Tensorflow tf AdamOptimizer

tf AdamOptimizer

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.