tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.n

tf.contrib.framework.assign_from_values_fn(var_names_to_values) Returns a function that assigns specific variables from the given values. This function provides a mechanism for performing assignment of variables to values in a way that does not fill the graph with large assignment values. Args: var_names_to_values: A map from variable names to values. Returns: A function that takes a single argument, a tf.Session, that applies the assignment operation. Raises: ValueError: if any of the giv

virtual uint64 tensorflow::Env::NowSeconds() Returns the number of seconds since some fixed point in time. Only useful for computing deltas of time.

tf.WholeFileReader.serialize_state(name=None) Produce a string tensor that encodes the state of a reader. Not all Readers support being serialized, so this can produce an Unimplemented error. Args: name: A name for the operation (optional). Returns: A string Tensor.

int64 tensorflow::PartialTensorShape::dim_size(int d) const Returns the number of elements in dimension d. REQUIRES: 0 <= d < dims()

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

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.input_dict

tf.contrib.distributions.Poisson.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.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.graph