contrib.training.create_train_op
tf.contrib.training.create_train_op
tf.contrib.training.create_train_op
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
: ATensor
representing the total loss. -
optimizer
: A tf.Optimizer to use for computing the gradients. -
global_step
: ATensor
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. Ifupdate_ops
isNone
, then the update ops are set to the contents of thetf.GraphKeys.UPDATE_OPS
collection. Ifupdate_ops
is notNone
, but it doesn't contain all of the update ops intf.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 classAggregationMethod
. -
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.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/training/create_train_op