tf.rsqrt(x, name=None) Computes reciprocal of square root of x element-wise. I.e., \(y = 1 / \sqrt{x}\). Args: x: A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.reduce_prod(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the product of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is retur

tf.reduce_sum(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the sum of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is returned.

tf.reduce_mean(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the mean of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is returned

tf.reduce_min(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the minimum of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is return

tf.reduce_any(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the "logical or" of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is r

tf.reduce_max(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the maximum of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is return

tf.reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes log(sum(exp(elements across dimensions of a tensor))). Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is

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

tf.reduce_all(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the "logical and" of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is