tensorflow backpropagation with all variables without updating non-trainable variables-CodePudding

I want to calculate gradients for both trainable and non-trainable variables.

And update only trainable parameters.

At first, I implemented it as follows

with tf.GradientTape(persistent = True) as g:
     preds = model(data)
     loss  = criterion(labels, preds)

gradients = g.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))

non_train_gradients = g.gradient(loss, model.non_trainable_variables)

However, the above code do twice backpropagation to calculate gradients.

I want to estimate the gradients of both trainable and non-trainable variables simultaneosuly,

but only updates trainable parameters.

How can I do it?

CodePudding user response：

We can use the fact that the gradients are just a list and are returned in the same order as the variables we put in:

n_trainable = len(model.trainable_variables)

gradients = g.gradient(loss, model.trainable_variables   model.non_trainable_variables)

trainable_gradients = gradients[:n_trainable]
non_trainable_gradients = gradients[n_trainable:]

optimizer.apply_gradients(zip(trainable_gradients, model.trainable_variables))

That is, we just put all the non-trainable variables at the end, and then split the gradients at that point.