tf.contrib.distributions.QuantizedDistribution.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.nn.rnn_cell.RNNCell.state_size size(s) of state(s) used by this cell. It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.

tf.contrib.learn.monitors.GraphDump.__init__(ignore_ops=None) Initializes GraphDump monitor. Args: ignore_ops: list of string. Names of ops to ignore. If None, GraphDump.IGNORE_OPS is used.

tf.contrib.learn.monitors.GraphDump.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.acos(x, name=None) Computes acos of x element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.edit_distance(hypothesis, truth, normalize=True, name='edit_distance') Computes the Levenshtein distance between sequences. This operation takes variable-length sequences (hypothesis and truth), each provided as a SparseTensor, and computes the Levenshtein distance. You can normalize the edit distance by length of truth by setting normalize to true. For example, given the following input: # 'hypothesis' is a tensor of shape `[2, 1]` with variable-length values: # (0,0) = ["a"] # (1,0) =

tf.igammac(a, x, name=None) Compute the upper regularized incomplete Gamma function Q(a, x). The upper regularized incomplete Gamma function is defined as: Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x) where Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt is the upper incomplete Gama function. Note, above P(a, x) (Igamma) is the lower regularized complete Gamma function. Args: a: A Tensor. Must be one of the following types: float32, float64. x: A Tensor. Must have the same type as a. n

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

tf.OpError.op The operation that failed, if known. N.B. If the failed op was synthesized at runtime, e.g. a Send or Recv op, there will be no corresponding Operation object. In that case, this will return None, and you should instead use the OpError.node_def to discover information about the op. Returns: The Operation that failed, or None.

tf.mod(x, y, name=None) Returns element-wise remainder of division. NOTE: Mod supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: int32, int64, float32, float64. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.