print_activations.py

from glob import glob


def get_activations(model, model_inputs, print_shape_only=False, layer_name=None):
    import keras.backend as K
    print('----- activations -----')
    activations = []
    inp = model.input

    model_multi_inputs_cond = True
    if not isinstance(inp, list):
        # only one input! let's wrap it in a list.
        inp = [inp]
        model_multi_inputs_cond = False

    outputs = [layer.output for layer in model.layers if
               layer.name == layer_name or layer_name is None]  # all layer outputs

    funcs = [K.function(inp + [K.learning_phase()], [out]) for out in outputs]  # evaluation functions

    if model_multi_inputs_cond:
        list_inputs = []
        list_inputs.extend(model_inputs)
        list_inputs.append(1.)
    else:
        list_inputs = [model_inputs, 1.]

    # Learning phase. 1 = Test mode (no dropout or batch normalization)
    # layer_outputs = [func([model_inputs, 1.])[0] for func in funcs]
    layer_outputs = [func(list_inputs)[0] for func in funcs]
    for layer_activations in layer_outputs:
        activations.append(layer_activations)
        if print_shape_only:
            print(layer_activations.shape)
        else:
            print(layer_activations)
    return activations

if __name__ == '__main__':

    checkpoints = glob('checkpoints/*.h5')
    # pip3 install natsort
    from natsort import natsorted

    from keras.models import load_model

    if len(checkpoints) > 0:

        checkpoints = natsorted(checkpoints)
        assert len(checkpoints) != 0, 'No checkpoints found.'
        checkpoint_file = checkpoints[-1]
        print('Loading [{}]'.format(checkpoint_file))
        model = load_model(checkpoint_file)

        model.compile(optimizer='adam',
                      loss='mse ',
                      metrics=['accuracy'])

        print(model.summary())


        get_activations(model, x_test[0:1], print_shape_only=True)  # with just one sample.

        get_activations(model, x_test[0:200], print_shape_only=True)  # with 200 samples.