Guided Backpropagation

Hi @keisen, thanks for putting this together. Is there a way to generate a guided backpropagation saliency map with a custom function in gradient-modifier?

[eyaler]

maybe something like:

@tf.custom_gradient
def guidedRelu(x):
    def grad(dy):
        return tf.cast(dy>0,"float32") * tf.cast(x>0, "float32") * dy
    return tf.nn.relu(x), grad

def modify_model(model):
  for layer in [layer for layer in model.layers[1:] if hasattr(layer,"activation")]:
      if layer.activation == tf.keras.activations.relu:
          layer.activation = guidedRelu
  return model

[keisen]

@ISipi , Thank you for your great suggestion! I’m going to include the warning comment in the code of guide-backprop.

Thanks!

(To be honest, I want to publish API and GettingStarted documentation on a website. It’s ideal if it included knowledge of visualization like above. If I could find the time, I would also do it. )