tf.contrib.distribute.StandardSingleLossStep
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A step function that implements a training step for a feed forward network.
Inherits From: StandardInputStep
tf.contrib.distribute.StandardSingleLossStep(
dataset_fn, loss_fn, optimizer, distribution, iterations_per_step=1
)
An instance of this class is intended to be used as a callable:
...
step = step_fn.StandardSingleLossStep(
dataset, loss_fn, optimizer, distribution)
# Run a single training step on a given DistributionStrategy:
step(distribution)
...
Args |
dataset_fn
|
a function that returns a tf.data Dataset that produces the
input for the model.
|
loss_fn
|
a function that takes a context and inputs as arguments. It returns
the loss for those inputs. context is an instance of
values.MultiStepContext that will be passed when loss_fn is run.
context can be used to specify the outputs to be returned from
loss_fn , among other things.
|
optimizer
|
an optimizer that implements an update rule.
|
distribution
|
a DistributionStrategy object.
|
Methods
initialize
View source
initialize()
__call__
View source
__call__()
Perform one step of this training algorithm.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-10-01 UTC.
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