tf.contrib.distributions.Uniform.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.add(x, y, name=None) Returns x + y element-wise. NOTE: Add supports broadcasting. AddN does not. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape sample_shape(x) + sel

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.dtype

class tf.nn.rnn_cell.MultiRNNCell RNN cell composed sequentially of multiple simple cells.

tf.sparse_segment_mean(data, indices, segment_ids, name=None) Computes the mean along sparse segments of a tensor. Read the section on Segmentation for an explanation of segments. Like SegmentMean, but segment_ids can have rank less than data's first dimension, selecting a subset of dimension 0, specified by indices. Args: data: A Tensor. Must be one of the following types: float32, float64. indices: A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Has same rank as s

tf.scalar_mul(scalar, x) Multiplies a scalar times a Tensor or IndexedSlices object. Intended for use in gradient code which might deal with IndexedSlices objects, which are easy to multiply by a scalar but more expensive to multiply with arbitrary tensors. Args: scalar: A 0-D scalar Tensor. Must have known shape. x: A Tensor or IndexedSlices to be scaled. Returns: scalar * x of the same type (Tensor or IndexedSlices) as x. Raises: ValueError: if scalar is not a 0-D scalar.

tf.contrib.distributions.MultivariateNormalDiag.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.StudentTTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.distribution