chore: rebase 2

notebooks/internal_priming.ipynb

@@ -32,10 +32,14 @@
     "# importing the libraries\n",
     "import pandas as pd\n",
     "import numpy as np\n",
+<<<<<<< HEAD
 <<<<<<< HEAD
     "import matplotlib.pyplot as plt\n",
 =======
 >>>>>>> d2ef840 (chore: started cnn notebook)
+=======
+    "import matplotlib.pyplot as plt\n",
+>>>>>>> 93ea318 (chore: added training function for cnn)
     "\n",
     "# for creating validation set\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -100,6 +104,9 @@
     "        x = x.view(x.size(0), -1)\n",
     "        x = self.linear_layers(x)\n",
 <<<<<<< HEAD
+<<<<<<< HEAD
+=======
+>>>>>>> 93ea318 (chore: added training function for cnn)
     "        return x\n",
     "\n",
     "# defining training function\n",
@@ -134,9 +141,12 @@
     "    tr_loss = loss_train.item()\n",
     "\n",
     "    return loss_train, loss_val"
+<<<<<<< HEAD
 =======
     "        return x"
 >>>>>>> d2ef840 (chore: started cnn notebook)
+=======
+>>>>>>> 93ea318 (chore: added training function for cnn)
    ],
    "metadata": {
     "collapsed": false,
@@ -325,16 +335,25 @@
    "source": [
     "# defining the model\n",
     "model = Net()\n",
+    "\n",
     "# defining the optimizer\n",
     "optimizer = Adam(model.parameters(), lr=0.07)\n",
+    "\n",
     "# defining the loss function\n",
     "criterion = CrossEntropyLoss()\n",
+<<<<<<< HEAD
 >>>>>>> d2ef840 (chore: started cnn notebook)
+=======
+    "\n",
+>>>>>>> 93ea318 (chore: added training function for cnn)
     "# checking if GPU is available\n",
     "if torch.cuda.is_available():\n",
     "    model = model.cuda()\n",
     "    criterion = criterion.cuda()\n",
 <<<<<<< HEAD
+<<<<<<< HEAD
+=======
+>>>>>>> 93ea318 (chore: added training function for cnn)
     "\n",
     "# defining the number of epochs\n",
     "n_epochs = 25\n",
@@ -346,6 +365,7 @@
     "val_losses = []\n",
     "\n",
     "# training the model\n",
+<<<<<<< HEAD
     "for epoch in tqdm(range(n_epochs)):\n",
     "    train_loss, val_loss = train()\n",
     "    train_losses.append(train_loss)\n",
@@ -465,6 +485,18 @@
 =======
     "\n"
 >>>>>>> d2ef840 (chore: started cnn notebook)
+=======
+    "for epoch in range(n_epochs):\n",
+    "    train_loss, val_loss = train()\n",
+    "    train_losses.append(train_loss)\n",
+    "    val_losses.append(val_loss)\n",
+    "\n",
+    "# plotting the training and validation loss\n",
+    "plt.plot(train_losses, label='Training loss')\n",
+    "plt.plot(val_losses, label='Validation loss')\n",
+    "plt.legend()\n",
+    "plt.show()"
+>>>>>>> 93ea318 (chore: added training function for cnn)
    ],
    "metadata": {
     "collapsed": false,