Ever wanted a pretty, Keras-like fit method for your PyTorch Modules? Here's one. It lacks some of the advanced functionality, but it's easy to use:
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])Just clone this repo and add it to your Python path. You'll need
- PyTorch
- NumPy
- Scikit-Learn (just for the example)
all of which are available via Anaconda.
Try out a simple example with the included script:
python run_example.pyEpoch 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