Tensorflow AdamOptimizer apply_gradients

tf.AdamOptimizer apply_gradients

apply_gradients

apply_gradients(
    grads_and_vars,
    global_step=None,
    name=None
)

Apply gradients to variables.

This is the second part of minimize(). It returns an Operation that applies gradients.

• Args:

  grads_and_vars: List of (gradient, variable) pairs as returned by compute_gradients(). global_step: Optional Variable to increment by one after the variables have been updated. name: Optional name for the returned operation. Default to the name passed to the Optimizer constructor.

• Returns:

  An Operation that applies the specified gradients. If global_step was not None, that operation also increments global_step.

• Raises:

  TypeError: If grads_and_vars is malformed. ValueError: If none of the variables have gradients. RuntimeError: If you should use _distributed_apply() instead.