tf.self_adjoint_eigvals(tensor, name=None) Computes the eigenvalues of one or more self-adjoint matrices. Args: tensor: Tensor of shape [..., N, N]. name: string, optional name of the operation. Returns: e: Eigenvalues. Shape is [..., N]. The vector e[..., :] contains the N eigenvalues of tensor[..., :, :].

tf.self_adjoint_eig(tensor, name=None) Computes the eigen decomposition of a batch of self-adjoint matrices. Computes the eigenvalues and eigenvectors of the innermost N-by-N matrices in tensor such that tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i], for i=0...N-1. Args: tensor: Tensor of shape [..., N, N]. Only the lower triangular part of each inner inner matrix is referenced. name: string, optional name of the operation. Returns: e: Eigenvalues. Shape is [..., N]. v: Eigenvec

tf.segment_sum(data, segment_ids, name=None) Computes the sum along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that \(output_i = \sum_j data_j\) where sum is over j such that segment_ids[j] == i. Args: data: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. segment_ids: A Tensor. Must be one of the following types: in

tf.segment_prod(data, segment_ids, name=None) Computes the product along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that \(output_i = \prod_j data_j\) where the product is over j such that segment_ids[j] == i. Args: data: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. segment_ids: A Tensor. Must be one of the follo

tf.segment_min(data, segment_ids, name=None) Computes the minimum along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that \(output_i = \min_j(data_j)\) where min is over j such that segment_ids[j] == i. Args: data: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half. segment_ids: A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose rank is eq

tf.segment_mean(data, segment_ids, name=None) Computes the mean along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that \(output_i = \frac{\sum_j data_j}{N}\) where mean is over j such that segment_ids[j] == i and N is the total number of values summed. Args: data: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half. segment_ids: A Tensor. Must be one of the following

tf.segment_max(data, segment_ids, name=None) Computes the maximum along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that \(output_i = \max_j(data_j)\) where max is over j such that segment_ids[j] == i. Args: data: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half. segment_ids: A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose rank is eq

tf.scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, infer_shape=True, name=None) scan on the list of tensors unpacked from elems on dimension 0. The simplest version of scan repeatedly applies the callable fn to a sequence of elements from first to last. The elements are made of the tensors unpacked from elems on dimension 0. The callable fn takes two tensors as arguments. The first argument is the accumulated value computed from the preceding invoca

tf.scalar_mul(scalar, x) Multiplies a scalar times a Tensor or IndexedSlices object. Intended for use in gradient code which might deal with IndexedSlices objects, which are easy to multiply by a scalar but more expensive to multiply with arbitrary tensors. Args: scalar: A 0-D scalar Tensor. Must have known shape. x: A Tensor or IndexedSlices to be scaled. Returns: scalar * x of the same type (Tensor or IndexedSlices) as x. Raises: ValueError: if scalar is not a 0-D scalar.

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.