size_t tensorflow::Tensor::TotalBytes() const Returns the estimated memory usage of this tensor.

tf.contrib.distributions.Binomial.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalCholesky.is_reparameterized

tf.contrib.distributions.Binomial.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.clone(name=None, **dist_args)

tf.errors.NotFoundError.__init__(node_def, op, message) Creates a NotFoundError.

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.

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.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.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.