Using Op Inputs When Defining Custom Gradients In Tensorflow

I'm trying to define a gradient method for my custom TF operation. Most of the solutions I have found online seem to based on a gist by harpone. I'm reluctant to use that approach

Solution 1:

There is no problem with your code:

Let's first do the forward pass:

var_foo = 5 -> bar = 125 -> tf.identity(bar) = 125

Now let's backpropagate:

The gradient of tf.identity(bar) with respect to its argument bar equals (by your definition) to bar, that is, 125. The gradient of bar with respect to var_foo equals 3 times the square of var_foo which is 75. Multiply, and you get 9375, which is indeed the output of your code.

op.inputs[0] contains the forward-pass value of the op. In this case, the forward pass of the identity op is 125.