tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.mean(name='mean') Mean. Additional documentation from StudentT: The mean of Student's T equals mu if df > 1, otherwise it is NaN. If self.allow_nan_stats=True, then an exception will be raised rather than returning NaN.

tf.contrib.learn.TensorFlowEstimator.get_variable_names() Returns list of all variable names in this model. Returns: List of names.

tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob') 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: prob: a Tensor of sh

tf.nn.rnn_cell.BasicRNNCell.state_size

tf.contrib.framework.VariableDeviceChooser.__call__(op)

tf.contrib.distributions.Chi2.__init__(df, validate_args=False, allow_nan_stats=True, name='Chi2') Construct Chi2 distributions with parameter df. Args: df: Floating point tensor, the degrees of freedom of the distribution(s). df must contain only positive values. validate_args: Boolean, default False. Whether to assert that df > 0, and that x > 0 in the methods prob(x) and log_prob(x). If validate_args is False and the inputs are invalid, correct behavior is not guaranteed. allow_nan

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.framework.get_variables_by_name(given_name, scope=None) Gets the list of variables that were given that name. Args: given_name: name given to the variable without any scope. scope: an optional scope for filtering the variables to return. Returns: a copied list of variables with the given name and scope.

tf.ReaderBase.num_records_produced(name=None) Returns the number of records this reader has produced. This is the same as the number of Read executions that have succeeded. Args: name: A name for the operation (optional). Returns: An int64 Tensor.