tf.nn.rnn_cell.GRUCell.output_size

tf.contrib.learn.TensorFlowRNNClassifier.get_variable_names() Returns list of all variable names in this model. Returns: List of names.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.variance(name='variance') Variance. Additional documentation from StudentT: The variance for Student's T equals df / (df - 2), when df > 2 infinity, when 1 < df <= 2 NaN, when df <= 1

tf.contrib.distributions.Dirichlet.is_reparameterized

tf.contrib.distributions.LaplaceWithSoftplusScale.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

return tensorflow::Status::OK()

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.clone(name=None, **dist_args)

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

tf.svd(tensor, compute_uv=True, full_matrices=False, name=None) Computes the singular value decompositions of one or more matrices. Computes the SVD of each inner matrix in tensor such that tensor[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :]) # a is a tensor. # s is a tensor of singular values. # u is a tensor of left singular vectors. # v is a tensor of right singular vectors. s, u, v = svd(a) s = svd(a, compute_uv=False) Args: matrix: Tensor of shape [..., M, N].