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- input = Input(shape=(224,224,3), name='input_1')
- base_model = keras.applications.VGG16(weights='imagenet', include_top=False)
- for layer in base_model.layers:
- layer.trainable=False
- x = base_model(input)
- x = Conv2DTranspose(filters=256, kernel_size=(4,4), strides=(2, 2), activation='relu',
- name='deconv1', padding='valid',
- output_padding=(1,1),
- kernel_initializer='random_uniform')(x)
- x = BatchNormalization()(x)
- x = Dropout(0.5)(x)
- x = Conv2D(filters=15, kernel_size=(1,1), name='final_conv', padding='same')(x)
- <tf.Tensor 'vgg16/block5_pool/MaxPool:0' shape=(?, 7, 7, 512) dtype=float32>
- Layer (type) Output Shape Param #
- =================================================================
- input_1 (InputLayer) (None, 224, 224, 3) 0
- _________________________________________________________________
- vgg16 (Model) multiple 14714688
- _________________________________________________________________
- deconv1 (Conv2DTranspose) (None, 17, 17, 256) 2097408
- _________________________________________________________________
- batch_normalization_11 (Batc (None, 17, 17, 256) 1024
- _________________________________________________________________
- dropout_7 (Dropout) (None, 17, 17, 256) 0
- _________________________________________________________________
- final_conv (Conv2D) (None, 17, 17, 15) 3855
- =================================================================
- Total params: 16,816,975
- Trainable params: 2,101,775
- Non-trainable params: 14,715,200
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