o ?e@sPdZddlmZddlmZGdddeZGdddeZGdd d eZ d S) z@The step function abstraction represents a single training step.)backprop) optimizerc@s4eZdZdZddZeddZddZdd Zd S) Stepz;Interface for performing each step of a training algorithm.cCs ||_dSN _distribution)self distributionr e/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/distribute/step_fn.py__init__s z Step.__init__cCs|jSrrrr r r r szStep.distributioncCsgSrr r r r r initializeszStep.initializecCstd)z,Perform one step of this training algorithm.z"must be implemented in descendants)NotImplementedErrorr r r r __call__"sz Step.__call__N) __name__ __module__ __qualname____doc__r propertyr rrr r r r rs  rcs(eZdZdZfddZddZZS)StandardInputStepzStep with a standard implementation of input handling. Args: dataset_fn: a function that returns a tf.data Dataset that produces the input for the model. cs(tt|||fdd|_dS)NcsSrr )_ dataset_fnr r 3sz,StandardInputStep.__init__..)superrr Zmake_input_fn_iterator _iterator)rrr  __class__rr r 1szStandardInputStep.__init__cCs|jjSr)rZ initializerr r r r r5szStandardInputStep.initialize)rrrrr r __classcell__r r rr r)s rcs,eZdZdZ dfdd ZddZZS)StandardSingleLossStepaA step function that implements a training step for a feed forward network. An instance of this class is intended to be used as a callable: ```python ... 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. cs(tt|||||_||_||_dSr)rr r _loss_fn _optimizer_iterations_per_step)rrZloss_fnrr Ziterations_per_steprr r r Ts zStandardSingleLossStep.__init__csVjfdd}jj|jj}|jWdS1s$wYdS)Ncs<tj}t|}jjj|||fd}j j|S)z-Function to run one iteration with one input.)args) rZ implicit_gradr" optimizer_libZget_filtered_grad_fnr extendedZcall_for_each_replicar#Z_distributed_apply)ctxZinputsZ gradients_fnZgrads_and_varsr r r step_fn]s  z0StandardSingleLossStep.__call__..step_fn)rscoper r'Z"experimental_run_steps_on_iteratorrr$Zrun_op)rr)r(r r r r[s   $zStandardSingleLossStep.__call__)r!)rrrrr rrr r rr r 9s r N) rZtensorflow.python.eagerrZtensorflow.python.trainingrr&objectrrr r r r r s