https://github.com/henryre/pytorch-fitmodule
Super simple fit method for PyTorch Modules
https://github.com/henryre/pytorch-fitmodule
Last synced: 5 months ago
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Super simple fit method for PyTorch Modules
- Host: GitHub
- URL: https://github.com/henryre/pytorch-fitmodule
- Owner: henryre
- Archived: true
- Created: 2017-08-02T18:56:03.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2020-06-24T16:31:00.000Z (over 5 years ago)
- Last Synced: 2024-11-14T05:33:46.275Z (11 months ago)
- Language: Python
- Homepage:
- Size: 9.77 KB
- Stars: 98
- Watchers: 5
- Forks: 18
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-pytorch-list-CNVersion - pytorch-fitmodule
- Awesome-pytorch-list - pytorch-fitmodule
README
# A super simple `fit` method for PyTorch `Module`s
Ever wanted a pretty, Keras-like `fit` method for your PyTorch `Module`s?
Here's one. It lacks some of the advanced functionality, but it's easy to use:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_fitmodule import FitModule
X, Y, n_classes = torch.get_me_some_data()
class MLP(FitModule):
def __init__(self, n_feats, n_classes, hidden_size=50):
super(MLP, self).__init__()
self.fc1 = nn.Linear(n_feats, hidden_size)
self.fc2 = nn.Linear(hidden_size, n_classes)
def forward(self, x):
return F.log_softmax(self.fc2(F.relu(self.fc1(x))))
f = MLP(X.size()[1], n_classes)
def n_correct(y_true, y_pred):
return (y_true == torch.max(y_pred, 1)[1]).sum()
f.fit(X, Y, epochs=5, validation_split=0.3, metrics=[n_correct])
```
## Installation
Just clone this repo and add it to your Python path. You'll need
* [PyTorch](http://pytorch.org)
* [NumPy](http://numpy.org/)
* [Scikit-Learn](http://scikit-learn.org/) (just for the example)
all of which are available via [Anaconda](https://www.continuum.io/downloads).
## Example
Try out a simple example with the included script:
```bash
python run_example.py
```
```bash
Epoch 1 / 10
[========================================] 100% loss: 1.3285 accuracy: 0.5676 val_loss: 1.0450 val_accuracy: 0.5693
Epoch 2 / 10
[========================================] 100% loss: 0.8004 accuracy: 0.8900 val_loss: 0.5804 val_accuracy: 0.8900
Epoch 3 / 10
[========================================] 100% loss: 0.4638 accuracy: 0.8981 val_loss: 0.3845 val_accuracy: 0.8983
Epoch 4 / 10
[========================================] 100% loss: 0.3357 accuracy: 0.9033 val_loss: 0.2998 val_accuracy: 0.9043
Epoch 5 / 10
[========================================] 100% loss: 0.2684 accuracy: 0.9196 val_loss: 0.2462 val_accuracy: 0.9213
Epoch 6 / 10
[========================================] 100% loss: 0.2215 accuracy: 0.9374 val_loss: 0.2061 val_accuracy: 0.9423
Epoch 7 / 10
[========================================] 100% loss: 0.1841 accuracy: 0.9586 val_loss: 0.1738 val_accuracy: 0.9590
Epoch 8 / 10
[========================================] 100% loss: 0.1543 accuracy: 0.9704 val_loss: 0.1478 val_accuracy: 0.9673
Epoch 9 / 10
[========================================] 100% loss: 0.1298 accuracy: 0.9806 val_loss: 0.1266 val_accuracy: 0.9747
Epoch 10 / 10
[========================================] 100% loss: 0.1099 accuracy: 0.9861 val_loss: 0.1094 val_accuracy: 0.9800
```