Convolutional_neural_network.py

import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow import keras
import time
import os

os.environ['CUDA_VISIBLE_DEVICES'] = '0'

cifar10 = keras.datasets.cifar10
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = X_train / 255.0
X_test = X_test / 255.0

#definindo o modelo
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding="same", activation="relu", input_shape=[32, 32, 3]))
model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding="same", activation="relu"))
model.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='valid'))
model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding="same", activation="relu"))
model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding="same", activation="relu"))
model.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='valid'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(units=128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(units=128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(units = 10, activation = 'softmax')) 
model.summary()


#compilando
model.compile(loss="sparse_categorical_crossentropy", optimizer="Adam", metrics=["sparse_categorical_accuracy"])

starting_time = time.time()
model.fit(X_train, y_train, epochs=15)
print("Training time: {}".format(time.time() - starting_time))


#avaliando
test_loss, test_accuracy = model.evaluate(X_test, y_test)
print("Test accuracy: {}".format(test_accuracy))
print(test_loss)

#salvando modelo
imagens = model.to_json()
with open("My_trained_models/imagens.json", "w") as json_file:
    json_file.write(imagens)

#salvando os pesos da rede
model.save_weights("My_trained_models/imagens.weights.h5")