diff options
Diffstat (limited to 'tensorflow/python/ops/linalg_ops.py')
| -rw-r--r-- | tensorflow/python/ops/linalg_ops.py | 31 |
1 files changed, 21 insertions, 10 deletions
diff --git a/tensorflow/python/ops/linalg_ops.py b/tensorflow/python/ops/linalg_ops.py index 2cb467c891..14a039ffd0 100644 --- a/tensorflow/python/ops/linalg_ops.py +++ b/tensorflow/python/ops/linalg_ops.py @@ -30,6 +30,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops.gen_linalg_ops import * # pylint: enable=wildcard-import from tensorflow.python.util import compat +from tensorflow.python.util.deprecation import deprecated_args # Names below are lower_case. # pylint: disable=invalid-name @@ -438,7 +439,10 @@ def svd(tensor, full_matrices=False, compute_uv=True, name=None): # pylint: disable=redefined-builtin -def norm(tensor, ord='euclidean', axis=None, keep_dims=False, name=None): +@deprecated_args(None, "keep_dims is deprecated, use keepdims instead", + "keep_dims") +def norm(tensor, ord='euclidean', axis=None, keepdims=None, name=None, + keep_dims=None): r"""Computes the norm of vectors, matrices, and tensors. This function can compute several different vector norms (the 1-norm, the @@ -471,13 +475,13 @@ def norm(tensor, ord='euclidean', axis=None, keep_dims=False, name=None): can be either a matrix or a batch of matrices at runtime, pass `axis=[-2,-1]` instead of `axis=None` to make sure that matrix norms are computed. - keep_dims: If True, the axis indicated in `axis` are kept with size 1. + keepdims: If True, the axis indicated in `axis` are kept with size 1. Otherwise, the dimensions in `axis` are removed from the output shape. name: The name of the op. Returns: output: A `Tensor` of the same type as tensor, containing the vector or - matrix norms. If `keep_dims` is True then the rank of output is equal to + matrix norms. If `keepdims` is True then the rank of output is equal to the rank of `tensor`. Otherwise, if `axis` is none the output is a scalar, if `axis` is an integer, the rank of `output` is one less than the rank of `tensor`, if `axis` is a 2-tuple the rank of `output` is two less @@ -497,6 +501,13 @@ def norm(tensor, ord='euclidean', axis=None, keep_dims=False, name=None): @end_compatibility """ + if keep_dims is not None: + if keepdims is not None: + raise ValueError("Cannot specify both 'keep_dims' and 'keepdims'") + keepdims = keep_dims + if keepdims is None: + keepdims = False + is_matrix_norm = ((isinstance(axis, tuple) or isinstance(axis, list)) and len(axis) == 2) if is_matrix_norm: @@ -528,25 +539,25 @@ def norm(tensor, ord='euclidean', axis=None, keep_dims=False, name=None): # matrices. result = math_ops.sqrt( math_ops.reduce_sum( - tensor * math_ops.conj(tensor), axis, keep_dims=True)) + tensor * math_ops.conj(tensor), axis, keepdims=True)) else: result = math_ops.abs(tensor) if ord == 1: sum_axis = None if axis is None else axis[0] - result = math_ops.reduce_sum(result, sum_axis, keep_dims=True) + result = math_ops.reduce_sum(result, sum_axis, keepdims=True) if is_matrix_norm: - result = math_ops.reduce_max(result, axis[-1], keep_dims=True) + result = math_ops.reduce_max(result, axis[-1], keepdims=True) elif ord == np.inf: if is_matrix_norm: - result = math_ops.reduce_sum(result, axis[1], keep_dims=True) + result = math_ops.reduce_sum(result, axis[1], keepdims=True) max_axis = None if axis is None else axis[0] - result = math_ops.reduce_max(result, max_axis, keep_dims=True) + result = math_ops.reduce_max(result, max_axis, keepdims=True) else: # General p-norms (positive p only) result = math_ops.pow( math_ops.reduce_sum( - math_ops.pow(result, ord), axis, keep_dims=True), 1.0 / ord) - if not keep_dims: + math_ops.pow(result, ord), axis, keepdims=True), 1.0 / ord) + if not keepdims: result = array_ops.squeeze(result, axis) return result |