Removing then Inserting a New Middle Layer in a Keras Model

Question

Given a predefined Keras model, I am trying to first load in pre-trained weights, then remove one to three of the models internal (non-last few) layers, and then replace it with another layer.

I can't seem to find any documentation on keras.io about to do such a thing or remove layers from a predefined model at all.

The model I am using is a good ole VGG-16 network which is instantiated in a function as shown 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

So as an example, I'd like to take the two Conv layers in Block 1 and replace them with just one Conv layer, after loading the original weights into all of the other layers.

Any ideas?

user_2329503 · Answer

Another way to do this is by building a Sequential model. See the following example where I swap ReLU layers for PReLU. You would need to simply not add the layers you don't want, and add a new layer.

def convert_model_relu(model):
    from keras.layers.advanced_activations import PReLU
    from keras.activations import linear as linear_activation
    from keras.models import Sequential
    new_model = Sequential()
    # Go through all layers, if it has a ReLU activation, replace it with PrELU
    for layer in tuple(model.layers):
        layer_type = type(layer).__name__
        if hasattr(layer, 'activation') and layer.activation.__name__ == 'relu':
            # Set activation to linear, add PReLU
            prelu_name = layer.name + "_prelu"
            prelu = PReLU(shared_axes=(1, 2), name=prelu_name) \ 
                if layer_type == "Conv2D" else PReLU(name=prelu_name)
            layer.activation = linear_activation
            new_model.add(layer)
            new_model.add(prelu)
        else:
            new_model.add(layer)
    return new_model

Removing then Inserting a New Middle Layer in a Keras Model

2 Answers