Removing then inserting a new middle layer into the Keras model
Given the predefined Keras model, I am trying to load in the pretrained weights first and then remove one to three of the models of the inner (not the last) layers and then replace it with another layer.
I can't find any documentation on keras.io to do such a thing or remove layers from a predefined model in everything.
The model I am using is a good VGG-16 ole network which is generated in a function like below:
def model(self, output_shape):
# Prepare image for input to model
img_input = Input(shape=self._input_shape)
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
# Classification block
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dropout(0.5)(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dropout(0.5)(x)
x = Dense(output_shape, activation='softmax', name='predictions')(x)
inputs = img_input
# Create model.
model = Model(inputs, x, name=self._name)
return model
As an example, I would like to take two Conv layers in block 1 and replace them with only one Conv layer, after loading the original weights into all other layers.
Any ideas?
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1 answer
Assuming you have a model vgg16_model
initialized with either your function above or keras.applications.VGG16(weights='imagenet')
. Now you need to insert a new layer in the middle so that the weights of the other layers are preserved.
The idea is to take apart the entire network to separate the layers and then put it back together. Here is some code specifically tailored for your task:
vgg_model = applications.VGG16(include_top=True, weights='imagenet')
# Disassemble layers
layers = [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-trainable
x = new_conv
for i in range(3, len(layers)):
layers[i].trainable = False
x = layers[i](x)
# Final touch
result_model = Model(input=layer[0].input, output=x)
result_model.summary()
And the above code:
_________________________________________________________________
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: 138,320,616
Trainable params: 1,792
Non-trainable params: 138,318,824
_________________________________________________________________
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