tf.InteractiveSession.close() Closes an InteractiveSession.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.is_reparameterized

tf.TextLineReader.num_records_produced(name=None) Returns the number of records this reader has produced. This is the same as the number of Read executions that have succeeded. Args: name: A name for the operation (optional). Returns: An int64 Tensor.

tf.contrib.distributions.MultivariateNormalCholesky.mean(name='mean') Mean.

tf.contrib.training.bucket(tensors, which_bucket, batch_size, num_buckets, num_threads=1, capacity=32, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, keep_input=None, shared_name=None, name=None) Lazy bucketing of input tensors according to which_bucket. The argument tensors can be a list or a dictionary of tensors. The value returned by the function will be of the same type as tensors. The tensors entering this function are put into the bucket given by which_bucket. Each buc

tf.contrib.distributions.Mixture.mean(name='mean') Mean.

tf.contrib.learn.monitors.NanLoss.__init__(loss_tensor, every_n_steps=100, fail_on_nan_loss=True) Initializes NanLoss monitor. Args: loss_tensor: Tensor, the loss tensor. every_n_steps: int, run check every this many steps. fail_on_nan_loss: bool, whether to raise exception when loss is NaN.

int64 tensorflow::TensorShapeDim::size

tf.contrib.distributions.WishartFull.is_reparameterized

tf.contrib.learn.DNNClassifier.config