-
Notifications
You must be signed in to change notification settings - Fork 610
/
lisht.py
60 lines (44 loc) · 1.96 KB
/
lisht.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
# Copyright 2019 The TensorFlow 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.
# ==============================================================================
import tensorflow as tf
from tensorflow_addons.utils import types
from tensorflow_addons.utils.resource_loader import LazySO
from tensorflow_addons import options
_activation_so = LazySO("custom_ops/activations/_activation_ops.so")
@tf.keras.utils.register_keras_serializable(package="Addons")
def lisht(x: types.TensorLike) -> tf.Tensor:
"""LiSHT: Non-Parameteric Linearly Scaled Hyperbolic Tangent Activation Function.
Computes linearly scaled hyperbolic tangent (LiSHT): `x * tanh(x)`
See [LiSHT: Non-Parameteric Linearly Scaled Hyperbolic Tangent Activation Function for Neural Networks](https://arxiv.org/abs/1901.05894).
Args:
x: A `Tensor`. Must be one of the following types:
`float16`, `float32`, `float64`.
Returns:
A `Tensor`. Has the same type as `x`.
"""
x = tf.convert_to_tensor(x)
if not options.TF_ADDONS_PY_OPS:
try:
return _lisht_custom_op(x)
except tf.errors.NotFoundError:
options.warn_fallback("lisht")
return _lisht_py(x)
def _lisht_custom_op(x):
return _activation_so.ops.addons_lisht(x)
@tf.RegisterGradient("Addons>Lisht")
def _lisht_grad(op, grad):
return _activation_so.ops.addons_lisht_grad(grad, op.inputs[0])
def _lisht_py(x):
return x * tf.math.tanh(x)