-
Notifications
You must be signed in to change notification settings - Fork 5.5k
/
activation.py
180 lines (138 loc) · 5.99 KB
/
activation.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = []
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid.framework import dygraph_only
from paddle import in_dynamic_mode
from paddle.fluid.layer_helper import LayerHelper
def relu(x, name=None):
"""
sparse relu activation, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = max(x, 0)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.sparse.nn.functional.relu(sparse_x)
# [0., 0., 1.]
"""
if in_dynamic_mode():
return _C_ops.sparse_relu(x)
else:
op_type = 'sparse_relu'
helper = LayerHelper(op_type)
out = helper.create_sparse_variable_for_type_inference(x.dtype)
helper.append_op(type=op_type,
inputs={'x': x},
outputs={'out': out},
attrs={})
return out
@dygraph_only
def softmax(x, axis=-1, name=None):
"""
sparse softmax activation, requiring x to be a SparseCooTensor or SparseCsrTensor.
Note:
Only support axis=-1 for SparseCsrTensor, which is faster when read data
by row (axis=-1).
From the point of view of dense matrix, for each row :math:`i` and each column :math:`j`
in the matrix, we have:
.. math::
softmax_ij = \frac{\exp(x_ij - max_j(x_ij))}{\sum_j(exp(x_ij - max_j(x_ij))}
Parameters:
x (Tensor): The input tensor. It can be SparseCooTensor/SparseCsrTensor. The data type can be float32 or float64.
axis (int, optional): The axis along which to perform softmax calculations. Only support -1 for SparseCsrTensor.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: SparseCoo or SparseCsr, whose layout is the same with `x` .
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.seed(100)
mask = np.random.rand(3, 4) < 0.5
np_x = np.random.rand(3, 4) * mask
# [[0. 0. 0.96823406 0.19722934]
# [0.94373937 0. 0.02060066 0.71456372]
# [0. 0. 0. 0.98275049]]
csr = paddle.to_tensor(np_x).to_sparse_csr()
# Tensor(shape=[3, 4], dtype=paddle.float64, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 5, 6],
# cols=[2, 3, 0, 2, 3, 3],
# values=[0.96823406, 0.19722934, 0.94373937, 0.02060066, 0.71456372,
# 0.98275049])
out = paddle.sparse.nn.functional.softmax(csr)
# Tensor(shape=[3, 4], dtype=paddle.float64, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 5, 6],
# cols=[2, 3, 0, 2, 3, 3],
# values=[0.68373820, 0.31626180, 0.45610887, 0.18119845, 0.36269269,
# 1. ])
"""
return _C_ops.sparse_softmax(x, axis)
@dygraph_only
def relu6(x, name=None):
"""
sparse relu6 activation, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
relu6(x) = min(max(0, x), 6)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 8.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.sparse.nn.functional.relu6(sparse_x)
"""
return _C_ops.sparse_relu6(x, 6.0)
@dygraph_only
def leaky_relu(x, negative_slope=0.01, name=None):
"""
sparse leaky_relu activation, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
leaky\_relu(x)=
\left\{
\begin{array}{rcl}
x, & & if \ x >= 0 \\
negative\_slope * x, & & otherwise \\
\end{array}
\right.
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
negative_slope (float, optional): Slope of the activation function at
:math:`x < 0` . Default is 0.01.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 5.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.sparse.nn.functional.leaky_relu(sparse_x, 0.5)
"""
return _C_ops.sparse_leaky_relu(x, negative_slope)