tf.contrib.distributions.Normal.variance(name='variance') Variance.

tf.contrib.distributions.Uniform.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is u

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.graph

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.Poisson.__init__(lam, validate_args=False, allow_nan_stats=True, name='Poisson') Construct Poisson distributions. Args: lam: Floating point tensor, the rate parameter of the distribution(s). lam must be positive. validate_args: Boolean, default False. Whether to assert that lam > 0 as well as inputs to pmf computations are non-negative integers. If validate_args is False, then pmf computations might return NaN, but can be evaluated at any real value. allow_nan_st

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

tf.contrib.graph_editor.make_list_of_t(ts, check_graph=True, allow_graph=True, ignore_ops=False) Convert ts to a list of tf.Tensor. Args: ts: can be an iterable of tf.Tensor, a tf.Graph or a single tensor. check_graph: if True check if all the tensors belong to the same graph. allow_graph: if False a tf.Graph cannot be converted. ignore_ops: if True, silently ignore tf.Operation. Returns: A newly created list of tf.Tensor. Raises: TypeError: if ts cannot be converted to a list of tf.Ten

tf.contrib.graph_editor.filter_ops_from_regex(ops, regex) Get all the operations that match the given regex. Args: ops: an object convertible to a list of tf.Operation. regex: a regular expression matching the operation's name. For example, "^foo(/.*)?$" will match all the operations in the "foo" scope. Returns: A list of tf.Operation. Raises: TypeError: if ops cannot be converted to a list of tf.Operation.

tf.contrib.rnn.GridLSTMCell.__call__(inputs, state, scope=None) Run one step of LSTM. Args: inputs: input Tensor, 2D, batch x num_units. state: state Tensor, 2D, batch x state_size. scope: VariableScope for the created subgraph; defaults to "LSTMCell". Returns: A tuple containing: - A 2D, batch x output_dim, Tensor representing the output of the LSTM after reading "inputs" when previous state was "state". Here output_dim is num_units. - A 2D, batch x state_size, Tensor representing the ne

tf.contrib.rnn.TimeFreqLSTMCell.__call__(inputs, state, scope=None) Run one step of LSTM. Args: inputs: input Tensor, 2D, batch x num_units. state: state Tensor, 2D, batch x state_size. scope: VariableScope for the created subgraph; defaults to "TimeFreqLSTMCell". Returns: A tuple containing: - A 2D, batch x output_dim, Tensor representing the output of the LSTM after reading "inputs" when previous state was "state". Here output_dim is num_units. - A 2D, batch x state_size, Tensor represe