| 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 |