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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"import torch.optim\n",
"import pytorch_lightning as pl"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"class LitTrainer(pl.LightningModule):\n",
" def __init__(self, model, loss_fn, optim):\n",
" super().__init__()\n",
" self.model = model\n",
" self.loss_fn = loss_fn\n",
" self.optim = optim\n",
"\n",
" def training_step(self, batch, batch_idx):\n",
" x, y = batch\n",
" x = x.to(torch.float32)\n",
"\n",
" y_pred = self.model(x).reshape(1, -1)\n",
" train_loss = self.loss_fn(y_pred, y)\n",
"\n",
" self.log(\"train_loss\", train_loss)\n",
" return train_loss\n",
"\n",
" def validation_step(self, batch, batch_idx):\n",
" # this is the validation loop\n",
" x, y = batch\n",
" x = x.to(torch.float32)\n",
"\n",
" y_pred = self.model(x).reshape(1, -1)\n",
" validate_loss = self.loss_fn(y_pred, y)\n",
"\n",
" self.log(\"val_loss\", validate_loss)\n",
"\n",
" def test_step(self, batch, batch_idx):\n",
" # this is the test loop\n",
" x, y = batch\n",
" x = x.to(torch.float32)\n",
"\n",
" y_pred = self.model(x).reshape(1, -1)\n",
" test_loss = self.loss_fn(y_pred, y)\n",
"\n",
" self.log(\"test_loss\", test_loss)\n",
"\n",
" def configure_optimizers(self):\n",
" return self.optim\n"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |