tf.contrib.training.create_train_op

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

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
)

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.compat.v1.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.