er('block3_conv3').output, block7_up])
    block7_conv1 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_merge)
    block7_conv2 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_conv1)
    block7_conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_conv2)

    block8_up = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(block7_conv3))
    block8_merge = Concatenate(axis=3)([vgg16_model.get_layer('block2_conv2').output, block8_up])
    block8_conv1 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block8_merge)
    block8_conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block8_conv1)

    block9_up = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(block8_conv2))
    block9_merge = Concatenate(axis=3)([vgg16_model.get_layer('block1_conv2').output, block9_up])
    block9_conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block9_merge)
    block9_conv2 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block9_conv1)

    block10_conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block9_conv2)
    block10_conv2 = Conv2D(1, 1, activation='sigmoid')(block10_conv1)

    model = Model(inputs=vgg16_model.input, outputs=block10_conv2)
    return model


if __name__ == '__main__':
    is_train = False
    if is_train:
        model = vgg10_unet(input_shape=(512,512,3), weights='imagenet')

        for index in range(15):
            model.layers[index].trainable = True
        model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
        model_checkpoint = ModelCheckpoint('unet.h5', monitor='loss', verbose=1, save_best_only=True)
        model.fit_generator(train_generator(batch_size=4),
                            steps_per_epoch=200,
                            epochs=50,
                            validation_data=train_generator(ba