tf.contrib.distributions.Mixture.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.QuantizedDistribution.base_distribution Base distribution, p(x).

tf.where(input, name=None) Returns locations of true values in a boolean tensor. This operation returns the coordinates of true elements in input. The coordinates are returned in a 2-D tensor where the first dimension (rows) represents the number of true elements, and the second dimension (columns) represents the coordinates of the true elements. Keep in mind, the shape of the output tensor can vary depending on how many true values there are in input. Indices are output in row-major order. Fo

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.distribution

tf.contrib.learn.monitors.PrintTensor.step_begin(step) Overrides BaseMonitor.step_begin. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. Returns: A list, the result of every_n_step_begin, if that was called this step, or an empty list otherwise. Raises: ValueError: if called more than once during a step.

tf.image.adjust_saturation(image, saturation_factor, name=None) Adjust saturation of an RGB image. This is a convenience method that converts an RGB image to float representation, converts it to HSV, add an offset to the saturation channel, converts back to RGB and then back to the original data type. If several adjustments are chained it is advisable to minimize the number of redundant conversions. image is an RGB image. The image saturation is adjusted by converting the image to HSV and mult

class tf.contrib.distributions.MultivariateNormalFull The multivariate normal distribution on R^k. This distribution is defined by a 1-D mean mu and covariance matrix sigma. Evaluation of the pdf, determinant, and sampling are all O(k^3) operations.

tf.matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None) Solves one or more linear least-squares problems. matrix is a tensor of shape [..., M, N] whose inner-most 2 dimensions form M-by-N matrices. Rhs is a tensor of shape [..., M, K] whose inner-most 2 dimensions form M-by-K matrices. The computed output is a Tensor of shape [..., N, K] whose inner-most 2 dimensions form M-by-K matrices that solve the equations matrix[..., :, :] * output[..., :, :] = rhs[..., :, :] in the le

tf.nn.rnn_cell.RNNCell.__call__(inputs, state, scope=None) Run this RNN cell on inputs, starting from the given state. Args: inputs: 2-D tensor with shape [batch_size x input_size]. state: if self.state_size is an integer, this should be a 2-D Tensor with shape [batch_size x self.state_size]. Otherwise, if self.state_size is a tuple of integers, this should be a tuple with shapes [batch_size x s] for s in self.state_size. scope: VariableScope for the created subgraph; defaults to class name

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.parameters Dictionary of parameters used by this Distribution.