Trainer¶

class tfsnippet.Trainer(loop, train_op, inputs, data_flow, feed_dict=None, metrics=None, summaries=None, ensure_variables_initialized=True)¶

Bases: tfsnippet.trainer.base_trainer.BaseTrainer

A subclass of BaseTrainer, executing a training operation per step. This might be the most commonly used Trainer. Code example:

import tfsnippet as spt

# build the model
input_x = tf.placeholder(...)
input_y = tf.placeholder(...)
learning_rate = tf.placeholder(...)  # learning rate annealing

# prepare for the data and
train_data = spt.DataFlow.arrays(
    [train_x, train_y], batch_size=128, shuffle=True,
    skip_incomplete=True
)
valid_data = spt.DataFlow.arrays(
    [valid_x, valid_y], batch_size=512)
...

# derive the training operation
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss)

# run the trainer
learning_rate = spt.AnnealingVariable('learning_rate', 0.001, 0.75)

with spt.TrainLoop(param_vars,
                   max_epoch=10,
                   early_stopping=True) as loop:
    trainer = spt.Trainer(
        loop, train_op, [input_x, input_y], train_data,
        metrics={'loss': loss'}
    )
    evaluator = spt.Evaluator(
        loop, {'loss': loss}, [input_x, input_y], valid_data)

    # validate after every epoch
    trainer.evaluate_after_epochs(evaluator, freq=1)

    # log after every epoch (and after validation, since
    # ``HookPriority.VALIDATION < HookPriority.LOGGING``)
    trainer.log_after_epochs(freq=1)

    # anneal the learning rate after every 10 epochs
    trainer.anneal_after_epochs(learning_rate, freq=10)

    # run the main training loop
    trainer.run()

See also

tfsnippet.trainer.BaseTrainer

Attributes Summary

`data_flow`	Get the training data flow.
`events`	Get the event source object.
`feed_dict`	Get the feed dict for training.
`inputs`	Get the input placeholders.
`loop`	Get the training loop object.
`metrics`	Get the metrics to be computed along with train_op.
`summaries`	Get the summaries to be computed along with train_op.
`train_op`	Get the training operation.

Methods Summary

`anneal_after`(value[, epochs, steps])	Add an annealing hook to run after every few epochs or steps.
`anneal_after_epochs`(value, freq)	Add an annealing hook to run after every few epochs.
`anneal_after_steps`(value, freq)	Add an annealing hook to run after every few steps.
`evaluate_after`(evaluator[, epochs, steps])	Add an evaluation hook to run after every few epochs or steps.
`evaluate_after_epochs`(evaluator, freq)	Add an evaluation hook to run after every few epochs.
`evaluate_after_steps`(evaluator, freq)	Add an evaluation hook to run after every few steps.
`log_after`([epochs, steps])	Add a logging hook to run after every few epochs or steps.
`log_after_epochs`(freq)	Add a logging hook to run after every few epochs.
`log_after_steps`(freq)	Add a logging hook to run after every few steps.
`remove_annealing_hooks`()	Remove annealing hooks from all lists.
`remove_evaluation_hooks`()	Remove evaluation hooks from all lists.
`remove_log_hooks`()	Remove logging hooks from all lists.
`remove_validation_hooks`()	Remove evaluation hooks from all lists.
`run`()	Run training loop.
`validate_after`(evaluator[, epochs, steps])	Add an evaluation hook to run after every few epochs or steps.
`validate_after_epochs`(evaluator, freq)	Add an evaluation hook to run after every few epochs.
`validate_after_steps`(evaluator, freq)	Add an evaluation hook to run after every few steps.

Attributes Documentation

data_flow¶

Get the training data flow.

Returns:	The training data flow.
Return type:	DataFlow

events¶

Get the event source object.

Returns:	The event source object.
Return type:	EventSource

feed_dict¶

Get the feed dict for training.

Returns:	The feed dict for training.
Return type:	dict[tf.Tensor, any]

inputs¶

Get the input placeholders.

Returns:	The input placeholders.
Return type:	list[tf.Tensor]

loop¶

Get the training loop object.

Returns:	The training loop object.
Return type:	TrainLoop

metrics¶: Get the metrics to be computed along with train_op.

summaries¶: Get the summaries to be computed along with train_op.

train_op¶: Get the training operation.

Methods Documentation

anneal_after(value, epochs=None, steps=None)¶

Add an annealing hook to run after every few epochs or steps.

Parameters:	value (AnnealingVariable or () -> any) – An annealing variable (which has `.anneal()`), or any callable object. epochs (None or int) – Run validation after every this few epochs. steps (None or int) – Run validation after every this few steps.
Raises:	`ValueError` – If both epochs and steps are specified, or neither is specified.

anneal_after_epochs(value, freq)¶

Add an annealing hook to run after every few epochs.

Parameters:	value (AnnealingVariable or () -> any) – An annealing variable (which has `.anneal()`), or any callable object. freq (int) – The frequency for this annealing hook to run.

anneal_after_steps(value, freq)¶

Add an annealing hook to run after every few steps.

Parameters:	value (AnnealingVariable or () -> any) – An annealing variable (which has `.anneal()`), or any callable object. freq (int) – The frequency for this annealing hook to run.

evaluate_after(evaluator, epochs=None, steps=None)¶

Add an evaluation hook to run after every few epochs or steps.

Parameters:	evaluator (Evaluator or () -> any) – A evaluator object (which has `.run()`), or any callable object. epochs (None or int) – Run validation after every this few epochs. steps (None or int) – Run validation after every this few steps.
Raises:	`ValueError` – If both epochs and steps are specified, or neither is specified.

evaluate_after_epochs(evaluator, freq)¶

Add an evaluation hook to run after every few epochs.

Parameters:	evaluator (Evaluator or () -> any) – A evaluator object (which has `.run()`), or any callable object. freq (int) – The frequency for this evaluation hook to run.

evaluate_after_steps(evaluator, freq)¶

Add an evaluation hook to run after every few steps.

Parameters:	evaluator (Evaluator or () -> any) – A evaluator object (which has `.run()`), or any callable object. freq (int) – The frequency for this evaluation hook to run.

log_after(epochs=None, steps=None)¶

Add a logging hook to run after every few epochs or steps.

Parameters:	epochs (None or int) – Run validation after every this few epochs. steps (None or int) – Run validation after every this few steps.
Raises:	`ValueError` – If both epochs and steps are specified, or neither is specified.

log_after_epochs(freq)¶

Add a logging hook to run after every few epochs.

Parameters:	freq (int) – The frequency for this logging hook to run.

log_after_steps(freq)¶

Add a logging hook to run after every few steps.

Parameters:	freq (int) – The frequency for this logging hook to run.

remove_annealing_hooks()¶

Remove annealing hooks from all lists.

Returns:	The number of removed hooks.
Return type:	int

remove_evaluation_hooks()¶

Remove evaluation hooks from all lists.

Returns:	The number of removed hooks.
Return type:	int

remove_log_hooks()¶

Remove logging hooks from all lists.

Returns:	The number of removed hooks.
Return type:	int

remove_validation_hooks()¶

Remove evaluation hooks from all lists.

Returns:	The number of removed hooks.
Return type:	int

run()¶: Run training loop.

validate_after(evaluator, epochs=None, steps=None)¶

Add an evaluation hook to run after every few epochs or steps.

Parameters:	evaluator (Evaluator or () -> any) – A evaluator object (which has `.run()`), or any callable object. epochs (None or int) – Run validation after every this few epochs. steps (None or int) – Run validation after every this few steps.
Raises:	`ValueError` – If both epochs and steps are specified, or neither is specified.

validate_after_epochs(evaluator, freq)¶

Add an evaluation hook to run after every few epochs.

Parameters:	evaluator (Evaluator or () -> any) – A evaluator object (which has `.run()`), or any callable object. freq (int) – The frequency for this evaluation hook to run.

validate_after_steps(evaluator, freq)¶

Add an evaluation hook to run after every few steps.

Parameters:	evaluator (Evaluator or () -> any) – A evaluator object (which has `.run()`), or any callable object. freq (int) – The frequency for this evaluation hook to run.