training_q.py 文件源码

def _clip_grad_norms(self, gradients_to_variables, max_norm=5):
    """Clips the gradients by the given value.

    Args:
        gradients_to_variables: A list of gradient to variable pairs (tuples).
        max_norm: the maximum norm value.

    Returns:
        A list of clipped gradient to variable pairs.
     """
    grads_and_vars = []
    for grad, var in gradients_to_variables:
        if grad is not None:
            if isinstance(grad, tf.IndexedSlices):
                tmp = tf.clip_by_norm(grad.values, max_norm)
                grad = tf.IndexedSlices(tmp, grad.indices, grad.dense_shape)
            else:
                grad = tf.clip_by_norm(grad, max_norm)
        grads_and_vars.append((grad, var))
    return grads_and_vars