tf.contrib.training

 

upper level

 

Modules

 

None

 

Classes

 

FeedingQueueRunner:

A queue runner that allows the feeding of values such as numpy arrays.

GreedyLoadBalancingStrategy:

Returns the least-loaded ps task for op placement.

HParams:

Class to hold a set of hyperparameters as name-value pairs.

NextQueuedSequenceBatch:

NextQueuedSequenceBatch stores deferred SequenceQueueingStateSaver data.

RandomStrategy:

Returns a random PS task for op placement.

SequenceQueueingStateSaver:

SequenceQueueingStateSaver provides access to stateful values from input.

StopAfterNEvalsHook:

Run hook used by the evaluation routines to run the eval_ops N times.

SummaryAtEndHook:

A run hook that saves a summary with the results of evaluation.

 

Functions

 

add_gradients_summaries(…):

Add summaries to gradients.

batch_sequences_with_states(…):

Creates batches of segments of sequential input.

bucket(…):

Lazy bucketing of input tensors according to which_bucket.

bucket_by_sequence_length(…):

Lazy bucketing of inputs according to their length.

byte_size_load_fn(…):

Load function that computes the byte size of a single-output Operation.

checkpoints_iterator(…):

Continuously yield new checkpoint files as they appear.

clip_gradient_norms(…):

Clips the gradients by the given value.

clip_gradient_norms_fn(…):

Returns a transform_grads_fn function for gradient clipping.

create_train_op(…):

Creates an Operation that evaluates the gradients and returns the loss.

evaluate_once(…):

Evaluates the model at the given checkpoint path.

evaluate_repeatedly(…):

Repeatedly searches for a checkpoint in checkpoint_dir and evaluates it.

get_or_create_eval_step(…):

Gets or creates the eval step Tensor.

multiply_gradients(…):

Multiply specified gradients.

parse_values(…):

Parses hyperparameter values from a string into a python map.

rejection_sample(…):

Stochastically creates batches by rejection sampling.

resample_at_rate(…):

Given inputs tensors, stochastically resamples each at a given rate.

stratified_sample(…):

Stochastically creates batches based on per-class probabilities.

train(…):

Runs the training loop.

wait_for_new_checkpoint(…):

Waits until a new checkpoint file is found.

weighted_resample(…):

Performs an approximate weighted resampling of inputs.