generate_filter_image.py

# This code is not mine. Source: https://github.com/keras-team/keras/blob/master/examples/conv_filter_visualization.py
def _generate_filter_image(input_img,
                               layer_output,
                               filter_index):
        """Generates image for one particular filter.
        # Arguments
            input_img: The input-image Tensor.
            layer_output: The output-image Tensor.
            filter_index: The to be processed filter number.
                          Assumed to be valid.
        #Returns
            Either None if no image could be generated.
            or a tuple of the image (array) itself and the last loss.
        """
        # we build a loss function that maximizes the activation
        # of the nth filter of the layer considered
        if K.image_data_format() == 'channels_first':
            loss = K.mean(layer_output[:, filter_index, :, :])
        else:
            loss = K.mean(layer_output[:, :, :, filter_index])

        # we compute the gradient of the input picture wrt this loss
        grads = K.gradients(loss, input_img)[0]

        # normalization trick: we normalize the gradient
        grads = normalize(grads)

        # this function returns the loss and grads given the input picture
        iterate = K.function([input_img], [loss, grads])

        # we start from a gray image with some random noise
        intermediate_dim = tuple(
            int(x / (upscaling_factor ** upscaling_steps)) for x in output_dim)
        if K.image_data_format() == 'channels_first':
            input_img_data = np.random.random(
                (1, 3, intermediate_dim[0], intermediate_dim[1]))
        else:
            input_img_data = np.random.random(
                (1, intermediate_dim[0], intermediate_dim[1], 3))
        input_img_data = (input_img_data - 0.5) * 20 + 128

        # Slowly upscaling towards the original size prevents
        # a dominating high-frequency of the to visualized structure
        # as it would occur if we directly compute the 412d-image.
        # Behaves as a better starting point for each following dimension
        # and therefore avoids poor local minima
        for up in reversed(range(upscaling_steps)):
            # we run gradient ascent for e.g. 20 steps
            for _ in range(epochs):
                loss_value, grads_value = iterate([input_img_data])
                input_img_data += grads_value * step

                # some filters get stuck to 0, we can skip them
                if loss_value <= K.epsilon():
                    return None

            # Calculate upscaled dimension
            intermediate_dim = tuple(
                int(x / (upscaling_factor ** up)) for x in output_dim)
            # Upscale
            img = deprocess_image(input_img_data[0])
            img = np.array(pil_image.fromarray(img).resize(intermediate_dim,
                                                           pil_image.BICUBIC))
            input_img_data = np.expand_dims(
                process_image(img, input_img_data[0]), 0)

        # decode the resulting input image
        img = deprocess_image(input_img_data[0])
        e_time = time.time()
        print('Costs of filter {:3}: {:5.0f} ( {:4.2f}s )'.format(filter_index,
                                                                  loss_value,
                                                                  e_time - s_time))
        return img, loss_value