tf.contrib.graph_editor.reroute_a2b(sgv0, sgv1) Re-route the inputs and outputs of sgv0 to sgv1 (see _reroute).

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.

TTypes< T, NDIMS >::ConstTensor tensorflow::Tensor::flat_inner_dims() const

class tf.OpError A generic error that is raised when TensorFlow execution fails. Whenever possible, the session will raise a more specific subclass of OpError from the tf.errors module.

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

tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from _MultivariateNormalOperatorPD: x is a batch vector with compatible shape if x is a Tensor whose shape can be broadcast up to either: self.batch_shape + self.event_shape or [M1,...,Mm] + self.batch_shape + self.event_shape Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a

tf.nn.rnn_cell.BasicRNNCell.__init__(num_units, input_size=None, activation=tanh)

tf.parse_example(serialized, features, name=None, example_names=None) Parses Example protos into a dict of tensors. Parses a number of serialized Example protos given in serialized. example_names may contain descriptive names for the corresponding serialized protos. These may be useful for debugging purposes, but they have no effect on the output. If not None, example_names must be the same length as serialized. This op parses serialized examples into a dictionary mapping keys to Tensor and Sp

tf.contrib.learn.monitors.RunHookAdapterForMonitors.after_run(run_context, run_values)

tf.contrib.learn.monitors.CaptureVariable.every_n_step_begin(step)