tf.contrib.distributions.Chi2.alpha Shape parameter.

tf.contrib.learn.monitors.GraphDump.data

tf.set_random_seed(seed) Sets the graph-level random seed. Operations that rely on a random seed actually derive it from two seeds: the graph-level and operation-level seeds. This sets the graph-level seed. Its interactions with operation-level seeds is as follows: If neither the graph-level nor the operation seed is set: A random seed is used for this op. If the graph-level seed is set, but the operation seed is not: The system deterministically picks an operation seed in conjunction with the

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.MeanValue.popped_above(unused_value_type)

tf.contrib.distributions.Bernoulli.name Name prepended to all ops created by this Distribution.

tf.contrib.learn.BaseEstimator.get_variable_value(name) Returns value of the variable given by name. Args: name: string, name of the tensor. Returns: Numpy array - value of the tensor.

tf.contrib.framework.with_same_shape(expected_tensor, tensor) Assert tensors are the same shape, from the same graph. Args: expected_tensor: Tensor with expected shape. tensor: Tensor of actual values. Returns: Tuple of (actual_tensor, label_tensor), possibly with assert ops added.

tf.contrib.distributions.StudentT.mu Locations of these Student's t distribution(s).

tf.contrib.distributions.DirichletMultinomial.dtype The DType of Tensors handled by this Distribution.