BaseTrainer

Inheritance Diagram

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class ashpy.trainers.BaseTrainer(epochs, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/v0.1.3/docs/source/log', log_eval_mode=<LogEvalMode.TEST: 0>, global_step=<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=0>, post_process_callback=None)

Bases: abc.ABC

BaseTrainer provide an interface for all trainers to inherit from.

Methods

__init__(epochs[, logdir, log_eval_mode, …])	Primitive trainer interface.
call(dataset)	Execute the training process.

__init__(epochs, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/v0.1.3/docs/source/log', log_eval_mode=<LogEvalMode.TEST: 0>, global_step=<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=0>, post_process_callback=None)

Primitive trainer interface. Handles model saving and restore.

Parameters

• epochs (int) – Number of training epochs.

• logdir (str) – Checkpoint and log directory.

• log_eval_mode – models’ mode to use when evaluating and logging.

• global_step – tf.Variable that keeps track of the training steps.

• post_process_callback – the function to postprocess the model output, if needed.

_current_epoch()

Get the current epoch using the (restored) variables.

Returns

current_epoch (int)

_epoch_completed(epoch)

Handle the end of the training epoch.

Parameters

epoch (int) – the just completed training epoch.

_log(name, out)

Log the out tensor using name as its name in tensorboard.

Parameters

• name – summary name.

• out – the tensor to log.

_reduce(per_replica_tensor, reduce_op)

Given the input tensor, reduces it in a distributed fashion, using the specified op.

_restore_or_init()

Restores or initializes the persistence layer (checkpoint).

_save()

Save the current checkpointable object status.

_update_global_batch_size(dataset, executors=None)

Given a dataset and the current distribution strategy sets the self._global_batch_size variable where needed. :param dataset: a dataset from wich the batch size will be extracted. :param executors: a list of executor with the property “global_batch_size” settable.

abstract call(dataset)

Execute the training process.

Iterate over the elements of a tf.data.Dataset. The dataset must contain everything needed to train the model.

Parameters

dataset (DatasetV2) – A tf.data.Dataset to loop on to train the model.