{
 "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"
    }
   }
  }
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