Hugging Face's logo

Brosnan
/
Universal_Activation_Function

PyTorch
activation-function
activation_function
dynamic_activation_function
dynamic-activation-function
Model card Files
xet
Community
Universal_Activation_Function
File size: 2,453 Bytes 
8a97c08
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
717576e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 

---
license: cc-by-nc-sa-4.0
library_name: pytorch
tags:
- activation-function
- activation_function
- dynamic_activation_function
- dynamic-activation-function
---








# Universal Activation Function

Tensorflow and Pytorch source code for the paper 

[Yuen, Brosnan, Minh Tu Hoang, Xiaodai Dong, and Tao Lu. "Universal activation function for machine learning." Scientific reports 11, no. 1 (2021): 1-11.](https://www.nature.com/articles/s41598-021-96723-8)


# Getting the code

Requires [Docker](https://docs.docker.com/get-docker/) 


Use git to pull this repo
```
git clone https://github.com/SensorOrgNet/Universal_Activation_Function.git
```


# Running the Tensorflow 2 version


Install CUDA 11.2 container
```
docker run --name UAF --gpus all  -v /home/username/UAF/:/workspace  -w /workspace    -it  nvcr.io/nvidia/cuda:11.2.0-cudnn8-devel-ubuntu20.04   bash
```


Install python
```
apt update
apt install python3-pip
```


Install pytorch and pytorch geometric
```
pip3 install tensorflow==2.7.0
```




Run the MLP with UAF for MNIST dataset
```
cd   Universal_Activation_Function/tensorflow/
python3   ./mnist_UAF.py 
```






# Running the Pytorch version


Install CUDA 11.3 container
```
docker run --name UAF --gpus all  -v /home/username/UAF/:/workspace  -w /workspace    -it  nvcr.io/nvidia/cuda:11.3.0-cudnn8-devel-ubuntu20.04   bash
```

Install python
```
apt update
apt install python3-pip
```

Install pytorch and pytorch geometric
```
pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
pip3 install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
pip3 install torch-sparse -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
pip3 install torch-cluster -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
pip3 install torch-spline-conv -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
pip3 install torch-geometric
```




Run the CNN with UAF for MNIST dataset
```
cd   Universal_Activation_Function/pytorch/
python3   ./mnist_UAF.py 
```







Run the GCN2 with UAF for CORA dataset. The fold number is represented by the number at the end
```
cd   Universal_Activation_Function/pytorch/
python3   ./gcn2_cora_UAF.py  0
```





Run the PNA with UAF for ZNC dataset. The fold number is represented by the number at the end
```
cd   Universal_Activation_Function/pytorch/
python3   ./pna_UAF.py  0
```