How to replace (or insert) intermediate layer in Keras model?

Question

I have a trained Keras model and I would like:

1) to replace Con2D layer with the same but without bias.

2) to add BatchNormalization layer before first Activati

Answer 1

You can use the following functions:

def replace_intermediate_layer_in_keras(model, layer_id, new_layer):
    from keras.models import Model

    layers = [l for l in model.layers]

    x = layers[0].output
    for i in range(1, len(layers)):
        if i == layer_id:
            x = new_layer(x)
        else:
            x = layers[i](x)

    new_model = Model(input=layers[0].input, output=x)
    return new_model

def insert_intermediate_layer_in_keras(model, layer_id, new_layer):
    from keras.models import Model

    layers = [l for l in model.layers]

    x = layers[0].output
    for i in range(1, len(layers)):
        if i == layer_id:
            x = new_layer(x)
        x = layers[i](x)

    new_model = Model(input=layers[0].input, output=x)
    return new_model

Example:

if __name__ == '__main__':
    from keras.layers import Conv2D, BatchNormalization
    model = keras_simple_model()
    print(model.summary())
    model = replace_intermediate_layer_in_keras(model, 3, Conv2D(4, (3, 3), activation=None, padding='same', name='conv2_repl', use_bias=False))
    print(model.summary())
    model = insert_intermediate_layer_in_keras(model, 4, BatchNormalization())
    print(model.summary())

There are some limitation on replacements due to layer shapes etc.