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

tf.Session.run(fetches, feed_dict=None, options=None, run_metadata=None) Runs operations and evaluates tensors in fetches. This method runs one "step" of TensorFlow computation, by running the necessary graph fragment to execute every Operation and evaluate every Tensor in fetches, substituting the values in feed_dict for the corresponding input values. The fetches argument may be a single graph element, or an arbitrarily nested list, tuple, namedtuple, or dict containing graph elements at its

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

tf.TensorArray.dtype The data type of this TensorArray.

class tf.errors.ResourceExhaustedError Some resource has been exhausted. For example, this error might be raised if a per-user quota is exhausted, or perhaps the entire file system is out of space.

tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.mean(name='mean')

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

tf.TensorArray.grad(source, flow=None, name=None)

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.entropy(name='entropy')