tf.contrib.learn.monitors.NanLoss.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.name

tf.contrib.graph_editor.ControlOutputs.graph

tf.contrib.training.NextQueuedSequenceBatch.batch_size The batch_size of the given batch. Usually, this is the batch_size requested when initializing the SQSS, but if allow_small_batch=True this will become smaller when inputs are exhausted. Returns: A scalar integer tensor, the batch_size

tf.contrib.distributions.InverseGamma.dtype The DType of Tensors handled by this Distribution.

tf.Session.__exit__(exec_type, exec_value, exec_tb)

tf.contrib.learn.monitors.StopAtStep.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

void tensorflow::Tensor::AsProtoField(TensorProto *proto) const Fills in proto with *this tensor's content. AsProtoField() fills in the repeated field for proto.dtype(), while AsProtoTensorContent() encodes the content in proto.tensor_content() in a compact form.

tf.contrib.distributions.Normal.dtype The DType of Tensors handled by this Distribution.

tf.contrib.distributions.Beta.a Shape parameter.