tf.contrib.learn.monitors.ExportMonitor.epoch_end(epoch) End epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've not begun an epoch, or epoch number does not match.

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.value_type

tf.contrib.learn.run_n(output_dict, feed_dict=None, restore_checkpoint_path=None, n=1) Run output_dict tensors n times, with the same feed_dict each run. Args: output_dict: A dict mapping string names to tensors to run. Must all be from the same graph. feed_dict: dict of input values to feed each run. restore_checkpoint_path: A string containing the path to a checkpoint to restore. n: Number of times to repeat. Returns: A list of n dict objects, each containing values read from output_di

void tensorflow::EnvWrapper::SleepForMicroseconds(int64 micros) override Sleeps/delays the thread for the prescribed number of micro-seconds.

uint64 tensorflow::EnvWrapper::NowMicros() override Returns the number of micro-seconds since some fixed point in time. Only useful for computing deltas of time.

tf.nn.rnn_cell.MultiRNNCell.__call__(inputs, state, scope=None) Run this multi-layer cell on inputs, starting from state.

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.loss(final_loss, name='Loss')

tf.train.string_input_producer(string_tensor, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None) Output strings (e.g. filenames) to a queue for an input pipeline. Args: string_tensor: A 1-D string tensor with the strings to produce. num_epochs: An integer (optional). If specified, string_input_producer produces each string from string_tensor num_epochs times before generating an OutOfRange error. If not specified, string_input_producer can cycle through the s

tf.parse_single_example(serialized, features, name=None, example_names=None) Parses a single Example proto. Similar to parse_example, except: For dense tensors, the returned Tensor is identical to the output of parse_example, except there is no batch dimension, the output shape is the same as the shape given in dense_shape. For SparseTensors, the first (batch) column of the indices matrix is removed (the indices matrix is a column vector), the values vector is unchanged, and the first (batch_s

tf.contrib.distributions.Chi2WithAbsDf.mode(name='mode') Mode. Additional documentation from Gamma: The mode of a gamma distribution is (alpha - 1) / beta when alpha > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.