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diff --git a/tensorflow/docs_src/api_guides/python/math_ops.md b/tensorflow/docs_src/api_guides/python/math_ops.md deleted file mode 100644 index 6ec18f48ef..0000000000 --- a/tensorflow/docs_src/api_guides/python/math_ops.md +++ /dev/null @@ -1,200 +0,0 @@ -# Math - -Note: Functions taking `Tensor` arguments can also take anything accepted by -`tf.convert_to_tensor`. - -[TOC] - -Note: Elementwise binary operations in TensorFlow follow [numpy-style -broadcasting](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html). - -## Arithmetic Operators - -TensorFlow provides several operations that you can use to add basic arithmetic -operators to your graph. - -* `tf.add` -* `tf.subtract` -* `tf.multiply` -* `tf.scalar_mul` -* `tf.div` -* `tf.divide` -* `tf.truediv` -* `tf.floordiv` -* `tf.realdiv` -* `tf.truncatediv` -* `tf.floor_div` -* `tf.div_no_nan` -* `tf.truncatemod` -* `tf.floormod` -* `tf.mod` -* `tf.cross` - -## Basic Math Functions - -TensorFlow provides several operations that you can use to add basic -mathematical functions to your graph. - -* `tf.add_n` -* `tf.abs` -* `tf.negative` -* `tf.sign` -* `tf.reciprocal` -* `tf.square` -* `tf.round` -* `tf.sqrt` -* `tf.rsqrt` -* `tf.pow` -* `tf.exp` -* `tf.expm1` -* `tf.log` -* `tf.log1p` -* `tf.ceil` -* `tf.floor` -* `tf.maximum` -* `tf.minimum` -* `tf.cos` -* `tf.sin` -* `tf.lbeta` -* `tf.tan` -* `tf.acos` -* `tf.asin` -* `tf.atan` -* `tf.cosh` -* `tf.sinh` -* `tf.asinh` -* `tf.acosh` -* `tf.atanh` -* `tf.lgamma` -* `tf.digamma` -* `tf.erf` -* `tf.erfc` -* `tf.squared_difference` -* `tf.igamma` -* `tf.igammac` -* `tf.zeta` -* `tf.polygamma` -* `tf.betainc` -* `tf.rint` - -## Matrix Math Functions - -TensorFlow provides several operations that you can use to add linear algebra -functions on matrices to your graph. - -* `tf.diag` -* `tf.diag_part` -* `tf.trace` -* `tf.transpose` -* `tf.eye` -* `tf.matrix_diag` -* `tf.matrix_diag_part` -* `tf.matrix_band_part` -* `tf.matrix_set_diag` -* `tf.matrix_transpose` -* `tf.matmul` -* `tf.norm` -* `tf.matrix_determinant` -* `tf.matrix_inverse` -* `tf.cholesky` -* `tf.cholesky_solve` -* `tf.matrix_solve` -* `tf.matrix_triangular_solve` -* `tf.matrix_solve_ls` -* `tf.qr` -* `tf.self_adjoint_eig` -* `tf.self_adjoint_eigvals` -* `tf.svd` - - -## Tensor Math Function - -TensorFlow provides operations that you can use to add tensor functions to your -graph. - -* `tf.tensordot` - - -## Complex Number Functions - -TensorFlow provides several operations that you can use to add complex number -functions to your graph. - -* `tf.complex` -* `tf.conj` -* `tf.imag` -* `tf.angle` -* `tf.real` - - -## Reduction - -TensorFlow provides several operations that you can use to perform -common math computations that reduce various dimensions of a tensor. - -* `tf.reduce_sum` -* `tf.reduce_prod` -* `tf.reduce_min` -* `tf.reduce_max` -* `tf.reduce_mean` -* `tf.reduce_all` -* `tf.reduce_any` -* `tf.reduce_logsumexp` -* `tf.count_nonzero` -* `tf.accumulate_n` -* `tf.einsum` - -## Scan - -TensorFlow provides several operations that you can use to perform scans -(running totals) across one axis of a tensor. - -* `tf.cumsum` -* `tf.cumprod` - -## Segmentation - -TensorFlow provides several operations that you can use to perform common -math computations on tensor segments. -Here a segmentation is a partitioning of a tensor along -the first dimension, i.e. it defines a mapping from the first dimension onto -`segment_ids`. The `segment_ids` tensor should be the size of -the first dimension, `d0`, with consecutive IDs in the range `0` to `k`, -where `k<d0`. -In particular, a segmentation of a matrix tensor is a mapping of rows to -segments. - -For example: - -```python -c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]) -tf.segment_sum(c, tf.constant([0, 0, 1])) - ==> [[0 0 0 0] - [5 6 7 8]] -``` - -* `tf.segment_sum` -* `tf.segment_prod` -* `tf.segment_min` -* `tf.segment_max` -* `tf.segment_mean` -* `tf.unsorted_segment_sum` -* `tf.sparse_segment_sum` -* `tf.sparse_segment_mean` -* `tf.sparse_segment_sqrt_n` - - -## Sequence Comparison and Indexing - -TensorFlow provides several operations that you can use to add sequence -comparison and index extraction to your graph. You can use these operations to -determine sequence differences and determine the indexes of specific values in -a tensor. - -* `tf.argmin` -* `tf.argmax` -* `tf.setdiff1d` -* `tf.where` -* `tf.unique` -* `tf.edit_distance` -* `tf.invert_permutation` |