tf.contrib.distributions.Beta.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.mul(x, y, name=None) Returns x * y element-wise. NOTE: Mul supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128. 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.ExportMonitor.export_dir

tf.contrib.graph_editor.reroute_b2a_inputs(sgv0, sgv1) Re-route all the inputs of sgv1 to sgv0 (see reroute_inputs).

class tf.nn.rnn_cell.InputProjectionWrapper Operator adding an input projection to the given cell. Note: in many cases it may be more efficient to not use this wrapper, but instead concatenate the whole sequence of your inputs in time, do the projection on this batch-concatenated sequence, then split it.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

Tensor& tensorflow::Tensor::operator=(const Tensor &other) Assign operator. This tensor shares other's underlying storage.

tf.contrib.rnn.GRUBlockCell.output_size

tf.FixedLenSequenceFeature.__getnewargs__() Return self as a plain tuple. Used by copy and pickle.