Using TensorFlow ``grad_loss / grad_ys`` parameter to add gradients

I'm trying to use the grad_loss parameter in optimizer.minimize(loss, grad_loss=) to modify the network gradients with existing gradients. I followed the comments here: Use of grads_ys parameter in tf.gradients - TensorFlow

and I would like to run a toy example, in which I recreate the default 1 values for grad_ys, as specified in the documentation.

Here's the relevant code segment:

grads_and_vars = optimizer.compute_gradients(loss_op)  vars_with_grad = [v for g, v in grads_and_vars if g is not None]  grad_loss = []  for grad,var in grads_and_vars:     grad_loss.append(tf.ones_like(grad)) train_op = optimizer.minimize(loss_op, grad_loss=grad_loss) 

The first part extracts gradients using compute_gradients. The last line computes gradients of the loss function loss_op but attempts to use 1-filled vectors for the grads. As far as I understand, this should behave similarly to funning minimize without the grad_loss parameter.

Unfortunately, this fails since it expects grad_loss to be a Tensor (and have a dtype) and not a list. Looking into gradients_impl.py I see that the function expected grad_loss to be of the same dimension as loss (which in this case is a scalar).

I would appreciate any assistance in this simple example - how do I add elements to the gradients this way?

EDIT: I guess the question boils down to the definition of grad_loss: "A Tensor holding the gradient computed for loss." How do I generate such a tensor from a set of gradients obtained by compute_gradients?

Thanks.

1 Answers

Answers 1

You can make use of the tf.convert_to_tensor method to convert your list of gradients to a tensor, and then use tf.reduce_sum:

train_op = optimizer.minimize(loss_op, grad_loss=tf.reduce_sum(tf.convert_to_tensor(grad_loss))) 

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