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.reduce_join(inputs, reduction_indices, keep_dims=None, separator=None, name=None) Joins a string Tensor across the given dimensions. Computes the string join across dimensions in the given string Tensor of shape [d_0, d_1, ..., d_n-1]. Returns a new Tensor created by joining the input strings with the given separator (default: empty string). Negative indices are counted backwards from the end, with -1 being equivalent to n - 1. Passing an empty reduction_indices joins all strings in linear

tensorflow::PartialTensorShape::PartialTensorShape() Construct an unknown PartialTensorShape.

tf.contrib.layers.layer_norm(*args, **kwargs) Adds a Layer Normalization layer from https://arxiv.org/abs/1607.06450. "Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton Can be used as a normalizer function for conv2d and fully_connected. Args: inputs: a tensor with 2 or more dimensions. The normalization occurs over all but the first dimension. center: If True, subtract beta. If False, beta is ignored. scale: If True, multiply by gamma. If False, gamma is not used. Whe

tf.contrib.distributions.LaplaceWithSoftplusScale.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.sparse_reduce_sum_sparse(sp_input, reduction_axes=None, keep_dims=False) Computes the sum of elements across dimensions of a SparseTensor. This Op takes a SparseTensor and is the sparse counterpart to tf.reduce_sum(). In contrast to SparseReduceSum, this Op returns a SparseTensor. Reduces sp_input along the dimensions given in reduction_axes. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_axes. If keep_dims is true, the reduced dimensions are re

tf.test.compute_gradient_error(x, x_shape, y, y_shape, x_init_value=None, delta=0.001, init_targets=None) Computes the gradient error. Computes the maximum error for dy/dx between the computed Jacobian and the numerically estimated Jacobian. This function will modify the tensors passed in as it adds more operations and hence changing the consumers of the operations of the input tensors. This function adds operations to the current session. To compute the error using a particular device, such a

tf.contrib.distributions.WishartFull.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.VarLenFeature.__new__(_cls, dtype) Create new instance of VarLenFeature(dtype,)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.mean(name='mean')