From a6a25904df4bbe00c364b93bc49b1e89b413de09 Mon Sep 17 00:00:00 2001 From: wangrao Date: Thu, 11 Mar 2021 16:59:40 +0800 Subject: [PATCH] fix docstring errors --- mindspore/nn/layer/math.py | 2 ++ mindspore/numpy/array_creations.py | 13 +++++++++++-- mindspore/numpy/array_ops.py | 12 +++++++++++- mindspore/numpy/logic_ops.py | 11 +++++++++++ mindspore/numpy/math_ops.py | 30 +++++++++++++++++++++++++++++- 5 files changed, 64 insertions(+), 4 deletions(-) diff --git a/mindspore/nn/layer/math.py b/mindspore/nn/layer/math.py index ca3b750925..d2d0a97887 100644 --- a/mindspore/nn/layer/math.py +++ b/mindspore/nn/layer/math.py @@ -833,6 +833,8 @@ class MatMul(Cell): r""" Multiplies matrix `x1` by matrix `x2`. + nn.MatMul will be deprecated in future versions. Please use ops.matmul instead. + - If both x1 and x2 are 1-dimensional, the dot product is returned. - If the dimensions of x1 and x2 are all not greater than 2, the matrix-matrix product will be returned. Note if one of 'x1' and 'x2' is 1-dimensional, the argument will first be expanded to 2 dimension. After the matrix diff --git a/mindspore/numpy/array_creations.py b/mindspore/numpy/array_creations.py index 7b3574e009..c7837685d8 100644 --- a/mindspore/numpy/array_creations.py +++ b/mindspore/numpy/array_creations.py @@ -688,6 +688,7 @@ def empty(shape, dtype=mstype.float32): >>> import mindspore.numpy as np >>> output = np.empty((2, 3)) >>> print(output) + # result may vary Tensor(shape=[2, 3], dtype=Float32, value= ) """ @@ -756,9 +757,10 @@ def empty_like(prototype, dtype=None, shape=None): Examples: >>> import mindspore.numpy as np - >>> a = [[(1, 2)], onp.ones((1, 2)), [[2, 3]], onp.ones((1, 2))] + >>> a = [[(1, 2)], np.ones((1, 2)), [[2, 3]], np.ones((1, 2))] >>> output = np.empty_like(a) >>> print(output) + # result may vary Tensor(shape=[4, 1, 2], dtype=Float32, value= ) """ @@ -869,7 +871,7 @@ def full_like(a, fill_value, dtype=None, shape=None): Examples: >>> import mindspore.numpy as np - >>> a = [[(1, 2)], onp.ones((1, 2)), [[2, 3]], onp.ones((1, 2))] + >>> a = [[(1, 2)], np.ones((1, 2)), [[2, 3]], np.ones((1, 2))] >>> output = np.full_like(a, 0.5) >>> print(output) [[[0.5 0.5]] @@ -1028,6 +1030,7 @@ def diagonal(a, offset=0, axis1=0, axis2=1): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> a = np.arange(4).reshape(2,2) >>> print(a) [[0 1] @@ -1136,6 +1139,7 @@ def trace(a, offset=0, axis1=0, axis2=1, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.trace(np.eye(3)) >>> print(output) 3.0 @@ -1216,6 +1220,7 @@ def meshgrid(*xi, sparse=False, indexing='xy'): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x = np.linspace(0, 1, 3) >>> y = np.linspace(0, 1, 2) >>> xv, yv = np.meshgrid(x, y) @@ -1439,6 +1444,7 @@ def diag(v, k=0): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x = np.arange(9).reshape((3,3)) >>> print(x) [[0 1 2] @@ -1505,6 +1511,7 @@ def diagflat(v, k=0): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.diagflat(np.asarray([[1,2], [3,4]])) >>> print(output) [[1 0 0 0] @@ -1564,6 +1571,7 @@ def diag_indices(n, ndim=2): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.diag_indices(5, 3) >>> print(output) (Tensor(shape=[5], dtype=Int32, value= [0, 1, 2, 3, 4]), @@ -1605,6 +1613,7 @@ def ix_(*args): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> ixgrid = np.ix_(np.array([0, 1]), np.array([2, 4])) >>> print(ixgrid) [Tensor(shape=[2, 1], dtype=Int32, value= diff --git a/mindspore/numpy/array_ops.py b/mindspore/numpy/array_ops.py index 378a3d5bfe..1fa08687d5 100644 --- a/mindspore/numpy/array_ops.py +++ b/mindspore/numpy/array_ops.py @@ -495,7 +495,7 @@ def append(arr, values, axis=None): >>> a = np.ones((2, 3)) >>> b = np.ones((2, 1)) >>> print(np.append(a, b, axis=1).shape) - >>> (2, 4) + (2, 4) """ _check_input_tensor(arr) _check_input_tensor(values) @@ -1186,6 +1186,7 @@ def moveaxis(a, source, destination): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x = np.zeros((3, 4, 5)) >>> output = np.moveaxis(x, 0, -1) >>> print(output.shape) @@ -1244,6 +1245,7 @@ def tile(a, reps): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> a = np.array([0, 1, 2]) >>> output = np.tile(a, 2) >>> print(output) @@ -1298,6 +1300,7 @@ def broadcast_to(array, shape): ``Ascend`` ``GPU`` ``CPU`` Example: + >>> import mindspore.numpy as np >>> x = np.array([1, 2, 3]) >>> output = np.broadcast_to(x, (3, 3)) >>> print(output) @@ -1333,6 +1336,7 @@ def broadcast_arrays(*args): ``Ascend`` ``GPU`` ``CPU`` Example: + >>> import mindspore.numpy as np >>> x = np.array([[1,2,3]]) >>> y = np.array([[4],[5]]) >>> output = np.broadcast_arrays(x, y) @@ -1600,6 +1604,7 @@ def flip(m, axis=None): ``Ascend`` ``GPU`` ``CPU`` Example: + >>> import mindspore.numpy as np >>> A = np.arange(8.0).reshape((2,2,2)) >>> output = np.flip(A) >>> print(output) @@ -1653,6 +1658,7 @@ def flipud(m): ``Ascend`` ``GPU`` ``CPU`` Example: + >>> import mindspore.numpy as np >>> A = np.arange(8.0).reshape((2,2,2)) >>> output = np.flipud(A) >>> print(output) @@ -1685,6 +1691,7 @@ def fliplr(m): ``Ascend`` ``GPU`` ``CPU`` Example: + >>> import mindspore.numpy as np >>> A = np.arange(8.0).reshape((2,2,2)) >>> output = np.fliplr(A) >>> print(output) @@ -1723,6 +1730,7 @@ def take_along_axis(arr, indices, axis): ``Ascend`` ``GPU`` ``CPU`` Example: + >>> import mindspore.numpy as np >>> x = np.arange(12).reshape(3, 4) >>> indices = np.arange(3).reshape(1, 3) >>> output = np.take_along_axis(x, indices, 1) @@ -1818,6 +1826,7 @@ def take(a, indices, axis=None, mode='raise'): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> a = np.array([4, 3, 5, 7, 6, 8]) >>> indices = np.array([0, 1, 4]) >>> output = np.take(a, indices) @@ -1880,6 +1889,7 @@ def repeat(a, repeats, axis=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.repeat(np.array(3), 4) >>> print(output) [3 3 3 3] diff --git a/mindspore/numpy/logic_ops.py b/mindspore/numpy/logic_ops.py index 0b20001141..aab930e378 100644 --- a/mindspore/numpy/logic_ops.py +++ b/mindspore/numpy/logic_ops.py @@ -107,6 +107,7 @@ def less_equal(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.less_equal(np.array([4, 2, 1]), np.array([2, 2, 2])) >>> print(output) [False True True] @@ -154,6 +155,7 @@ def less(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.less(np.array([1, 2]), np.array([2, 2])) >>> print(output) [ True False] @@ -200,6 +202,7 @@ def greater_equal(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.greater_equal(np.array([4, 2, 1]), np.array([2, 2, 2])) >>> print(output) [ True True False] @@ -246,6 +249,7 @@ def greater(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.greater(np.array([4, 2]), np.array([2, 2])) >>> print(output) [ True False] @@ -292,6 +296,7 @@ def equal(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.equal(np.array([0, 1, 3]), np.arange(3)) >>> print(output) [ True True False] @@ -338,6 +343,7 @@ def isfinite(x, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.isfinite(np.array([np.inf, 1., np.nan]).astype('float32')) >>> print(output) [False True False] @@ -390,6 +396,7 @@ def isnan(x, out=None, where=True, dtype=None): ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.isnan(np.array(np.nan, np.float32)) >>> print(output) True @@ -451,6 +458,7 @@ def isinf(x, out=None, where=True, dtype=None): ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.isinf(np.array(np.inf, np.float32)) >>> print(output) True @@ -495,6 +503,7 @@ def isposinf(x): ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.isposinf(np.array([-np.inf, 0., np.inf], np.float32)) >>> print(output) [False False True] @@ -525,6 +534,7 @@ def isneginf(x): ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.isneginf(np.array([-np.inf, 0., np.inf], np.float32)) >>> print(output) [ True False False] @@ -561,6 +571,7 @@ def isscalar(element): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.isscalar(3.1) >>> print(output) True diff --git a/mindspore/numpy/math_ops.py b/mindspore/numpy/math_ops.py index 5039ef2eda..17d30d7956 100644 --- a/mindspore/numpy/math_ops.py +++ b/mindspore/numpy/math_ops.py @@ -261,6 +261,7 @@ def rad2deg(x, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x = np.asarray([1, 2, 3, -4, -5]) >>> output = np.rad2deg(x) >>> print(output) @@ -310,6 +311,7 @@ def add(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x1 = np.full((3, 2), [1, 2]) >>> x2 = np.full((3, 2), [3, 4]) >>> output = np.add(x1, x2) @@ -363,6 +365,7 @@ def subtract(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x1 = np.full((3, 2), [1, 2]) >>> x2 = np.full((3, 2), [3, 4]) >>> output = np.subtract(x1, x2) @@ -411,6 +414,7 @@ def multiply(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x1 = np.full((3, 2), [1, 2]) >>> x2 = np.full((3, 2), [3, 4]) >>> output = np.multiply(x1, x2) @@ -468,6 +472,7 @@ def divide(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x1 = np.full((3, 2), [1, 2]) >>> x2 = np.full((3, 2), [3, 4]) >>> output = np.divide(x1, x2) @@ -520,6 +525,7 @@ def true_divide(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x1 = np.full((3, 2), [1, 2]) >>> x2 = np.full((3, 2), [3, 4]) >>> output = np.true_divide(x1, x2) @@ -571,6 +577,7 @@ def power(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x1 = np.full((3, 2), [1, 2]).astype('float32') >>> x2 = np.full((3, 2), [3, 4]).astype('float32') >>> output = np.power(x1, x2) @@ -627,6 +634,7 @@ def float_power(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x1 = np.arange(6) >>> x2 = np.array(3) >>> output = np.float_power(x1, x2) @@ -1007,6 +1015,7 @@ def tensordot(a, b, axes=2): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> a = np.ones((3, 4, 5)) >>> b = np.ones((4, 3, 2)) >>> output = np.tensordot(a, b, axes=([1,0],[0,1])) @@ -1284,6 +1293,7 @@ def matmul(x1, x2, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x1 = np.arange(2*3*4).reshape(2, 3, 4).astype('float32') >>> x2 = np.arange(4*5).reshape(4, 5).astype('float32') >>> output = np.matmul(x1, x2) @@ -1335,6 +1345,7 @@ def square(x, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x = np.square(np.arange(6).reshape(2, 3).astype('float32')) >>> print(x) [[ 0. 1. 4.] @@ -1381,6 +1392,7 @@ def sqrt(x, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x = np.arange(6).reshape(2, 3).astype('float32') >>> x_squared = np.square(x) >>> output = np.sqrt(x_squared) @@ -1430,6 +1442,7 @@ def reciprocal(x, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x = np.arange(1, 7).reshape(2, 3).astype('float32') >>> output = np.reciprocal(x) >>> print(output) @@ -1482,6 +1495,7 @@ def log(x, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> x = np.array([2, 3, 4]).astype('float32') >>> output = np.log(x) >>> print(output) @@ -1534,6 +1548,7 @@ def maximum(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.maximum(np.array([2, 3, 4]), np.array([1, 5, 2])) >>> print(output) [2 5 4] @@ -1598,6 +1613,7 @@ def heaviside(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.heaviside(np.array([-1.5, 0, 2.0]), np.array(0.5)) >>> print(output) [0. 0.5 1. ] @@ -1666,6 +1682,7 @@ def amax(a, axis=None, keepdims=False, initial=None, where=True): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> a = np.arange(4).reshape((2,2)).astype('float32') >>> output = np.amax(a) >>> print(output) @@ -1721,6 +1738,7 @@ def amin(a, axis=None, keepdims=False, initial=None, where=True): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> a = np.arange(4).reshape((2,2)).astype('float32') >>> output = np.amin(a) >>> print(output) @@ -1784,6 +1802,7 @@ def hypot(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.hypot(3*np.ones((3, 3)), 4*np.ones((3, 3))) >>> print(output) [[5. 5. 5.] @@ -1847,6 +1866,7 @@ def floor(x, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.floor(np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])) >>> print(output) [-2. -2. -1. 0. 1. 1. 2.] @@ -1892,6 +1912,7 @@ def floor_divide(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.floor_divide(np.array([1., 2., 3., 4.]), np.array(2.5)) >>> print(output) [0. 0. 1. 1.] @@ -1964,6 +1985,7 @@ def remainder(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.remainder(np.array([4, 7]), np.array([2, 3])) >>> print(output) [0 1] @@ -1997,6 +2019,7 @@ def fix(x): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.fix(np.array([2.1, 2.9, -2.1, -2.9])) >>> print(output) [ 2. 2. -2. -2.] @@ -2052,6 +2075,7 @@ def fmod(x1, x2, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.fmod(np.array([-3, -2, -1, 1, 2, 3]), np.array(2)) >>> print(output) [-1 0 -1 1 0 1] @@ -2099,6 +2123,7 @@ def trunc(x, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.trunc(np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])) >>> print(output) [-1. -1. -0. 0. 1. 1. 2.] @@ -2144,6 +2169,7 @@ def exp(x, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.exp(np.arange(5).astype(np.float32)) >>> print(output) [ 1. 2.718282 7.3890557 20.085537 54.598145 ] @@ -2189,9 +2215,10 @@ def expm1(x, out=None, where=True, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.expm1(np.arange(5).astype(np.float32)) >>> print(output) - [ 0. 1.7182819 6.389056 19.085537 53.59815 ] + [ 0. 1.7182819 6.389056 19.085537 53.59815 ] """ return _apply_tensor_op(F.tensor_expm1, x, out=out, where=where, dtype=dtype) @@ -2377,6 +2404,7 @@ def cumsum(a, axis=None, dtype=None): ``Ascend`` ``GPU`` ``CPU`` Examples: + >>> import mindspore.numpy as np >>> output = np.cumsum(np.ones((3,3)), axis=0) >>> print(output) [[1. 1. 1.]