Modifying Neural Network weights using estimators in tensorflow

Question

I need to modify the weight values during the execution, more specifically between the compute_gradients() and apply_gradients() functions. I was able to modify the gradients themselves, but i could not change the weights.

I'm using the tutorial for the Iris NN in tensorflow: https://github.com/tensorflow/models/blob/master/samples/core/get_started/custom_estimator.py , the only difference being that i changed the minimize() function for the compute_gradients() and the apply_gradients() function.

grads_and_vars = optimizer.compute_gradients(loss)
// some way to change the weights
train_op = optimizer.apply_gradients(grads_and_vars, global_step=tf.train.get_global_step())

Thanks in advance.

Answer 1

My best guess is that you are looking for tf.assign (from here ) to assign values to your Variable tensors.

According to the docs:

Update 'ref' by assigning 'value' to it.

This operation outputs a Tensor that holds the new value of 'ref' after the value has been assigned. This makes it easier to chain operations that need to use the reset value.