tf.contrib.training.create_train_op

tf.contrib.training.create_train_op(
    total_loss,
    optimizer,
    global_step=_USE_GLOBAL_STEP,
    update_ops=None,
    variables_to_train=None,
    transform_grads_fn=None,
    summarize_gradients=False,
    gate_gradients=tf_optimizer.Optimizer.GATE_OP,
    aggregation_method=None,
    colocate_gradients_with_ops=False,
    check_numerics=True
)

Defined in tensorflow/contrib/training/python/training/training.py.

Creates an Operation that evaluates the gradients and returns the loss.

Args:

• total_loss: A Tensor representing the total loss.
• optimizer: A tf.Optimizer to use for computing the gradients.
• global_step: A Tensor representing the global step variable. If left as _USE_GLOBAL_STEP, then tf.contrib.framework.global_step() is used.
• update_ops: An optional list of updates to execute. If update_ops is None, then the update ops are set to the contents of the tf.GraphKeys.UPDATE_OPS collection. If update_ops is not None, but it doesn't contain all of the update ops in tf.GraphKeys.UPDATE_OPS, a warning will be displayed.
• variables_to_train: an optional list of variables to train. If None, it will default to all tf.trainable_variables().
• transform_grads_fn: A function which takes a single argument, a list of gradient to variable pairs (tuples), performs any requested gradient updates, such as gradient clipping or multipliers, and returns the updated list.
• summarize_gradients: Whether or not add summaries for each gradient.
• gate_gradients: How to gate the computation of gradients. See tf.Optimizer.
• aggregation_method: Specifies the method used to combine gradient terms. Valid values are defined in the class AggregationMethod.
• colocate_gradients_with_ops: Whether or not to try colocating the gradients with the ops that generated them.
• check_numerics: Whether or not we apply check_numerics.

Returns:

A Tensor that when evaluated, computes the gradients and returns the total loss value.