假设您有一个模型
vgg16_model，可以通过上面的函数或通过初始化
keras.applications.VGG16(weights='imagenet')。现在，您需要在中间插入一个新层，以节省其他层的权重。
想法是将整个网络分解为单独的层，然后将其重新组装。这是专门用于您的任务的代码：
vgg_model = applications.VGG16(include_top=True, weights='imagenet')# Disassemble layerslayers = [l for l in vgg_model.layers]# Defining new convolutional layer.# important: the number of filters should be the same!# Note: the receiptive field of two 3x3 convolutions is 5x5.new_conv = Conv2D(filters=64, kernel_size=(5, 5), name='new_conv', padding='same')(layers[0].output)# Now stack everything back# Note: If you are going to fine tune the model, do not forget to# mark other layers as un-trainablex = new_convfor i in range(3, len(layers)): layers[i].trainable = False x = layers[i](x)# Final touchresult_model = Model(input=layer[0].input, output=x)result_model.summary()
上面代码的输出是：
_________________________________________________________________Layer (type) Output Shape Param # =================================================================input_50 (InputLayer) (None, 224, 224, 3) 0 _________________________________________________________________new_conv (Conv2D) (None, 224, 224, 64) 1792 _________________________________________________________________block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 _________________________________________________________________block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 _________________________________________________________________block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 _________________________________________________________________block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 _________________________________________________________________block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 _________________________________________________________________block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 _________________________________________________________________block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 _________________________________________________________________block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 _________________________________________________________________block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 _________________________________________________________________flatten (Flatten) (None, 25088) 0 _________________________________________________________________fc1 (Dense) (None, 4096) 102764544 _________________________________________________________________fc2 (Dense) (None, 4096) 16781312 _________________________________________________________________predictions (Dense) (None, 1000) 4097000 =================================================================Total params: 138,320,616Trainable params: 1,792Non-trainable params: 138,318,824_________________________________________________________________
