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https://github.com/0h-n0/toy_neural_network_generator
https://github.com/0h-n0/toy_neural_network_generator
deep-learning neural-architecture-search pytorch tensorflow
Last synced: 15 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/0h-n0/toy_neural_network_generator
- Owner: 0h-n0
- Created: 2020-03-05T04:08:20.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-03-15T18:40:53.000Z (over 4 years ago)
- Last Synced: 2024-10-12T04:51:38.962Z (26 days ago)
- Topics: deep-learning, neural-architecture-search, pytorch, tensorflow
- Language: Python
- Size: 50.8 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Toy Neural Network Generator
## Installation
```shell
$ pip install tnng
```
## Simple Model Generator
```python
#!/usr/bin/env python
import torch
import torch.nn as nn
import torchex.nn as exnn
from tnng import Generator, MultiHeadLinkedListLayer
m = MultiHeadLinkedListLayer()
# all layers can be lazy evaluation.
m.append([exnn.Linear(64), exnn.Linear(128), exnn.Linear(256)])
m.append([nn.ReLU(), nn.ELU()])
m.append([exnn.Linear(16), exnn.Linear(32), exnn.Linear(64),])
m.append([nn.ReLU(), nn.ELU()])
m.append([exnn.Linear(10)])
g = Generator(m)
x = torch.randn(128, 256)
class Model(nn.Module):
def __init__(self, idx=0):
super(Model, self).__init__()
self.model = nn.ModuleList([l[0] for l in g[idx]])
def forward(self, x):
for m in self.model:
x = m(x)
return x
m = Model(0)
o = m(x)
'''
ModuleList(
(0): Linear(in_features=256, out_features=64, bias=True)
(1): ReLU()
(2): Linear(in_features=64, out_features=16, bias=True)
(3): ReLU()
(4): Linear(in_features=16, out_features=10, bias=True)
)
'''
```
## Multimodal Model Generator
```python
#!/usr/bin/env python
import torch
import torch.nn as nn
import torchex.nn as exnn
from tnng import Generator, MultiHeadLinkedListLayer
m = MultiHeadLinkedListLayer()
m1 = MultiHeadLinkedListLayer()
# all layers can be lazy evaluation.
m.append([exnn.Linear(64), exnn.Linear(128), exnn.Linear(256)])
m.append([nn.ReLU(), nn.ELU()])
m.append([exnn.Linear(16), exnn.Linear(32), exnn.Linear(64),])
m.append([nn.ReLU(), nn.ELU()])
m1.append([exnn.Conv2d(16, 1), exnn.Conv2d(32, 1), exnn.Conv2d(64, 1)])
m1.append([nn.MaxPool2d(2), nn.AvgPool2d(2)])
m1.append([nn.ReLU(), nn.ELU(), nn.Identity()])
m1.append([exnn.Conv2d(32, 1), exnn.Conv2d(64, 1), exnn.Conv2d(128, 1)])
m1.append([nn.MaxPool2d(2), nn.AvgPool2d(2)])
m1.append([exnn.Flatten(),])
m = m + m1
m.append([exnn.Linear(128)])
m.append([nn.ReLU(), nn.ELU(), nn.Identity()])
m.append([exnn.Linear(10)])
g = Generator(m)
class Model(nn.Module):
def __init__(self, idx=0):
super(Model, self).__init__()
self.model = g[idx]
for layers in self.model:
for layer in layers:
self.add_module(f'{layer}', layer)
def forward(self, x, img):
for m in self.model:
if len(m) == 2:
if m[0] is not None:
x = m[0](x)
img = m[1](img)
elif len(m) == 1 and m[0] is None:
x = torch.cat((x, img), 1)
else:
x = m[0](x)
return x
x = torch.randn(128, 256)
img = torch.randn(128, 3, 28, 28)
m = Model()
o = m(x, img)
print(o.shape)
```