Module: contrib.training

Module: tf.contrib.training

Module tf.contrib.training

Defined in tensorflow/contrib/training/__init__.py.

Training and input utilities.

See Training (contrib) guide.

Classes

class FailureTolerator: Helper for tolerating certain exceptions.

class GreedyLoadBalancingStrategy: Returns the least-loaded ps task for op placement.

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

class NextQueuedSequenceBatch: NextQueuedSequenceBatch stores deferred SequenceQueueingStateSaver data.

class SequenceQueueingStateSaver: SequenceQueueingStateSaver provides access to stateful values from input.

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

class 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.

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..

python_input(...): Easily feed data from a python generator into TensorFlow queues.

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.