Example #1
def output_at_layer(input_image, model, layer_num, verbose=False):
    This function is used to visualize activations at any layer in a
    Convolutional Neural Network model in Keras. It returns the output image
    for a given input image at the layer_num layer of the model (layer numbers
    starting at 1). The model should be Sequential type. This function will
    not mutate the model.

    Reference: https://github.com/fchollet/keras/issues/431

    The idea is to keep the first layer_num layers of a trained model and
    remove the rest. Then compile the model and use the predict function to get
    the ouput.

        input_image: Image in Numpy format from the dataset
        model: Name to be used with the load_model() function
        layer_num: Layer number between 1 and len(model.layers)
        verbose: Prints layer info

        output_image: Numpy array of the output at the layer_num layer
    model_temp = Sequential()
    model_temp.layers = model.layers[:layer_num]  # Truncates layers
    model_temp.compile(loss=model.loss, optimizer=model.optimizer)  # Recompiles model_temp
    output_image = model_temp.predict(np.array([input_image]))  # Uses predict to get ouput
    if verbose:
        # Print layer and image info
        layer = model_temp.layers[layer_num-1]
        print layer
            print layer.W_shape
        print "Image dimensions: " + str(output_image.shape)
    return output_image