From 91e0841697f8eb0bc3b47357fae9b95d7c313379 Mon Sep 17 00:00:00 2001
From: oscarchaufour <101994223+oscarchaufour@users.noreply.github.com>
Date: Thu, 30 Nov 2023 15:27:02 +0100
Subject: [PATCH] Update TD2 Deep Learning.ipynb

---
 TD2 Deep Learning.ipynb | 473 ++++++++++++++++++++++++----------------
 1 file changed, 285 insertions(+), 188 deletions(-)

diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index e4b8455..c6538b6 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "b1950f0a",
    "metadata": {},
    "outputs": [
@@ -69,34 +69,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([[ 1.6064e-01, -8.3096e-01, -5.7560e-01, -5.5221e-01, -2.4734e+00,\n",
-      "         -9.5960e-01, -6.1278e-03, -1.4512e+00, -3.7847e-01,  1.4202e+00],\n",
-      "        [-9.2481e-01, -1.1975e+00, -1.3714e+00, -2.2159e-01, -6.9335e-01,\n",
-      "          5.9759e-01, -1.0556e+00, -1.6124e+00, -1.3782e+00,  5.0452e-01],\n",
-      "        [-5.6611e-01,  1.9770e+00,  5.0722e-02,  1.2445e+00,  6.6420e-01,\n",
-      "         -7.4535e-01, -6.7633e-01,  3.6921e-01, -1.3451e-01, -4.2435e-01],\n",
-      "        [ 1.5304e+00,  2.8679e-01,  1.4755e+00, -3.2790e+00,  6.7065e-01,\n",
-      "          6.2163e-01, -7.1354e-01,  4.3174e-01, -1.0341e+00, -2.3934e+00],\n",
-      "        [ 2.3164e+00, -2.7928e-03,  4.1310e-01, -6.5861e-01, -5.6625e-01,\n",
-      "         -7.9415e-01, -1.3316e+00, -1.1399e+00, -3.0817e-01,  9.1052e-01],\n",
-      "        [-6.2689e-01,  8.6980e-01, -1.0182e+00, -3.8407e-01, -5.0964e-01,\n",
-      "          2.0581e+00, -3.2808e-01, -1.0505e+00, -9.4926e-02,  3.3163e-01],\n",
-      "        [ 2.8618e-01, -1.3192e+00, -1.1055e+00, -5.3056e-02,  1.4341e+00,\n",
-      "          2.8907e-01, -5.0532e-01,  9.2871e-01, -3.3850e-02,  9.2353e-01],\n",
-      "        [ 9.4972e-01,  8.4687e-01, -7.6490e-01, -1.4787e-01, -4.3975e-01,\n",
-      "          2.3979e+00,  5.5934e-01,  9.8858e-02, -1.3084e+00, -4.0068e-01],\n",
-      "        [-1.2574e-01, -3.9157e-01,  1.9478e-01, -8.0233e-01, -7.4159e-01,\n",
-      "         -3.1866e-01,  1.3065e+00,  9.6804e-02,  8.9880e-01, -1.2927e-01],\n",
-      "        [-1.7127e-01,  9.2458e-01,  8.8092e-01, -7.3623e-01, -7.3029e-01,\n",
-      "         -1.6389e+00, -3.9760e-01,  9.5078e-01, -7.9384e-01,  1.3524e-01],\n",
-      "        [ 2.1211e+00,  3.0165e-01, -7.1339e-01, -5.0282e-01,  1.6750e-01,\n",
-      "          7.1006e-01,  8.6247e-01,  4.3677e-01,  1.3093e+00, -1.5271e+00],\n",
-      "        [-1.8020e-01, -7.1857e-01, -1.1063e+00, -1.6508e+00, -4.9902e-01,\n",
-      "          1.0612e+00,  1.1554e+00, -5.2150e-01,  6.2228e-01, -5.4746e-01],\n",
-      "        [-1.6428e+00, -1.2118e+00,  1.4600e-01,  8.4214e-01,  6.7059e-01,\n",
-      "          5.2342e-02, -6.4501e-01, -1.0193e+00,  4.1927e-01,  1.1333e+00],\n",
-      "        [ 7.8724e-01,  7.4030e-01,  2.9120e-01,  7.6239e-01,  4.1124e-01,\n",
-      "          1.0952e+00,  7.1367e-02,  7.4975e-02,  2.4040e-02,  9.6980e-01]])\n",
+      "tensor([[ 1.8854e+00,  3.3594e-01,  2.4385e-01,  9.3441e-01,  3.8804e-01,\n",
+      "          5.8961e-02, -3.7622e-02, -1.2529e+00, -6.6612e-01, -5.8072e-01],\n",
+      "        [-1.4671e+00,  1.4231e+00, -1.2025e+00, -5.3109e-01,  4.3720e-02,\n",
+      "          2.1798e+00,  6.4931e-01,  9.6299e-01, -1.1575e+00, -1.9343e-01],\n",
+      "        [-4.3447e-01,  1.7466e+00, -7.1663e-01,  1.0507e-01, -4.4889e-01,\n",
+      "          7.2018e-02, -8.7205e-01,  1.4163e+00, -2.2866e-01, -6.6632e-01],\n",
+      "        [ 1.0448e+00,  2.2115e-01,  1.3330e+00,  2.0327e+00, -1.1046e+00,\n",
+      "         -1.7296e-01,  1.5189e+00, -3.8984e-01,  7.6002e-01, -8.2957e-01],\n",
+      "        [-1.6815e-01, -1.0889e+00, -4.4035e-01, -4.6792e-02,  8.3255e-01,\n",
+      "         -1.3879e-01,  7.3910e-01, -4.8541e-01,  7.1943e-01, -1.4042e+00],\n",
+      "        [ 7.2299e-01,  6.7934e-01,  3.1603e-01,  2.8441e+00, -2.5268e-01,\n",
+      "          2.5929e-01, -1.5108e+00, -2.8074e-01, -4.1456e-01, -5.1746e-01],\n",
+      "        [-2.1776e-01,  3.4326e-01,  9.3110e-01, -1.8498e-01,  3.6421e-01,\n",
+      "         -1.0885e+00,  1.5954e+00,  1.0334e+00,  4.1926e-02, -8.9267e-01],\n",
+      "        [-1.0552e+00, -2.3193e-01,  2.9310e-01, -2.4087e+00, -4.8483e-01,\n",
+      "          6.2572e-01,  3.2118e-01, -1.1077e+00, -2.3259e+00,  3.8126e-01],\n",
+      "        [-2.0087e-01,  4.7602e-01,  4.1493e-01,  2.2908e-03,  9.4581e-01,\n",
+      "         -1.2542e+00,  4.6698e-01, -2.1633e-01,  1.1841e-01, -1.3105e+00],\n",
+      "        [-7.1432e-01,  1.7955e+00,  2.2020e+00,  1.5325e+00, -8.2356e-01,\n",
+      "         -7.2211e-01,  8.3963e-01,  4.1870e-01, -3.7944e-01, -5.9342e-01],\n",
+      "        [-9.0255e-01,  6.6934e-01,  1.9344e-01, -8.0582e-03, -7.2458e-01,\n",
+      "          6.1677e-01, -2.1813e+00,  1.4867e+00, -5.3238e-01, -1.7710e+00],\n",
+      "        [ 3.9705e-01, -5.0827e-01, -7.8566e-01,  1.3220e+00, -3.0925e+00,\n",
+      "          4.4828e-01,  1.2272e+00,  2.9801e-01, -2.4118e-01,  1.8077e-02],\n",
+      "        [-1.1333e+00,  2.4575e+00,  6.1330e-01,  9.0629e-01, -1.3946e+00,\n",
+      "          9.2362e-01,  1.1205e-01, -1.2964e-01, -8.0516e-01,  1.3768e+00],\n",
+      "        [-1.1775e+00,  3.2316e-01,  1.3902e+00,  1.4906e+00,  4.4133e-01,\n",
+      "         -4.0164e-02,  3.7911e-01, -1.5541e+00,  4.1250e-01, -4.5086e-01]])\n",
       "AlexNet(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 10,
    "id": "6e18f2fd",
    "metadata": {},
    "outputs": [
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 11,
    "id": "462666a2",
    "metadata": {},
    "outputs": [
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 12,
    "id": "317bf070",
    "metadata": {},
    "outputs": [
@@ -346,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 13,
    "id": "4b53f229",
    "metadata": {},
    "outputs": [
@@ -354,23 +354,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 \tTraining Loss: 43.985662 \tValidation Loss: 38.691828\n",
-      "Validation loss decreased (inf --> 38.691828).  Saving model ...\n",
-      "Epoch: 1 \tTraining Loss: 34.737945 \tValidation Loss: 32.245164\n",
-      "Validation loss decreased (38.691828 --> 32.245164).  Saving model ...\n",
-      "Epoch: 2 \tTraining Loss: 30.932543 \tValidation Loss: 29.559662\n",
-      "Validation loss decreased (32.245164 --> 29.559662).  Saving model ...\n",
-      "Epoch: 3 \tTraining Loss: 28.841259 \tValidation Loss: 28.510968\n",
-      "Validation loss decreased (29.559662 --> 28.510968).  Saving model ...\n",
-      "Epoch: 4 \tTraining Loss: 27.152388 \tValidation Loss: 26.944222\n",
-      "Validation loss decreased (28.510968 --> 26.944222).  Saving model ...\n",
-      "Epoch: 5 \tTraining Loss: 25.761013 \tValidation Loss: 26.533953\n",
-      "Validation loss decreased (26.944222 --> 26.533953).  Saving model ...\n",
-      "Epoch: 6 \tTraining Loss: 24.576015 \tValidation Loss: 26.304483\n",
-      "Validation loss decreased (26.533953 --> 26.304483).  Saving model ...\n",
-      "Epoch: 7 \tTraining Loss: 23.546151 \tValidation Loss: 24.197494\n",
-      "Validation loss decreased (26.304483 --> 24.197494).  Saving model ...\n",
-      "Epoch: 8 \tTraining Loss: 22.601423 \tValidation Loss: 25.205635\n"
+      "Epoch: 0 \tTraining Loss: 43.136620 \tValidation Loss: 37.220863\n",
+      "Validation loss decreased (inf --> 37.220863).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 33.819963 \tValidation Loss: 30.998808\n",
+      "Validation loss decreased (37.220863 --> 30.998808).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 29.764550 \tValidation Loss: 28.375328\n",
+      "Validation loss decreased (30.998808 --> 28.375328).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 27.633869 \tValidation Loss: 27.134280\n",
+      "Validation loss decreased (28.375328 --> 27.134280).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 26.085778 \tValidation Loss: 25.679973\n",
+      "Validation loss decreased (27.134280 --> 25.679973).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 24.806852 \tValidation Loss: 24.887339\n",
+      "Validation loss decreased (25.679973 --> 24.887339).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 23.640785 \tValidation Loss: 23.775639\n",
+      "Validation loss decreased (24.887339 --> 23.775639).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 22.565121 \tValidation Loss: 23.255059\n",
+      "Validation loss decreased (23.775639 --> 23.255059).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 21.663921 \tValidation Loss: 22.763115\n",
+      "Validation loss decreased (23.255059 --> 22.763115).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 20.766016 \tValidation Loss: 22.159910\n",
+      "Validation loss decreased (22.763115 --> 22.159910).  Saving model ...\n",
+      "Epoch: 10 \tTraining Loss: 19.935057 \tValidation Loss: 22.163762\n"
      ]
     }
    ],
@@ -466,13 +470,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 14,
    "id": "d39df818",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -501,7 +505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 15,
    "id": "e93efdfc",
    "metadata": {},
    "outputs": [
@@ -509,20 +513,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 23.820829\n",
+      "Test Loss: 22.092326\n",
       "\n",
-      "Test Accuracy of airplane: 61% (610/1000)\n",
-      "Test Accuracy of automobile: 84% (849/1000)\n",
-      "Test Accuracy of  bird: 42% (423/1000)\n",
-      "Test Accuracy of   cat: 32% (322/1000)\n",
-      "Test Accuracy of  deer: 42% (420/1000)\n",
-      "Test Accuracy of   dog: 45% (452/1000)\n",
-      "Test Accuracy of  frog: 75% (759/1000)\n",
-      "Test Accuracy of horse: 72% (729/1000)\n",
-      "Test Accuracy of  ship: 67% (679/1000)\n",
-      "Test Accuracy of truck: 55% (551/1000)\n",
+      "Test Accuracy of airplane: 62% (626/1000)\n",
+      "Test Accuracy of automobile: 63% (637/1000)\n",
+      "Test Accuracy of  bird: 45% (458/1000)\n",
+      "Test Accuracy of   cat: 51% (514/1000)\n",
+      "Test Accuracy of  deer: 46% (467/1000)\n",
+      "Test Accuracy of   dog: 42% (429/1000)\n",
+      "Test Accuracy of  frog: 76% (760/1000)\n",
+      "Test Accuracy of horse: 69% (698/1000)\n",
+      "Test Accuracy of  ship: 82% (826/1000)\n",
+      "Test Accuracy of truck: 72% (722/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 57% (5794/10000)\n"
+      "Test Accuracy (Overall): 61% (6137/10000)\n"
      ]
     }
    ],
@@ -615,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -638,7 +642,6 @@
    "source": [
     "# define the CNN architecture\n",
     "\n",
-    "\n",
     "class Net(nn.Module):\n",
     "    def __init__(self):\n",
     "        super(Net, self).__init__()\n",
@@ -681,40 +684,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 \tTraining Loss: 44.312457 \tValidation Loss: 40.273668\n",
-      "Validation loss decreased (inf --> 40.273668).  Saving model ...\n",
-      "Epoch: 1 \tTraining Loss: 36.289314 \tValidation Loss: 32.158548\n",
-      "Validation loss decreased (40.273668 --> 32.158548).  Saving model ...\n",
-      "Epoch: 2 \tTraining Loss: 30.851595 \tValidation Loss: 28.817860\n",
-      "Validation loss decreased (32.158548 --> 28.817860).  Saving model ...\n",
-      "Epoch: 3 \tTraining Loss: 27.730793 \tValidation Loss: 27.938577\n",
-      "Validation loss decreased (28.817860 --> 27.938577).  Saving model ...\n",
-      "Epoch: 4 \tTraining Loss: 25.182311 \tValidation Loss: 25.716466\n",
-      "Validation loss decreased (27.938577 --> 25.716466).  Saving model ...\n",
-      "Epoch: 5 \tTraining Loss: 22.998916 \tValidation Loss: 22.586595\n",
-      "Validation loss decreased (25.716466 --> 22.586595).  Saving model ...\n",
-      "Epoch: 6 \tTraining Loss: 21.008817 \tValidation Loss: 22.228286\n",
-      "Validation loss decreased (22.586595 --> 22.228286).  Saving model ...\n",
-      "Epoch: 7 \tTraining Loss: 19.318290 \tValidation Loss: 20.138872\n",
-      "Validation loss decreased (22.228286 --> 20.138872).  Saving model ...\n",
-      "Epoch: 8 \tTraining Loss: 17.760859 \tValidation Loss: 19.191882\n",
-      "Validation loss decreased (20.138872 --> 19.191882).  Saving model ...\n",
-      "Epoch: 9 \tTraining Loss: 16.270090 \tValidation Loss: 18.723222\n",
-      "Validation loss decreased (19.191882 --> 18.723222).  Saving model ...\n",
-      "Epoch: 10 \tTraining Loss: 14.886328 \tValidation Loss: 18.159567\n",
-      "Validation loss decreased (18.723222 --> 18.159567).  Saving model ...\n",
-      "Epoch: 11 \tTraining Loss: 13.544485 \tValidation Loss: 17.597254\n",
-      "Validation loss decreased (18.159567 --> 17.597254).  Saving model ...\n",
-      "Epoch: 12 \tTraining Loss: 12.293319 \tValidation Loss: 17.118693\n",
-      "Validation loss decreased (17.597254 --> 17.118693).  Saving model ...\n",
-      "Epoch: 13 \tTraining Loss: 10.956016 \tValidation Loss: 17.155066\n"
+      "Epoch: 0 \tTraining Loss: 45.722326 \tValidation Loss: 43.194956\n",
+      "Validation loss decreased (inf --> 43.194956).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 38.789553 \tValidation Loss: 35.145603\n",
+      "Validation loss decreased (43.194956 --> 35.145603).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 32.115269 \tValidation Loss: 31.001964\n",
+      "Validation loss decreased (35.145603 --> 31.001964).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 28.380890 \tValidation Loss: 26.939841\n",
+      "Validation loss decreased (31.001964 --> 26.939841).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 25.778531 \tValidation Loss: 24.737312\n",
+      "Validation loss decreased (26.939841 --> 24.737312).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 23.454600 \tValidation Loss: 22.869931\n",
+      "Validation loss decreased (24.737312 --> 22.869931).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 21.373382 \tValidation Loss: 21.206488\n",
+      "Validation loss decreased (22.869931 --> 21.206488).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 19.464826 \tValidation Loss: 19.695314\n",
+      "Validation loss decreased (21.206488 --> 19.695314).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 17.783432 \tValidation Loss: 18.822976\n",
+      "Validation loss decreased (19.695314 --> 18.822976).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 16.272115 \tValidation Loss: 19.394120\n"
      ]
     }
    ],
@@ -801,12 +796,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -833,6 +828,13 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CNN2 loss decreases more rapidly than CNN1 loss. Thus for the same number of epochs, CNN2 has a lower loss, which is a poperty of a better model."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bc381cf4",
@@ -850,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 19,
    "id": "ef623c26",
    "metadata": {},
    "outputs": [
@@ -867,7 +869,7 @@
        "2330946"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -875,7 +877,6 @@
    "source": [
     "import os\n",
     "\n",
-    "\n",
     "def print_size_of_model(model, label=\"\"):\n",
     "    torch.save(model.state_dict(), \"temp.p\")\n",
     "    size = os.path.getsize(\"temp.p\")\n",
@@ -897,7 +898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 20,
    "id": "c4c65d4b",
    "metadata": {},
    "outputs": [
@@ -914,7 +915,7 @@
        "659806"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -931,7 +932,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The quantized model size is only 30% of the non quantized model size."
+    "The quantized model size is only 30% of the non quantized model size. This is an important size reduction."
    ]
   },
   {
@@ -958,29 +959,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 20.890845\n",
+      "Test Loss: 19.002333\n",
       "\n",
-      "Test Loss, quantized model: 20.870204\n",
+      "Test Loss, quantized model: 18.995148\n",
       "\n",
-      "Test Accuracy of airplane: 64% (1288/2000); quantized: 64% (643/1000);\n",
-      "Test Accuracy of automobile: 74% (1486/2000); quantized: 74% (742/1000);\n",
-      "Test Accuracy of  bird: 43% (874/2000); quantized: 43% (435/1000);\n",
-      "Test Accuracy of   cat: 42% (856/2000); quantized: 42% (426/1000);\n",
-      "Test Accuracy of  deer: 42% (852/2000); quantized: 42% (426/1000);\n",
-      "Test Accuracy of   dog: 69% (1392/2000); quantized: 69% (691/1000);\n",
-      "Test Accuracy of  frog: 68% (1368/2000); quantized: 68% (685/1000);\n",
-      "Test Accuracy of horse: 66% (1328/2000); quantized: 66% (665/1000);\n",
-      "Test Accuracy of  ship: 85% (1714/2000); quantized: 85% (856/1000);\n",
-      "Test Accuracy of truck: 76% (1528/2000); quantized: 76% (766/1000);\n",
+      "Test Accuracy of airplane: 59% (1198/2000); quantized: 59% (597/1000);\n",
+      "Test Accuracy of automobile: 80% (1606/2000); quantized: 80% (801/1000);\n",
+      "Test Accuracy of  bird: 64% (1288/2000); quantized: 64% (642/1000);\n",
+      "Test Accuracy of   cat: 63% (1262/2000); quantized: 62% (628/1000);\n",
+      "Test Accuracy of  deer: 50% (1012/2000); quantized: 51% (510/1000);\n",
+      "Test Accuracy of   dog: 52% (1056/2000); quantized: 53% (530/1000);\n",
+      "Test Accuracy of  frog: 66% (1336/2000); quantized: 66% (666/1000);\n",
+      "Test Accuracy of horse: 72% (1446/2000); quantized: 72% (720/1000);\n",
+      "Test Accuracy of  ship: 83% (1674/2000); quantized: 83% (837/1000);\n",
+      "Test Accuracy of truck: 71% (1426/2000); quantized: 71% (715/1000);\n",
       "\n",
-      "Test Accuracy (Overall): 63% (12686/20000); quantized: 63% (6335/10000)\n"
+      "Test Accuracy (Overall): 66% (13304/20000); quantized: 66% (6646/10000)\n"
      ]
     }
    ],
@@ -1090,12 +1091,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The accuracies are plotted to be compared."
+    "The accuracies for each class are the same (or close, because of the round number). The quantized model classify as wall as the non quantized model. The accuracies are also plotted to be compared."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1115,13 +1116,13 @@
        "  Text(10, 0, 'overall')])"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1161,22 +1162,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
-     "ename": "IndexError",
-     "evalue": "Target 7 is out of bounds.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32mc:\\Users\\oscar\\Documents\\GitHub\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 42\u001b[0m line \u001b[0;36m3\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y164sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m output \u001b[39m=\u001b[39m quantized_model(data)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y164sZmlsZQ%3D%3D?line=31'>32</a>\u001b[0m \u001b[39m# Calculate the batch loss\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y164sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m loss \u001b[39m=\u001b[39m criterion(output, target)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y164sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m \u001b[39m# Backward pass: compute gradient of the loss with respect to model parameters\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y164sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m loss\u001b[39m.\u001b[39mbackward()\n",
-      "File \u001b[1;32mc:\\Users\\oscar\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1516\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
-      "File \u001b[1;32mc:\\Users\\oscar\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1525\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1526\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m   1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   1530\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\oscar\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\loss.py:1179\u001b[0m, in \u001b[0;36mCrossEntropyLoss.forward\u001b[1;34m(self, input, target)\u001b[0m\n\u001b[0;32m   1178\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor, target: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m-> 1179\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mcross_entropy(\u001b[39minput\u001b[39;49m, target, weight\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight,\n\u001b[0;32m   1180\u001b[0m                            ignore_index\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mignore_index, reduction\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mreduction,\n\u001b[0;32m   1181\u001b[0m                            label_smoothing\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlabel_smoothing)\n",
-      "File \u001b[1;32mc:\\Users\\oscar\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\functional.py:3053\u001b[0m, in \u001b[0;36mcross_entropy\u001b[1;34m(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)\u001b[0m\n\u001b[0;32m   3051\u001b[0m \u001b[39mif\u001b[39;00m size_average \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mor\u001b[39;00m reduce \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   3052\u001b[0m     reduction \u001b[39m=\u001b[39m _Reduction\u001b[39m.\u001b[39mlegacy_get_string(size_average, reduce)\n\u001b[1;32m-> 3053\u001b[0m \u001b[39mreturn\u001b[39;00m torch\u001b[39m.\u001b[39;49m_C\u001b[39m.\u001b[39;49m_nn\u001b[39m.\u001b[39;49mcross_entropy_loss(\u001b[39minput\u001b[39;49m, target, weight, _Reduction\u001b[39m.\u001b[39;49mget_enum(reduction), ignore_index, label_smoothing)\n",
-      "\u001b[1;31mIndexError\u001b[0m: Target 7 is out of bounds."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0 \tTraining Loss: 16.182903 \tValidation Loss: 18.824253\n",
+      "Validation loss decreased (inf --> 18.824253).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 16.181128 \tValidation Loss: 18.821360\n",
+      "Validation loss decreased (18.824253 --> 18.821360).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 16.177818 \tValidation Loss: 18.828244\n"
      ]
     }
    ],
@@ -1184,12 +1181,15 @@
     "import torch.optim as optim\n",
     "\n",
     "# Apply quantization to the model\n",
-    "quantized_model = torch.quantization.quantize_dynamic(\n",
+    "AwareQuantized_model = torch.quantization.quantize_dynamic(\n",
     "    model, {torch.nn.Linear}, dtype=torch.qint8\n",
     ")\n",
     "\n",
     "# Prepare the quantized model for training\n",
-    "quantized_model.train()\n",
+    "AwareQuantized_model.train()\n",
+    "AwareQuantized_model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')\n",
+    "torch.quantization.prepare(AwareQuantized_model, inplace=True)\n",
+    "\n",
     "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
     "optimizer = optim.SGD(model.parameters(), lr=0.01)  # specify optimizer\n",
     "\n",
@@ -1203,7 +1203,8 @@
     "    valid_loss = 0.0\n",
     "\n",
     "    # Train the model\n",
-    "    model.train()\n",
+    "    AwareQuantized_model.train()\n",
+    "    # torch.quantization.prepare_qat(quantized_model, inplace=True)\n",
     "    for data, target in train_loader:\n",
     "        # Move tensors to GPU if CUDA is available\n",
     "        if train_on_gpu:\n",
@@ -1211,10 +1212,11 @@
     "        # Clear the gradients of all optimized variables\n",
     "        optimizer.zero_grad()\n",
     "        # Forward pass: compute predicted quantized outputs by passing inputs to the model\n",
-    "        output = quantized_model(data)\n",
+    "        output = AwareQuantized_model(data)\n",
     "        # Calculate the batch loss\n",
     "        loss = criterion(output, target)\n",
     "        # Backward pass: compute gradient of the loss with respect to model parameters\n",
+    "        loss.requires_grad = True\n",
     "        loss.backward()\n",
     "        # Perform a single optimization step (parameter update)\n",
     "        optimizer.step()\n",
@@ -1222,7 +1224,7 @@
     "        train_loss += loss.item() * data.size(0)\n",
     "\n",
     "    # Validate the model\n",
-    "    model.eval()\n",
+    "    AwareQuantized_model.eval()\n",
     "    for data, target in valid_loader:\n",
     "        # Move tensors to GPU if CUDA is available\n",
     "        if train_on_gpu:\n",
@@ -1253,7 +1255,7 @@
     "                valid_loss_min, valid_loss\n",
     "            )\n",
     "        )\n",
-    "        torch.save(model.state_dict(), \"model_cifar_CNN2.pt\") # the model is saved under a new name, so it does not erase the former model version\n",
+    "        torch.save(model.state_dict(), \"model_cifar_CNN3.pt\") # the model is saved under a new name, so it does not erase the former model version\n",
     "        valid_loss_min = valid_loss\n",
     "    # break stops the loop when the validation loss increase. i.e when overfit occures. No need to calculate the models with higher number of epoch\n",
     "    else : \n",
@@ -1261,6 +1263,149 @@
     "        break "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  int8  \t Size (KB): 666.592\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "666592"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print_size_of_model(AwareQuantized_model, \"int8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The aware quantized model is the same size as the post quantized model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the aware quantized model is trained, lets compute its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss, aware quantization model: 19.002453\n",
+      "\n",
+      "Test Accuracy (aware quantized model) of airplane: 60% (603/1000)\n",
+      "Test Accuracy (aware quantized model) of automobile: 80% (805/1000)\n",
+      "Test Accuracy (aware quantized model) of  bird: 64% (641/1000)\n",
+      "Test Accuracy (aware quantized model) of   cat: 62% (629/1000)\n",
+      "Test Accuracy (aware quantized model) of  deer: 50% (503/1000)\n",
+      "Test Accuracy (aware quantized model) of   dog: 52% (529/1000)\n",
+      "Test Accuracy (aware quantized model) of  frog: 66% (661/1000)\n",
+      "Test Accuracy (aware quantized model) of horse: 72% (723/1000)\n",
+      "Test Accuracy (aware quantized model) of  ship: 83% (839/1000)\n",
+      "Test Accuracy (aware quantized model) of truck: 71% (715/1000)\n",
+      "\n",
+      "Test Accuracy (aware quantized model), Overall: 66% (6648/10000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# track test loss\n",
+    "test_loss = 0.0\n",
+    "class_correct = list(0.0 for i in range(10))\n",
+    "class_total = list(0.0 for i in range(10))\n",
+    "\n",
+    "# loading of the model and quantized model\n",
+    "\n",
+    "AwareQuantized_model.eval()\n",
+    "# iterate over test data\n",
+    "for data, target in test_loader:\n",
+    "    test_accuracy_list = [] # list that will store the accuracy values for each class\n",
+    "    test_accuracy_list_quantized = [] # list that will store the accuracy values for each class, for the quantized model\n",
+    "    # move tensors to GPU if CUDA is available\n",
+    "    if train_on_gpu:\n",
+    "        data, target = data.cuda(), target.cuda()\n",
+    "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+    "    output = AwareQuantized_model(data)\n",
+    "    # calculate the batch loss\n",
+    "    loss = criterion(output, target)\n",
+    "    # update test loss\n",
+    "    test_loss += loss.item() * data.size(0)\n",
+    "    # convert output probabilities to predicted class\n",
+    "    _, pred = torch.max(output, 1)\n",
+    "\n",
+    "    # compare predictions to true label\n",
+    "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+    "    correct = (\n",
+    "        np.squeeze(correct_tensor.numpy())\n",
+    "        if not train_on_gpu\n",
+    "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+    "    )\n",
+    "    # calculate test accuracy for each object class\n",
+    "    for i in range(batch_size):\n",
+    "        label = target.data[i]\n",
+    "        class_correct[label] += correct[i].item()\n",
+    "        class_total[label] += 1\n",
+    "\n",
+    "# average test loss\n",
+    "test_loss = test_loss / len(test_loader)\n",
+    "print(\"Test Loss, aware quantization model: {:.6f}\\n\".format(test_loss))\n",
+    "\n",
+    "\n",
+    "for i in range(10):\n",
+    "    if class_total[i] > 0:\n",
+    "        print(\n",
+    "            \"Test Accuracy (aware quantized model) of %5s: %2d%% (%2d/%2d)\"\n",
+    "            % (\n",
+    "                classes[i],\n",
+    "                100 * class_correct[i] / class_total[i],\n",
+    "                np.sum(class_correct[i]),\n",
+    "                np.sum(class_total[i]),\n",
+    "            )\n",
+    "        )\n",
+    "        test_accuracy_list.append(100 * class_correct[i] / class_total[i])\n",
+    "    else:\n",
+    "        print(\"Test Accuracy (aware quantized model) of %5s: N/A (no training examples)\" % (classes[i]))\n",
+    "\n",
+    "print(\n",
+    "    \"\\nTest Accuracy (aware quantized model), Overall: %2d%% (%2d/%2d)\"\n",
+    "    % (\n",
+    "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+    "        np.sum(class_correct),\n",
+    "        np.sum(class_total),\n",
+    "    )\n",
+    ")\n",
+    "test_accuracy_list.append(100.0 * np.sum(class_correct) / np.sum(class_total))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The aware quantized model has the same overall accuracy than the two previous models (quantized and non quantized). We note that for some classes the accuracy differ."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "201470f9",
@@ -2673,54 +2818,6 @@
     "The accuracy of the quantized model is the same as the accuracy of the non quantized model."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lets try the quantization aware. The model is trained from the start with quantized weigths and activations, instead of converting them post training. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/10\n",
-      "----------\n"
-     ]
-    },
-    {
-     "ename": "RuntimeError",
-     "evalue": "element 0 of tensors does not require grad and does not have a grad_fn",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[1;32mc:\\Users\\oscar\\Documents\\GitHub\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 71\u001b[0m line \u001b[0;36m8\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y162sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m optimizer_conv \u001b[39m=\u001b[39m optim\u001b[39m.\u001b[39mSGD(awareQuantized_model\u001b[39m.\u001b[39mparameters(), lr\u001b[39m=\u001b[39m\u001b[39m0.001\u001b[39m, momentum\u001b[39m=\u001b[39m\u001b[39m0.9\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y162sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m exp_lr_scheduler \u001b[39m=\u001b[39m lr_scheduler\u001b[39m.\u001b[39mStepLR(optimizer_conv, step_size\u001b[39m=\u001b[39m\u001b[39m7\u001b[39m, gamma\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y162sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m awareQuantized_model, epoch_time \u001b[39m=\u001b[39m train_model(\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y162sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m     awareQuantized_model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y162sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m )\n",
-      "\u001b[1;32mc:\\Users\\oscar\\Documents\\GitHub\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 71\u001b[0m line \u001b[0;36m1\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y162sZmlsZQ%3D%3D?line=130'>131</a>\u001b[0m     \u001b[39m# backward + optimize only if in training phase\u001b[39;00m\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y162sZmlsZQ%3D%3D?line=131'>132</a>\u001b[0m     \u001b[39mif\u001b[39;00m phase \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m--> <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y162sZmlsZQ%3D%3D?line=132'>133</a>\u001b[0m         loss\u001b[39m.\u001b[39;49mbackward()\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y162sZmlsZQ%3D%3D?line=133'>134</a>\u001b[0m         optimizer\u001b[39m.\u001b[39mstep()\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/oscar/Documents/GitHub/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y162sZmlsZQ%3D%3D?line=135'>136</a>\u001b[0m \u001b[39m# Statistics\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\oscar\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\_tensor.py:492\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    482\u001b[0m \u001b[39mif\u001b[39;00m has_torch_function_unary(\u001b[39mself\u001b[39m):\n\u001b[0;32m    483\u001b[0m     \u001b[39mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    484\u001b[0m         Tensor\u001b[39m.\u001b[39mbackward,\n\u001b[0;32m    485\u001b[0m         (\u001b[39mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    490\u001b[0m         inputs\u001b[39m=\u001b[39minputs,\n\u001b[0;32m    491\u001b[0m     )\n\u001b[1;32m--> 492\u001b[0m torch\u001b[39m.\u001b[39;49mautograd\u001b[39m.\u001b[39;49mbackward(\n\u001b[0;32m    493\u001b[0m     \u001b[39mself\u001b[39;49m, gradient, retain_graph, create_graph, inputs\u001b[39m=\u001b[39;49minputs\n\u001b[0;32m    494\u001b[0m )\n",
-      "File \u001b[1;32mc:\\Users\\oscar\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\autograd\\__init__.py:251\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    246\u001b[0m     retain_graph \u001b[39m=\u001b[39m create_graph\n\u001b[0;32m    248\u001b[0m \u001b[39m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m    249\u001b[0m \u001b[39m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    250\u001b[0m \u001b[39m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 251\u001b[0m Variable\u001b[39m.\u001b[39;49m_execution_engine\u001b[39m.\u001b[39;49mrun_backward(  \u001b[39m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    252\u001b[0m     tensors,\n\u001b[0;32m    253\u001b[0m     grad_tensors_,\n\u001b[0;32m    254\u001b[0m     retain_graph,\n\u001b[0;32m    255\u001b[0m     create_graph,\n\u001b[0;32m    256\u001b[0m     inputs,\n\u001b[0;32m    257\u001b[0m     allow_unreachable\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[0;32m    258\u001b[0m     accumulate_grad\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[0;32m    259\u001b[0m )\n",
-      "\u001b[1;31mRuntimeError\u001b[0m: element 0 of tensors does not require grad and does not have a grad_fn"
-     ]
-    }
-   ],
-   "source": [
-    "# Perform quantization-aware training\n",
-    "awareQuantized_model = torch.quantization.quantize_dynamic(\n",
-    "    model, {torch.nn.Linear}, dtype=torch.qint8\n",
-    ")\n",
-    "criterion = torch.nn.CrossEntropyLoss()\n",
-    "optimizer_conv = optim.SGD(awareQuantized_model.parameters(), lr=0.001, momentum=0.9)\n",
-    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
-    "awareQuantized_model, epoch_time = train_model(\n",
-    "    awareQuantized_model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
-    ")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "04a263f0",
-- 
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