cnn_model.py

import tensorflow as tf
import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import RMSprop

# CNNのモデルを定義する
def def_model(in_shape, nb_classes):
    model = Sequential()
    model.add(Conv2D(32,
              kernel_size=(3,3),
              activation="relu",
              input_shape=in_shape))
    model.add(Conv2D(32, (3, 3), activation="relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(64, (3, 3), activation="relu"))
    model.add(Conv2D(64, (3, 3), activation="relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512, activation="relu"))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes, activation="softmax"))
    return model


# コンパイル済みのCNNのモデルを返す
def get_model(in_shape, nb_classes):
    model = def_model(in_shape, nb_classes)
    model.compile(
        loss="categorical_crossentropy",
        optimizer=RMSprop(),
        metrics=["accuracy"])
    return model