Hands-On Deep Learning Algorithms with Python

Defining BPTT

Now, we will perform the BPTT, with Adam as our optimizer. We will also perform gradient clipping to avoid the exploding gradients problem:

1. Initialize the Adam optimizer:

minimizer = tf.train.AdamOptimizer()

2. Compute the gradients of the loss with the Adam optimizer:

gradients = minimizer.compute_gradients(loss)

3. Set the threshold for the gradient clipping:

threshold = tf.constant(5.0, name="grad_clipping")

4. Clip the gradients that exceed the threshold and bring it to the range:

clipped_gradients = []for grad, var in gradients:    clipped_grad = tf.clip_by_value(grad, -threshold, threshold)    clipped_gradients.append((clipped_grad, var))

5. Update the gradients with the clipped gradients:

updated_gradients = minimizer.apply_gradients(clipped_gradients) ...