tf.contrib.distributions.Multinomial.mean(name='mean') Mean.

tf.image.resize_area(images, size, align_corners=None, name=None) Resize images to size using area interpolation. Input images can be of different types but output images are always float. Args: images: A Tensor. Must be one of the following types: uint8, int8, int16, int32, int64, half, float32, float64. 4-D with shape [batch, height, width, channels]. size: A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images. align_corners: An optional bool. Defaults to Fa

tf.contrib.distributions.Beta.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.distributions.MultivariateNormalCholesky.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.bayesflow.stochastic_tensor.Chi2Tensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.value_type

tf.contrib.distributions.Binomial.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.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.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.dtype