEeOJLjWJlsdOnhEEREFlT17dp07d87sK1qksPmAPX78hNvNsxrnFllSwHz00Ufq0aOHDhw4oLlz515VcZ4zWyMsLKcqVrheO3f9bPZVqVJRsbG/zaTBHzuYeFw9xn2ofw57TkUK5DP7tsUeNoXLB4tXadOuA5r8fFvf/Xfsi9P1xcJNl5IzzTrND3sOqu87C/Vh/xiF58vtys9iszWrN6hnz2eUM2eo+SvXcUu92ukGS8I/GzdtMWM06ta5SctX/JZM168fbWYoMibur+Hc+snDubgmowtjjRgxwnw/atQoXU1+2rlHX3yxWO+8M0I1a1ZVk8a3q1evzpo8eYbbTbOi26hameJ69d1Ptftggr7bvNNMi36qeQPdfmMlrfspVtO+WqH98Uf14Tdr9dmKTXq8aT3TreR0I6VtzroxDud7Z0YTMua771bpwIHDmjhpmKpWrWiKmajakZr2/ly3m2YdJ42dPmO+xo0bpNpRkbr//qbq0T1Go8dOdbtp1uPcIssG8TpFzPDhw7V69cUrpQa7x9s/q5EjB+ibfy8wceaEie9r3Pj33G5WwMt+zTUa1e0RDZz1pdq99a7CQnKoTeNoszndjcM6/03jF/5H4xb+R8Uj8mtgTEtFVijldrODjsfj0SMPP6XxE4Zo2fJF2rN7r1r/PYZF7C5Tr+f7m4H8i7+epxMnTuq1AcP18cdfut2soMC59YPHnunOWSWbN0AyudCcfGBllRNLBrndhKAU3qSf200IWilnf59CC9jibOrvYySz2q9dm2XZc+cd+4VswDRqAABs4wmI7MFVFDAAANjGQwHDpQQAAIB1SGAAALCMNzCGr7qKBAYAAFiHBAYAANt4SGBIYAAAgHVIYAAAsI2HBIYEBgAAWIcEBgAAy3hJYChgAACwjocChi4kAABgHRIYAABs43G7Ae4jgQEAANYhgQEAwDJexsCQwAAAAPuQwAAAYBsPCQwJDAAAsA4JDAAAtvG43QD3kcAAAADrUMAAAGDhLCRvFm0ZERcXp2effVbR0dFq0KCBBg4cqJSUFHPsjTfeUOXKldNtM2fO9D120aJFaty4sSIjI9WlSxcdPXo0Q69NFxIAALbxuN0Ayev1muIlX758mjVrlk6cOKG+ffvqmmuu0QsvvKDdu3erZ8+eevDBB32PyZMnj/m6efNm9evXT6+99pqqVKmiN998U3369NGkSZP8fn0SGAAAkGF79uzRxo0bTepSsWJF1a5d2xQ0TrLicAqYatWqqVChQr4tLCzMHHOSmHvuuUcPPPCAKWCGDBmipUuXav/+/X6/PgUMAACW8QZAF5JTkEyZMkURERHp9p86dcpsTvdS2bJlL/nYTZs2mYInTbFixVS8eHGz3190IQEAAJ/U1FSznS8kJMRs53O6jpxxL2k8Ho9JVurWrWvSl2zZsmnixIn69ttvlT9/fnXo0MHXnRQfH6/ChQune77w8HAdOXJE/qKAAQDANp6se2pnHMrYsWPT7evatau6dev2p48bOnSotm3bpvnz52vr1q2mgClXrpwee+wxrVmzRi+//LIZA9OkSRMlJydfVBA5ty8snP4MBQwAAPCJiYkxacn5Liw2LlW8TJs2TSNHjlSlSpXMmJhGjRqZ5MXhjHPZu3evZs+ebQqY0NDQi4oV53baGBl/UMAAAGAZbxYmMJfqLvozr7/+uilMnCKmadOmZp+TvqQVL2mcNOb777833xcpUkSJiYnpjju3nXE1/mIQLwAAuCxOV9OcOXM0YsQI3Xvvvb79b7/9ttq3b5/uvj/++KMpYhzO2i/r1q3zHTt8+LDZnP3+IoEBAMA2Hrcb8Ns06fHjx6tTp06KiopSQkKC75jTfTR58mRNnTrVdBktW7ZMH3/8saZPn26Ot27dWm3bttWNN96omjVrmnVgGjZsqFKlSvn9+hQwAABYxhsABcySJUt07tw5TZgwwWzn27Fjh0lhRo8ebb6WKFFCw4cPV61atcxx5+uAAQPMcWcBvPr165uuqIzI5nWW0gsAoTn9r7qQMSeWDHK7CUEpvEk/t5sQtFLOnnG7CUCGnU09eMVeK/Ge27PsuSO+XCobkMAAAGAbj9sNcB+DeAEAgHVIYAAAsIyXBIYEBgAA2IcEBgAAy3hJYEhgAACAfUhgAACwjJcEhgIGAADreLPpakcBcxW47s4X3W5CUDo+M8btJgStvG3Sr+oJABeigAEAwDJeupAYxAsAAOxDAgMAgGW8HsbAkMAAAADrkMAAAGAZL2NgSGAAAIB9SGAAALCMl3VgKGAAALCNly4kupAAAIB9SGAAALCMl2nUJDAAAMA+JDAAAFjG63W7Be4jgQEAANYhgQEAwDJexsCQwAAAAPuQwAAAYBkvCQwFDAAAtvEyiJcuJAAAYB8SGAAALOOlC4kEBgAA2IcEBgAAy3i5GjUJDAAAsA8JDAAAlvF63G6B+0hgAACAdUhgAACwjIcxMBQwAADYxksBQxcSAACwDwkMAACW8bKQHQkMAACwDwkMAACW8XIxRxIYAABgHxIYAAAs42UMDAkMAACwDwkMAACW8bAODAUMAAC28VLA0IUEAADsQwIDAIBlvEyjJoEBAAD2oYABAMAyziDerNoyIi4uTs8++6yio6PVoEEDDRw4UCkpKebY/v371b59e914441q1qyZli1blu6xK1asUPPmzRUZGal27dqZ+2cEBUwG3H//3UpJ3p9um/3BRLebFRRCQkL09qg3dOTwD9oXu14DBrzgdpOsse+Xk3rmvcWq99ps3T3kI73/3VbfsRU7D+nhMYtU59UPzNdlOw6me+z3uw6r1dufqm7/D/TU1H/pwNFfXfgJ7BQaGqrJk4YpMX6b9seuV/fnYtxuUtDg3NrB6/Wa4iUpKUmzZs3SyJEj9c0332jUqFHmWJcuXRQREaGPPvpILVq0UNeuXXXo0CHzWOerc7xly5aaP3++ChYsqM6dO5vH+YsxMBlQtWpFLVr0tTp3+f3DNTn5t0oTf82I4f3VsGF9Nb+vrfLmza0Z08dp374DmjJllttNC2gej1fdpn+j6iXCNafLvdr3y6/qM/c7Fc6XSzVKhqvHrP+oS5Mb1ahqKX2zbb+6z/qPPu7eQiUK5NHh4/81t5+5M1K3VCyuyd9sVveZ/9GH3ZorWzZmOPwvgwe9pKioSDW562GVLlNS700dpdh9B7RgweduN816nFs7ZiHt2bNHGzdu1PLly02h4nAKmsGDB+u2224zicqcOXOUK1culS9fXitXrjTFTLdu3TRv3jzVqFFDHTt2NI9zkpv69etr9erVqlOnjl+vTwKTAVWqVNDWbTsUF5fg206cOOl2s6xXoEB+tW//dz3T+QWtXbtR33yzXKPenqybb67ldtMC3i+nklS5WAH1a1FHZSLyqUHlEoouX1Qb9sYr7sRptby5otrWr6aSBfOq7a3VFBZyrbYcSDSPXbh2p6qVCFe7W6upQpH8eq3lLTp0/L9a+3Oc2z9WwMuVK0xPdGytHj1e0YaNW/TJJ19p2PAJ6vJMe7ebZj3OrftSU1N16tSpdJuz70KFChXSlClTfMVLGuf+mzZtUrVq1UzxkiYqKsoUPA7neO3atX3HwsLCVL16dd9xf1DAZEDVKhW1c+cet5sRdOrfcrNOnPhV3333vW/fsGHjFRPTy9V22aBQvlwa8vfblDs0h4leN8TGa/3eeNUuV0Q3lyuq3vfebO535pzHFCypZ8+pRsnfftls3p+oqLKFfc/lFDdVihfU5v0Jrv08toi8obpy5MihFSvX+vYtX75a0dG1SK/+Is6tf7zerNsmTZpkio3zN2ffhfLly2fGvaTxeDyaOXOm6tatq4SEBBUu/PvvF0d4eLiOHDlivv9fx/1BF1IGVKpUXk2a3K4XendV9uzZ9dFHi/TagOE6c+aM202z2vXXl1Zs7AE9+mgrc25DQnJo2vR5GjRodIb6Q692zYYtNN1Ct1UuocbVS6cbI/PgqE91zuPVP5rWMt1HjsRfk1Qo7+9/HTnC8+Q0yQ3+XNFihZWYeDTdv/24+ATzV2R4eAFzDJeHc+v+SrwxMTHq0KHDReMU/5ehQ4dq27ZtZkzL+++/f9FjnNtpSY4zbubPjmdaAeM84dtvv61Fixbp119/1S233KLu3bubPq00iYmJphLbvn27glHp0iWUO3cupaSkqs2jz6hs2dIaMeI1hYXlVM9e/d1untVy58mtChXK6sknH9VTnXqqaNHCGjd2kJJOJ5muJPhnWOvbTZfSm5+u0rAv1uqF5tFmf4HcOTXrmWbatC9Bw79cq1IF86pxjTJKPnNWOa5NH8KGZM+uM2c9Lv0EdnVzOL8Lzpd22xmAisvHuXVfSEiIXwXLhcXLtGnTzEDeSpUqmf9Xx48fv6iWyJkzp/neOX5hseLcdlKdTC1gRowYYUYW9+7d2/xF7ERErVq10rBhw9S4cWPf/YL5r+V9+w6qaLGaOnbst/8hmzdv0zXXZNP7743W870HmOgMl+fs2bO67rp8evzxbuY8O0qXKqGYmHYUMBlQvWS4+Zpy9pz6frhMPe6OUo5rsytvzhDTNeRse+JPaPb3O0wBE3LtxcVK6rlzyhuWw6WfwB7O4P3Q0PS/4NNunz6d5FKrggPn1p5BvGlef/11zZ492xQxTZs2NfuKFCmiXbt26XxO0JHWbeQcd25feLxq1arK1DEwX375pd566y3de++9Zs6209DWrVvrueeeM8fSBHv/ZFrxkubHH3eZBKZgwfyutSkYHDkSr6SkZF/x4vjpp90qWbK4q+2ygZO4/HvbvnT7yhW+zox52bQ/Uev3xl107Ph/k833zkylxFPpPxB++TVJEXnDrkDL7Xbo4BFFRBQ0XclpihYpbD5gjx8/4WrbbMe5tcvYsWPNTCMn6HBqhDTO2i5bt25VcvJvv28c69atM/vTjju30zhdSk73U9rxTCtgnAbkz58/XaHywgsv6PHHH9fzzz+vr7/+WsGuSePbdejgZlOwpImMrG76Y+mT/WtWr1pvzmvFCtf79lWpUlGxsRlb1OhqdPDoKfX8YGm6cSvbDx5Vgdyh2rwvQQMWfp8uGd1+6BddX/g68/0NpSK0MTbedywp9ax+PHxMN5QqdIV/Cvts3LTFjNGoW+cm37769aPNLLpgTqKvBM6tPQvZ7d69W+PHj9dTTz1lBvo6A3PTNmdhu2LFiqlPnz7auXOnJk+erM2bN+uhhx4yj3V6cdavX2/2O8ed+5UsWdLvKdR+FzDOEw4ZMkRHj6b/oHaKl0ceecSMh/nggw8UzFZ+v9akBBMnDlWliuXU9K6GGvhWP40YMcHtplnvp5179MUXi/XOOyNUs2ZVUyz26tVZkyfPcLtpVnQbVS0erv4LVmh3/HF9t+OgRn61Tk82rKl7b7zeDNR9+58bFJt4UnO+36HPN/6sJ26rYR7bIqqCNsYm6N2lW7Qr7rheXbDCDPCtfX0Rt3+sgOf8Lpg+Y77GjRuk2lGRuv/+purRPUajx051u2nW49zaY8mSJTp37pwmTJigW2+9Nd3mJGhOceMUM85idZ9++qnGjRun4sV/S9adYmXMmDFmXRinqHHGyzjHM9KTk83rR0mbtlSwUz05c76dxWYujJCcH8AZB3K5g3hDc5ZSoKtatZKGD3tV0dE36ddf/6spU2fqzTdHud2soJAvX16NHDlALe6/20TFEydN01tvva1AdnxmYKwOGn/ytAZ9tlqrdx8xU6EfqVtZT9xew/wicFKYoV+s1c4jx1Q8fx4927SWGlb9/d+aszKvczzuxH8VWbqQXnmgrkoUzCu35W0T+H8YOKmhM9i85YPNzHpQw0dM1OgxU9xuVlCw9dyeTU2/0nVW+r54yyx77rqHFsgGfhUw56+65yxckzdv3ktGSU411qlTp6AtYIBALGCCkQ0FDHAhCpgrK0PrwJQrV+4PjzlTqs+fVg0AAOxbB8YWLGQHAIBlvBQwXEoAAADYhwQGAADLeNxuQAAggQEAANYhgQEAwDJeMQaGBAYAAFiHBAYAAMt4uKoCCQwAALAPCQwAAJbxMAaGBAYAANiHBAYAAMt4SWAoYAAAsI3H7QYEALqQAACAdUhgAACwjJcuJBIYAABgHxIYAAAs43G7AQGABAYAAFiHBAYAAMt43G5AACCBAQAA1iGBAQDAMl5mIVHAAABgGw/1C11IAADAPiQwAABYxkMXEgkMAACwDwkMAACW8brdgABAAgMAAKxDAgMAgGU8bjcgAJDAAAAA65DAAABgGU82ZiFRwAAAYBmv2w0IAHQhAQAA65DAAABgGY/bDQgAJDAAAMA6JDAAAFjGwxheEhgAAGAfEhgAACzj4WKOJDAAAMA+JDAAAFjG63YDAgAFDAAAlvHQgxQ4Bcw5D7PaYZe8bSa43YSg9UCxKLebELQ+PrzO7SYAwVXAAAAA/3jcbkAAYBAvAACwDgkMAACW8brdgABAAgMAAKxDAQMAgIWzkDxZtF2O1NRUNW/eXKtWrfLte+ONN1S5cuV028yZM33HFy1apMaNGysyMlJdunTR0aNHM/SaFDAAAOCypaSkqEePHtq5c2e6/bt371bPnj21bNky39aqVStzbPPmzerXr5+6du2quXPn6uTJk+rTp0+GXpcxMAAAWMajwLBr1y5TpHi9F4/KcQqYJ554QoUKFbromJPE3HPPPXrggQfM7SFDhqhRo0bav3+/SpUq5ddrk8AAAGBhAePJoi0jVq9erTp16pgU5XynTp1SXFycypYte8nHbdq0SbVr1/bdLlasmIoXL272+4sEBgAApBvP4mznCwkJMduF2rRpo0tx0pds2bJp4sSJ+vbbb5U/f3516NBBDz74oDkeHx+vwoULp3tMeHi4jhw5In9RwAAAYBlvFl5KYNKkSRo7dmy6fc5YlW7duvn9HHv27DEFTLly5fTYY49pzZo1evnll5UnTx41adJEycnJFxVEzu0LC6c/QwEDAAB8YmJiTFpyvkulL3/GGdvijGlxkhdHlSpVtHfvXs2ePdsUMKGhoRcVK87tsLAwv1+DAgYAAMt4svC5/6i7KCOc9CWteEnjpDHff/+9+b5IkSJKTExMd9y5fakBv3+EQbwAACBTvf3222rfvn26fT/++KMpYhzO2i/r1v1+YdHDhw+bzdnvLwoYAAAs4wmQWUh/xOk+csa9TJ06Vfv27dMHH3ygjz/+WB07djTHW7durU8++UTz5s0zhU3v3r3VsGFDv6dQO+hCAgAAmeqGG24wKczo0aPN1xIlSmj48OGqVauWOe58HTBggDl+4sQJ1a9fX6+//nqGXiOb91Krz7jg2pASbjcBQIB4oFiU200IWh8f/j22R+Y6m3rwir3WmFKPZdlzd9v/+3L/gYwEBgAAy3iycBq1LRgDAwAArEMCAwCAZTxuNyAAkMAAAADrkMAAAGAZj9sNCAAkMAAAwDokMAAAWMbrdgMCAAkMAACwDgkMAACW8bAODAUMAAC28bjdgABAFxIAALAOCQwAAJbxut2AAEACAwAArEMCAwCAZTxkMCQwAADAPiQwAABYxuN2AwIACQwAALAOCQwAAJbxut2AAEABAwCAZTxuNyAA0IUEAACsQwIDAIBlPFwLiQQGAADYhwQGAADLeBjGSwJzOUJCQrRxwxLdfls9t5sSNEJDQzV50jAlxm/T/tj16v5cjNtNCgqc18wTXixCfd59WdO3zNH4Ze/o3o73+46VrlxGr88fpFk75mn4P0erer2arrbVdrxv4Q8SmMv4hzVzxljVqF7F7aYElcGDXlJUVKSa3PWwSpcpqfemjlLsvgNasOBzt5tmNc5r5ukxvrcSD8Srd/PuKlWxtP4xuqcSDsZry4rNennmAK1dvFrjer6t21o2VO9JfdSt0TM6+csJt5ttJd63/5vX7QYEAAqYDKhataJmTB+nbNkYPZWZcuUK0xMdW6v5fW21YeMWsw2rNkFdnmnPL6y/gPOaeXLny63KN1XRxBfG6sjew2bbuHS9ataPVESxCCWfTtY7/SbI4/How5GzdVOj2ip/QwVt+Gad2023Du9b+IsupAy4rUE9Lf3PCt3a4D63mxJUIm+orhw5cmjFyrW+fcuXr1Z0dC2Kxb+A85p5UlNSTZHS6OHGyn5tdhUvV0KVo6rq5617THfRmq9XmeIlzYv396R4uUy8b/3jycLtqklgzp49q1OnTil//vwKdpMmT3e7CUGpaLHCSkw8qjNnzvj2xcUnKCwsTOHhBcwxZBznNfOcSTmjKS9P1BMDYnRvh/tMEfPvDxfr33O/1j2P36udG39SzMAuqt04WgkH4jXtzXe1Y+12t5ttJd63yJIE5vPPP9eAAQP0z3/+U16vV2+88YZuuukm1atXT/Xr19fMmTMz8nSALzJOSUlNty/ttjPmCJeH85q5SlYopXWL16jvA89rbM9RqtfsFjV44HblzB2mB59ppWPxR/VW+9e0bdUWvTzjNTPoFxnH+9b/WUieLNqCLoGZOnWqJkyYYIqVV199VR9//LG2b9+uoUOHqkKFCvrhhx80bNgwnT59Wp06dcraViOoJCenKDQ0JN2+tNunTye51Cr7cV4zT836N+jOvzdRTJ2Opjtp9w+7VLBouFp1e1ies+f087Y9ZuyLw+lWirytlm5v2UgLxs1zu+nW4X3rH6/bDbCpgJk1a5ZGjBih2267TevWrdNjjz2miRMn6vbbbzfHy5cvrwIFCujll1+mgEGGHDp4RBERBZU9e3adO3fO7CtapLD5ZXX8OLM4LhfnNfOUq1FBh38+bIqXNE6h0qrrw9q5cYcO7jqY7v6H9hwkgblMvG+R6V1Ix44dU9myZc33UVFRKlasmCIi0v8DLVmypJKSqJCRMRs3bTH93XXr3OTbV79+tNau3Wi6KnF5OK+Z52j8URUtW0zX5vj9b74S5Usqfn+cflr/k8pW++134/nHEg7EudBS+/G+9Y+HQbz+FzDOWJdx48aZLiLHv//9b1WvXt13PD4+XgMHDjRdTEBGJCUla/qM+Ro3bpBqR0Xq/vubqkf3GI0eO9XtplmN85p51i1erXNnz+qZwV1V7PriirrzZrXs8jd98d5n+tesL1W6Slk9/FxrFS1TTI/0aKMipYvo24X/cbvZVuJ9C39l8/pZ0u7bt890DVWrVs10JZ1v8eLF6tatm2rUqKHx48erUKFCyqhrQ0rIJmdTD+rOxg9p6bcr3W5KUAgLy6lxYwep5YPNdOLESQ0fMVGjx0xxu1nWs/W8PlAsSoGmZMVS6vDqU6oQWVEnj57UV9M+1+fvfmqOVa5dVR37P2UWuDu4+4De7f+Otq/eqkD08eHAn95t6/vW+Vy4UnqU/XuWPfeIvXMUVAWMw7lrYmLiRQXKL7/8ogMHDqhmzZq65prLW1rGtgIGwNVVwAQLGwoYW1HABPA6MM4iQpdKV8LDw80GAACyntftBgQAVuIFAADW4VpIAABYxuN2AwIABQwAAJbx0olEFxIAALAPCQwAAJbxuN2AAEACAwAArEMCAwCAZTyMgSGBAQAA9iGBAQDAMl63GxAASGAAAIB1SGAAALCMhwyGBAYAANt4snC7HKmpqWrevLlWrVrl27d//361b99eN954o5o1a6Zly5ale8yKFSvMYyIjI9WuXTtz/4yggAEAAJctJSVFPXr00M6dO337vF6vunTpooiICH300Udq0aKFunbtqkOHDpnjzlfneMuWLTV//nwVLFhQnTt3No/zFwUMAAAWXkrAm0X/ZcSuXbv08MMPa9++fen2f//99yZRGTBggMqXL6+YmBiTxDjFjGPevHmqUaOGOnbsqIoVK2rgwIE6ePCgVq9e7fdrU8AAAIB03UGnTp1Ktzn7LsUpOOrUqaO5c+em279p0yZVq1ZNuXLl8u2LiorSxo0bfcdr167tOxYWFqbq1av7jvuDQbwAAFjGk4XPPWnSJI0dOzbdPqf7p1u3bhfdt02bNpd8joSEBBUuXDjdvvDwcB05csSv4/6ggAEAAD5Od0+HDh1+3yEpJCREGZGUlHTRY5zbaUnO/zruDwoYAAAs483CadROIZHRguVCoaGhOn78eLp9TnGSM2dO3/ELixXndr58+fx+DcbAAACATFWkSBElJiam2+fcTus2+qPjhQoV8vs1KGAAALCMJ8DWgbmQs7bL1q1blZyc7Nu3bt06sz/tuHM7jdOltG3bNt9xf1DAAABgGY/Xm2VbZoiOjlaxYsXUp08fsz7M5MmTtXnzZj300EPmeKtWrbR+/Xqz3znu3K9kyZJmRpO/KGAAAECmyp49u8aPH29mGzmL1X366acaN26cihcvbo47xcqYMWPMujBOUeOMl3GOZ8uWze/XyObNyLJ3WejakBJuNwFAgHigWJTbTQhaHx/+PbZH5jqbevCKvdZjZVpm2XPPjF0gG5DAAAAA6zCNGgAAy3i4GjUJDAAAsA8JDAAAlvGSwJDAAAAA+5DAAABgGY/bDQgAFDAAAFjGQxcSXUgAAMA+JDAAAFjGSwJDAgMAAOxDAgMAgGU8bjcgAJDAAAAA65DAAABgGW9gXIfZVSQwAADAOiQwAABYxsMsJAoYAABs43G7AQGALiQAAGAdEhjgMmVzuwFB7OPD69xuQtAqkTfc7SYgE3jpQiKBAQAA9iGBAQDAMh4SGBIYAABgHxIYAAAs42UhOxIYAABgHxIYAAAs43G7AQGAAgYAAMt4GcRLFxIAALAPCQwAAJbxkMCQwAAAAPuQwAAAYBkv06hJYAAAgH1IYAAAsIyHMTAkMAAAwD4kMAAAWMZLAkMBAwCAbTwM4qULCQAA2IcEBgAAy3jdbkAAIIEBAADWIYEBAMAyHjIYEhgAAGAfEhgAACzjIYEhgQEAAPYhgQEAwDJe1oEhgQEAAPYhgQEAwDIexsBQwAAAYBsvBQxdSAAAwD4UMAAAWDiI15tFW0Z8/fXXqly5crrt2WefNce2bdumv/3tb4qMjFSrVq20ZcuWTD0HFDAAAOCy7Nq1S40aNdKyZct82xtvvKHTp0+rU6dOql27thYsWKBatWopJibG7M8sFDAAAFg4iNeTRVtG7N69W5UqVVKhQoV8W758+fTFF18oNDRUvXv3Vvny5dWvXz/lzp1bX331VaadAwoYAABwWZwCpmzZshft37Rpk6KiopQtWzZz2/l60003aePGjcoszEICAMAy3ixcyC41NdVs5wsJCTHbhW34+eefTbfRpEmTdO7cOd19991mDExCQoIqVKiQ7v7h4eHauXNnprWTAgYAAPg4xcjYsWN/3yGpa9eu6tatW7p9hw4dUlJSkilsRo0apQMHDpjxL8nJyb7953NuX1gY/RUUMAAAWMaThevAOINtO3TokG7fhcWIo0SJElq1apWuu+4600VUtWpVeTwePf/884qOjr6oWHFu58yZM9PaSQEDAIBlvFlYwFyqu+iP5M+fP91tZ8BuSkqKGcybmJiY7phzu3DhwpnWTgbxAgCADPvuu+9Up04d012UZvv27aaocQbwbtiwwTdWx/m6fv16syZMZqGAAQDAMh6vN8s2fzlruzhTpV966SXt2bNHS5cu1ZAhQ/Tkk0+awbwnT57Um2++adaKcb46hc4999yTaeeAAgYAAGRYnjx5NHXqVB09etSstOus9fLII4+YAsY55gwGXrdunVq2bGmmVU+ePFm5cuVSZqGAyQCn0pw8aZgS47dpf+x6dX8uxu0mBQ3ObdYoXryo5syZrLgjW7T357UaOuRVc67x1/GezTxlri+l6fMmaFvs91qx6Z+K6dred6xW7Ru04Mvp5ti/V32qvz/W0tW2BtIYGG8W/ZcRFStW1HvvvWe6i5zp1M5spbS1X2644QYtXLhQmzdv1rx581StWrVMPQcM4s2AwYNeUlRUpJrc9bBKlymp96aOUuy+A1qw4HO3m2Y9zm3WmDtnso4dO65Gd7RUgQL59c7kEWathhf7vOF206zHezZzOB92780Zp80btqhZo4d1fbnSGv3OYB05HK8V363StLnjNeO9D9Wjy0uqGVlNw8YMUHxcgv799XduNx0uy+bNytVwMuDakBIKZLlyhSnu8A9qfl9bLf12pdnXt88/dOcdt+rOJn9zu3lWs/Xc/vY3RuCqXLm8tvzwrUqUjFR8/G+zAR55pIUGD3pZZa+vrUAWEL+UgvA96yiRN1yBpHCRCL3yZm+98Fx//ffUb9fJmTRthBLif9H2rT+pY8yjurPeA777vzX8ZeXOnUv/eLqPAk3sL5uv2GtVLRydZc+9PX61bEAXkp8ib6iuHDlyaMXKtb59y5evVnR0LV9chsvDuc0aR44kqNm9bXzFS5rrrsvnWpuCBe/ZzBMfl6iuT/b2FS+1o29UdL0orVy2RkuXLFevbq9c9Ji8+fK40FIEGgoYPxUtVliJiUd15swZ3764+ASFhYUpPLyAq22zHec2a5w4cVJff73Ud9v5YO38TAf9+5tlrrYrGPCezRrLN36lj76crvVrNuvLzxbrwP5D2rD291QjPKKg7mt5t5Z/u0pXO2+AjIGxuoBxLs60f/9+BTsnMk5JSb+qYNptBkX+NZzbK2PQwJdUq1YNvfLKYLebYj3es1nj6fY91KF1V1WrWVmvvPl8umOhOUM18f3fupZmTZuvq50nAKZRu82vQbx9+vxxX6OzNPDQoUPNZbIdAwcOVDBKTk5RaGj6lQnTbp8+/fsiPsg4zm3We+utvnr22SfV5tFntHXrDrebYz3es1njh43bzNfX+w3VqEkD9eYrw3XmzFnlyh2mKTNH6/ryZfTQvY8rOSnZ7abClgTml19+MVOhnMtmX60OHTyiiIiCyp49u29f0SKFzS+r48dPuNo223Fus9aoka+bKb6Pt++mhQu/cLs5QYH3bOaJKFRQdzVrlG7fzp92m4IwT948ypM3t2bMm6hKVSqo9QNPau+efa61NZB46ULyL4FxFp/5/PPPTdJSr149denSxXedhK+++spcuKlUqVIKZhs3bTH93XXr3KTlK9aYffXrR2vt2o1ZelnzqwHnNuu89FJ3derUVo8+1pnpvZmI92zmKVWmpCZNG6m6N9yluMPxZl+NyGpKTDiq48dOaOZHk8w09Ufu76DdO/e63VzYOAbm3nvv1SeffKKEhATdd999WrFiha4mSUnJmj5jvsaNG6TaUZG6//6m6tE9RqPHTnW7adbj3GaNKlUqqF/f5zRk6DgzQ6ZIkUK+DX8N79nMs2n9Fv2waZuGjn5NFSuXU6PGt6pv/x4aO+IdPfJYS9W79WYzxfrkiV9VqHC42a7Lz0w6D2NgLm8dmJUrV6p///6qUaOGlixZos8+++wvJzCBvg6MIywsp8aNHaSWDzYzMzyGj5io0WOmuN2soGDjuQ30ybLPP99Fb73Z95LHcgT4vzcbfoXa+J4NxHVgHIWLFtLrg/voltvqKOl0kqZNmaNxI6do2ocT1PDO+hfd35li/fcWT+hqXgemfMRNWfbcuxPXK6gXsnMG744ZM0ZffPGFZs6cqWLFigV9AQPYVMDYzIYCxlaBWMAEiytZwJSLqJVlz70ncYNswEq8wGWigMk6AfFLKUhRwGQdCpgri2shAQBgGa/Xo6sdBQwAAJbxkFNyKQEAAGAfEhgAACzjDYzhq64igQEAANYhgQEAwDIexsCQwAAAAPuQwAAAYBkvY2BIYAAAgH1IYAAAsIyHBIYCBgAA23gZxEsXEgAAsA8JDAAAlvHShUQCAwAA7EMCAwCAZTyMgSGBAQAA9iGBAQDAMl7GwJDAAAAA+5DAAABgGQ8JDAUMAAC28VLA0IUEAADsQwIDAIBlPEyjJoEBAAD2IYEBAMAyXsbAkMAAAAD7kMAAAGAZDwkMCQwAALAPCQwAAJbxMguJAgYAANt46EKiCwkAANiHBAYAAMt4SWBIYAAAgH1IYAAAsIyXQbwkMAAAwD4kMAAAWMbLGBgSGAAAcHlSUlLUt29f1a5dW7feeqveffddXSkkMAAAWMYbIAnMkCFDtGXLFk2bNk2HDh3SCy+8oOLFi+vuu+/O8temgAEAwDJetxsg6fTp05o3b57eeecdVa9e3Ww7d+7UrFmzrkgBQxcSAADwSU1N1alTp9Jtzr4L/fjjjzp79qxq1arl2xcVFaVNmzbJ4/HoqklgzqYedLsJAABY4WwWfmaOGTNGY8eOTbeva9eu6tatW7p9CQkJKlCggEJCQnz7IiIizLiY48ePq2DBgroqChgAAOC+mJgYdejQId2+84uUNElJSRftT7t9qcQms1HAAACAdEXIpQqWC4WGhl5UqKTdzpkzp7IaY2AAAECGFSlSRMeOHTPjYM7vVnKKl3z58imrUcAAAIAMq1q1qq699lpt3LjRt2/dunWqWbOmrrkm68sLChgAAJBhYWFheuCBB9S/f39t3rxZixcvNgvZtWvXTldCNm+grIYDAACskpSUZAqYf/3rX8qTJ4+eeOIJtW/f/oq8NgUMAACwDl1IAADAOhQwAADAOhQwAADAOhQwllw2/GrhLILUvHlzrVq1yu2mBIW4uDg9++yzio6OVoMGDTRw4EDzPsZfFxsbawYsOteBadiwoaZMmeJ2k4JSp06d9OKLL7rdDAQgVuK15LLhVwPng7Vnz57maqb465zx+U7x4iwo5Vwd9sSJE6YAd9ZncN67uHzOheqcD1ZnvYuFCxeaYqZHjx5mYa/77rvP7eYFjc8//1xLly7Vgw8+6HZTEIBIYDJ42fB+/fqZS4Y3adJETz75pPlgwF+3a9cuPfzww9q3b5/bTQkae/bsMQtMOalLxYoVTXLoFDSLFi1yu2nWS0xMNIt4OdNHy5Ytq9tvv1316tUzi3ghczgXA3T+aHSKROBSKGD85PZlw4Pd6tWrVadOHc2dO9ftpgSNQoUKmW4N5+qw5zt16pRrbQoWhQsX1qhRo8y6F07S5RQua9asMV11yByDBw9WixYtVKFCBbebggBFF5Kf3L5seLBr06aN200IOk7XkTPuJY1TaM+cOVN169Z1tV3B5o477jBdyo0aNVLTpk3dbk5QWLlypdauXavPPvvMpFzApZDA+Mnty4YDf9XQoUO1bds2de/e3e2mBJXRo0dr4sSJ2r59u+muw1/j/FH46quv6pVXXrkiVzSGvUhg/OT2ZcOBv1q8OIPPR44cqUqVKrndnKCSNkbD+eDt1auXevfufdEfO/Df2LFjVaNGjXTpIXApFDCXcdlw5+qbV/qy4cDlev311zV79mxTxNDFkXmDeJ0B0o0bN/btc8ZqnDlzxowxokv5r808cs5v2njDtD8U//nPf2rDhg0utw6BhALmMi4b7szmuNKXDQcu96/ZOXPmaMSIEUz3z0QHDhxQ165dzRRf548bh7PEglO4ULz8NTNmzDB/KKYZNmyY+eqkW8D5KGAu47Lhb731luLj481CdvR5I1Dt3r1b48ePN+uVODPmnMTw/BlKuHzOHy7OcgrOujp9+vTRwYMHTcL19NNPu90065UoUSLd7dy5c5uvZcqUcalFCFQUMBng/KJyCpjHH3/cTJ/s1q2b7rrrLrebBVzSkiVLdO7cOU2YMMFs59uxY4dr7QoG2bNnN8Wh0z33yCOPmD9w2rZtq3bt2rndNOCqkc3rLGIAAABgEQZvAAAA61DAAAAA61DAAAAA61DAAAAA61DAAAAA61DAAAAA61DAAAAA61DAAAAA61DAAAAA61DAAAAA61DAAAAA2eb/AOM5qbL0NytTAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification Report\n", + " precision recall f1-score support\n", + "\n", + " 0 0.97 0.99 0.98 398\n", + " 1 0.98 0.96 0.97 370\n", + " 2 0.96 0.97 0.96 341\n", + " 3 1.00 0.99 0.99 87\n", + " 4 1.00 0.94 0.97 34\n", + "\n", + " accuracy 0.97 1230\n", + " macro avg 0.98 0.97 0.98 1230\n", + "weighted avg 0.97 0.97 0.97 1230\n", + "\n" + ] + } + ], + "execution_count": 15 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FNP6aqzc9hE5" + }, + "source": [ + "# Convert to model for Tensorflow-Lite" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ODjnYyld9hE6", + "ExecuteTime": { + "end_time": "2025-04-06T14:42:46.525052Z", + "start_time": "2025-04-06T14:42:46.495530Z" + } + }, + "source": [ + "# Save as a model dedicated to inference\n", + "model.save(model_save_path, include_optimizer=False)" + ], + "outputs": [], + "execution_count": 16 + }, + { + "cell_type": "code", + "metadata": { + "id": "zRfuK8Y59hE6", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a4ca585c-b5d5-4244-8291-8674063209bb", + "ExecuteTime": { + "end_time": "2025-04-06T14:42:46.780128Z", + "start_time": "2025-04-06T14:42:46.542059Z" + } + }, + "source": [ + "# Transform model (quantization)\n", + "\n", + "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n", + "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n", + "tflite_quantized_model = converter.convert()\n", + "\n", + "open(tflite_save_path, 'wb').write(tflite_quantized_model)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: C:\\Users\\grimm\\AppData\\Local\\Temp\\tmpdi8ap89u\\assets\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: C:\\Users\\grimm\\AppData\\Local\\Temp\\tmpdi8ap89u\\assets\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved artifact at 'C:\\Users\\grimm\\AppData\\Local\\Temp\\tmpdi8ap89u'. The following endpoints are available:\n", + "\n", + "* Endpoint 'serve'\n", + " args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 42), dtype=tf.float32, name='input_layer')\n", + "Output Type:\n", + " TensorSpec(shape=(None, 5), dtype=tf.float32, name=None)\n", + "Captures:\n", + " 2451841247968: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", + " 2451842012384: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", + " 2451842010800: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", + " 2451842419344: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", + " 2451842010624: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", + " 2451842422512: TensorSpec(shape=(), dtype=tf.resource, name=None)\n" + ] + }, + { + "data": { + "text/plain": [ + "6600" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 17 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CHBPBXdx9hE6" + }, + "source": [ + "# Inference test" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mGAzLocO9hE7", + "ExecuteTime": { + "end_time": "2025-04-06T14:42:46.812178Z", + "start_time": "2025-04-06T14:42:46.797128Z" + } + }, + "source": [ + "interpreter = tf.lite.Interpreter(model_path=tflite_save_path)\n", + "interpreter.allocate_tensors()" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\.venv\\lib\\site-packages\\tensorflow\\lite\\python\\interpreter.py:457: UserWarning: Warning: tf.lite.Interpreter is deprecated and is scheduled for deletion in\n", + " TF 2.20. Please use the LiteRT interpreter from the ai_edge_litert package.\n", + " See the [migration guide](https://ai.google.dev/edge/litert/migration)\n", + " for details.\n", + " \n", + " warnings.warn(_INTERPRETER_DELETION_WARNING)\n" + ] + } + ], + "execution_count": 18 + }, + { + "cell_type": "code", + "metadata": { + "id": "oQuDK8YS9hE7", + "ExecuteTime": { + "end_time": "2025-04-06T14:42:46.844102Z", + "start_time": "2025-04-06T14:42:46.829106Z" + } + }, + "source": [ + "# Get I / O tensor\n", + "input_details = interpreter.get_input_details()\n", + "output_details = interpreter.get_output_details()" + ], + "outputs": [], + "execution_count": 19 + }, + { + "cell_type": "code", + "metadata": { + "id": "2_ixAf_l9hE7", + "ExecuteTime": { + "end_time": "2025-04-06T14:42:46.875277Z", + "start_time": "2025-04-06T14:42:46.861102Z" + } + }, + "source": [ + "interpreter.set_tensor(input_details[0]['index'], np.array([X_test[0]]))" + ], + "outputs": [], + "execution_count": 20 + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "id": "s4FoAnuc9hE7", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "91f18257-8d8b-4ef3-c558-e9b5f94fabbf", + "ExecuteTime": { + "end_time": "2025-04-06T14:42:46.906147Z", + "start_time": "2025-04-06T14:42:46.891643Z" + } + }, + "source": [ + "%%time\n", + "# Inference implementation\n", + "interpreter.invoke()\n", + "tflite_results = interpreter.get_tensor(output_details[0]['index'])" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: total: 0 ns\n", + "Wall time: 0 ns\n" + ] + } + ], + "execution_count": 21 + }, + { + "cell_type": "code", + "metadata": { + "id": "vONjp19J9hE8", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "77205e24-fd00-42c4-f7b6-e06e527c2cba", + "ExecuteTime": { + "end_time": "2025-04-06T14:42:46.937996Z", + "start_time": "2025-04-06T14:42:46.923157Z" + } + }, + "source": [ + "print(np.squeeze(tflite_results))\n", + "print(np.argmax(np.squeeze(tflite_results)))" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.6097244e-02 5.2194584e-02 9.3088049e-01 7.2499836e-04 1.0261366e-04]\n", + "2\n" + ] + } + ], + "execution_count": 22 + } + ] +} diff --git a/point_history_classification.ipynb b/point_history_classification.ipynb index 53e0b022..b6cffc0d 100644 --- a/point_history_classification.ipynb +++ b/point_history_classification.ipynb @@ -1,2006 +1,2143 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import csv\n", - "\n", - "import numpy as np\n", - "import tensorflow as tf\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "RANDOM_SEED = 42" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 各パス指定" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = 'model/point_history_classifier/point_history.csv'\n", - "model_save_path = 'model/point_history_classifier/point_history_classifier.hdf5'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 分類数設定" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "NUM_CLASSES = 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 入力長" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "TIME_STEPS = 16\n", - "DIMENSION = 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 学習データ読み込み" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "X_dataset = np.loadtxt(dataset, delimiter=',', dtype='float32', usecols=list(range(1, (TIME_STEPS * DIMENSION) + 1)))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "y_dataset = np.loadtxt(dataset, delimiter=',', dtype='int32', usecols=(0))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, train_size=0.75, random_state=RANDOM_SEED)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# モデル構築" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "use_lstm = False\n", - "model = None\n", - "\n", - "if use_lstm:\n", - " model = tf.keras.models.Sequential([\n", - " tf.keras.layers.InputLayer(input_shape=(TIME_STEPS * DIMENSION, )),\n", - " tf.keras.layers.Reshape((TIME_STEPS, DIMENSION), input_shape=(TIME_STEPS * DIMENSION, )), \n", - " tf.keras.layers.Dropout(0.2),\n", - " tf.keras.layers.LSTM(16, input_shape=[TIME_STEPS, DIMENSION]),\n", - " tf.keras.layers.Dropout(0.5),\n", - " tf.keras.layers.Dense(10, activation='relu'),\n", - " tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n", - " ])\n", - "else:\n", - " model = tf.keras.models.Sequential([\n", - " tf.keras.layers.InputLayer(input_shape=(TIME_STEPS * DIMENSION, )),\n", - " tf.keras.layers.Dropout(0.2),\n", - " tf.keras.layers.Dense(24, activation='relu'),\n", - " tf.keras.layers.Dropout(0.5),\n", - " tf.keras.layers.Dense(10, activation='relu'),\n", - " tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n", - " ])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dropout (Dropout) (None, 32) 0 \n", - "_________________________________________________________________\n", - "dense (Dense) (None, 24) 792 \n", - "_________________________________________________________________\n", - "dropout_1 (Dropout) (None, 24) 0 \n", - "_________________________________________________________________\n", - "dense_1 (Dense) (None, 10) 250 \n", - "_________________________________________________________________\n", - "dense_2 (Dense) (None, 4) 44 \n", - "=================================================================\n", - "Total params: 1,086\n", - "Trainable params: 1,086\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "model.summary() # tf.keras.utils.plot_model(model, show_shapes=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# モデルチェックポイントのコールバック\n", - "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", - " model_save_path, verbose=1, save_weights_only=False)\n", - "# 早期打ち切り用コールバック\n", - "es_callback = tf.keras.callbacks.EarlyStopping(patience=20, verbose=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# モデルコンパイル\n", - "model.compile(\n", - " optimizer='adam',\n", - " loss='sparse_categorical_crossentropy',\n", - " metrics=['accuracy']\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# モデル訓練" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/1000\n", - "30/32 [===========================>..] - ETA: 0s - loss: 1.3800 - accuracy: 0.4279\n", - "Epoch 00001: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 1s 25ms/step - loss: 1.3800 - accuracy: 0.4270 - val_loss: 1.3584 - val_accuracy: 0.5219\n", - "Epoch 2/1000\n", - "31/32 [============================>.] - ETA: 0s - loss: 1.3587 - accuracy: 0.5066\n", - "Epoch 00002: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 1.3586 - accuracy: 0.5068 - val_loss: 1.3365 - val_accuracy: 0.5310\n", - "Epoch 3/1000\n", - "32/32 [==============================] - ETA: 0s - loss: 1.3348 - accuracy: 0.5050\n", - "Epoch 00003: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 1.3348 - accuracy: 0.5050 - val_loss: 1.3091 - val_accuracy: 0.5249\n", - "Epoch 4/1000\n", - "31/32 [============================>.] - ETA: 0s - loss: 1.3090 - accuracy: 0.5237\n", - "Epoch 00004: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 1.3090 - accuracy: 0.5239 - val_loss: 1.2752 - val_accuracy: 0.5906\n", - "Epoch 5/1000\n", - "30/32 [===========================>..] - ETA: 0s - loss: 1.2776 - accuracy: 0.5560\n", - "Epoch 00005: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 13ms/step - loss: 1.2768 - accuracy: 0.5554 - val_loss: 1.2332 - val_accuracy: 0.6178\n", - "Epoch 6/1000\n", - "31/32 [============================>.] - ETA: 0s - loss: 1.2342 - accuracy: 0.5597\n", - "Epoch 00006: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 1.2342 - accuracy: 0.5599 - val_loss: 1.1817 - val_accuracy: 0.6858\n", - "Epoch 7/1000\n", - "26/32 [=======================>......] - ETA: 0s - loss: 1.1957 - accuracy: 0.5721\n", - "Epoch 00007: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 1.1932 - accuracy: 0.5780 - val_loss: 1.1206 - val_accuracy: 0.7122\n", - "Epoch 8/1000\n", - "29/32 [==========================>...] - ETA: 0s - loss: 1.1480 - accuracy: 0.5717\n", - "Epoch 00008: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 1.1455 - accuracy: 0.5743 - val_loss: 1.0555 - val_accuracy: 0.7492\n", - "Epoch 9/1000\n", - "31/32 [============================>.] - ETA: 0s - loss: 1.0875 - accuracy: 0.6001\n", - "Epoch 00009: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 1.0879 - accuracy: 0.5997 - val_loss: 0.9879 - val_accuracy: 0.7847\n", - "Epoch 10/1000\n", - "30/32 [===========================>..] - ETA: 0s - loss: 1.0461 - accuracy: 0.6023\n", - "Epoch 00010: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 10ms/step - loss: 1.0451 - accuracy: 0.6037 - val_loss: 0.9241 - val_accuracy: 0.7878\n", - "Epoch 11/1000\n", - "27/32 [========================>.....] - ETA: 0s - loss: 0.9968 - accuracy: 0.6238\n", - "Epoch 00011: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 10ms/step - loss: 0.9958 - accuracy: 0.6256 - val_loss: 0.8632 - val_accuracy: 0.8180\n", - "Epoch 12/1000\n", - "31/32 [============================>.] - ETA: 0s - loss: 0.9525 - accuracy: 0.6356\n", - "Epoch 00012: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 0.9523 - accuracy: 0.6357 - val_loss: 0.8063 - val_accuracy: 0.8270\n", - "Epoch 13/1000\n", - "31/32 [============================>.] - ETA: 0s - loss: 0.9175 - accuracy: 0.6560\n", - "Epoch 00013: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 0.9174 - accuracy: 0.6558 - val_loss: 0.7573 - val_accuracy: 0.8527\n", - "Epoch 14/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.8813 - accuracy: 0.6709\n", - "Epoch 00014: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.8738 - accuracy: 0.6689 - val_loss: 0.7132 - val_accuracy: 0.8739\n", - "Epoch 15/1000\n", - "26/32 [=======================>......] - ETA: 0s - loss: 0.8613 - accuracy: 0.6659\n", - "Epoch 00015: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.8577 - accuracy: 0.6692 - val_loss: 0.6770 - val_accuracy: 0.8897\n", - "Epoch 16/1000\n", - "31/32 [============================>.] - ETA: 0s - loss: 0.8275 - accuracy: 0.6815\n", - "Epoch 00016: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 0.8273 - accuracy: 0.6815 - val_loss: 0.6460 - val_accuracy: 0.9026\n", - "Epoch 17/1000\n", - "30/32 [===========================>..] - ETA: 0s - loss: 0.8088 - accuracy: 0.6958\n", - "Epoch 00017: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 0.8066 - accuracy: 0.6966 - val_loss: 0.6152 - val_accuracy: 0.9063\n", - "Epoch 18/1000\n", - "30/32 [===========================>..] - ETA: 0s - loss: 0.7810 - accuracy: 0.6953\n", - "Epoch 00018: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.7807 - accuracy: 0.6954 - val_loss: 0.5880 - val_accuracy: 0.9162\n", - "Epoch 19/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.7743 - accuracy: 0.6991\n", - "Epoch 00019: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.7695 - accuracy: 0.7029 - val_loss: 0.5688 - val_accuracy: 0.9230\n", - "Epoch 20/1000\n", - "26/32 [=======================>......] - ETA: 0s - loss: 0.7537 - accuracy: 0.7121\n", - "Epoch 00020: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.7469 - accuracy: 0.7127 - val_loss: 0.5462 - val_accuracy: 0.9222\n", - "Epoch 21/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.7440 - accuracy: 0.7175\n", - "Epoch 00021: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 1s 31ms/step - loss: 0.7457 - accuracy: 0.7168 - val_loss: 0.5261 - val_accuracy: 0.9313\n", - "Epoch 22/1000\n", - "29/32 [==========================>...] - ETA: 0s - loss: 0.7107 - accuracy: 0.7322\n", - "Epoch 00022: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 10ms/step - loss: 0.7106 - accuracy: 0.7329 - val_loss: 0.5073 - val_accuracy: 0.9313\n", - "Epoch 23/1000\n", - "32/32 [==============================] - ETA: 0s - loss: 0.7039 - accuracy: 0.7356\n", - "Epoch 00023: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 9ms/step - loss: 0.7039 - accuracy: 0.7356 - val_loss: 0.4947 - val_accuracy: 0.9358\n", - "Epoch 24/1000\n", - "24/32 [=====================>........] - ETA: 0s - loss: 0.7048 - accuracy: 0.7279\n", - "Epoch 00024: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 10ms/step - loss: 0.7119 - accuracy: 0.7251 - val_loss: 0.4782 - val_accuracy: 0.9350\n", - "Epoch 25/1000\n", - "31/32 [============================>.] - ETA: 0s - loss: 0.6741 - accuracy: 0.7485\n", - "Epoch 00025: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 0.6737 - accuracy: 0.7485 - val_loss: 0.4624 - val_accuracy: 0.9366\n", - "Epoch 26/1000\n", - "23/32 [====================>.........] - ETA: 0s - loss: 0.6698 - accuracy: 0.7480\n", - "Epoch 00026: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 9ms/step - loss: 0.6700 - accuracy: 0.7472 - val_loss: 0.4484 - val_accuracy: 0.9388\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 27/1000\n", - "28/32 [=========================>....] - ETA: 0s - loss: 0.6723 - accuracy: 0.74 - ETA: 0s - loss: 0.6671 - accuracy: 0.7444\n", - "Epoch 00027: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.6640 - accuracy: 0.7447 - val_loss: 0.4387 - val_accuracy: 0.9418\n", - "Epoch 28/1000\n", - "31/32 [============================>.] - ETA: 0s - loss: 0.6625 - accuracy: 0.7533\n", - "Epoch 00028: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 0.6622 - accuracy: 0.7535 - val_loss: 0.4314 - val_accuracy: 0.9411\n", - "Epoch 29/1000\n", - "30/32 [===========================>..] - ETA: 0s - loss: 0.6503 - accuracy: 0.7568\n", - "Epoch 00029: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 0.6505 - accuracy: 0.7570 - val_loss: 0.4200 - val_accuracy: 0.9449\n", - "Epoch 30/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.6271 - accuracy: 0.7675\n", - "Epoch 00030: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.6296 - accuracy: 0.7656 - val_loss: 0.4093 - val_accuracy: 0.9524\n", - "Epoch 31/1000\n", - "29/32 [==========================>...] - ETA: 0s - loss: 0.6360 - accuracy: 0.7586\n", - "Epoch 00031: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 0.6362 - accuracy: 0.7593 - val_loss: 0.4019 - val_accuracy: 0.9479\n", - "Epoch 32/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.6253 - accuracy: 0.7700\n", - "Epoch 00032: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.6186 - accuracy: 0.7717 - val_loss: 0.3927 - val_accuracy: 0.9464\n", - "Epoch 33/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.6255 - accuracy: 0.7613\n", - "Epoch 00033: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.6217 - accuracy: 0.7623 - val_loss: 0.3868 - val_accuracy: 0.9547\n", - "Epoch 34/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.6175 - accuracy: 0.7678\n", - "Epoch 00034: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.6136 - accuracy: 0.7694 - val_loss: 0.3795 - val_accuracy: 0.9547\n", - "Epoch 35/1000\n", - "32/32 [==============================] - ETA: 0s - loss: 0.6162 - accuracy: 0.7598\n", - "Epoch 00035: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.6162 - accuracy: 0.7598 - val_loss: 0.3737 - val_accuracy: 0.9554\n", - "Epoch 36/1000\n", - 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0.8602\n", - "Epoch 00325: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 10ms/step - loss: 0.3718 - accuracy: 0.8610 - val_loss: 0.1629 - val_accuracy: 0.9690\n", - "Epoch 326/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.3637 - accuracy: 0.8631\n", - "Epoch 00326: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3738 - accuracy: 0.8590 - val_loss: 0.1627 - val_accuracy: 0.9705\n", - "Epoch 327/1000\n", - "32/32 [==============================] - ETA: 0s - loss: 0.3681 - accuracy: 0.8691\n", - "Epoch 00327: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3681 - accuracy: 0.8691 - val_loss: 0.1621 - val_accuracy: 0.9683\n", - "Epoch 328/1000\n", - "25/32 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0.9713\n", - "Epoch 331/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.3729 - accuracy: 0.8641\n", - "Epoch 00331: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3792 - accuracy: 0.8610 - val_loss: 0.1590 - val_accuracy: 0.9705\n", - "Epoch 332/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.3809 - accuracy: 0.8634\n", - "Epoch 00332: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3784 - accuracy: 0.8666 - val_loss: 0.1589 - val_accuracy: 0.9705\n", - "Epoch 333/1000\n", - "30/32 [===========================>..] - ETA: 0s - loss: 0.3621 - accuracy: 0.8690\n", - "Epoch 00333: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 0.3607 - accuracy: 0.8701 - val_loss: 0.1596 - val_accuracy: 0.9698\n", - "Epoch 334/1000\n", - "26/32 [=======================>......] - ETA: 0s - loss: 0.3794 - accuracy: 0.8576\n", - "Epoch 00334: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 10ms/step - loss: 0.3713 - accuracy: 0.8605 - val_loss: 0.1589 - val_accuracy: 0.9690\n", - "Epoch 335/1000\n", - "28/32 [=========================>....] - ETA: 0s - loss: 0.3656 - accuracy: 0.8650\n", - "Epoch 00335: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 10ms/step - loss: 0.3624 - accuracy: 0.8663 - val_loss: 0.1588 - val_accuracy: 0.9698\n", - "Epoch 336/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.3751 - accuracy: 0.8609\n", - "Epoch 00336: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3773 - accuracy: 0.8600 - val_loss: 0.1557 - val_accuracy: 0.9683\n", - "Epoch 337/1000\n", - "30/32 [===========================>..] - ETA: 0s - loss: 0.3691 - accuracy: 0.8641\n", - "Epoch 00337: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 0.3670 - accuracy: 0.8653 - val_loss: 0.1569 - val_accuracy: 0.9713\n", - "Epoch 338/1000\n", - "28/32 [=========================>....] - ETA: 0s - loss: 0.3592 - accuracy: 0.8597\n", - "Epoch 00338: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3578 - accuracy: 0.8598 - val_loss: 0.1565 - val_accuracy: 0.9713\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 339/1000\n", - "32/32 [==============================] - ETA: 0s - loss: 0.3769 - accuracy: 0.8633\n", - "Epoch 00339: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3769 - accuracy: 0.8633 - val_loss: 0.1553 - val_accuracy: 0.9683\n", - "Epoch 340/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.3625 - accuracy: 0.8669\n", - "Epoch 00340: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3648 - accuracy: 0.8653 - val_loss: 0.1565 - val_accuracy: 0.9698\n", - "Epoch 341/1000\n", - "26/32 [=======================>......] - ETA: 0s - loss: 0.3605 - accuracy: 0.8669\n", - "Epoch 00341: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3588 - accuracy: 0.8688 - val_loss: 0.1580 - val_accuracy: 0.9675\n", - "Epoch 342/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.3730 - accuracy: 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0s 11ms/step - loss: 0.3686 - accuracy: 0.8635 - val_loss: 0.1579 - val_accuracy: 0.9690\n", - "Epoch 354/1000\n", - "28/32 [=========================>....] - ETA: 0s - loss: 0.3689 - accuracy: 0.8652\n", - "Epoch 00354: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 10ms/step - loss: 0.3731 - accuracy: 0.8633 - val_loss: 0.1575 - val_accuracy: 0.9705\n", - "Epoch 355/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.3796 - accuracy: 0.8584\n", - "Epoch 00355: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3829 - accuracy: 0.8588 - val_loss: 0.1584 - val_accuracy: 0.9675\n", - "Epoch 356/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.3776 - accuracy: 0.8591\n", - "Epoch 00356: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 12ms/step - loss: 0.3737 - accuracy: 0.8623 - val_loss: 0.1568 - val_accuracy: 0.9675\n", - "Epoch 357/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.3539 - accuracy: 0.8697\n", - "Epoch 00357: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 10ms/step - loss: 0.3696 - accuracy: 0.8635 - val_loss: 0.1571 - val_accuracy: 0.9683\n", - "Epoch 358/1000\n", - "32/32 [==============================] - ETA: 0s - loss: 0.3729 - accuracy: 0.8671\n", - "Epoch 00358: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3729 - accuracy: 0.8671 - val_loss: 0.1568 - val_accuracy: 0.9675\n", - "Epoch 359/1000\n", - "25/32 [======================>.......] - ETA: 0s - loss: 0.3893 - accuracy: 0.8666\n", - "Epoch 00359: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", - "32/32 [==============================] - 0s 11ms/step - loss: 0.3871 - accuracy: 0.8666 - val_loss: 0.1558 - val_accuracy: 0.9683\n", - "Epoch 00359: early stopping\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit(\n", - " X_train,\n", - " y_train,\n", - " epochs=1000,\n", - " batch_size=128,\n", - " validation_data=(X_test, y_test),\n", - " callbacks=[cp_callback, es_callback]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# 保存したモデルのロード\n", - "model = tf.keras.models.load_model(model_save_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.9438375 0.01624594 0.01188833 0.02802826]\n", - "0\n" - ] - } - ], - "source": [ - "# 推論テスト\n", - "predict_result = model.predict(np.array([X_test[0]]))\n", - "print(np.squeeze(predict_result))\n", - "print(np.argmax(np.squeeze(predict_result)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 混同行列" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Classification Report\n", - " precision recall f1-score support\n", - "\n", - " 0 0.99 1.00 0.99 395\n", - " 1 0.93 0.99 0.96 295\n", - " 2 0.98 0.97 0.98 307\n", - " 3 0.96 0.91 0.93 327\n", - "\n", - " accuracy 0.97 1324\n", - " macro avg 0.97 0.97 0.97 1324\n", - "weighted avg 0.97 0.97 0.97 1324\n", - "\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import confusion_matrix, classification_report\n", - "\n", - "def print_confusion_matrix(y_true, y_pred, report=True):\n", - " labels = sorted(list(set(y_true)))\n", - " cmx_data = confusion_matrix(y_true, y_pred, labels=labels)\n", - " \n", - " df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)\n", - " \n", - " fig, ax = plt.subplots(figsize=(7, 6))\n", - " sns.heatmap(df_cmx, annot=True, fmt='g' ,square=False)\n", - " ax.set_ylim(len(set(y_true)), 0)\n", - " plt.show()\n", - " \n", - " if report:\n", - " print('Classification Report')\n", - " print(classification_report(y_test, y_pred))\n", - "\n", - "Y_pred = model.predict(X_test)\n", - "y_pred = np.argmax(Y_pred, axis=1)\n", - "\n", - "print_confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tensorflow-Lite用のモデルへ変換" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 推論専用のモデルとして保存\n", - "model.save(model_save_path, include_optimizer=False)\n", - "model = tf.keras.models.load_model(model_save_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "tflite_save_path = 'model/point_history_classifier/point_history_classifier.tflite'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# モデルを変換(量子化\n", - "converter = tf.lite.TFLiteConverter.from_keras_model(model) # converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)\n", - "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n", - "tflite_quantized_model = converter.convert()\n", - "\n", - "open(tflite_save_path, 'wb').write(tflite_quantized_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 推論テスト" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "interpreter = tf.lite.Interpreter(model_path=tflite_save_path)\n", - "interpreter.allocate_tensors()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'name': 'input_1', 'index': 0, 'shape': array([ 1, 32]), 'shape_signature': array([-1, 32]), 'dtype': , 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n" - ] - } - ], - "source": [ - "# 入出力テンソルを取得\n", - "input_details = interpreter.get_input_details()\n", - "output_details = interpreter.get_output_details()\n", - "print(input_details)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "interpreter.set_tensor(input_details[0]['index'], np.array([X_test[0]]))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wall time: 0 ns\n" - ] - } - ], - "source": [ - "%%time\n", - "# 推論実施\n", - "interpreter.invoke()\n", - "tflite_results = interpreter.get_tensor(output_details[0]['index'])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.9579909 0.01342559 0.00907356 0.01950999]\n", - "0\n" - ] - } - ], - "source": [ - "print(np.squeeze(tflite_results))\n", - "print(np.argmax(np.squeeze(tflite_results)))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} +{ + "cells": [ + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T14:36:10.579371Z", + "start_time": "2025-04-06T14:36:08.972242Z" + } + }, + "source": [ + "import csv\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "RANDOM_SEED = 42" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 各パス指定" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T14:36:10.595154Z", + "start_time": "2025-04-06T14:36:10.582373Z" + } + }, + "source": [ + "dataset = 'model/point_history_classifier/point_history.csv'\n", + "model_save_path = 'model/point_history_classifier/point_history_classifier.hdf5'" + ], + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 分類数設定" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T14:36:10.737285Z", + "start_time": "2025-04-06T14:36:10.722287Z" + } + }, + "source": [ + "NUM_CLASSES = 4" + ], + "outputs": [], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 入力長" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T14:36:10.768196Z", + "start_time": "2025-04-06T14:36:10.754066Z" + } + }, + "source": [ + "TIME_STEPS = 16\n", + "DIMENSION = 2" + ], + "outputs": [], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 学習データ読み込み" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T14:36:10.816368Z", + "start_time": "2025-04-06T14:36:10.786257Z" + } + }, + "source": [ + "X_dataset = np.loadtxt(dataset, delimiter=',', dtype='float32', usecols=list(range(1, (TIME_STEPS * DIMENSION) + 1)))" + ], + "outputs": [], + "execution_count": 5 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T14:36:10.847851Z", + "start_time": "2025-04-06T14:36:10.833239Z" + } + }, + "source": [ + "y_dataset = np.loadtxt(dataset, delimiter=',', dtype='int32', usecols=(0))" + ], + "outputs": [], + "execution_count": 6 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T14:36:10.879849Z", + "start_time": "2025-04-06T14:36:10.864853Z" + } + }, + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, train_size=0.75, random_state=RANDOM_SEED)" + ], + "outputs": [], + "execution_count": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# モデル構築" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T14:36:10.942614Z", + "start_time": "2025-04-06T14:36:10.896107Z" + } + }, + "source": [ + "use_lstm = False\n", + "model = None\n", + "\n", + "if use_lstm:\n", + " model = tf.keras.models.Sequential([\n", + " tf.keras.layers.InputLayer(input_shape=(TIME_STEPS * DIMENSION, )),\n", + " tf.keras.layers.Reshape((TIME_STEPS, DIMENSION), input_shape=(TIME_STEPS * DIMENSION, )), \n", + " tf.keras.layers.Dropout(0.2),\n", + " tf.keras.layers.LSTM(16, input_shape=[TIME_STEPS, DIMENSION]),\n", + " tf.keras.layers.Dropout(0.5),\n", + " tf.keras.layers.Dense(10, activation='relu'),\n", + " tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n", + " ])\n", + "else:\n", + " model = tf.keras.models.Sequential([\n", + " tf.keras.layers.InputLayer(input_shape=(TIME_STEPS * DIMENSION, )),\n", + " tf.keras.layers.Dropout(0.2),\n", + " tf.keras.layers.Dense(24, activation='relu'),\n", + " tf.keras.layers.Dropout(0.5),\n", + " tf.keras.layers.Dense(10, activation='relu'),\n", + " tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n", + " ])" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\.venv\\lib\\site-packages\\keras\\src\\layers\\core\\input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.\n", + " warnings.warn(\n" + ] + } + ], + "execution_count": 8 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-06T14:36:10.973905Z", + "start_time": "2025-04-06T14:36:10.958902Z" + } + }, + "source": [ + "model.summary() # tf.keras.utils.plot_model(model, show_shapes=True)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001B[1mModel: \"sequential\"\u001B[0m\n" + ], + "text/html": [ + "
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Received: filepath=model/point_history_classifier/point_history_classifier.hdf5", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[10], line 2\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;66;03m# モデルチェックポイントのコールバック\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m cp_callback \u001B[38;5;241m=\u001B[39m \u001B[43mtf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mkeras\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mModelCheckpoint\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 3\u001B[0m \u001B[43m \u001B[49m\u001B[43mmodel_save_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msave_weights_only\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m 4\u001B[0m \u001B[38;5;66;03m# 早期打ち切り用コールバック\u001B[39;00m\n\u001B[0;32m 5\u001B[0m es_callback \u001B[38;5;241m=\u001B[39m tf\u001B[38;5;241m.\u001B[39mkeras\u001B[38;5;241m.\u001B[39mcallbacks\u001B[38;5;241m.\u001B[39mEarlyStopping(patience\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m20\u001B[39m, verbose\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n", + "File \u001B[1;32m~\\PycharmProjects\\hand-gesture-recognition-mediapipe\\.venv\\lib\\site-packages\\keras\\src\\callbacks\\model_checkpoint.py:194\u001B[0m, in \u001B[0;36mModelCheckpoint.__init__\u001B[1;34m(self, filepath, monitor, verbose, save_best_only, save_weights_only, mode, save_freq, initial_value_threshold)\u001B[0m\n\u001B[0;32m 190\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 191\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28many\u001B[39m(\n\u001B[0;32m 192\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfilepath\u001B[38;5;241m.\u001B[39mendswith(ext) \u001B[38;5;28;01mfor\u001B[39;00m ext \u001B[38;5;129;01min\u001B[39;00m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m.keras\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m.h5\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 193\u001B[0m ):\n\u001B[1;32m--> 194\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m 195\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe filepath provided must end in `.keras` \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 196\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m(Keras model format). Received: \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 197\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfilepath=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfilepath\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 198\u001B[0m )\n", + "\u001B[1;31mValueError\u001B[0m: The filepath provided must end in `.keras` (Keras model format). Received: filepath=model/point_history_classifier/point_history_classifier.hdf5" + ] + } + ], + "execution_count": 10 + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# モデルコンパイル\n", + "model.compile(\n", + " optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# モデル訓練" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "30/32 [===========================>..] - ETA: 0s - loss: 1.3800 - accuracy: 0.4279\n", + "Epoch 00001: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 1s 25ms/step - loss: 1.3800 - accuracy: 0.4270 - val_loss: 1.3584 - val_accuracy: 0.5219\n", + "Epoch 2/1000\n", + "31/32 [============================>.] - ETA: 0s - loss: 1.3587 - accuracy: 0.5066\n", + "Epoch 00002: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 1.3586 - accuracy: 0.5068 - val_loss: 1.3365 - val_accuracy: 0.5310\n", + "Epoch 3/1000\n", + "32/32 [==============================] - ETA: 0s - loss: 1.3348 - accuracy: 0.5050\n", + "Epoch 00003: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 1.3348 - accuracy: 0.5050 - val_loss: 1.3091 - val_accuracy: 0.5249\n", + "Epoch 4/1000\n", + "31/32 [============================>.] - ETA: 0s - loss: 1.3090 - accuracy: 0.5237\n", + "Epoch 00004: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 1.3090 - accuracy: 0.5239 - val_loss: 1.2752 - val_accuracy: 0.5906\n", + "Epoch 5/1000\n", + "30/32 [===========================>..] - ETA: 0s - loss: 1.2776 - accuracy: 0.5560\n", + "Epoch 00005: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 13ms/step - loss: 1.2768 - accuracy: 0.5554 - val_loss: 1.2332 - val_accuracy: 0.6178\n", + "Epoch 6/1000\n", + "31/32 [============================>.] - ETA: 0s - loss: 1.2342 - accuracy: 0.5597\n", + "Epoch 00006: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 1.2342 - accuracy: 0.5599 - val_loss: 1.1817 - val_accuracy: 0.6858\n", + "Epoch 7/1000\n", + "26/32 [=======================>......] - ETA: 0s - loss: 1.1957 - accuracy: 0.5721\n", + "Epoch 00007: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 1.1932 - accuracy: 0.5780 - val_loss: 1.1206 - val_accuracy: 0.7122\n", + "Epoch 8/1000\n", + "29/32 [==========================>...] - ETA: 0s - loss: 1.1480 - accuracy: 0.5717\n", + "Epoch 00008: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 1.1455 - accuracy: 0.5743 - val_loss: 1.0555 - val_accuracy: 0.7492\n", + "Epoch 9/1000\n", + "31/32 [============================>.] - ETA: 0s - loss: 1.0875 - accuracy: 0.6001\n", + "Epoch 00009: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 1.0879 - accuracy: 0.5997 - val_loss: 0.9879 - val_accuracy: 0.7847\n", + "Epoch 10/1000\n", + "30/32 [===========================>..] - ETA: 0s - loss: 1.0461 - accuracy: 0.6023\n", + "Epoch 00010: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 10ms/step - loss: 1.0451 - accuracy: 0.6037 - val_loss: 0.9241 - val_accuracy: 0.7878\n", + "Epoch 11/1000\n", + "27/32 [========================>.....] - ETA: 0s - loss: 0.9968 - accuracy: 0.6238\n", + "Epoch 00011: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 10ms/step - loss: 0.9958 - accuracy: 0.6256 - val_loss: 0.8632 - val_accuracy: 0.8180\n", + "Epoch 12/1000\n", + "31/32 [============================>.] - ETA: 0s - loss: 0.9525 - accuracy: 0.6356\n", + "Epoch 00012: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 0.9523 - accuracy: 0.6357 - val_loss: 0.8063 - val_accuracy: 0.8270\n", + "Epoch 13/1000\n", + "31/32 [============================>.] - ETA: 0s - loss: 0.9175 - accuracy: 0.6560\n", + "Epoch 00013: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 0.9174 - accuracy: 0.6558 - val_loss: 0.7573 - val_accuracy: 0.8527\n", + "Epoch 14/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.8813 - accuracy: 0.6709\n", + "Epoch 00014: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.8738 - accuracy: 0.6689 - val_loss: 0.7132 - val_accuracy: 0.8739\n", + "Epoch 15/1000\n", + "26/32 [=======================>......] - ETA: 0s - loss: 0.8613 - accuracy: 0.6659\n", + "Epoch 00015: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.8577 - accuracy: 0.6692 - val_loss: 0.6770 - val_accuracy: 0.8897\n", + "Epoch 16/1000\n", + "31/32 [============================>.] - ETA: 0s - loss: 0.8275 - accuracy: 0.6815\n", + "Epoch 00016: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 0.8273 - accuracy: 0.6815 - val_loss: 0.6460 - val_accuracy: 0.9026\n", + "Epoch 17/1000\n", + "30/32 [===========================>..] - ETA: 0s - loss: 0.8088 - accuracy: 0.6958\n", + "Epoch 00017: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 0.8066 - accuracy: 0.6966 - val_loss: 0.6152 - val_accuracy: 0.9063\n", + "Epoch 18/1000\n", + "30/32 [===========================>..] - ETA: 0s - loss: 0.7810 - accuracy: 0.6953\n", + "Epoch 00018: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.7807 - accuracy: 0.6954 - val_loss: 0.5880 - val_accuracy: 0.9162\n", + "Epoch 19/1000\n", + "25/32 [======================>.......] 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"29/32 [==========================>...] - ETA: 0s - loss: 0.7107 - accuracy: 0.7322\n", + "Epoch 00022: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 10ms/step - loss: 0.7106 - accuracy: 0.7329 - val_loss: 0.5073 - val_accuracy: 0.9313\n", + "Epoch 23/1000\n", + "32/32 [==============================] - ETA: 0s - loss: 0.7039 - accuracy: 0.7356\n", + "Epoch 00023: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 9ms/step - loss: 0.7039 - accuracy: 0.7356 - val_loss: 0.4947 - val_accuracy: 0.9358\n", + "Epoch 24/1000\n", + "24/32 [=====================>........] - ETA: 0s - loss: 0.7048 - accuracy: 0.7279\n", + "Epoch 00024: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 10ms/step - loss: 0.7119 - accuracy: 0.7251 - val_loss: 0.4782 - val_accuracy: 0.9350\n", + "Epoch 25/1000\n", + "31/32 [============================>.] - ETA: 0s - loss: 0.6741 - accuracy: 0.7485\n", + "Epoch 00025: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 0.6737 - accuracy: 0.7485 - val_loss: 0.4624 - val_accuracy: 0.9366\n", + "Epoch 26/1000\n", + "23/32 [====================>.........] - ETA: 0s - loss: 0.6698 - accuracy: 0.7480\n", + "Epoch 00026: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 9ms/step - loss: 0.6700 - accuracy: 0.7472 - val_loss: 0.4484 - val_accuracy: 0.9388\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 27/1000\n", + "28/32 [=========================>....] - ETA: 0s - loss: 0.6723 - accuracy: 0.74 - ETA: 0s - loss: 0.6671 - accuracy: 0.7444\n", + "Epoch 00027: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.6640 - accuracy: 0.7447 - val_loss: 0.4387 - val_accuracy: 0.9418\n", + "Epoch 28/1000\n", + "31/32 [============================>.] - ETA: 0s - loss: 0.6625 - accuracy: 0.7533\n", + "Epoch 00028: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 0.6622 - accuracy: 0.7535 - val_loss: 0.4314 - val_accuracy: 0.9411\n", + "Epoch 29/1000\n", + "30/32 [===========================>..] - ETA: 0s - loss: 0.6503 - accuracy: 0.7568\n", + "Epoch 00029: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 0.6505 - accuracy: 0.7570 - val_loss: 0.4200 - val_accuracy: 0.9449\n", + "Epoch 30/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.6271 - accuracy: 0.7675\n", + "Epoch 00030: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.6296 - accuracy: 0.7656 - val_loss: 0.4093 - val_accuracy: 0.9524\n", + "Epoch 31/1000\n", + "29/32 [==========================>...] - ETA: 0s - loss: 0.6360 - accuracy: 0.7586\n", + "Epoch 00031: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 0.6362 - accuracy: 0.7593 - val_loss: 0.4019 - val_accuracy: 0.9479\n", + "Epoch 32/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.6253 - accuracy: 0.7700\n", + "Epoch 00032: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.6186 - accuracy: 0.7717 - val_loss: 0.3927 - val_accuracy: 0.9464\n", + "Epoch 33/1000\n", + "25/32 [======================>.......] 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val_accuracy: 0.9524\n", + "Epoch 39/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.5917 - accuracy: 0.7831\n", + "Epoch 00039: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.5975 - accuracy: 0.7782 - val_loss: 0.3579 - val_accuracy: 0.9547\n", + "Epoch 40/1000\n", + "26/32 [=======================>......] - ETA: 0s - loss: 0.5938 - accuracy: 0.7749\n", + "Epoch 00040: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.5941 - accuracy: 0.7734 - val_loss: 0.3521 - val_accuracy: 0.9569\n", + "Epoch 41/1000\n", + "26/32 [=======================>......] - ETA: 0s - loss: 0.5979 - accuracy: 0.7656\n", + "Epoch 00041: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.5977 - 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model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.5552 - accuracy: 0.7933 - val_loss: 0.3244 - val_accuracy: 0.9585\n", + "Epoch 48/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.5754 - accuracy: 0.7809\n", + "Epoch 00048: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.5751 - accuracy: 0.7827 - val_loss: 0.3202 - val_accuracy: 0.9577\n", + "Epoch 49/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.5672 - accuracy: 0.7816\n", + "Epoch 00049: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.5630 - accuracy: 0.7842 - val_loss: 0.3181 - val_accuracy: 0.9585\n", + "Epoch 50/1000\n", + "29/32 [==========================>...] - ETA: 0s - loss: 0.5576 - accuracy: 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accuracy: 0.8155 - val_loss: 0.2721 - val_accuracy: 0.9600\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 79/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.5063 - accuracy: 0.8122\n", + "Epoch 00079: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.5016 - accuracy: 0.8099 - val_loss: 0.2682 - val_accuracy: 0.9600\n", + "Epoch 80/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.4965 - accuracy: 0.8166\n", + "Epoch 00080: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.4887 - accuracy: 0.8200 - val_loss: 0.2658 - val_accuracy: 0.9600\n", + "Epoch 81/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.4992 - accuracy: 0.8169\n", + "Epoch 00081: saving model to 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to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3769 - accuracy: 0.8633 - val_loss: 0.1553 - val_accuracy: 0.9683\n", + "Epoch 340/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.3625 - accuracy: 0.8669\n", + "Epoch 00340: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3648 - accuracy: 0.8653 - val_loss: 0.1565 - val_accuracy: 0.9698\n", + "Epoch 341/1000\n", + "26/32 [=======================>......] - ETA: 0s - loss: 0.3605 - accuracy: 0.8669\n", + "Epoch 00341: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3588 - accuracy: 0.8688 - val_loss: 0.1580 - val_accuracy: 0.9675\n", + "Epoch 342/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.3730 - accuracy: 0.8650\n", + "Epoch 00342: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3672 - accuracy: 0.8686 - val_loss: 0.1579 - val_accuracy: 0.9675\n", + "Epoch 343/1000\n", + "31/32 [============================>.] - ETA: 0s - loss: 0.3742 - accuracy: 0.8616\n", + "Epoch 00343: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 8ms/step - loss: 0.3748 - accuracy: 0.8615 - val_loss: 0.1585 - val_accuracy: 0.9660\n", + "Epoch 344/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.3826 - accuracy: 0.8641\n", + "Epoch 00344: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3755 - accuracy: 0.8668 - val_loss: 0.1620 - val_accuracy: 0.9675\n", + "Epoch 345/1000\n", + "31/32 [============================>.] - ETA: 0s - loss: 0.3707 - accuracy: 0.8644\n", + "Epoch 00345: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 0.3706 - accuracy: 0.8646 - val_loss: 0.1571 - val_accuracy: 0.9698\n", + "Epoch 346/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.3740 - accuracy: 0.8537\n", + "Epoch 00346: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3666 - accuracy: 0.8578 - val_loss: 0.1587 - val_accuracy: 0.9683\n", + "Epoch 347/1000\n", + "32/32 [==============================] - ETA: 0s - loss: 0.3786 - accuracy: 0.8583\n", + "Epoch 00347: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3786 - accuracy: 0.8583 - val_loss: 0.1588 - val_accuracy: 0.9698\n", + "Epoch 348/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.3673 - accuracy: 0.8694\n", + "Epoch 00348: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3631 - accuracy: 0.8696 - val_loss: 0.1585 - val_accuracy: 0.9690\n", + "Epoch 349/1000\n", + "32/32 [==============================] - ETA: 0s - loss: 0.3887 - accuracy: 0.8557\n", + "Epoch 00349: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3887 - accuracy: 0.8557 - val_loss: 0.1573 - val_accuracy: 0.9690\n", + "Epoch 350/1000\n", + "32/32 [==============================] - ETA: 0s - loss: 0.3770 - accuracy: 0.8575\n", + "Epoch 00350: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3770 - accuracy: 0.8575 - val_loss: 0.1573 - val_accuracy: 0.9683\n", + "Epoch 351/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.3831 - accuracy: 0.8603\n", + "Epoch 00351: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3783 - accuracy: 0.8610 - val_loss: 0.1587 - val_accuracy: 0.9683\n", + "Epoch 352/1000\n", + "30/32 [===========================>..] - ETA: 0s - loss: 0.3559 - accuracy: 0.8708\n", + "Epoch 00352: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 0.3571 - accuracy: 0.8698 - val_loss: 0.1571 - val_accuracy: 0.9690\n", + "Epoch 353/1000\n", + "30/32 [===========================>..] - ETA: 0s - loss: 0.3684 - accuracy: 0.8641\n", + "Epoch 00353: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3686 - accuracy: 0.8635 - val_loss: 0.1579 - val_accuracy: 0.9690\n", + "Epoch 354/1000\n", + "28/32 [=========================>....] - ETA: 0s - loss: 0.3689 - accuracy: 0.8652\n", + "Epoch 00354: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 10ms/step - loss: 0.3731 - accuracy: 0.8633 - val_loss: 0.1575 - val_accuracy: 0.9705\n", + "Epoch 355/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.3796 - accuracy: 0.8584\n", + "Epoch 00355: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3829 - accuracy: 0.8588 - val_loss: 0.1584 - val_accuracy: 0.9675\n", + "Epoch 356/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.3776 - accuracy: 0.8591\n", + "Epoch 00356: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 12ms/step - loss: 0.3737 - accuracy: 0.8623 - val_loss: 0.1568 - val_accuracy: 0.9675\n", + "Epoch 357/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.3539 - accuracy: 0.8697\n", + "Epoch 00357: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 10ms/step - loss: 0.3696 - accuracy: 0.8635 - val_loss: 0.1571 - val_accuracy: 0.9683\n", + "Epoch 358/1000\n", + "32/32 [==============================] - ETA: 0s - loss: 0.3729 - accuracy: 0.8671\n", + "Epoch 00358: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3729 - accuracy: 0.8671 - val_loss: 0.1568 - val_accuracy: 0.9675\n", + "Epoch 359/1000\n", + "25/32 [======================>.......] - ETA: 0s - loss: 0.3893 - accuracy: 0.8666\n", + "Epoch 00359: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n", + "32/32 [==============================] - 0s 11ms/step - loss: 0.3871 - accuracy: 0.8666 - val_loss: 0.1558 - val_accuracy: 0.9683\n", + "Epoch 00359: early stopping\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(\n", + " X_train,\n", + " y_train,\n", + " epochs=1000,\n", + " batch_size=128,\n", + " validation_data=(X_test, y_test),\n", + " callbacks=[cp_callback, es_callback]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# 保存したモデルのロード\n", + "model = tf.keras.models.load_model(model_save_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.9438375 0.01624594 0.01188833 0.02802826]\n", + "0\n" + ] + } + ], + "source": [ + "# 推論テスト\n", + "predict_result = model.predict(np.array([X_test[0]]))\n", + "print(np.squeeze(predict_result))\n", + "print(np.argmax(np.squeeze(predict_result)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 混同行列" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification Report\n", + " precision recall f1-score support\n", + "\n", + " 0 0.99 1.00 0.99 395\n", + " 1 0.93 0.99 0.96 295\n", + " 2 0.98 0.97 0.98 307\n", + " 3 0.96 0.91 0.93 327\n", + "\n", + " accuracy 0.97 1324\n", + " macro avg 0.97 0.97 0.97 1324\n", + "weighted avg 0.97 0.97 0.97 1324\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import confusion_matrix, classification_report\n", + "\n", + "def print_confusion_matrix(y_true, y_pred, report=True):\n", + " labels = sorted(list(set(y_true)))\n", + " cmx_data = confusion_matrix(y_true, y_pred, labels=labels)\n", + " \n", + " df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)\n", + " \n", + " fig, ax = plt.subplots(figsize=(7, 6))\n", + " sns.heatmap(df_cmx, annot=True, fmt='g' ,square=False)\n", + " ax.set_ylim(len(set(y_true)), 0)\n", + " plt.show()\n", + " \n", + " if report:\n", + " print('Classification Report')\n", + " print(classification_report(y_test, y_pred))\n", + "\n", + "Y_pred = model.predict(X_test)\n", + "y_pred = np.argmax(Y_pred, axis=1)\n", + "\n", + "print_confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tensorflow-Lite用のモデルへ変換" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 推論専用のモデルとして保存\n", + "model.save(model_save_path, include_optimizer=False)\n", + "model = tf.keras.models.load_model(model_save_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "tflite_save_path = 'model/point_history_classifier/point_history_classifier.tflite'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# モデルを変換(量子化\n", + "converter = tf.lite.TFLiteConverter.from_keras_model(model) # converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)\n", + "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n", + "tflite_quantized_model = converter.convert()\n", + "\n", + "open(tflite_save_path, 'wb').write(tflite_quantized_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 推論テスト" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "interpreter = tf.lite.Interpreter(model_path=tflite_save_path)\n", + "interpreter.allocate_tensors()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'name': 'input_1', 'index': 0, 'shape': array([ 1, 32]), 'shape_signature': array([-1, 32]), 'dtype': , 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n" + ] + } + ], + "source": [ + "# 入出力テンソルを取得\n", + "input_details = interpreter.get_input_details()\n", + "output_details = interpreter.get_output_details()\n", + "print(input_details)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "interpreter.set_tensor(input_details[0]['index'], np.array([X_test[0]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 0 ns\n" + ] + } + ], + "source": [ + "%%time\n", + "# 推論実施\n", + "interpreter.invoke()\n", + "tflite_results = interpreter.get_tensor(output_details[0]['index'])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.9579909 0.01342559 0.00907356 0.01950999]\n", + "0\n" + ] + } + ], + "source": [ + "print(np.squeeze(tflite_results))\n", + "print(np.argmax(np.squeeze(tflite_results)))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 1f0622e88cf665ade471d89e9dfa64ec3147e8f4 Mon Sep 17 00:00:00 2001 From: Grimspen66 Date: Sun, 6 Apr 2025 20:28:02 -0500 Subject: [PATCH 2/3] Update app.py added a comment --- app.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/app.py b/app.py index df953dab..0f3317e2 100644 --- a/app.py +++ b/app.py @@ -61,6 +61,8 @@ def main(): mp_hands = mp.solutions.hands hands = mp_hands.Hands( static_image_mode=use_static_image_mode, + + #Changed this to recognize 2 hands max_num_hands=2, min_detection_confidence=min_detection_confidence, min_tracking_confidence=min_tracking_confidence, From 6305fe937f4a9437c39ec358d84fbb1b9c00915d Mon Sep 17 00:00:00 2001 From: grimm Date: Sun, 6 Apr 2025 20:56:39 -0500 Subject: [PATCH 3/3] getting the project back to speed with correct fork from original branch --- .gitignore | 135 -- app.py | 4 +- keypoint_classification.ipynb | 1156 +++++++---------- model/keypoint_classifier/keypoint.csv | 1038 +++++++-------- .../keypoint_classifier.hdf5 | Bin 22264 -> 0 bytes .../keypoint_classifier.keras | Bin 0 -> 43080 bytes .../keypoint_classifier.tflite | Bin 6352 -> 6644 bytes .../keypoint_classifier_label.csv | 4 +- 8 files changed, 887 insertions(+), 1450 deletions(-) delete mode 100644 .gitignore delete mode 100644 model/keypoint_classifier/keypoint_classifier.hdf5 create mode 100644 model/keypoint_classifier/keypoint_classifier.keras diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 241bf184..00000000 --- a/.gitignore +++ /dev/null @@ -1,135 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# bat -*.bat -.idea/ - -.DS_Store diff --git a/app.py b/app.py index 0f3317e2..3dadce2b 100644 --- a/app.py +++ b/app.py @@ -61,8 +61,6 @@ def main(): mp_hands = mp.solutions.hands hands = mp_hands.Hands( static_image_mode=use_static_image_mode, - - #Changed this to recognize 2 hands max_num_hands=2, min_detection_confidence=min_detection_confidence, min_tracking_confidence=min_tracking_confidence, @@ -141,7 +139,7 @@ def main(): logging_csv(number, mode, pre_processed_landmark_list, pre_processed_point_history_list) - # Hand sign classification + #Hand sign classification hand_sign_id = keypoint_classifier(pre_processed_landmark_list) if hand_sign_id == 2: # Point gesture point_history.append(landmark_list[8]) diff --git a/keypoint_classification.ipynb b/keypoint_classification.ipynb index 1b1f4500..afe91123 100644 --- a/keypoint_classification.ipynb +++ b/keypoint_classification.ipynb @@ -4,8 +4,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:09:19.924935Z", - "start_time": "2025-04-06T15:09:19.920937Z" + "end_time": "2025-04-07T01:32:07.043144Z", + "start_time": "2025-04-07T01:32:05.172985Z" } }, "source": [ @@ -18,7 +18,7 @@ "RANDOM_SEED = 42" ], "outputs": [], - "execution_count": 23 + "execution_count": 1 }, { "cell_type": "markdown", @@ -31,8 +31,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:09:19.940212Z", - "start_time": "2025-04-06T15:09:19.927609Z" + "end_time": "2025-04-07T01:32:07.121511Z", + "start_time": "2025-04-07T01:32:07.047147Z" } }, "source": [ @@ -52,7 +52,7 @@ "# model.fit(X_train, y_train, epochs=100, callbacks=[cp_callback, es_callback])\n" ], "outputs": [], - "execution_count": 24 + "execution_count": 2 }, { "cell_type": "markdown", @@ -65,13 +65,13 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:09:19.972212Z", - "start_time": "2025-04-06T15:09:19.957213Z" + "end_time": "2025-04-07T01:32:07.436206Z", + "start_time": "2025-04-07T01:32:07.421206Z" } }, "source": "NUM_CLASSES = 6", "outputs": [], - "execution_count": 25 + "execution_count": 3 }, { "cell_type": "markdown", @@ -84,41 +84,41 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:09:20.034998Z", - "start_time": "2025-04-06T15:09:19.989213Z" + "end_time": "2025-04-07T01:32:07.498522Z", + "start_time": "2025-04-07T01:32:07.454210Z" } }, "source": [ "X_dataset = np.loadtxt(dataset, delimiter=',', dtype='float32', usecols=list(range(1, (21 * 2) + 1)))" ], "outputs": [], - "execution_count": 26 + "execution_count": 4 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:09:20.066452Z", - "start_time": "2025-04-06T15:09:20.051453Z" + "end_time": "2025-04-07T01:32:07.530529Z", + "start_time": "2025-04-07T01:32:07.515530Z" } }, "source": "y_dataset = np.loadtxt(dataset, delimiter=',', dtype='int32', usecols=(0))", "outputs": [], - "execution_count": 27 + "execution_count": 5 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:09:20.097742Z", - "start_time": "2025-04-06T15:09:20.082910Z" + "end_time": "2025-04-07T01:32:07.562532Z", + "start_time": "2025-04-07T01:32:07.547533Z" } }, "source": [ "X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, train_size=0.75, random_state=RANDOM_SEED)" ], "outputs": [], - "execution_count": 28 + "execution_count": 6 }, { "cell_type": "markdown", @@ -131,8 +131,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:09:20.144382Z", - "start_time": "2025-04-06T15:09:20.114353Z" + "end_time": "2025-04-07T01:32:07.625657Z", + "start_time": "2025-04-07T01:32:07.579540Z" } }, "source": [ @@ -146,14 +146,14 @@ "])" ], "outputs": [], - "execution_count": 29 + "execution_count": 7 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:09:20.175513Z", - "start_time": "2025-04-06T15:09:20.160513Z" + "end_time": "2025-04-07T01:32:07.735177Z", + "start_time": "2025-04-07T01:32:07.642660Z" } }, "source": [ @@ -163,10 +163,10 @@ { "data": { "text/plain": [ - "\u001B[1mModel: \"sequential_1\"\u001B[0m\n" + "\u001B[1mModel: \"sequential\"\u001B[0m\n" ], "text/html": [ - "
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@@ -250,14 +250,14 @@
      "output_type": "display_data"
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+   "execution_count": 8
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@@ -268,14 +268,14 @@
     "es_callback = tf.keras.callbacks.EarlyStopping(patience=20, verbose=1)"
    ],
    "outputs": [],
-   "execution_count": 31
+   "execution_count": 9
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   {
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@@ -287,7 +287,7 @@
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@@ -301,8 +301,8 @@
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@@ -321,963 +321,675 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m9s\u001B[0m 349ms/step - accuracy: 0.0625 - loss: 2.0222\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m10s\u001B[0m 391ms/step - accuracy: 0.2031 - loss: 1.8582\n",
       "Epoch 1: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.0773 - loss: 1.9791 - val_accuracy: 0.2572 - val_loss: 1.7389\n",
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       "Epoch 2/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.2109 - loss: 1.7578\n",
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       "Epoch 2: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.2342 - loss: 1.7574 - val_accuracy: 0.4045 - val_loss: 1.6213\n",
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       "Epoch 3/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3438 - loss: 1.6719\n",
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       "Epoch 3: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.3428 - loss: 1.6372 - val_accuracy: 0.4080 - val_loss: 1.5256\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.3706 - loss: 1.5399 - val_accuracy: 0.5129 - val_loss: 1.3903\n",
       "Epoch 4/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.2734 - loss: 1.5504\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.4297 - loss: 1.4812\n",
       "Epoch 4: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.3527 - loss: 1.5440 - val_accuracy: 0.4568 - val_loss: 1.4422\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4189 - loss: 1.4393 - val_accuracy: 0.5680 - val_loss: 1.2911\n",
       "Epoch 5/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4219 - loss: 1.5022\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.4609 - loss: 1.3419\n",
       "Epoch 5: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4197 - loss: 1.4810 - val_accuracy: 0.5283 - val_loss: 1.3709\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4306 - loss: 1.3669 - val_accuracy: 0.5783 - val_loss: 1.2160\n",
       "Epoch 6/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4062 - loss: 1.4479\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.5391 - loss: 1.2843\n",
       "Epoch 6: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4316 - loss: 1.4221 - val_accuracy: 0.5388 - val_loss: 1.3062\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4702 - loss: 1.3001 - val_accuracy: 0.5878 - val_loss: 1.1506\n",
       "Epoch 7/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.3906 - loss: 1.4662\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5000 - loss: 1.2149\n",
       "Epoch 7: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4453 - loss: 1.3788 - val_accuracy: 0.5632 - val_loss: 1.2367\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4694 - loss: 1.2643 - val_accuracy: 0.6179 - val_loss: 1.0986\n",
       "Epoch 8/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.4688 - loss: 1.3573\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.5156 - loss: 1.1944\n",
       "Epoch 8: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4599 - loss: 1.3226 - val_accuracy: 0.5859 - val_loss: 1.1669\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5048 - loss: 1.2167 - val_accuracy: 0.6489 - val_loss: 1.0489\n",
       "Epoch 9/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4609 - loss: 1.2706\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4922 - loss: 1.3093\n",
       "Epoch 9: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.4964 - loss: 1.2402 - val_accuracy: 0.6085 - val_loss: 1.0906\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5100 - loss: 1.1991 - val_accuracy: 0.6523 - val_loss: 1.0065\n",
       "Epoch 10/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.5234 - loss: 1.2300\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.5234 - loss: 1.1329\n",
       "Epoch 10: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5111 - loss: 1.1958 - val_accuracy: 0.6173 - val_loss: 1.0209\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5074 - loss: 1.1542 - val_accuracy: 0.6532 - val_loss: 0.9697\n",
       "Epoch 11/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5391 - loss: 1.1013\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.5625 - loss: 1.0692\n",
       "Epoch 11: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5180 - loss: 1.1655 - val_accuracy: 0.6460 - val_loss: 0.9645\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5286 - loss: 1.1447 - val_accuracy: 0.6566 - val_loss: 0.9418\n",
       "Epoch 12/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5625 - loss: 1.1091\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4766 - loss: 1.1607\n",
       "Epoch 12: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5249 - loss: 1.1306 - val_accuracy: 0.6879 - val_loss: 0.9138\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5491 - loss: 1.1235 - val_accuracy: 0.7212 - val_loss: 0.9057\n",
       "Epoch 13/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5469 - loss: 1.0677\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5469 - loss: 1.0851\n",
       "Epoch 13: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5443 - loss: 1.1049 - val_accuracy: 0.7306 - val_loss: 0.8627\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5515 - loss: 1.0980 - val_accuracy: 0.7315 - val_loss: 0.8763\n",
       "Epoch 14/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4766 - loss: 1.1436\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.5703 - loss: 1.0917\n",
       "Epoch 14: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5409 - loss: 1.0798 - val_accuracy: 0.7384 - val_loss: 0.8244\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5622 - loss: 1.0640 - val_accuracy: 0.7349 - val_loss: 0.8491\n",
       "Epoch 15/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6328 - loss: 0.9176\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6016 - loss: 0.9433\n",
       "Epoch 15: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5931 - loss: 1.0027 - val_accuracy: 0.7515 - val_loss: 0.7856\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5726 - loss: 1.0341 - val_accuracy: 0.7410 - val_loss: 0.8234\n",
       "Epoch 16/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.5547 - loss: 1.0661\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.5781 - loss: 1.0564\n",
       "Epoch 16: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5589 - loss: 1.0331 - val_accuracy: 0.7454 - val_loss: 0.7571\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5815 - loss: 1.0361 - val_accuracy: 0.7410 - val_loss: 0.7998\n",
       "Epoch 17/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6797 - loss: 0.9360\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.5625 - loss: 1.1101\n",
       "Epoch 17: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5960 - loss: 0.9712 - val_accuracy: 0.7733 - val_loss: 0.7259\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5799 - loss: 1.0349 - val_accuracy: 0.7410 - val_loss: 0.7795\n",
       "Epoch 18/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6406 - loss: 0.9400\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.5938 - loss: 0.9689\n",
       "Epoch 18: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.5959 - loss: 0.9798 - val_accuracy: 0.7951 - val_loss: 0.6978\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6052 - loss: 0.9979 - val_accuracy: 0.7556 - val_loss: 0.7609\n",
       "Epoch 19/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.5547 - loss: 1.0904\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6797 - loss: 0.9491\n",
       "Epoch 19: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6070 - loss: 0.9680 - val_accuracy: 0.8073 - val_loss: 0.6693\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6192 - loss: 0.9721 - val_accuracy: 0.7556 - val_loss: 0.7409\n",
       "Epoch 20/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6641 - loss: 0.8054\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5781 - loss: 0.9907\n",
       "Epoch 20: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6246 - loss: 0.9202 - val_accuracy: 0.8152 - val_loss: 0.6566\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6130 - loss: 0.9839 - val_accuracy: 0.7539 - val_loss: 0.7239\n",
       "Epoch 21/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6328 - loss: 0.8654\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6016 - loss: 1.0174\n",
       "Epoch 21: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6358 - loss: 0.8990 - val_accuracy: 0.8204 - val_loss: 0.6292\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6171 - loss: 0.9808 - val_accuracy: 0.7754 - val_loss: 0.7056\n",
       "Epoch 22/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6484 - loss: 0.8508\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5781 - loss: 1.0118\n",
       "Epoch 22: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6250 - loss: 0.9057 - val_accuracy: 0.8387 - val_loss: 0.6121\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6163 - loss: 0.9518 - val_accuracy: 0.7806 - val_loss: 0.6868\n",
       "Epoch 23/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7031 - loss: 0.8454\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6094 - loss: 0.9426\n",
       "Epoch 23: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6503 - loss: 0.8875 - val_accuracy: 0.8370 - val_loss: 0.5931\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6464 - loss: 0.9330 - val_accuracy: 0.7978 - val_loss: 0.6692\n",
       "Epoch 24/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6328 - loss: 0.9711\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5625 - loss: 1.0196\n",
       "Epoch 24: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6464 - loss: 0.8882 - val_accuracy: 0.8439 - val_loss: 0.5758\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6322 - loss: 0.9297 - val_accuracy: 0.7995 - val_loss: 0.6616\n",
       "Epoch 25/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.5938 - loss: 1.0014\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6641 - loss: 0.8983\n",
       "Epoch 25: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6365 - loss: 0.9033 - val_accuracy: 0.8457 - val_loss: 0.5629\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6306 - loss: 0.9202 - val_accuracy: 0.7935 - val_loss: 0.6470\n",
       "Epoch 26/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6641 - loss: 0.8698\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6484 - loss: 0.8380\n",
       "Epoch 26: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6581 - loss: 0.8625 - val_accuracy: 0.8527 - val_loss: 0.5412\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6354 - loss: 0.9170 - val_accuracy: 0.7995 - val_loss: 0.6350\n",
       "Epoch 27/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6719 - loss: 0.7902\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7422 - loss: 0.7610\n",
       "Epoch 27: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6539 - loss: 0.8377 - val_accuracy: 0.8596 - val_loss: 0.5299\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6436 - loss: 0.8954 - val_accuracy: 0.8150 - val_loss: 0.6187\n",
       "Epoch 28/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6250 - loss: 0.8936\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6016 - loss: 0.9117\n",
       "Epoch 28: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6500 - loss: 0.8674 - val_accuracy: 0.8666 - val_loss: 0.5235\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6634 - loss: 0.8719 - val_accuracy: 0.8158 - val_loss: 0.6031\n",
       "Epoch 29/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7109 - loss: 0.7589\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6094 - loss: 0.9059\n",
       "Epoch 29: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6793 - loss: 0.8141 - val_accuracy: 0.8684 - val_loss: 0.5123\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6440 - loss: 0.8866 - val_accuracy: 0.8244 - val_loss: 0.5950\n",
       "Epoch 30/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6250 - loss: 0.8867\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.5938 - loss: 0.9291\n",
       "Epoch 30: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6680 - loss: 0.8343 - val_accuracy: 0.8710 - val_loss: 0.4999\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6517 - loss: 0.8803 - val_accuracy: 0.8305 - val_loss: 0.5826\n",
       "Epoch 31/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6641 - loss: 0.9109\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6016 - loss: 0.8839\n",
       "Epoch 31: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6822 - loss: 0.8166 - val_accuracy: 0.8718 - val_loss: 0.4865\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6588 - loss: 0.8540 - val_accuracy: 0.8348 - val_loss: 0.5723\n",
       "Epoch 32/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 19ms/step - accuracy: 0.7266 - loss: 0.7984\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6641 - loss: 0.8743\n",
       "Epoch 32: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6785 - loss: 0.8232 - val_accuracy: 0.8666 - val_loss: 0.4868\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6526 - loss: 0.8670 - val_accuracy: 0.8305 - val_loss: 0.5630\n",
       "Epoch 33/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7109 - loss: 0.7727\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6094 - loss: 0.9990\n",
       "Epoch 33: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6978 - loss: 0.7922 - val_accuracy: 0.8701 - val_loss: 0.4778\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6605 - loss: 0.8517 - val_accuracy: 0.8408 - val_loss: 0.5448\n",
       "Epoch 34/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.7798\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7109 - loss: 0.8068\n",
       "Epoch 34: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7039 - loss: 0.7837 - val_accuracy: 0.8745 - val_loss: 0.4668\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6727 - loss: 0.8343 - val_accuracy: 0.8434 - val_loss: 0.5375\n",
       "Epoch 35/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6172 - loss: 0.8736\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6953 - loss: 0.8557\n",
       "Epoch 35: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6690 - loss: 0.8212 - val_accuracy: 0.8771 - val_loss: 0.4574\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6901 - loss: 0.8066 - val_accuracy: 0.8494 - val_loss: 0.5259\n",
       "Epoch 36/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m2s\u001B[0m 78ms/step - accuracy: 0.6953 - loss: 0.6885\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6719 - loss: 0.8049\n",
       "Epoch 36: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6829 - loss: 0.7829 - val_accuracy: 0.8858 - val_loss: 0.4441\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6569 - loss: 0.8250 - val_accuracy: 0.8468 - val_loss: 0.5155\n",
       "Epoch 37/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6406 - loss: 0.9419\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6953 - loss: 0.8077\n",
       "Epoch 37: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6929 - loss: 0.8005 - val_accuracy: 0.8788 - val_loss: 0.4418\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6761 - loss: 0.8154 - val_accuracy: 0.8537 - val_loss: 0.5109\n",
       "Epoch 38/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6905\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7188 - loss: 0.7073\n",
       "Epoch 38: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7031 - loss: 0.7612 - val_accuracy: 0.8788 - val_loss: 0.4334\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6825 - loss: 0.7886 - val_accuracy: 0.8589 - val_loss: 0.5002\n",
       "Epoch 39/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6797 - loss: 0.8182\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7422 - loss: 0.7702\n",
       "Epoch 39: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6940 - loss: 0.7918 - val_accuracy: 0.9024 - val_loss: 0.4231\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6793 - loss: 0.8211 - val_accuracy: 0.8503 - val_loss: 0.4984\n",
       "Epoch 40/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6406 - loss: 0.8019\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7109 - loss: 0.8079\n",
       "Epoch 40: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6839 - loss: 0.7694 - val_accuracy: 0.9032 - val_loss: 0.4128\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6851 - loss: 0.7898 - val_accuracy: 0.8503 - val_loss: 0.4901\n",
       "Epoch 41/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6641 - loss: 0.9634\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6953 - loss: 0.7663\n",
       "Epoch 41: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6943 - loss: 0.7897 - val_accuracy: 0.8858 - val_loss: 0.4184\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6789 - loss: 0.8184 - val_accuracy: 0.8589 - val_loss: 0.4887\n",
       "Epoch 42/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8047 - loss: 0.5583\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7031 - loss: 0.7185\n",
       "Epoch 42: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7236 - loss: 0.7259 - val_accuracy: 0.8963 - val_loss: 0.4082\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7046 - loss: 0.7474 - val_accuracy: 0.8632 - val_loss: 0.4769\n",
       "Epoch 43/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6077\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7109 - loss: 0.7371\n",
       "Epoch 43: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7190 - loss: 0.7192 - val_accuracy: 0.8971 - val_loss: 0.4047\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6986 - loss: 0.7808 - val_accuracy: 0.8735 - val_loss: 0.4713\n",
       "Epoch 44/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7500 - loss: 0.6481\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6953 - loss: 0.8347\n",
       "Epoch 44: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7204 - loss: 0.7467 - val_accuracy: 0.8875 - val_loss: 0.3997\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6944 - loss: 0.7750 - val_accuracy: 0.8657 - val_loss: 0.4664\n",
       "Epoch 45/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6998\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7109 - loss: 0.7636\n",
       "Epoch 45: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7163 - loss: 0.7389 - val_accuracy: 0.8945 - val_loss: 0.3954\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7002 - loss: 0.7671 - val_accuracy: 0.8709 - val_loss: 0.4525\n",
       "Epoch 46/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.7393\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6953 - loss: 0.8086\n",
       "Epoch 46: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7241 - loss: 0.7325 - val_accuracy: 0.9058 - val_loss: 0.3874\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7115 - loss: 0.7537 - val_accuracy: 0.8735 - val_loss: 0.4425\n",
       "Epoch 47/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6875 - loss: 0.7247\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7188 - loss: 0.7727\n",
       "Epoch 47: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7255 - loss: 0.7239 - val_accuracy: 0.9058 - val_loss: 0.3832\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7123 - loss: 0.7438 - val_accuracy: 0.8709 - val_loss: 0.4342\n",
       "Epoch 48/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7031 - loss: 0.8094\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5850\n",
       "Epoch 48: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7219 - loss: 0.7399 - val_accuracy: 0.8989 - val_loss: 0.3788\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7007 - loss: 0.7578 - val_accuracy: 0.8890 - val_loss: 0.4388\n",
       "Epoch 49/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7422 - loss: 0.7648\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7188 - loss: 0.8205\n",
       "Epoch 49: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7165 - loss: 0.7276 - val_accuracy: 0.9067 - val_loss: 0.3711\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7089 - loss: 0.7593 - val_accuracy: 0.8821 - val_loss: 0.4268\n",
       "Epoch 50/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6797 - loss: 0.7365\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6484 - loss: 0.8416\n",
       "Epoch 50: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7138 - loss: 0.7280 - val_accuracy: 0.9102 - val_loss: 0.3631\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.6965 - loss: 0.7558 - val_accuracy: 0.8787 - val_loss: 0.4253\n",
       "Epoch 51/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7422 - loss: 0.6953\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6631\n",
       "Epoch 51: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7216 - loss: 0.7328 - val_accuracy: 0.9224 - val_loss: 0.3581\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7139 - loss: 0.7293 - val_accuracy: 0.8881 - val_loss: 0.4171\n",
       "Epoch 52/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7109 - loss: 0.8351\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7422 - loss: 0.7197\n",
       "Epoch 52: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7119 - loss: 0.7598 - val_accuracy: 0.9198 - val_loss: 0.3601\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7032 - loss: 0.7491 - val_accuracy: 0.8976 - val_loss: 0.4164\n",
       "Epoch 53/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 32ms/step - accuracy: 0.6172 - loss: 0.8963\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6719 - loss: 0.7503\n",
       "Epoch 53: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7344 - loss: 0.7193 - val_accuracy: 0.9128 - val_loss: 0.3577\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7227 - loss: 0.7238 - val_accuracy: 0.8950 - val_loss: 0.4040\n",
       "Epoch 54/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6406 - loss: 0.9208\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 34ms/step - accuracy: 0.7734 - loss: 0.6965\n",
       "Epoch 54: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7072 - loss: 0.7303 - val_accuracy: 0.9180 - val_loss: 0.3552\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7255 - loss: 0.7256 - val_accuracy: 0.9002 - val_loss: 0.4023\n",
       "Epoch 55/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.6616\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 19ms/step - accuracy: 0.6328 - loss: 0.8631\n",
       "Epoch 55: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7326 - loss: 0.6984 - val_accuracy: 0.9250 - val_loss: 0.3499\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7152 - loss: 0.7355 - val_accuracy: 0.8976 - val_loss: 0.3992\n",
       "Epoch 56/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6953 - loss: 0.7095\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7422 - loss: 0.6186\n",
       "Epoch 56: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7316 - loss: 0.7040 - val_accuracy: 0.9085 - val_loss: 0.3502\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7322 - loss: 0.6887 - val_accuracy: 0.9114 - val_loss: 0.3900\n",
       "Epoch 57/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7031 - loss: 0.6687\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7109 - loss: 0.8086\n",
       "Epoch 57: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7242 - loss: 0.7037 - val_accuracy: 0.9276 - val_loss: 0.3347\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7209 - loss: 0.7228 - val_accuracy: 0.9122 - val_loss: 0.3883\n",
       "Epoch 58/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5950\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.5965\n",
       "Epoch 58: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7249 - loss: 0.7049 - val_accuracy: 0.9224 - val_loss: 0.3354\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7405 - loss: 0.6989 - val_accuracy: 0.9053 - val_loss: 0.3824\n",
       "Epoch 59/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6875 - loss: 0.7105\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7266 - loss: 0.7023\n",
       "Epoch 59: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7245 - loss: 0.6898 - val_accuracy: 0.9076 - val_loss: 0.3374\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7173 - loss: 0.7465 - val_accuracy: 0.9148 - val_loss: 0.3846\n",
       "Epoch 60/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.7135\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7344 - loss: 0.6516\n",
       "Epoch 60: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7400 - loss: 0.6977 - val_accuracy: 0.9224 - val_loss: 0.3325\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7283 - loss: 0.7035 - val_accuracy: 0.9071 - val_loss: 0.3754\n",
       "Epoch 61/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6953 - loss: 0.7222\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.6875 - loss: 0.6921\n",
       "Epoch 61: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7408 - loss: 0.6716 - val_accuracy: 0.9303 - val_loss: 0.3204\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7357 - loss: 0.6865 - val_accuracy: 0.9174 - val_loss: 0.3667\n",
       "Epoch 62/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6953 - loss: 0.7629\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.6512\n",
       "Epoch 62: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7210 - loss: 0.7136 - val_accuracy: 0.9215 - val_loss: 0.3329\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7288 - loss: 0.7069 - val_accuracy: 0.9260 - val_loss: 0.3686\n",
       "Epoch 63/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6797 - loss: 0.7956\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.6649\n",
       "Epoch 63: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7465 - loss: 0.6779 - val_accuracy: 0.9346 - val_loss: 0.3217\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7419 - loss: 0.6831 - val_accuracy: 0.9165 - val_loss: 0.3661\n",
       "Epoch 64/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.6471\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7188 - loss: 0.7692\n",
       "Epoch 64: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7539 - loss: 0.6725 - val_accuracy: 0.9381 - val_loss: 0.3256\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7325 - loss: 0.7048 - val_accuracy: 0.9122 - val_loss: 0.3651\n",
       "Epoch 65/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7500 - loss: 0.7264\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7266 - loss: 0.7385\n",
       "Epoch 65: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7441 - loss: 0.7074 - val_accuracy: 0.9364 - val_loss: 0.3285\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7279 - loss: 0.7036 - val_accuracy: 0.9200 - val_loss: 0.3632\n",
       "Epoch 66/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.7449\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5607\n",
       "Epoch 66: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7395 - loss: 0.6798 - val_accuracy: 0.9364 - val_loss: 0.3282\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7346 - loss: 0.6827 - val_accuracy: 0.9139 - val_loss: 0.3599\n",
       "Epoch 67/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7109 - loss: 0.7946\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7891 - loss: 0.6296\n",
       "Epoch 67: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7326 - loss: 0.6987 - val_accuracy: 0.9268 - val_loss: 0.3246\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7379 - loss: 0.6967 - val_accuracy: 0.9200 - val_loss: 0.3534\n",
       "Epoch 68/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.7638\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7500 - loss: 0.7527\n",
       "Epoch 68: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7447 - loss: 0.6932 - val_accuracy: 0.9381 - val_loss: 0.3191\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7229 - loss: 0.7117 - val_accuracy: 0.9200 - val_loss: 0.3477\n",
       "Epoch 69/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6797 - loss: 0.7415\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7891 - loss: 0.7847\n",
       "Epoch 69: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7433 - loss: 0.6551 - val_accuracy: 0.9390 - val_loss: 0.3144\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7465 - loss: 0.6864 - val_accuracy: 0.9243 - val_loss: 0.3472\n",
       "Epoch 70/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.6711\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.7225\n",
       "Epoch 70: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7574 - loss: 0.6524 - val_accuracy: 0.9329 - val_loss: 0.3120\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7452 - loss: 0.6903 - val_accuracy: 0.9251 - val_loss: 0.3453\n",
       "Epoch 71/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.6528\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.6907\n",
       "Epoch 71: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7535 - loss: 0.6642 - val_accuracy: 0.9355 - val_loss: 0.3092\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7332 - loss: 0.6949 - val_accuracy: 0.9303 - val_loss: 0.3446\n",
       "Epoch 72/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.7630\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7031 - loss: 0.7499\n",
       "Epoch 72: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7539 - loss: 0.6652 - val_accuracy: 0.9416 - val_loss: 0.3094\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7478 - loss: 0.6780 - val_accuracy: 0.9286 - val_loss: 0.3437\n",
       "Epoch 73/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7422 - loss: 0.6481\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.6485\n",
       "Epoch 73: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7373 - loss: 0.6876 - val_accuracy: 0.9337 - val_loss: 0.3103\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7472 - loss: 0.6539 - val_accuracy: 0.9329 - val_loss: 0.3288\n",
       "Epoch 74/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7266 - loss: 0.7056\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7500 - loss: 0.6294\n",
       "Epoch 74: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7349 - loss: 0.6759 - val_accuracy: 0.9425 - val_loss: 0.3064\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7493 - loss: 0.6633 - val_accuracy: 0.9449 - val_loss: 0.3229\n",
       "Epoch 75/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.6441\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7344 - loss: 0.6618\n",
       "Epoch 75: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7595 - loss: 0.6500 - val_accuracy: 0.9547 - val_loss: 0.2963\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7498 - loss: 0.6739 - val_accuracy: 0.9441 - val_loss: 0.3313\n",
       "Epoch 76/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5595\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7812 - loss: 0.5738\n",
       "Epoch 76: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7595 - loss: 0.6330 - val_accuracy: 0.9538 - val_loss: 0.2915\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7566 - loss: 0.6539 - val_accuracy: 0.9389 - val_loss: 0.3255\n",
       "Epoch 77/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.5877\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7422 - loss: 0.7673\n",
       "Epoch 77: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7503 - loss: 0.6423 - val_accuracy: 0.9529 - val_loss: 0.2879\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7556 - loss: 0.6532 - val_accuracy: 0.9475 - val_loss: 0.3217\n",
       "Epoch 78/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6448\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7344 - loss: 0.6526\n",
       "Epoch 78: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7581 - loss: 0.6498 - val_accuracy: 0.9582 - val_loss: 0.2861\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7499 - loss: 0.6605 - val_accuracy: 0.9432 - val_loss: 0.3215\n",
       "Epoch 79/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.7513\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6546\n",
       "Epoch 79: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7631 - loss: 0.6580 - val_accuracy: 0.9529 - val_loss: 0.2934\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7505 - loss: 0.6706 - val_accuracy: 0.9398 - val_loss: 0.3178\n",
       "Epoch 80/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 20ms/step - accuracy: 0.7422 - loss: 0.6293\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5725\n",
       "Epoch 80: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7506 - loss: 0.6667 - val_accuracy: 0.9390 - val_loss: 0.2949\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7589 - loss: 0.6338 - val_accuracy: 0.9406 - val_loss: 0.3098\n",
       "Epoch 81/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6252\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5650\n",
       "Epoch 81: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7688 - loss: 0.6295 - val_accuracy: 0.9538 - val_loss: 0.2898\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7575 - loss: 0.6452 - val_accuracy: 0.9441 - val_loss: 0.3083\n",
       "Epoch 82/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7188 - loss: 0.6518\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6175\n",
       "Epoch 82: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7460 - loss: 0.6422 - val_accuracy: 0.9573 - val_loss: 0.2781\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7636 - loss: 0.6282 - val_accuracy: 0.9501 - val_loss: 0.3053\n",
       "Epoch 83/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7266 - loss: 0.6937\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.7500\n",
       "Epoch 83: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7439 - loss: 0.6609 - val_accuracy: 0.9503 - val_loss: 0.2829\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7653 - loss: 0.6613 - val_accuracy: 0.9492 - val_loss: 0.2988\n",
       "Epoch 84/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5661\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7891 - loss: 0.5794\n",
       "Epoch 84: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7619 - loss: 0.6163 - val_accuracy: 0.9660 - val_loss: 0.2658\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7537 - loss: 0.6509 - val_accuracy: 0.9596 - val_loss: 0.2937\n",
       "Epoch 85/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.5787\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7188 - loss: 0.6468\n",
       "Epoch 85: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7615 - loss: 0.6401 - val_accuracy: 0.9660 - val_loss: 0.2697\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7535 - loss: 0.6399 - val_accuracy: 0.9561 - val_loss: 0.3011\n",
       "Epoch 86/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.5988\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5994\n",
       "Epoch 86: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7484 - loss: 0.6443 - val_accuracy: 0.9590 - val_loss: 0.2783\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7553 - loss: 0.6433 - val_accuracy: 0.9570 - val_loss: 0.2936\n",
       "Epoch 87/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7500 - loss: 0.6345\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7109 - loss: 0.7525\n",
       "Epoch 87: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7585 - loss: 0.6592 - val_accuracy: 0.9538 - val_loss: 0.2806\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7589 - loss: 0.6469 - val_accuracy: 0.9570 - val_loss: 0.2972\n",
       "Epoch 88/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.7078\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6422\n",
       "Epoch 88: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7586 - loss: 0.6501 - val_accuracy: 0.9582 - val_loss: 0.2738\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7740 - loss: 0.6129 - val_accuracy: 0.9527 - val_loss: 0.2888\n",
       "Epoch 89/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6031\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7891 - loss: 0.5555\n",
       "Epoch 89: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7550 - loss: 0.6698 - val_accuracy: 0.9564 - val_loss: 0.2767\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7594 - loss: 0.6345 - val_accuracy: 0.9544 - val_loss: 0.2935\n",
       "Epoch 90/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7344 - loss: 0.6736\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.6284\n",
       "Epoch 90: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7487 - loss: 0.6666 - val_accuracy: 0.9564 - val_loss: 0.2798\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7506 - loss: 0.6513 - val_accuracy: 0.9501 - val_loss: 0.2933\n",
       "Epoch 91/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7344 - loss: 0.6264\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6719 - loss: 0.7132\n",
       "Epoch 91: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7578 - loss: 0.6019 - val_accuracy: 0.9520 - val_loss: 0.2776\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7531 - loss: 0.6450 - val_accuracy: 0.9484 - val_loss: 0.3018\n",
       "Epoch 92/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.6732\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6180\n",
       "Epoch 92: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7549 - loss: 0.6583 - val_accuracy: 0.9643 - val_loss: 0.2647\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7723 - loss: 0.6284 - val_accuracy: 0.9501 - val_loss: 0.2897\n",
       "Epoch 93/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.5483\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7344 - loss: 0.6934\n",
       "Epoch 93: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7742 - loss: 0.6153 - val_accuracy: 0.9547 - val_loss: 0.2792\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7581 - loss: 0.6604 - val_accuracy: 0.9518 - val_loss: 0.2963\n",
       "Epoch 94/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7188 - loss: 0.7038\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7188 - loss: 0.7213\n",
       "Epoch 94: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7667 - loss: 0.6522 - val_accuracy: 0.9538 - val_loss: 0.2709\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7507 - loss: 0.6594 - val_accuracy: 0.9596 - val_loss: 0.2916\n",
       "Epoch 95/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6660\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5452\n",
       "Epoch 95: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7662 - loss: 0.6430 - val_accuracy: 0.9582 - val_loss: 0.2694\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7578 - loss: 0.6310 - val_accuracy: 0.9535 - val_loss: 0.2898\n",
       "Epoch 96/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5784\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6491\n",
       "Epoch 96: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7763 - loss: 0.6094 - val_accuracy: 0.9625 - val_loss: 0.2579\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7680 - loss: 0.6237 - val_accuracy: 0.9587 - val_loss: 0.2816\n",
       "Epoch 97/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7109 - loss: 0.7181\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.6797 - loss: 0.7056\n",
       "Epoch 97: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7669 - loss: 0.6159 - val_accuracy: 0.9564 - val_loss: 0.2700\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7578 - loss: 0.6371 - val_accuracy: 0.9587 - val_loss: 0.2815\n",
       "Epoch 98/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5916\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7344 - loss: 0.6382\n",
       "Epoch 98: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7673 - loss: 0.6355 - val_accuracy: 0.9590 - val_loss: 0.2620\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7555 - loss: 0.6450 - val_accuracy: 0.9604 - val_loss: 0.2881\n",
       "Epoch 99/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8125 - loss: 0.5287\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.5643\n",
       "Epoch 99: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7845 - loss: 0.6140 - val_accuracy: 0.9608 - val_loss: 0.2598\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7698 - loss: 0.6209 - val_accuracy: 0.9613 - val_loss: 0.2829\n",
       "Epoch 100/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6493\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.5383\n",
       "Epoch 100: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7731 - loss: 0.6279 - val_accuracy: 0.9582 - val_loss: 0.2605\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7617 - loss: 0.6301 - val_accuracy: 0.9630 - val_loss: 0.2784\n",
       "Epoch 101/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7109 - loss: 0.8103\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.6154\n",
       "Epoch 101: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7497 - loss: 0.6695 - val_accuracy: 0.9582 - val_loss: 0.2614\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7700 - loss: 0.6402 - val_accuracy: 0.9561 - val_loss: 0.2816\n",
       "Epoch 102/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8047 - loss: 0.6126\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.7120\n",
       "Epoch 102: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7838 - loss: 0.6041 - val_accuracy: 0.9608 - val_loss: 0.2557\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7725 - loss: 0.6373 - val_accuracy: 0.9621 - val_loss: 0.2783\n",
       "Epoch 103/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7422 - loss: 0.7509\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.6977\n",
       "Epoch 103: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7654 - loss: 0.6345 - val_accuracy: 0.9625 - val_loss: 0.2576\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7693 - loss: 0.6250 - val_accuracy: 0.9647 - val_loss: 0.2794\n",
       "Epoch 104/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.5592\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5512\n",
       "Epoch 104: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7920 - loss: 0.5897 - val_accuracy: 0.9634 - val_loss: 0.2491\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7755 - loss: 0.5980 - val_accuracy: 0.9621 - val_loss: 0.2752\n",
       "Epoch 105/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8281 - loss: 0.4830\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.6955\n",
       "Epoch 105: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7775 - loss: 0.6139 - val_accuracy: 0.9599 - val_loss: 0.2533\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7746 - loss: 0.6184 - val_accuracy: 0.9621 - val_loss: 0.2698\n",
       "Epoch 106/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.7242\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.6158\n",
       "Epoch 106: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7698 - loss: 0.6416 - val_accuracy: 0.9625 - val_loss: 0.2566\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7622 - loss: 0.6341 - val_accuracy: 0.9613 - val_loss: 0.2673\n",
       "Epoch 107/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5654\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6896\n",
       "Epoch 107: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7763 - loss: 0.6012 - val_accuracy: 0.9634 - val_loss: 0.2449\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7612 - loss: 0.6328 - val_accuracy: 0.9647 - val_loss: 0.2683\n",
       "Epoch 108/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.6409\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7344 - loss: 0.6880\n",
       "Epoch 108: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7752 - loss: 0.6369 - val_accuracy: 0.9651 - val_loss: 0.2491\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7664 - loss: 0.6137 - val_accuracy: 0.9578 - val_loss: 0.2737\n",
       "Epoch 109/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8438 - loss: 0.4575\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8047 - loss: 0.5546\n",
       "Epoch 109: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7760 - loss: 0.6142 - val_accuracy: 0.9704 - val_loss: 0.2486\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7800 - loss: 0.6060 - val_accuracy: 0.9639 - val_loss: 0.2695\n",
       "Epoch 110/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8203 - loss: 0.5242\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6572\n",
       "Epoch 110: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7705 - loss: 0.6172 - val_accuracy: 0.9686 - val_loss: 0.2460\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7739 - loss: 0.6084 - val_accuracy: 0.9621 - val_loss: 0.2712\n",
       "Epoch 111/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8281 - loss: 0.5177\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7422 - loss: 0.6870\n",
       "Epoch 111: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7850 - loss: 0.5857 - val_accuracy: 0.9669 - val_loss: 0.2394\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7708 - loss: 0.6112 - val_accuracy: 0.9630 - val_loss: 0.2675\n",
       "Epoch 112/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5381\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5765\n",
       "Epoch 112: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7873 - loss: 0.5639 - val_accuracy: 0.9721 - val_loss: 0.2352\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7796 - loss: 0.6045 - val_accuracy: 0.9682 - val_loss: 0.2662\n",
       "Epoch 113/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.5871\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7266 - loss: 0.7352\n",
       "Epoch 113: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7702 - loss: 0.6352 - val_accuracy: 0.9634 - val_loss: 0.2433\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7669 - loss: 0.6301 - val_accuracy: 0.9647 - val_loss: 0.2699\n",
       "Epoch 114/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5980\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6066\n",
       "Epoch 114: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7894 - loss: 0.5816 - val_accuracy: 0.9669 - val_loss: 0.2301\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7742 - loss: 0.5984 - val_accuracy: 0.9742 - val_loss: 0.2706\n",
       "Epoch 115/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.5700\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.5582\n",
       "Epoch 115: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7765 - loss: 0.5956 - val_accuracy: 0.9721 - val_loss: 0.2284\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7935 - loss: 0.5709 - val_accuracy: 0.9664 - val_loss: 0.2617\n",
       "Epoch 116/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5322\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5494\n",
       "Epoch 116: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7893 - loss: 0.5923 - val_accuracy: 0.9616 - val_loss: 0.2418\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7780 - loss: 0.6019 - val_accuracy: 0.9690 - val_loss: 0.2586\n",
       "Epoch 117/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7344 - loss: 0.6899\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7578 - loss: 0.6459\n",
       "Epoch 117: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7638 - loss: 0.6227 - val_accuracy: 0.9555 - val_loss: 0.2467\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7729 - loss: 0.6118 - val_accuracy: 0.9673 - val_loss: 0.2609\n",
       "Epoch 118/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7812 - loss: 0.5865\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7266 - loss: 0.6792\n",
       "Epoch 118: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7773 - loss: 0.5944 - val_accuracy: 0.9677 - val_loss: 0.2354\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7579 - loss: 0.6410 - val_accuracy: 0.9630 - val_loss: 0.2687\n",
       "Epoch 119/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5463\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7656 - loss: 0.5031\n",
       "Epoch 119: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7695 - loss: 0.6114 - val_accuracy: 0.9677 - val_loss: 0.2298\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7642 - loss: 0.6140 - val_accuracy: 0.9621 - val_loss: 0.2637\n",
       "Epoch 120/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.6110\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7266 - loss: 0.7073\n",
       "Epoch 120: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7693 - loss: 0.6375 - val_accuracy: 0.9686 - val_loss: 0.2340\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7855 - loss: 0.5824 - val_accuracy: 0.9673 - val_loss: 0.2617\n",
       "Epoch 121/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7891 - loss: 0.5587\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.5905\n",
       "Epoch 121: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7871 - loss: 0.5946 - val_accuracy: 0.9643 - val_loss: 0.2286\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7787 - loss: 0.6035 - val_accuracy: 0.9656 - val_loss: 0.2524\n",
       "Epoch 122/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7188 - loss: 0.7024\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7422 - loss: 0.6116\n",
       "Epoch 122: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7688 - loss: 0.6131 - val_accuracy: 0.9677 - val_loss: 0.2359\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7664 - loss: 0.6061 - val_accuracy: 0.9664 - val_loss: 0.2529\n",
       "Epoch 123/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5494\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8047 - loss: 0.5779\n",
       "Epoch 123: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7797 - loss: 0.5854 - val_accuracy: 0.9651 - val_loss: 0.2450\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7795 - loss: 0.6039 - val_accuracy: 0.9750 - val_loss: 0.2556\n",
       "Epoch 124/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5866\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.6194\n",
       "Epoch 124: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7832 - loss: 0.5969 - val_accuracy: 0.9651 - val_loss: 0.2334\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7759 - loss: 0.5879 - val_accuracy: 0.9682 - val_loss: 0.2531\n",
       "Epoch 125/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8359 - loss: 0.4527\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5162\n",
       "Epoch 125: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7991 - loss: 0.5792 - val_accuracy: 0.9704 - val_loss: 0.2312\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7872 - loss: 0.5794 - val_accuracy: 0.9733 - val_loss: 0.2561\n",
       "Epoch 126/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7188 - loss: 0.6996\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7812 - loss: 0.5067\n",
       "Epoch 126: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7721 - loss: 0.6165 - val_accuracy: 0.9669 - val_loss: 0.2352\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7962 - loss: 0.5688 - val_accuracy: 0.9707 - val_loss: 0.2476\n",
       "Epoch 127/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.6257\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.4736\n",
       "Epoch 127: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7860 - loss: 0.5916 - val_accuracy: 0.9695 - val_loss: 0.2305\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7693 - loss: 0.5868 - val_accuracy: 0.9733 - val_loss: 0.2491\n",
       "Epoch 128/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.7016\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5966\n",
       "Epoch 128: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7817 - loss: 0.5928 - val_accuracy: 0.9643 - val_loss: 0.2326\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7805 - loss: 0.6043 - val_accuracy: 0.9733 - val_loss: 0.2537\n",
       "Epoch 129/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6301\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.5287\n",
       "Epoch 129: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7720 - loss: 0.6227 - val_accuracy: 0.9634 - val_loss: 0.2300\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7964 - loss: 0.5617 - val_accuracy: 0.9759 - val_loss: 0.2474\n",
       "Epoch 130/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5741\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8750 - loss: 0.4464\n",
       "Epoch 130: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7778 - loss: 0.6205 - val_accuracy: 0.9643 - val_loss: 0.2281\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8063 - loss: 0.5574 - val_accuracy: 0.9733 - val_loss: 0.2344\n",
       "Epoch 131/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5895\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5644\n",
       "Epoch 131: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7834 - loss: 0.5788 - val_accuracy: 0.9686 - val_loss: 0.2241\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7871 - loss: 0.5726 - val_accuracy: 0.9742 - val_loss: 0.2407\n",
       "Epoch 132/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.6158\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.6953 - loss: 0.7481\n",
       "Epoch 132: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7946 - loss: 0.5877 - val_accuracy: 0.9686 - val_loss: 0.2217\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7706 - loss: 0.6085 - val_accuracy: 0.9725 - val_loss: 0.2520\n",
       "Epoch 133/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5522\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6536\n",
       "Epoch 133: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7835 - loss: 0.6043 - val_accuracy: 0.9669 - val_loss: 0.2245\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7851 - loss: 0.5934 - val_accuracy: 0.9682 - val_loss: 0.2479\n",
       "Epoch 134/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.6516\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5989\n",
       "Epoch 134: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7921 - loss: 0.5971 - val_accuracy: 0.9677 - val_loss: 0.2298\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7899 - loss: 0.5783 - val_accuracy: 0.9716 - val_loss: 0.2437\n",
       "Epoch 135/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5581\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8125 - loss: 0.5642\n",
       "Epoch 135: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7910 - loss: 0.5781 - val_accuracy: 0.9686 - val_loss: 0.2268\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7943 - loss: 0.5603 - val_accuracy: 0.9690 - val_loss: 0.2442\n",
       "Epoch 136/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5913\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.5905\n",
       "Epoch 136: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7888 - loss: 0.5802 - val_accuracy: 0.9651 - val_loss: 0.2260\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7894 - loss: 0.5748 - val_accuracy: 0.9768 - val_loss: 0.2409\n",
       "Epoch 137/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5728\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 19ms/step - accuracy: 0.7891 - loss: 0.5676\n",
       "Epoch 137: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7920 - loss: 0.5850 - val_accuracy: 0.9712 - val_loss: 0.2189\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7872 - loss: 0.5815 - val_accuracy: 0.9768 - val_loss: 0.2416\n",
       "Epoch 138/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.6518\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7812 - loss: 0.6264\n",
       "Epoch 138: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7768 - loss: 0.6069 - val_accuracy: 0.9616 - val_loss: 0.2280\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7693 - loss: 0.6066 - val_accuracy: 0.9793 - val_loss: 0.2394\n",
       "Epoch 139/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.6347\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8203 - loss: 0.5130\n",
       "Epoch 139: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7782 - loss: 0.6206 - val_accuracy: 0.9677 - val_loss: 0.2307\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7814 - loss: 0.5888 - val_accuracy: 0.9742 - val_loss: 0.2390\n",
       "Epoch 140/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5122\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5216\n",
       "Epoch 140: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7862 - loss: 0.5758 - val_accuracy: 0.9721 - val_loss: 0.2253\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7843 - loss: 0.5856 - val_accuracy: 0.9742 - val_loss: 0.2312\n",
       "Epoch 141/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.5543\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.7443\n",
       "Epoch 141: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7889 - loss: 0.5646 - val_accuracy: 0.9686 - val_loss: 0.2281\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7798 - loss: 0.5840 - val_accuracy: 0.9742 - val_loss: 0.2284\n",
       "Epoch 142/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5501\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5053\n",
       "Epoch 142: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7812 - loss: 0.5992 - val_accuracy: 0.9721 - val_loss: 0.2231\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7863 - loss: 0.5702 - val_accuracy: 0.9759 - val_loss: 0.2264\n",
       "Epoch 143/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5760\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7266 - loss: 0.7747\n",
       "Epoch 143: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7969 - loss: 0.5820 - val_accuracy: 0.9634 - val_loss: 0.2256\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7817 - loss: 0.6156 - val_accuracy: 0.9699 - val_loss: 0.2301\n",
       "Epoch 144/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7812 - loss: 0.6266\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8125 - loss: 0.5390\n",
       "Epoch 144: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7913 - loss: 0.5928 - val_accuracy: 0.9669 - val_loss: 0.2318\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7910 - loss: 0.5857 - val_accuracy: 0.9759 - val_loss: 0.2341\n",
       "Epoch 145/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7891 - loss: 0.5853\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 20ms/step - accuracy: 0.7422 - loss: 0.6955\n",
       "Epoch 145: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7865 - loss: 0.5908 - val_accuracy: 0.9677 - val_loss: 0.2295\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7698 - loss: 0.6111 - val_accuracy: 0.9776 - val_loss: 0.2376\n",
       "Epoch 146/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5663\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.5849\n",
       "Epoch 146: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7903 - loss: 0.5989 - val_accuracy: 0.9677 - val_loss: 0.2338\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7795 - loss: 0.5752 - val_accuracy: 0.9785 - val_loss: 0.2346\n",
       "Epoch 147/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6402\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5535\n",
       "Epoch 147: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8003 - loss: 0.5763 - val_accuracy: 0.9677 - val_loss: 0.2298\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7970 - loss: 0.5795 - val_accuracy: 0.9819 - val_loss: 0.2286\n",
       "Epoch 148/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7891 - loss: 0.5296\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8594 - loss: 0.5247\n",
       "Epoch 148: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7952 - loss: 0.5684 - val_accuracy: 0.9686 - val_loss: 0.2288\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7994 - loss: 0.5534 - val_accuracy: 0.9768 - val_loss: 0.2307\n",
       "Epoch 149/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.5079\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7891 - loss: 0.5924\n",
       "Epoch 149: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8098 - loss: 0.5733 - val_accuracy: 0.9660 - val_loss: 0.2221\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7866 - loss: 0.5956 - val_accuracy: 0.9768 - val_loss: 0.2310\n",
       "Epoch 150/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5778\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7422 - loss: 0.6836\n",
       "Epoch 150: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7880 - loss: 0.5866 - val_accuracy: 0.9712 - val_loss: 0.2232\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7946 - loss: 0.5704 - val_accuracy: 0.9725 - val_loss: 0.2346\n",
       "Epoch 151/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5207\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.6361\n",
       "Epoch 151: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7791 - loss: 0.5876 - val_accuracy: 0.9712 - val_loss: 0.2222\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7926 - loss: 0.5672 - val_accuracy: 0.9725 - val_loss: 0.2309\n",
       "Epoch 152/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.5847\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.5591\n",
       "Epoch 152: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7905 - loss: 0.5643 - val_accuracy: 0.9721 - val_loss: 0.2134\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7965 - loss: 0.5665 - val_accuracy: 0.9742 - val_loss: 0.2275\n",
       "Epoch 153/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.5806\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7344 - loss: 0.6897\n",
       "Epoch 153: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7878 - loss: 0.5723 - val_accuracy: 0.9730 - val_loss: 0.2110\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7782 - loss: 0.6005 - val_accuracy: 0.9759 - val_loss: 0.2277\n",
       "Epoch 154/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.4988\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7422 - loss: 0.6317\n",
       "Epoch 154: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7963 - loss: 0.5629 - val_accuracy: 0.9704 - val_loss: 0.2186\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7835 - loss: 0.5750 - val_accuracy: 0.9768 - val_loss: 0.2345\n",
       "Epoch 155/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.5500\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8438 - loss: 0.4546\n",
       "Epoch 155: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7978 - loss: 0.5776 - val_accuracy: 0.9730 - val_loss: 0.2155\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8016 - loss: 0.5641 - val_accuracy: 0.9750 - val_loss: 0.2321\n",
       "Epoch 156/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8203 - loss: 0.4869\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7422 - loss: 0.6906\n",
       "Epoch 156: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7828 - loss: 0.6050 - val_accuracy: 0.9695 - val_loss: 0.2208\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7791 - loss: 0.6128 - val_accuracy: 0.9725 - val_loss: 0.2265\n",
       "Epoch 157/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7578 - loss: 0.6877\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.4455\n",
       "Epoch 157: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7981 - loss: 0.5727 - val_accuracy: 0.9695 - val_loss: 0.2170\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7949 - loss: 0.5442 - val_accuracy: 0.9742 - val_loss: 0.2297\n",
       "Epoch 158/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5425\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.7578 - loss: 0.6549\n",
       "Epoch 158: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7884 - loss: 0.5843 - val_accuracy: 0.9704 - val_loss: 0.2182\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7914 - loss: 0.5645 - val_accuracy: 0.9768 - val_loss: 0.2274\n",
       "Epoch 159/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8047 - loss: 0.5415\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.6366\n",
       "Epoch 159: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7896 - loss: 0.5767 - val_accuracy: 0.9721 - val_loss: 0.2196\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7872 - loss: 0.5927 - val_accuracy: 0.9802 - val_loss: 0.2305\n",
       "Epoch 160/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7266 - loss: 0.6685\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6118\n",
       "Epoch 160: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7913 - loss: 0.5859 - val_accuracy: 0.9738 - val_loss: 0.2158\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7822 - loss: 0.5880 - val_accuracy: 0.9785 - val_loss: 0.2348\n",
       "Epoch 161/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5529\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5556\n",
       "Epoch 161: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7805 - loss: 0.5922 - val_accuracy: 0.9695 - val_loss: 0.2194\n",
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7844 - loss: 0.5751 - val_accuracy: 0.9785 - val_loss: 0.2264\n",
       "Epoch 162/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8203 - loss: 0.4853\n",
+      "\u001B[1m 1/28\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8047 - loss: 0.5790\n",
       "Epoch 162: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7974 - loss: 0.5612 - val_accuracy: 0.9712 - val_loss: 0.2151\n",
-      "Epoch 163/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.5796\n",
-      "Epoch 163: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8022 - loss: 0.5668 - val_accuracy: 0.9677 - val_loss: 0.2146\n",
-      "Epoch 164/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.6269\n",
-      "Epoch 164: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7851 - loss: 0.5766 - val_accuracy: 0.9704 - val_loss: 0.2156\n",
-      "Epoch 165/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7656 - loss: 0.6097\n",
-      "Epoch 165: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7952 - loss: 0.5745 - val_accuracy: 0.9721 - val_loss: 0.2215\n",
-      "Epoch 166/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8516 - loss: 0.4536\n",
-      "Epoch 166: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8142 - loss: 0.5531 - val_accuracy: 0.9686 - val_loss: 0.2156\n",
-      "Epoch 167/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8438 - loss: 0.5172\n",
-      "Epoch 167: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8020 - loss: 0.5605 - val_accuracy: 0.9730 - val_loss: 0.2079\n",
-      "Epoch 168/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.8203 - loss: 0.5927\n",
-      "Epoch 168: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8056 - loss: 0.5605 - val_accuracy: 0.9695 - val_loss: 0.2079\n",
-      "Epoch 169/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 20ms/step - accuracy: 0.8047 - loss: 0.6494\n",
-      "Epoch 169: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7937 - loss: 0.5838 - val_accuracy: 0.9695 - val_loss: 0.2152\n",
-      "Epoch 170/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5463\n",
-      "Epoch 170: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8001 - loss: 0.5745 - val_accuracy: 0.9669 - val_loss: 0.2130\n",
-      "Epoch 171/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8203 - loss: 0.5899\n",
-      "Epoch 171: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7972 - loss: 0.5781 - val_accuracy: 0.9677 - val_loss: 0.2113\n",
-      "Epoch 172/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8438 - loss: 0.4425\n",
-      "Epoch 172: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7996 - loss: 0.5530 - val_accuracy: 0.9695 - val_loss: 0.2084\n",
-      "Epoch 173/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5852\n",
-      "Epoch 173: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7948 - loss: 0.5861 - val_accuracy: 0.9721 - val_loss: 0.2094\n",
-      "Epoch 174/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7656 - loss: 0.6026\n",
-      "Epoch 174: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7899 - loss: 0.5752 - val_accuracy: 0.9730 - val_loss: 0.2062\n",
-      "Epoch 175/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.8203 - loss: 0.4570\n",
-      "Epoch 175: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8049 - loss: 0.5561 - val_accuracy: 0.9730 - val_loss: 0.2061\n",
-      "Epoch 176/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8438 - loss: 0.4919\n",
-      "Epoch 176: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8105 - loss: 0.5427 - val_accuracy: 0.9747 - val_loss: 0.1993\n",
-      "Epoch 177/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.4823\n",
-      "Epoch 177: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7987 - loss: 0.5707 - val_accuracy: 0.9721 - val_loss: 0.2064\n",
-      "Epoch 178/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.4889\n",
-      "Epoch 178: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7993 - loss: 0.5592 - val_accuracy: 0.9747 - val_loss: 0.2077\n",
-      "Epoch 179/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5535\n",
-      "Epoch 179: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7976 - loss: 0.5620 - val_accuracy: 0.9738 - val_loss: 0.2032\n",
-      "Epoch 180/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5824\n",
-      "Epoch 180: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7998 - loss: 0.5565 - val_accuracy: 0.9704 - val_loss: 0.2099\n",
-      "Epoch 181/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.4362\n",
-      "Epoch 181: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8115 - loss: 0.5441 - val_accuracy: 0.9730 - val_loss: 0.2129\n",
-      "Epoch 182/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8672 - loss: 0.4783\n",
-      "Epoch 182: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8001 - loss: 0.5569 - val_accuracy: 0.9721 - val_loss: 0.2149\n",
-      "Epoch 183/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5520\n",
-      "Epoch 183: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7988 - loss: 0.5608 - val_accuracy: 0.9712 - val_loss: 0.2111\n",
-      "Epoch 184/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8516 - loss: 0.4611\n",
-      "Epoch 184: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8002 - loss: 0.5642 - val_accuracy: 0.9712 - val_loss: 0.2075\n",
-      "Epoch 185/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5170\n",
-      "Epoch 185: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7906 - loss: 0.5788 - val_accuracy: 0.9686 - val_loss: 0.2076\n",
-      "Epoch 186/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.6221\n",
-      "Epoch 186: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7828 - loss: 0.5824 - val_accuracy: 0.9738 - val_loss: 0.2102\n",
-      "Epoch 187/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8125 - loss: 0.5062\n",
-      "Epoch 187: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7874 - loss: 0.5760 - val_accuracy: 0.9738 - val_loss: 0.2071\n",
-      "Epoch 188/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.7137\n",
-      "Epoch 188: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7996 - loss: 0.5999 - val_accuracy: 0.9765 - val_loss: 0.1995\n",
-      "Epoch 189/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7188 - loss: 0.6855\n",
-      "Epoch 189: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7843 - loss: 0.5840 - val_accuracy: 0.9738 - val_loss: 0.2051\n",
-      "Epoch 190/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8594 - loss: 0.4604\n",
-      "Epoch 190: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8124 - loss: 0.5375 - val_accuracy: 0.9730 - val_loss: 0.2084\n",
-      "Epoch 191/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.8359 - loss: 0.5506\n",
-      "Epoch 191: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8139 - loss: 0.5482 - val_accuracy: 0.9730 - val_loss: 0.2033\n",
-      "Epoch 192/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8125 - loss: 0.5925\n",
-      "Epoch 192: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8040 - loss: 0.5540 - val_accuracy: 0.9738 - val_loss: 0.2073\n",
-      "Epoch 193/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7500 - loss: 0.5939\n",
-      "Epoch 193: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7946 - loss: 0.5501 - val_accuracy: 0.9730 - val_loss: 0.2028\n",
-      "Epoch 194/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8047 - loss: 0.5613\n",
-      "Epoch 194: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8188 - loss: 0.5278 - val_accuracy: 0.9747 - val_loss: 0.1980\n",
-      "Epoch 195/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.4923\n",
-      "Epoch 195: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7967 - loss: 0.5564 - val_accuracy: 0.9704 - val_loss: 0.2058\n",
-      "Epoch 196/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.4770\n",
-      "Epoch 196: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8013 - loss: 0.5379 - val_accuracy: 0.9738 - val_loss: 0.2090\n",
-      "Epoch 197/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7969 - loss: 0.5925\n",
-      "Epoch 197: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8057 - loss: 0.5529 - val_accuracy: 0.9738 - val_loss: 0.2016\n",
-      "Epoch 198/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5831\n",
-      "Epoch 198: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8131 - loss: 0.5349 - val_accuracy: 0.9738 - val_loss: 0.1982\n",
-      "Epoch 199/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.4914\n",
-      "Epoch 199: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8042 - loss: 0.5477 - val_accuracy: 0.9747 - val_loss: 0.2021\n",
-      "Epoch 200/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.4765\n",
-      "Epoch 200: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8212 - loss: 0.5181 - val_accuracy: 0.9756 - val_loss: 0.1932\n",
-      "Epoch 201/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.4399\n",
-      "Epoch 201: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7886 - loss: 0.5582 - val_accuracy: 0.9686 - val_loss: 0.2054\n",
-      "Epoch 202/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8438 - loss: 0.5077\n",
-      "Epoch 202: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7797 - loss: 0.6027 - val_accuracy: 0.9712 - val_loss: 0.2037\n",
-      "Epoch 203/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.5925\n",
-      "Epoch 203: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7958 - loss: 0.5549 - val_accuracy: 0.9721 - val_loss: 0.2013\n",
-      "Epoch 204/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.5804\n",
-      "Epoch 204: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8199 - loss: 0.5386 - val_accuracy: 0.9712 - val_loss: 0.2010\n",
-      "Epoch 205/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.5225\n",
-      "Epoch 205: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7965 - loss: 0.5663 - val_accuracy: 0.9721 - val_loss: 0.2024\n",
-      "Epoch 206/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7812 - loss: 0.5693\n",
-      "Epoch 206: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8049 - loss: 0.5511 - val_accuracy: 0.9704 - val_loss: 0.2048\n",
-      "Epoch 207/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.4656\n",
-      "Epoch 207: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8151 - loss: 0.5216 - val_accuracy: 0.9756 - val_loss: 0.1941\n",
-      "Epoch 208/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8281 - loss: 0.4725\n",
-      "Epoch 208: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8062 - loss: 0.5431 - val_accuracy: 0.9782 - val_loss: 0.1922\n",
-      "Epoch 209/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5688\n",
-      "Epoch 209: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7842 - loss: 0.5888 - val_accuracy: 0.9686 - val_loss: 0.2042\n",
-      "Epoch 210/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8672 - loss: 0.3775\n",
-      "Epoch 210: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8099 - loss: 0.5346 - val_accuracy: 0.9712 - val_loss: 0.2106\n",
-      "Epoch 211/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5993\n",
-      "Epoch 211: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8200 - loss: 0.5330 - val_accuracy: 0.9669 - val_loss: 0.2067\n",
-      "Epoch 212/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8828 - loss: 0.3879\n",
-      "Epoch 212: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8207 - loss: 0.5247 - val_accuracy: 0.9686 - val_loss: 0.2023\n",
-      "Epoch 213/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.5587\n",
-      "Epoch 213: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8088 - loss: 0.5496 - val_accuracy: 0.9712 - val_loss: 0.2021\n",
-      "Epoch 214/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8359 - loss: 0.4606\n",
-      "Epoch 214: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8054 - loss: 0.5312 - val_accuracy: 0.9756 - val_loss: 0.1850\n",
-      "Epoch 215/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7578 - loss: 0.6467\n",
-      "Epoch 215: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7851 - loss: 0.5892 - val_accuracy: 0.9730 - val_loss: 0.1966\n",
-      "Epoch 216/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7109 - loss: 0.7488\n",
-      "Epoch 216: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7757 - loss: 0.6108 - val_accuracy: 0.9669 - val_loss: 0.2035\n",
-      "Epoch 217/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 15ms/step - accuracy: 0.8125 - loss: 0.5911\n",
-      "Epoch 217: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8046 - loss: 0.5562 - val_accuracy: 0.9704 - val_loss: 0.1994\n",
-      "Epoch 218/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8750 - loss: 0.4520\n",
-      "Epoch 218: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8013 - loss: 0.5472 - val_accuracy: 0.9712 - val_loss: 0.2020\n",
-      "Epoch 219/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8047 - loss: 0.5736\n",
-      "Epoch 219: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8099 - loss: 0.5462 - val_accuracy: 0.9686 - val_loss: 0.2029\n",
-      "Epoch 220/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8281 - loss: 0.4038\n",
-      "Epoch 220: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8003 - loss: 0.5340 - val_accuracy: 0.9773 - val_loss: 0.1938\n",
-      "Epoch 221/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8359 - loss: 0.5431\n",
-      "Epoch 221: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7956 - loss: 0.5731 - val_accuracy: 0.9695 - val_loss: 0.2046\n",
-      "Epoch 222/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5285\n",
-      "Epoch 222: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7972 - loss: 0.5681 - val_accuracy: 0.9669 - val_loss: 0.2058\n",
-      "Epoch 223/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7969 - loss: 0.4823\n",
-      "Epoch 223: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8122 - loss: 0.5155 - val_accuracy: 0.9712 - val_loss: 0.1965\n",
-      "Epoch 224/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8203 - loss: 0.5541\n",
-      "Epoch 224: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8056 - loss: 0.5680 - val_accuracy: 0.9677 - val_loss: 0.1991\n",
-      "Epoch 225/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8125 - loss: 0.5274\n",
-      "Epoch 225: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8088 - loss: 0.5392 - val_accuracy: 0.9747 - val_loss: 0.1946\n",
-      "Epoch 226/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7812 - loss: 0.6112\n",
-      "Epoch 226: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7956 - loss: 0.5639 - val_accuracy: 0.9695 - val_loss: 0.2017\n",
-      "Epoch 227/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8125 - loss: 0.5353\n",
-      "Epoch 227: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7965 - loss: 0.5624 - val_accuracy: 0.9660 - val_loss: 0.2044\n",
-      "Epoch 228/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7734 - loss: 0.6194\n",
-      "Epoch 228: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8034 - loss: 0.5514 - val_accuracy: 0.9730 - val_loss: 0.1976\n",
-      "Epoch 229/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8750 - loss: 0.4060\n",
-      "Epoch 229: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8203 - loss: 0.5072 - val_accuracy: 0.9695 - val_loss: 0.2029\n",
-      "Epoch 230/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.8359 - loss: 0.5064\n",
-      "Epoch 230: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8003 - loss: 0.5466 - val_accuracy: 0.9738 - val_loss: 0.1990\n",
-      "Epoch 231/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7891 - loss: 0.5423\n",
-      "Epoch 231: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7937 - loss: 0.5568 - val_accuracy: 0.9712 - val_loss: 0.1984\n",
-      "Epoch 232/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.7578 - loss: 0.6099\n",
-      "Epoch 232: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8091 - loss: 0.5300 - val_accuracy: 0.9747 - val_loss: 0.1860\n",
-      "Epoch 233/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.7734 - loss: 0.6119\n",
-      "Epoch 233: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8058 - loss: 0.5446 - val_accuracy: 0.9730 - val_loss: 0.1926\n",
-      "Epoch 234/1000\n",
-      "\u001B[1m 1/27\u001B[0m \u001B[37m━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.8438 - loss: 0.5648\n",
-      "Epoch 234: saving model to grimm\\PycharmProjects\\hand-gesture-recognition-mediapipe\\model\\keypoint_classifier\\keypoint_classifier.keras\n",
-      "\u001B[1m27/27\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.8114 - loss: 0.5478 - val_accuracy: 0.9677 - val_loss: 0.2020\n",
-      "Epoch 234: early stopping\n"
+      "\u001B[1m28/28\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step - accuracy: 0.7942 - loss: 0.5536 - val_accuracy: 0.9776 - val_loss: 0.2327\n",
+      "Epoch 162: early stopping\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 33,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 33
+   "execution_count": 11
   },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-06T15:10:26.710697Z",
-     "start_time": "2025-04-06T15:10:26.649490Z"
+     "end_time": "2025-04-07T01:32:49.335407Z",
+     "start_time": "2025-04-07T01:32:49.268888Z"
     }
    },
    "source": [
@@ -1289,18 +1001,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[1m9/9\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.9692 - loss: 0.2051 \n"
+      "\u001B[1m10/10\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.9737 - loss: 0.2289 \n"
      ]
     }
    ],
-   "execution_count": 34
+   "execution_count": 12
   },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-06T15:10:26.772708Z",
-     "start_time": "2025-04-06T15:10:26.726968Z"
+     "end_time": "2025-04-07T01:32:49.414025Z",
+     "start_time": "2025-04-07T01:32:49.351409Z"
     }
    },
    "source": [
@@ -1308,14 +1020,14 @@
     "model = tf.keras.models.load_model(model_save_path)"
    ],
    "outputs": [],
-   "execution_count": 35
+   "execution_count": 13
   },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-06T15:10:26.850307Z",
-     "start_time": "2025-04-06T15:10:26.789232Z"
+     "end_time": "2025-04-07T01:32:49.493678Z",
+     "start_time": "2025-04-07T01:32:49.431099Z"
     }
    },
    "source": [
@@ -1330,13 +1042,13 @@
      "output_type": "stream",
      "text": [
       "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step\n",
-      "[4.1954637e-02 7.1722388e-02 8.7705678e-01 8.6475220e-03 5.7122717e-04\n",
-      " 4.7421348e-05]\n",
-      "2\n"
+      "[9.8055995e-01 1.9246580e-02 1.6972909e-04 1.5124833e-07 1.4379638e-05\n",
+      " 9.2182872e-06]\n",
+      "0\n"
      ]
     }
    ],
-   "execution_count": 36
+   "execution_count": 14
   },
   {
    "cell_type": "markdown",
@@ -1349,8 +1061,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-06T15:10:27.038162Z",
-     "start_time": "2025-04-06T15:10:26.866818Z"
+     "end_time": "2025-04-07T01:32:49.746395Z",
+     "start_time": "2025-04-07T01:32:49.510717Z"
     }
    },
    "source": [
@@ -1384,7 +1096,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[1m36/36\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 680us/step\n"
+      "\u001B[1m37/37\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 630us/step\n"
      ]
     },
     {
@@ -1392,7 +1104,7 @@
       "text/plain": [
        "
"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1404,21 +1116,21 @@
       "Classification Report\n",
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.99      0.99      0.99       408\n",
-      "           1       1.00      0.89      0.94       237\n",
-      "           2       0.91      0.99      0.95       329\n",
-      "           3       1.00      1.00      1.00        70\n",
-      "           4       1.00      0.95      0.98        64\n",
-      "           5       1.00      0.97      0.99        39\n",
+      "           0       0.98      1.00      0.99       394\n",
+      "           1       1.00      0.95      0.97       238\n",
+      "           2       0.96      0.99      0.97       333\n",
+      "           3       0.99      0.94      0.96        77\n",
+      "           4       1.00      0.97      0.99        71\n",
+      "           5       0.94      0.98      0.96        49\n",
       "\n",
-      "    accuracy                           0.97      1147\n",
-      "   macro avg       0.98      0.97      0.97      1147\n",
-      "weighted avg       0.97      0.97      0.97      1147\n",
+      "    accuracy                           0.98      1162\n",
+      "   macro avg       0.98      0.97      0.97      1162\n",
+      "weighted avg       0.98      0.98      0.98      1162\n",
       "\n"
      ]
     }
    ],
-   "execution_count": 37
+   "execution_count": 15
   },
   {
    "cell_type": "markdown",
@@ -1431,8 +1143,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-06T15:10:27.242924Z",
-     "start_time": "2025-04-06T15:10:27.054441Z"
+     "end_time": "2025-04-07T01:32:49.982375Z",
+     "start_time": "2025-04-07T01:32:49.763399Z"
     }
    },
    "source": [
@@ -1450,33 +1162,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Assets written to: C:\\Users\\grimm\\AppData\\Local\\Temp\\tmpx0n8ggq1\\assets\n"
+      "INFO:tensorflow:Assets written to: C:\\Users\\grimm\\AppData\\Local\\Temp\\tmp0n5lulxf\\assets\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Assets written to: C:\\Users\\grimm\\AppData\\Local\\Temp\\tmpx0n8ggq1\\assets\n"
+      "INFO:tensorflow:Assets written to: C:\\Users\\grimm\\AppData\\Local\\Temp\\tmp0n5lulxf\\assets\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Saved artifact at 'C:\\Users\\grimm\\AppData\\Local\\Temp\\tmpx0n8ggq1'. The following endpoints are available:\n",
+      "Saved artifact at 'C:\\Users\\grimm\\AppData\\Local\\Temp\\tmp0n5lulxf'. The following endpoints are available:\n",
       "\n",
       "* Endpoint 'serve'\n",
-      "  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 42), dtype=tf.float32, name='input_layer_1')\n",
+      "  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 42), dtype=tf.float32, name='input_layer')\n",
       "Output Type:\n",
       "  TensorSpec(shape=(None, 6), dtype=tf.float32, name=None)\n",
       "Captures:\n",
-      "  2614704504176: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
-      "  2614869353648: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
-      "  2614704496960: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
-      "  2614869345904: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
-      "  2614869344496: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
-      "  2614871934928: TensorSpec(shape=(), dtype=tf.resource, name=None)\n"
+      "  2256939976832: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  2256939988096: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  2256939987568: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  2256940372336: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  2256940377968: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  2256940368992: TensorSpec(shape=(), dtype=tf.resource, name=None)\n"
      ]
     },
     {
@@ -1485,12 +1197,12 @@
        "6644"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 38
+   "execution_count": 16
   },
   {
    "cell_type": "markdown",
@@ -1503,8 +1215,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-06T15:10:27.274132Z",
-     "start_time": "2025-04-06T15:10:27.259133Z"
+     "end_time": "2025-04-07T01:32:50.013297Z",
+     "start_time": "2025-04-07T01:32:49.998788Z"
     }
    },
    "source": [
@@ -1525,14 +1237,14 @@
      ]
     }
    ],
-   "execution_count": 39
+   "execution_count": 17
   },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-06T15:10:27.306131Z",
-     "start_time": "2025-04-06T15:10:27.291134Z"
+     "end_time": "2025-04-07T01:32:50.044788Z",
+     "start_time": "2025-04-07T01:32:50.030297Z"
     }
    },
    "source": [
@@ -1541,29 +1253,29 @@
     "output_details = interpreter.get_output_details()"
    ],
    "outputs": [],
-   "execution_count": 40
+   "execution_count": 18
   },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-06T15:10:27.337021Z",
-     "start_time": "2025-04-06T15:10:27.322449Z"
+     "end_time": "2025-04-07T01:32:50.075650Z",
+     "start_time": "2025-04-07T01:32:50.061303Z"
     }
    },
    "source": [
     "interpreter.set_tensor(input_details[0]['index'], np.array([X_test[0]]))"
    ],
    "outputs": [],
-   "execution_count": 41
+   "execution_count": 19
   },
   {
    "cell_type": "code",
    "metadata": {
     "scrolled": true,
     "ExecuteTime": {
-     "end_time": "2025-04-06T15:10:27.368247Z",
-     "start_time": "2025-04-06T15:10:27.354023Z"
+     "end_time": "2025-04-07T01:32:50.107549Z",
+     "start_time": "2025-04-07T01:32:50.092544Z"
     }
    },
    "source": [
@@ -1582,13 +1294,13 @@
      ]
     }
    ],
-   "execution_count": 42
+   "execution_count": 20
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-06T15:10:27.415239Z",
-     "start_time": "2025-04-06T15:10:27.384549Z"
+     "end_time": "2025-04-07T01:32:50.154657Z",
+     "start_time": "2025-04-07T01:32:50.124490Z"
     }
    },
    "cell_type": "code",
@@ -1597,13 +1309,13 @@
     "model.save(model_save_path, include_optimizer=False)"
    ],
    "outputs": [],
-   "execution_count": 43
+   "execution_count": 21
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-06T15:10:27.445830Z",
-     "start_time": "2025-04-06T15:10:27.431554Z"
+     "end_time": "2025-04-07T01:32:50.186660Z",
+     "start_time": "2025-04-07T01:32:50.171660Z"
     }
    },
    "cell_type": "code",
@@ -1616,13 +1328,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[4.1954637e-02 7.1722366e-02 8.7705678e-01 8.6475220e-03 5.7122711e-04\n",
-      " 4.7421348e-05]\n",
-      "2\n"
+      "[9.8055995e-01 1.9246584e-02 1.6972909e-04 1.5124817e-07 1.4379638e-05\n",
+      " 9.2182863e-06]\n",
+      "0\n"
      ]
     }
    ],
-   "execution_count": 44
+   "execution_count": 22
   }
  ],
  "metadata": {
diff --git a/model/keypoint_classifier/keypoint.csv b/model/keypoint_classifier/keypoint.csv
index e8a70f80..224d7226 100644
--- a/model/keypoint_classifier/keypoint.csv
+++ b/model/keypoint_classifier/keypoint.csv
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diff --git a/model/keypoint_classifier/keypoint_classifier.hdf5 b/model/keypoint_classifier/keypoint_classifier.hdf5
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