chore: rebase

notebooks/internal_priming.ipynb

@@ -22,13 +22,20 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 68,
+=======
+   "execution_count": null,
+>>>>>>> d2ef840 (chore: started cnn notebook)
    "outputs": [],
    "source": [
     "# importing the libraries\n",
     "import pandas as pd\n",
     "import numpy as np\n",
+<<<<<<< HEAD
     "import matplotlib.pyplot as plt\n",
+=======
+>>>>>>> d2ef840 (chore: started cnn notebook)
     "\n",
     "# for creating validation set\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -40,7 +47,11 @@
     "# PyTorch libraries and modules\n",
     "import torch\n",
     "from torch.autograd import Variable\n",
+<<<<<<< HEAD
     "from torch.nn import Linear, ReLU, CrossEntropyLoss, Sequential, MaxPool1d, Module, Softmax, BatchNorm1d, Dropout, Conv1d\n",
+=======
+    "from torch.nn import Linear, ReLU, CrossEntropyLoss, Sequential, Conv2d, MaxPool2d, Module, Softmax, BatchNorm2d, Dropout\n",
+>>>>>>> d2ef840 (chore: started cnn notebook)
     "from torch.optim import Adam, SGD\n",
     "\n",
     "\n",
@@ -50,6 +61,7 @@
     "        super(Net, self).__init__()\n",
     "\n",
     "        self.cnn_layers = Sequential(\n",
+<<<<<<< HEAD
     "            # Defining a 1D convolution layer\n",
     "            Conv1d(1, 4, kernel_size=3, stride=1, padding=1),\n",
     "            BatchNorm1d(4),\n",
@@ -64,6 +76,22 @@
     "\n",
     "        self.linear_layers = Sequential(\n",
     "            Linear(4 * 50, 10)\n",
+=======
+    "            # Defining a 2D convolution layer\n",
+    "            Conv2d(1, 4, kernel_size=3, stride=1, padding=1),\n",
+    "            BatchNorm2d(4),\n",
+    "            ReLU(inplace=True),\n",
+    "            MaxPool2d(kernel_size=2, stride=2),\n",
+    "            # Defining another 2D convolution layer\n",
+    "            Conv2d(4, 4, kernel_size=3, stride=1, padding=1),\n",
+    "            BatchNorm2d(4),\n",
+    "            ReLU(inplace=True),\n",
+    "            MaxPool2d(kernel_size=2, stride=2),\n",
+    "        )\n",
+    "\n",
+    "        self.linear_layers = Sequential(\n",
+    "            Linear(4 * 7 * 7, 10)\n",
+>>>>>>> d2ef840 (chore: started cnn notebook)
     "        )\n",
     "\n",
     "    # Defining the forward pass\n",
@@ -71,6 +99,7 @@
     "        x = self.cnn_layers(x)\n",
     "        x = x.view(x.size(0), -1)\n",
     "        x = self.linear_layers(x)\n",
+<<<<<<< HEAD
     "        return x\n",
     "\n",
     "# defining training function\n",
@@ -105,6 +134,9 @@
     "    tr_loss = loss_train.item()\n",
     "\n",
     "    return loss_train, loss_val"
+=======
+    "        return x"
+>>>>>>> d2ef840 (chore: started cnn notebook)
    ],
    "metadata": {
     "collapsed": false,
@@ -127,6 +159,7 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 69,
    "outputs": [
     {
@@ -206,6 +239,20 @@
     "\n",
     "val_x  = torch.from_numpy(val_x)\n",
     "val_y = torch.from_numpy(val_y)"
+=======
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# TODO: Get test data from issues 25 and 26\n",
+    "train_x = []\n",
+    "train_y = []\n",
+    "test_x = []\n",
+    "test_y = []\n",
+    "\n",
+    "train_x, val_x, train_y, val_y = train_test_split(train_x, train_y, test_size = 0.1)\n",
+    "\n",
+    "# TODO: reshape shape from [n, l] to [n, 1, l]\n"
+>>>>>>> d2ef840 (chore: started cnn notebook)
    ],
    "metadata": {
     "collapsed": false,
@@ -228,6 +275,7 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 71,
    "outputs": [
     {
@@ -271,10 +319,22 @@
     "# defining the loss function\n",
     "criterion = CrossEntropyLoss()\n",
     "\n",
+=======
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# defining the model\n",
+    "model = Net()\n",
+    "# defining the optimizer\n",
+    "optimizer = Adam(model.parameters(), lr=0.07)\n",
+    "# defining the loss function\n",
+    "criterion = CrossEntropyLoss()\n",
+>>>>>>> d2ef840 (chore: started cnn notebook)
     "# checking if GPU is available\n",
     "if torch.cuda.is_available():\n",
     "    model = model.cuda()\n",
     "    criterion = criterion.cuda()\n",
+<<<<<<< HEAD
     "\n",
     "# defining the number of epochs\n",
     "n_epochs = 25\n",
@@ -402,6 +462,9 @@
    "outputs": [],
    "source": [
     "torch.save(model.state_dict(), '../models/internal_priming.pth')"
+=======
+    "\n"
+>>>>>>> d2ef840 (chore: started cnn notebook)
    ],
    "metadata": {
     "collapsed": false,