tf.floordiv(x, y, name=None) Divides x / y elementwise, rounding down for floating point. The same as tf.div(x,y) for integers, but uses tf.floor(tf.div(x,y)) for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y floor division in Python 3 and in Python 2.7 with from __future__ import division. Note that for efficiency, floordiv uses C semantics for negative numbers (unlike Python and Nump

tf.contrib.distributions.QuantizedDistribution.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T f

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

tf.sparse_softmax(sp_input, name=None) Applies softmax to a batched N-D SparseTensor. The inputs represent an N-D SparseTensor with logical shape [..., B, C] (where N >= 2), and with indices sorted in the canonical lexicographic order. This op is equivalent to applying the normal tf.nn.softmax() to each innermost logical submatrix with shape [B, C], but with the catch that the implicitly zero elements do not participate. Specifically, the algorithm is equivalent to: (1) Applies tf.nn.softma

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.value(name='value')

tf.assert_type(tensor, tf_type, message=None, name=None) Statically asserts that the given Tensor is of the specified type. Args: tensor: A tensorflow Tensor. tf_type: A tensorflow type (dtypes.float32, tf.int64, dtypes.bool, etc). message: A string to prefix to the default message. name: A name to give this Op. Defaults to "assert_type" Raises: TypeError: If the tensors data type doesn't match tf_type. Returns: A no_op that does nothing. Type can be determined statically.

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

tf.cholesky(input, name=None) Computes the Cholesky decomposition of one or more square matrices. The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices, with the same constraints as the single matrix Cholesky decomposition above. The output is a tensor of the same shape as the input containing the Cholesky decompositions for all input submatrices [..., :, :]. Args: input: A Tensor. Must be one of the following types: float64, float32. Shape is [..., M,

tf.matrix_diag_part(input, name=None) Returns the batched diagonal part of a batched tensor. This operation returns a tensor with the diagonal part of the batched input. The diagonal part is computed as follows: Assume input has k dimensions [I, J, K, ..., N, N], then the output is a tensor of rank k - 1 with dimensions [I, J, K, ..., N] where: diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]. The input must be at least a matrix. For example: # 'input' is [[[1, 0, 0, 0] [0,

const string& tensorflow::Status::error_message() const