Given a keras model & some input, show output statistics for each layer

get_keras_activations.py

import tensorflow.keras.backend as K

def get_activations(model, model_inputs):
    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 = []
    names =[]
    for layer in model.layers:
        if not any(n in layer.name for n in ['input', 'model']):
            outputs.append(layer.output)
            names.append(layer.name)

    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(0.)
    else:
        list_inputs = [model_inputs, 0.]

    # Learning phase. 0 = Test mode (no dropout or batch normalization)
    # layer_outputs = [func([model_inputs, 0.])[0] for func in funcs]
    layer_outputs = [func(list_inputs)[0] for func in funcs]
    for i, layer_activations in enumerate(layer_outputs):
        activations.append(layer_activations)
        for a in layer_activations:
            print('Layer: {}, Shape: {}, Mean: {:.2f}, Std: {:.2f}'.format(
                names[i], a.shape, np.mean(a), np.std(a)
            ))
    return activations