tf.matrix_solve(matrix, rhs, adjoint=None, name=None) Solves systems of linear equations. Matrix is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. Rhs is a tensor of shape [..., M, K]. The output is a tensor shape [..., M, K]. If adjoint is False then each output matrix satisfies matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]. If adjoint is True then each output matrix satisfies adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]. Args: m

tf.matrix_set_diag(input, diagonal, name=None) Returns a batched matrix tensor with new batched diagonal values. Given input and diagonal, this operation returns a tensor with the same shape and values as input, except for the diagonals of the innermost matrices. These will be overwritten by the values in diagonal. The batched matrices must be square. The output is computed as follows: Assume input has k+1 dimensions [I, J, K, ..., N, N] and diagonal has k dimensions [I, J, K, ..., N]. Then th

tf.matrix_inverse(input, adjoint=None, name=None) Computes the inverse of one or more square invertible matrices or their adjoints (conjugate transposes). The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. The output is a tensor of the same shape as the input containing the inverse for all input submatrices [..., :, :]. The op uses LU decomposition with partial pivoting to compute the inverses. If a matrix is not invertible there is no guarantee what

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,

tf.matrix_diag(diagonal, name=None) Returns a batched diagonal tensor with a given batched diagonal values. Given a diagonal, this operation returns a tensor with the diagonal and everything else padded with zeros. The diagonal is computed as follows: Assume diagonal has k dimensions [I, J, K, ..., N], then the output is a tensor of rank k+1 with dimensions [I, J, K, ..., N, N]` where: output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]. For example: # 'diagonal' is [[1, 2, 3, 4],

tf.matrix_determinant(input, name=None) Computes the determinant of one ore more square matrices. The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. The output is a tensor containing the determinants for all input submatrices [..., :, :]. Args: input: A Tensor. Must be one of the following types: float32, float64. Shape is [..., M, M]. name: A name for the operation (optional). Returns: A Tensor. Has the same type as input. Shape is [...].

tf.matrix_band_part(input, num_lower, num_upper, name=None) Copy a tensor setting everything outside a central band in each innermost matrix to zero. The band part is computed as follows: Assume input has k dimensions [I, J, K, ..., M, N], then the output is a tensor with the same shape where band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]. The indicator function 'in_band(m, n)is one if(num_lower < 0 || (m-n) <= num_lower)) && (num_upper < 0 || (n-m) <=

tf.matmul(a, b, transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=False, name=None) Multiplies matrix a by matrix b, producing a * b. The inputs must be two-dimensional matrices, with matching inner dimensions, possibly after transposition. Both matrices must be of the same type. The supported types are: float32, float64, int32, complex64. Either matrix can be transposed on the fly by setting the corresponding flag to True. This is False by default. If one or both of the mat

tf.matching_files(pattern, name=None) Returns the set of files matching a pattern. Note that this routine only supports wildcard characters in the basename portion of the pattern, not in the directory portion. Args: pattern: A Tensor of type string. A (scalar) shell wildcard pattern. name: A name for the operation (optional). Returns: A Tensor of type string. A vector of matching filenames.

tf.map_fn(fn, elems, dtype=None, parallel_iterations=10, back_prop=True, swap_memory=False, infer_shape=True, name=None) map on the list of tensors unpacked from elems on dimension 0. The simplest version of map repeatedly applies the callable fn to a sequence of elements from first to last. The elements are made of the tensors unpacked from elems. dtype is the data type of the return value of fn. Users must provide dtype if it is different from the data type of elems. Suppose that elems is un