application_vnd.jupyter.stderr_output_1.png
def get_model():
    input_img = Input((256, 256, 3))
    X = BatchNormalization()(input_img)
    X = Convolution2D(16, (3, 3), activation='relu')(X)
    X = BatchNormalization()(X)
    X = Convolution2D(16, (3, 3), activation='relu')(X)
    X = MaxPooling2D()(X)
    X = Convolution2D(32, (3, 3), activation='relu')(X)
    X = BatchNormalization()(X)
    X = Convolution2D(32, (3, 3), activation='relu')(X)
    X = GlobalMaxPooling2D()(X)
    X = BatchNormalization()(X)
    X = Dense(512, activation='relu')(X)
    X = Dropout(0.2)(X)
    X = Dense(10, activation='softmax')(X)
    model = Model(inputs=input_img, outputs=X)
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
    model.summary()
    return model

model = get_model()