tf.contrib.distributions.Categorical.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.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.declare_inputs(unused_stochastic_tensor, unused_inputs_dict)

string tensorflow::PartialTensorShapeUtils::PartialShapeListString(const gtl::ArraySlice< PartialTensorShape > &shapes)

tf.contrib.graph_editor.transform_op_if_inside_handler(info, op, keep_if_possible=True) Transform an optional op only if it is inside the subgraph. This handler is typically use to handle original op: it is fine to keep them if they are inside the subgraph, otherwise they are just ignored. Args: info: Transform._Info instance. op: the optional op to transform (or ignore). keep_if_possible: re-attach to the original op if possible, that is, if the source graph and the destination graph are t

tf.contrib.distributions.TransformedDistribution.sample_n(n, seed=None, name='sample_n') Generate n samples. Additional documentation from TransformedDistribution: Samples from the base distribution and then passes through the transform. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer

tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None) Outputs random values from a normal distribution. Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. mean: A 0-D Tensor or Python value of type dtype. The mean of the normal distribution. stddev: A 0-D Tensor or Python value of type dtype. The standard deviation of the normal distribution. dtype: The type of the output. seed: A Python integer. Used to create a random seed

tf.contrib.distributions.Chi2WithAbsDf.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.contrib.learn.monitors.LoggingTrainable.end(session=None)

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.distribution

tf.contrib.framework.convert_to_tensor_or_sparse_tensor(value, dtype=None, name=None, as_ref=False) Converts value to a SparseTensor or Tensor. Args: value: A SparseTensor, SparseTensorValue, or an object whose type has a registered Tensor conversion function. dtype: Optional element type for the returned tensor. If missing, the type is inferred from the type of value. name: Optional name to use if a new Tensor is created. as_ref: True if we want the result as a ref tensor. Only used if a