tf.contrib.distributions.Uniform.dtype The DType of Tensors handled by this Distribution.

tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf') Log probability density function. 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. Raises: TypeError: if not is_continuous.

tf.nn.rnn_cell.LSTMCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [b

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.foldr(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None) foldr on the list of tensors unpacked from elems on dimension 0. This foldr operator repeatedly applies the callable fn to a sequence of elements from last to first. The elements are made of the tensors unpacked from elems. The callable fn takes two tensors as arguments. The first argument is the accumulated value computed from the preceding invocation of fn. If initializer is None, elem

tf.truediv(x, y, name=None) Divides x / y elementwise, always producing floating point results. The same as tf.div for floating point arguments, but casts integer arguments to floating point before dividing so that the result is always floating point. This op is generated by normal x / y division in Python 3 and in Python 2.7 with from __future__ import division. If you want integer division that rounds down, use x // y or tf.floordiv. x and y must have the same numeric type. If the inputs are

tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from QuantizedDistribution: For whole numbers y, P[Y = y] := P[X <= lower_cutoff], if y == lower_cutoff, := P[X > upper_cutoff - 1], y == upper_cutoff, := 0, if j < lower_cutoff or y > upper_cutoff, := P[y - 1 < X <= y], all other y. The base distribution's cdf method must be defined on

tf.contrib.crf.CrfForwardRnnCell.__call__(inputs, state, scope=None) Build the CrfForwardRnnCell. Args: inputs: A [batch_size, num_tags] matrix of unary potentials. state: A [batch_size, num_tags] matrix containing the previous alpha values. scope: Unused variable scope of this cell. Returns: new_alphas, new_alphas: A pair of [batch_size, num_tags] matrices values containing the new alpha values.

tf.contrib.distributions.Distribution.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.Exponential.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) + s