tf.contrib.learn.RunConfig.is_chief

class tf.contrib.learn.RunConfig This class specifies the specific configurations for the run. If you're a Google-internal user using command line flags with learn_runner.py (for instance, to do distributed training or to use parameter servers), you probably want to use learn_runner.EstimatorConfig instead.

tf.contrib.learn.read_batch_record_features(file_pattern, batch_size, features, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name='dequeue_record_examples') Reads TFRecord, queues, batches and parses Example proto. See more detailed description in read_examples. Args: file_pattern: List of files or pattern of file paths containing Example records. See tf.gfile.Glob for pattern rules. batch_size: An int or scalar Tensor specifying th

tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, feature_queue_capacity=100, reader_num_threads=1, parser_num_threads=1, parse_fn=None, name=None) Adds operations to read, queue, batch and parse Example protos. Given file pattern (or list of files), will setup a queue for file names, read Example proto using provided reader, use batch queue to create batches of examples of size batch_size and parse exa

tf.contrib.learn.read_batch_examples(file_pattern, batch_size, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, num_threads=1, read_batch_size=1, parse_fn=None, name=None) Adds operations to read, queue, batch Example protos. Given file pattern (or list of files), will setup a queue for file names, read Example proto using provided reader, use batch queue to create batches of examples of size batch_size. All queue runners are added to the queue runners collection, and may b

tf.contrib.learn.NanLossDuringTrainingError.__str__()

class tf.contrib.learn.NanLossDuringTrainingError

tf.contrib.learn.monitors.ValidationMonitor.__init__(x=None, y=None, input_fn=None, batch_size=None, eval_steps=None, every_n_steps=100, metrics=None, early_stopping_rounds=None, early_stopping_metric='loss', early_stopping_metric_minimize=True, name=None) Initializes a ValidationMonitor. Args: x: See BaseEstimator.evaluate. y: See BaseEstimator.evaluate. input_fn: See BaseEstimator.evaluate. batch_size: See BaseEstimator.evaluate. eval_steps: See BaseEstimator.evaluate. every_n_steps: C

tf.contrib.learn.monitors.ValidationMonitor.step_end(step, output) Overrides BaseMonitor.step_end. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. output: dict mapping string values representing tensor names to the value resulted from running these tensors. Values may be either scalars, for scalar tensors, or Numpy array, for non-scalar tensors. Returns: bool, the result of every_n_step_end, if that was called this

tf.contrib.learn.monitors.ValidationMonitor.step_begin(step) Overrides BaseMonitor.step_begin. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. Returns: A list, the result of every_n_step_begin, if that was called this step, or an empty list otherwise. Raises: ValueError: if called more than once during a step.