tf.contrib.distributions.Chi2.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.NormalWithSoftplusSigma.sample_n(n, seed=None, name='sample_n') Generate n samples. 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 type.

tf.contrib.distributions.NormalWithSoftplusSigma.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.StudentT.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.nn.rnn_cell.GRUCell.__call__(inputs, state, scope=None) Gated recurrent unit (GRU) with nunits cells.

tf.contrib.distributions.Binomial.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.nn.rnn_cell.OutputProjectionWrapper.__init__(cell, output_size) Create a cell with output projection. Args: cell: an RNNCell, a projection to output_size is added to it. output_size: integer, the size of the output after projection. Raises: TypeError: if cell is not an RNNCell. ValueError: if output_size is not positive.

tf.contrib.distributions.Multinomial.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.