From d42123b81a79422cf0cc2370c6dfe8a68c92bd7e Mon Sep 17 00:00:00 2001
From: Khalil <medkhalilbabay@gmail.com>
Date: Fri, 1 Dec 2023 18:25:17 +0100
Subject: [PATCH] commit

still having problems with quantization functions/parts.
---
 TD2 Deep Learning.ipynb                       | 1210 ++++++++++++++---
 hymenoptera_data/test/aunts/fourmiss.jfif     |  Bin 0 -> 7567 bytes
 hymenoptera_data/test/aunts/fourmissss.jfif   |  Bin 0 -> 7247 bytes
 hymenoptera_data/test/bees/bee.jfif           |  Bin 0 -> 5858 bytes
 hymenoptera_data/test/bees/bee.webp           |  Bin 0 -> 96410 bytes
 hymenoptera_data/test/bees/beess.jfif         |  Bin 0 -> 17538 bytes
 hymenoptera_data/test/bees/beesss.webp        |  Bin 0 -> 268168 bytes
 hymenoptera_data/train/ants/formica.jpeg      |  Bin 0 -> 7858 bytes
 hymenoptera_data/train/ants/imageNotFound.gif |  Bin 0 -> 5504 bytes
 9 files changed, 1005 insertions(+), 205 deletions(-)
 create mode 100644 hymenoptera_data/test/aunts/fourmiss.jfif
 create mode 100644 hymenoptera_data/test/aunts/fourmissss.jfif
 create mode 100644 hymenoptera_data/test/bees/bee.jfif
 create mode 100644 hymenoptera_data/test/bees/bee.webp
 create mode 100644 hymenoptera_data/test/bees/beess.jfif
 create mode 100644 hymenoptera_data/test/bees/beesss.webp
 create mode 100644 hymenoptera_data/train/ants/formica.jpeg
 create mode 100644 hymenoptera_data/train/ants/imageNotFound.gif

diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index c44f423..95604c3 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 30,
    "id": "330a42f5",
    "metadata": {},
    "outputs": [
@@ -41,7 +41,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: torch in c:\\users\\lenovo\\anaconda3\\envs\\new\\lib\\site-packages (2.1.1+cu118)\n",
+      "Requirement already satisfied: torch in c:\\users\\lenovo\\anaconda3\\envs\\new\\lib\\site-packages (2.1.1+cu118)Note: you may need to restart the kernel to use updated packages.\n",
+      "\n",
       "Requirement already satisfied: torchvision in c:\\users\\lenovo\\anaconda3\\envs\\new\\lib\\site-packages (0.16.1+cu118)\n",
       "Requirement already satisfied: filelock in c:\\users\\lenovo\\anaconda3\\envs\\new\\lib\\site-packages (from torch) (3.9.0)\n",
       "Requirement already satisfied: typing-extensions in c:\\users\\lenovo\\anaconda3\\envs\\new\\lib\\site-packages (from torch) (4.8.0)\n",
@@ -57,8 +58,7 @@
       "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\lenovo\\anaconda3\\envs\\new\\lib\\site-packages (from requests->torchvision) (3.4)\n",
       "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\lenovo\\anaconda3\\envs\\new\\lib\\site-packages (from requests->torchvision) (1.26.13)\n",
       "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\lenovo\\anaconda3\\envs\\new\\lib\\site-packages (from requests->torchvision) (2022.12.7)\n",
-      "Requirement already satisfied: mpmath>=0.19 in c:\\users\\lenovo\\anaconda3\\envs\\new\\lib\\site-packages (from sympy->torch) (1.3.0)\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
+      "Requirement already satisfied: mpmath>=0.19 in c:\\users\\lenovo\\anaconda3\\envs\\new\\lib\\site-packages (from sympy->torch) (1.3.0)\n"
      ]
     }
    ],
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 31,
    "id": "b1950f0a",
    "metadata": {},
    "outputs": [
@@ -85,34 +85,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([[ 0.6005, -0.8262, -1.1444,  1.8317,  0.2427,  1.2135,  0.5529,  0.3090,\n",
-      "          0.1768, -0.0671],\n",
-      "        [ 1.5132,  0.6618,  1.2110,  1.8297, -0.4285, -0.4998,  0.4473, -0.2253,\n",
-      "          1.4177, -0.0405],\n",
-      "        [-0.0883,  0.6764,  0.3432, -1.2304,  2.6615,  1.1792,  1.4577,  1.5665,\n",
-      "         -1.7107, -0.2310],\n",
-      "        [-1.2376,  0.3868,  0.4568, -1.3576,  1.7694,  1.4209,  2.0126, -0.2384,\n",
-      "         -0.9737, -0.5757],\n",
-      "        [ 0.1787, -0.5972,  0.9511,  0.0971,  0.0702,  0.6209, -0.5282,  0.1848,\n",
-      "         -0.4190,  3.4575],\n",
-      "        [ 0.3149,  2.1750, -1.6734, -0.0107, -0.2639,  0.3729, -0.3992,  1.0509,\n",
-      "         -0.1983, -0.5388],\n",
-      "        [-0.4991, -0.0539,  1.3420,  1.1376,  1.0812, -0.9487,  0.2711, -0.1039,\n",
-      "          0.6608,  0.1926],\n",
-      "        [-0.8309,  0.3424,  0.7537,  1.1209, -0.6249,  0.9049,  0.0279, -0.9683,\n",
-      "          1.5207,  1.3997],\n",
-      "        [-0.8293, -0.9169, -0.8743,  1.1780, -0.6684,  1.0099, -0.9231, -0.9749,\n",
-      "          0.6912, -0.9500],\n",
-      "        [ 0.4564,  0.5852,  1.0717, -0.9455, -0.6503,  1.3128,  0.3559,  0.1450,\n",
-      "          0.0160,  1.7602],\n",
-      "        [-1.3473, -0.6786,  0.2884,  1.3234,  0.6250, -0.0373,  0.5641,  0.5425,\n",
-      "          1.3457,  2.0471],\n",
-      "        [ 0.7055,  0.2446,  2.1138,  0.1851, -1.1334, -1.0468,  1.2929,  0.5373,\n",
-      "          0.9560,  0.1556],\n",
-      "        [-0.6760,  1.7166,  0.8109, -0.9636, -0.9815, -0.4989,  0.1184, -0.7103,\n",
-      "          0.3448,  0.4121],\n",
-      "        [ 0.6817, -1.2063,  0.8555, -0.7569, -1.3178, -0.9650, -1.0587,  1.2064,\n",
-      "         -0.0845,  0.6830]])\n",
+      "tensor([[-0.6581, -0.9375, -0.4236, -0.9730,  0.4997,  0.7691, -1.0877,  1.3272,\n",
+      "          0.9560,  1.0622],\n",
+      "        [ 0.8443,  0.1934,  1.2779, -2.1645,  0.0195,  0.8859,  0.8484,  0.7258,\n",
+      "         -1.0687, -0.0304],\n",
+      "        [-1.0095,  0.6243, -0.2460,  0.6983, -1.0557,  0.1040, -0.4034, -1.2477,\n",
+      "          0.2465,  0.5206],\n",
+      "        [-0.3276, -0.0150,  0.1173, -0.4353, -0.9920, -0.4391,  0.7819,  0.2454,\n",
+      "          0.2561, -0.1743],\n",
+      "        [ 0.9841, -1.4059,  0.4776,  0.4854,  0.4299,  0.4048,  1.2392,  0.1488,\n",
+      "          0.1296, -0.2167],\n",
+      "        [-1.2676, -0.2153,  1.2487, -0.6098, -0.3791, -1.0197,  0.7996, -0.1532,\n",
+      "         -0.1854,  1.1354],\n",
+      "        [-0.7575,  1.4485, -0.3636, -0.6507,  1.1930,  0.0662, -2.0674, -0.5319,\n",
+      "          0.6456,  0.2321],\n",
+      "        [ 1.3597,  1.4130, -0.0596,  2.2069, -0.6001,  1.2625, -0.4797, -0.3748,\n",
+      "          0.0629, -0.0761],\n",
+      "        [-0.4647,  0.7024, -0.1922,  0.6327, -0.4248,  0.0032,  0.2901,  0.4248,\n",
+      "         -0.3362, -0.0540],\n",
+      "        [ 0.9851,  0.2495,  0.8352,  0.0858, -0.0050,  0.1876, -1.5141, -0.4408,\n",
+      "         -0.7165,  1.2877],\n",
+      "        [ 0.6959, -0.3172,  1.4623, -0.2210, -0.4739, -1.8028,  1.6237, -0.6854,\n",
+      "         -1.2045,  0.9305],\n",
+      "        [-1.2063, -0.2017, -0.0025,  0.5756,  1.0949,  1.7396, -0.9888, -1.2622,\n",
+      "         -0.8575,  0.1792],\n",
+      "        [-0.5867, -0.4581,  0.0387, -0.4708,  0.1158, -0.0322, -0.9158,  0.3819,\n",
+      "         -1.0462, -0.4289],\n",
+      "        [-0.9968,  0.1882,  0.4067,  0.1511, -0.2564, -0.5459,  0.4712, -0.6354,\n",
+      "         -0.2393,  0.2999]])\n",
       "AlexNet(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 32,
    "id": "6e18f2fd",
    "metadata": {},
    "outputs": [
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 33,
    "id": "462666a2",
    "metadata": {},
    "outputs": [
@@ -297,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 34,
    "id": "317bf070",
    "metadata": {},
    "outputs": [
@@ -361,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 35,
    "id": "4b53f229",
    "metadata": {},
    "outputs": [
@@ -369,47 +369,52 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 \tTraining Loss: 43.400863 \tValidation Loss: 38.021109\n",
-      "Validation loss decreased (inf --> 38.021109).  Saving model ...\n",
-      "Epoch: 1 \tTraining Loss: 35.353367 \tValidation Loss: 32.136121\n",
-      "Validation loss decreased (38.021109 --> 32.136121).  Saving model ...\n",
-      "Epoch: 2 \tTraining Loss: 30.913728 \tValidation Loss: 29.369840\n",
-      "Validation loss decreased (32.136121 --> 29.369840).  Saving model ...\n",
-      "Epoch: 3 \tTraining Loss: 28.601452 \tValidation Loss: 27.490179\n",
-      "Validation loss decreased (29.369840 --> 27.490179).  Saving model ...\n",
-      "Epoch: 4 \tTraining Loss: 26.915667 \tValidation Loss: 26.303214\n",
-      "Validation loss decreased (27.490179 --> 26.303214).  Saving model ...\n",
-      "Epoch: 5 \tTraining Loss: 25.459954 \tValidation Loss: 25.887691\n",
-      "Validation loss decreased (26.303214 --> 25.887691).  Saving model ...\n",
-      "Epoch: 6 \tTraining Loss: 24.229395 \tValidation Loss: 24.873652\n",
-      "Validation loss decreased (25.887691 --> 24.873652).  Saving model ...\n",
-      "Epoch: 7 \tTraining Loss: 23.164625 \tValidation Loss: 23.284470\n",
-      "Validation loss decreased (24.873652 --> 23.284470).  Saving model ...\n",
-      "Epoch: 8 \tTraining Loss: 22.206993 \tValidation Loss: 23.291942\n",
-      "Epoch: 9 \tTraining Loss: 21.421621 \tValidation Loss: 23.335741\n",
-      "Epoch: 10 \tTraining Loss: 20.651834 \tValidation Loss: 22.401363\n",
-      "Validation loss decreased (23.284470 --> 22.401363).  Saving model ...\n",
-      "Epoch: 11 \tTraining Loss: 19.985618 \tValidation Loss: 24.326989\n",
-      "Epoch: 12 \tTraining Loss: 19.291143 \tValidation Loss: 21.739429\n",
-      "Validation loss decreased (22.401363 --> 21.739429).  Saving model ...\n",
-      "Epoch: 13 \tTraining Loss: 18.624268 \tValidation Loss: 21.852032\n",
-      "Epoch: 14 \tTraining Loss: 17.965765 \tValidation Loss: 21.446357\n",
-      "Validation loss decreased (21.739429 --> 21.446357).  Saving model ...\n",
-      "Epoch: 15 \tTraining Loss: 17.394854 \tValidation Loss: 22.545597\n",
-      "Epoch: 16 \tTraining Loss: 16.794884 \tValidation Loss: 22.042855\n",
-      "Epoch: 17 \tTraining Loss: 16.227497 \tValidation Loss: 22.688590\n",
-      "Epoch: 18 \tTraining Loss: 15.704653 \tValidation Loss: 22.186874\n",
-      "Epoch: 19 \tTraining Loss: 15.155005 \tValidation Loss: 22.500867\n",
-      "Epoch: 20 \tTraining Loss: 14.652102 \tValidation Loss: 22.332764\n",
-      "Epoch: 21 \tTraining Loss: 14.156440 \tValidation Loss: 22.655204\n",
-      "Epoch: 22 \tTraining Loss: 13.740071 \tValidation Loss: 22.890621\n",
-      "Epoch: 23 \tTraining Loss: 13.230510 \tValidation Loss: 23.827616\n",
-      "Epoch: 24 \tTraining Loss: 12.847678 \tValidation Loss: 23.486764\n",
-      "Epoch: 25 \tTraining Loss: 12.306719 \tValidation Loss: 24.269633\n",
-      "Epoch: 26 \tTraining Loss: 11.897007 \tValidation Loss: 24.318152\n",
-      "Epoch: 27 \tTraining Loss: 11.547802 \tValidation Loss: 24.906728\n",
-      "Epoch: 28 \tTraining Loss: 11.044322 \tValidation Loss: 25.903962\n",
-      "Epoch: 29 \tTraining Loss: 10.720178 \tValidation Loss: 25.918305\n"
+      "Epoch: 0 \tTraining Loss: 43.215441 \tValidation Loss: 37.683124\n",
+      "Validation loss decreased (inf --> 37.683124).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 34.673784 \tValidation Loss: 31.409208\n",
+      "Validation loss decreased (37.683124 --> 31.409208).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 30.805413 \tValidation Loss: 29.304087\n",
+      "Validation loss decreased (31.409208 --> 29.304087).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 28.766397 \tValidation Loss: 27.333866\n",
+      "Validation loss decreased (29.304087 --> 27.333866).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 27.169207 \tValidation Loss: 26.484749\n",
+      "Validation loss decreased (27.333866 --> 26.484749).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 25.691545 \tValidation Loss: 24.975418\n",
+      "Validation loss decreased (26.484749 --> 24.975418).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 24.405742 \tValidation Loss: 24.431968\n",
+      "Validation loss decreased (24.975418 --> 24.431968).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 23.308638 \tValidation Loss: 23.782930\n",
+      "Validation loss decreased (24.431968 --> 23.782930).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 22.316762 \tValidation Loss: 22.560685\n",
+      "Validation loss decreased (23.782930 --> 22.560685).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 21.413378 \tValidation Loss: 22.442556\n",
+      "Validation loss decreased (22.560685 --> 22.442556).  Saving model ...\n",
+      "Epoch: 10 \tTraining Loss: 20.611512 \tValidation Loss: 22.047878\n",
+      "Validation loss decreased (22.442556 --> 22.047878).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 19.799721 \tValidation Loss: 21.767571\n",
+      "Validation loss decreased (22.047878 --> 21.767571).  Saving model ...\n",
+      "Epoch: 12 \tTraining Loss: 19.087978 \tValidation Loss: 21.913128\n",
+      "Epoch: 13 \tTraining Loss: 18.407649 \tValidation Loss: 21.745113\n",
+      "Validation loss decreased (21.767571 --> 21.745113).  Saving model ...\n",
+      "Epoch: 14 \tTraining Loss: 17.684496 \tValidation Loss: 21.537710\n",
+      "Validation loss decreased (21.745113 --> 21.537710).  Saving model ...\n",
+      "Epoch: 15 \tTraining Loss: 17.021198 \tValidation Loss: 22.406302\n",
+      "Epoch: 16 \tTraining Loss: 16.468190 \tValidation Loss: 21.243899\n",
+      "Validation loss decreased (21.537710 --> 21.243899).  Saving model ...\n",
+      "Epoch: 17 \tTraining Loss: 15.804495 \tValidation Loss: 21.209673\n",
+      "Validation loss decreased (21.243899 --> 21.209673).  Saving model ...\n",
+      "Epoch: 18 \tTraining Loss: 15.286864 \tValidation Loss: 21.345999\n",
+      "Epoch: 19 \tTraining Loss: 14.751690 \tValidation Loss: 22.127771\n",
+      "Epoch: 20 \tTraining Loss: 14.224813 \tValidation Loss: 22.072356\n",
+      "Epoch: 21 \tTraining Loss: 13.685973 \tValidation Loss: 22.520048\n",
+      "Epoch: 22 \tTraining Loss: 13.272711 \tValidation Loss: 23.559879\n",
+      "Epoch: 23 \tTraining Loss: 12.842664 \tValidation Loss: 23.841250\n",
+      "Epoch: 24 \tTraining Loss: 12.350914 \tValidation Loss: 23.073734\n",
+      "Epoch: 25 \tTraining Loss: 11.915530 \tValidation Loss: 22.988760\n",
+      "Epoch: 26 \tTraining Loss: 11.476627 \tValidation Loss: 24.483539\n",
+      "Epoch: 27 \tTraining Loss: 11.095261 \tValidation Loss: 24.730821\n",
+      "Epoch: 28 \tTraining Loss: 10.728006 \tValidation Loss: 25.559204\n",
+      "Epoch: 29 \tTraining Loss: 10.407424 \tValidation Loss: 25.359876\n"
      ]
     }
    ],
@@ -503,18 +508,19 @@
     "If the model is trained for too many epochs, it may start fitting the noise in the training data, leading to a decrease in training loss but an increase in validation loss.\n",
     "Overfitting is often identified when the training loss continues to decrease while the validation loss starts to increase, indicating that the model is becoming too specialized for the training data. \n",
     "To adress overfitting, we perform an early stopping when the validation loss starts to increase. \n",
+    "\n",
     "---\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 36,
    "id": "d39df818",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -535,6 +541,16 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "we can see on the graph how the validation loss starts to inscrease while the training loss is decreasing : this is overfitting\n",
+    "\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "11df8fd4",
@@ -545,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 37,
    "id": "e93efdfc",
    "metadata": {},
    "outputs": [
@@ -553,20 +569,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 21.956040\n",
+      "Test Loss: 21.267196\n",
       "\n",
-      "Test Accuracy of airplane: 67% (670/1000)\n",
-      "Test Accuracy of automobile: 78% (786/1000)\n",
-      "Test Accuracy of  bird: 57% (574/1000)\n",
-      "Test Accuracy of   cat: 46% (462/1000)\n",
-      "Test Accuracy of  deer: 54% (548/1000)\n",
-      "Test Accuracy of   dog: 46% (460/1000)\n",
-      "Test Accuracy of  frog: 73% (734/1000)\n",
-      "Test Accuracy of horse: 61% (615/1000)\n",
-      "Test Accuracy of  ship: 70% (703/1000)\n",
-      "Test Accuracy of truck: 64% (641/1000)\n",
+      "Test Accuracy of airplane: 71% (716/1000)\n",
+      "Test Accuracy of automobile: 74% (747/1000)\n",
+      "Test Accuracy of  bird: 47% (474/1000)\n",
+      "Test Accuracy of   cat: 42% (428/1000)\n",
+      "Test Accuracy of  deer: 59% (598/1000)\n",
+      "Test Accuracy of   dog: 56% (564/1000)\n",
+      "Test Accuracy of  frog: 74% (748/1000)\n",
+      "Test Accuracy of horse: 69% (694/1000)\n",
+      "Test Accuracy of  ship: 81% (817/1000)\n",
+      "Test Accuracy of truck: 62% (622/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 61% (6193/10000)\n"
+      "Test Accuracy (Overall): 64% (6408/10000)\n"
      ]
     }
    ],
@@ -658,12 +674,13 @@
    "source": [
     "---\n",
     "Here is the new network\n",
+    "\n",
     "---"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -734,68 +751,70 @@
    "source": [
     "---\n",
     "Here we train the model2:\n",
+    "\n",
     "---"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 \tTraining Loss: 45.857789 \tValidation Loss: 44.560349\n",
-      "Validation loss decreased (inf --> 44.560349).  Saving model ...\n",
-      "Epoch: 1 \tTraining Loss: 41.778772 \tValidation Loss: 37.877069\n",
-      "Validation loss decreased (44.560349 --> 37.877069).  Saving model ...\n",
-      "Epoch: 2 \tTraining Loss: 37.083519 \tValidation Loss: 32.782693\n",
-      "Validation loss decreased (37.877069 --> 32.782693).  Saving model ...\n",
-      "Epoch: 3 \tTraining Loss: 33.402934 \tValidation Loss: 30.173893\n",
-      "Validation loss decreased (32.782693 --> 30.173893).  Saving model ...\n",
-      "Epoch: 4 \tTraining Loss: 31.229537 \tValidation Loss: 28.297551\n",
-      "Validation loss decreased (30.173893 --> 28.297551).  Saving model ...\n",
-      "Epoch: 5 \tTraining Loss: 29.413370 \tValidation Loss: 27.164286\n",
-      "Validation loss decreased (28.297551 --> 27.164286).  Saving model ...\n",
-      "Epoch: 6 \tTraining Loss: 27.889698 \tValidation Loss: 25.733314\n",
-      "Validation loss decreased (27.164286 --> 25.733314).  Saving model ...\n",
-      "Epoch: 7 \tTraining Loss: 26.490659 \tValidation Loss: 24.033053\n",
-      "Validation loss decreased (25.733314 --> 24.033053).  Saving model ...\n",
-      "Epoch: 8 \tTraining Loss: 25.185638 \tValidation Loss: 22.838416\n",
-      "Validation loss decreased (24.033053 --> 22.838416).  Saving model ...\n",
-      "Epoch: 9 \tTraining Loss: 23.968391 \tValidation Loss: 21.469535\n",
-      "Validation loss decreased (22.838416 --> 21.469535).  Saving model ...\n",
-      "Epoch: 10 \tTraining Loss: 22.870672 \tValidation Loss: 20.855845\n",
-      "Validation loss decreased (21.469535 --> 20.855845).  Saving model ...\n",
-      "Epoch: 11 \tTraining Loss: 21.748044 \tValidation Loss: 20.084015\n",
-      "Validation loss decreased (20.855845 --> 20.084015).  Saving model ...\n",
-      "Epoch: 12 \tTraining Loss: 20.823984 \tValidation Loss: 19.152056\n",
-      "Validation loss decreased (20.084015 --> 19.152056).  Saving model ...\n",
-      "Epoch: 13 \tTraining Loss: 19.860081 \tValidation Loss: 18.863754\n",
-      "Validation loss decreased (19.152056 --> 18.863754).  Saving model ...\n",
-      "Epoch: 14 \tTraining Loss: 19.011136 \tValidation Loss: 18.424995\n",
-      "Validation loss decreased (18.863754 --> 18.424995).  Saving model ...\n",
-      "Epoch: 15 \tTraining Loss: 18.079763 \tValidation Loss: 17.192690\n",
-      "Validation loss decreased (18.424995 --> 17.192690).  Saving model ...\n",
-      "Epoch: 16 \tTraining Loss: 17.376203 \tValidation Loss: 17.144983\n",
-      "Validation loss decreased (17.192690 --> 17.144983).  Saving model ...\n",
-      "Epoch: 17 \tTraining Loss: 16.608376 \tValidation Loss: 16.288037\n",
-      "Validation loss decreased (17.144983 --> 16.288037).  Saving model ...\n",
-      "Epoch: 18 \tTraining Loss: 15.988603 \tValidation Loss: 16.175762\n",
-      "Validation loss decreased (16.288037 --> 16.175762).  Saving model ...\n",
-      "Epoch: 19 \tTraining Loss: 15.412869 \tValidation Loss: 15.441982\n",
-      "Validation loss decreased (16.175762 --> 15.441982).  Saving model ...\n",
-      "Epoch: 20 \tTraining Loss: 14.778294 \tValidation Loss: 15.939022\n",
-      "Epoch: 21 \tTraining Loss: 14.146509 \tValidation Loss: 15.906133\n",
-      "Epoch: 22 \tTraining Loss: 13.569586 \tValidation Loss: 15.517570\n",
-      "Epoch: 23 \tTraining Loss: 13.081360 \tValidation Loss: 16.112445\n",
-      "Epoch: 24 \tTraining Loss: 12.587562 \tValidation Loss: 15.454825\n",
-      "Epoch: 25 \tTraining Loss: 12.073642 \tValidation Loss: 15.664324\n",
-      "Epoch: 26 \tTraining Loss: 11.623898 \tValidation Loss: 15.579594\n",
-      "Epoch: 27 \tTraining Loss: 11.123427 \tValidation Loss: 15.694448\n",
-      "Epoch: 28 \tTraining Loss: 10.639247 \tValidation Loss: 15.685701\n",
-      "Epoch: 29 \tTraining Loss: 10.170218 \tValidation Loss: 16.205130\n"
+      "Epoch: 0 \tTraining Loss: 45.482660 \tValidation Loss: 42.266271\n",
+      "Validation loss decreased (inf --> 42.266271).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 39.624139 \tValidation Loss: 34.163277\n",
+      "Validation loss decreased (42.266271 --> 34.163277).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 34.283577 \tValidation Loss: 31.137031\n",
+      "Validation loss decreased (34.163277 --> 31.137031).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 31.900688 \tValidation Loss: 29.190291\n",
+      "Validation loss decreased (31.137031 --> 29.190291).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 30.024970 \tValidation Loss: 27.142510\n",
+      "Validation loss decreased (29.190291 --> 27.142510).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 28.414487 \tValidation Loss: 27.079086\n",
+      "Validation loss decreased (27.142510 --> 27.079086).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 27.069440 \tValidation Loss: 24.907836\n",
+      "Validation loss decreased (27.079086 --> 24.907836).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 25.704398 \tValidation Loss: 24.346413\n",
+      "Validation loss decreased (24.907836 --> 24.346413).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 24.378956 \tValidation Loss: 21.848465\n",
+      "Validation loss decreased (24.346413 --> 21.848465).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 23.147784 \tValidation Loss: 21.119492\n",
+      "Validation loss decreased (21.848465 --> 21.119492).  Saving model ...\n",
+      "Epoch: 10 \tTraining Loss: 21.956576 \tValidation Loss: 19.890825\n",
+      "Validation loss decreased (21.119492 --> 19.890825).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 20.924181 \tValidation Loss: 19.145526\n",
+      "Validation loss decreased (19.890825 --> 19.145526).  Saving model ...\n",
+      "Epoch: 12 \tTraining Loss: 19.988317 \tValidation Loss: 18.641545\n",
+      "Validation loss decreased (19.145526 --> 18.641545).  Saving model ...\n",
+      "Epoch: 13 \tTraining Loss: 19.151696 \tValidation Loss: 17.945656\n",
+      "Validation loss decreased (18.641545 --> 17.945656).  Saving model ...\n",
+      "Epoch: 14 \tTraining Loss: 18.254571 \tValidation Loss: 17.666480\n",
+      "Validation loss decreased (17.945656 --> 17.666480).  Saving model ...\n",
+      "Epoch: 15 \tTraining Loss: 17.589327 \tValidation Loss: 17.185750\n",
+      "Validation loss decreased (17.666480 --> 17.185750).  Saving model ...\n",
+      "Epoch: 16 \tTraining Loss: 16.816361 \tValidation Loss: 17.874206\n",
+      "Epoch: 17 \tTraining Loss: 16.008379 \tValidation Loss: 16.766704\n",
+      "Validation loss decreased (17.185750 --> 16.766704).  Saving model ...\n",
+      "Epoch: 18 \tTraining Loss: 15.433616 \tValidation Loss: 16.187895\n",
+      "Validation loss decreased (16.766704 --> 16.187895).  Saving model ...\n",
+      "Epoch: 19 \tTraining Loss: 14.788092 \tValidation Loss: 16.156564\n",
+      "Validation loss decreased (16.187895 --> 16.156564).  Saving model ...\n",
+      "Epoch: 20 \tTraining Loss: 14.267352 \tValidation Loss: 16.259150\n",
+      "Epoch: 21 \tTraining Loss: 13.589933 \tValidation Loss: 15.988910\n",
+      "Validation loss decreased (16.156564 --> 15.988910).  Saving model ...\n",
+      "Epoch: 22 \tTraining Loss: 13.048423 \tValidation Loss: 16.629794\n",
+      "Epoch: 23 \tTraining Loss: 12.562278 \tValidation Loss: 16.529023\n",
+      "Epoch: 24 \tTraining Loss: 11.877198 \tValidation Loss: 16.545573\n",
+      "Epoch: 25 \tTraining Loss: 11.499595 \tValidation Loss: 15.879724\n",
+      "Validation loss decreased (15.988910 --> 15.879724).  Saving model ...\n",
+      "Epoch: 26 \tTraining Loss: 11.120298 \tValidation Loss: 16.102519\n",
+      "Epoch: 27 \tTraining Loss: 10.664357 \tValidation Loss: 16.182902\n",
+      "Epoch: 28 \tTraining Loss: 10.180806 \tValidation Loss: 16.197553\n",
+      "Epoch: 29 \tTraining Loss: 9.665137 \tValidation Loss: 17.009737\n"
      ]
     }
    ],
@@ -874,12 +893,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -906,7 +925,8 @@
    "metadata": {},
    "source": [
     "----\n",
-    "We note that validation voss are less than the first model. lets see the accuracy on the test data now:\n",
+    "We note that validation voss are less than the first model.The graph shows that there is less overfitting.\n",
+    " lets see the accuracy on the test data now:\n",
     "\n",
     "---"
    ]
@@ -920,27 +940,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 16.039122\n",
+      "Test Loss: 15.826936\n",
       "\n",
-      "Test Accuracy of airplane: 79% (799/1000)\n",
-      "Test Accuracy of automobile: 82% (820/1000)\n",
-      "Test Accuracy of  bird: 58% (584/1000)\n",
-      "Test Accuracy of   cat: 55% (552/1000)\n",
-      "Test Accuracy of  deer: 72% (723/1000)\n",
-      "Test Accuracy of   dog: 55% (551/1000)\n",
-      "Test Accuracy of  frog: 80% (807/1000)\n",
-      "Test Accuracy of horse: 76% (766/1000)\n",
-      "Test Accuracy of  ship: 86% (862/1000)\n",
-      "Test Accuracy of truck: 79% (790/1000)\n",
+      "Test Accuracy of airplane: 77% (773/1000)\n",
+      "Test Accuracy of automobile: 86% (867/1000)\n",
+      "Test Accuracy of  bird: 58% (585/1000)\n",
+      "Test Accuracy of   cat: 50% (501/1000)\n",
+      "Test Accuracy of  deer: 71% (719/1000)\n",
+      "Test Accuracy of   dog: 61% (610/1000)\n",
+      "Test Accuracy of  frog: 85% (852/1000)\n",
+      "Test Accuracy of horse: 80% (806/1000)\n",
+      "Test Accuracy of  ship: 84% (847/1000)\n",
+      "Test Accuracy of truck: 84% (849/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 72% (7254/10000)\n"
+      "Test Accuracy (Overall): 74% (7409/10000)\n"
      ]
     }
    ],
@@ -1012,7 +1032,7 @@
    "metadata": {},
    "source": [
     "---\n",
-    " Models comparaison: During the training, it's noticibale that the validation loss values are less than the one in the first model. It gives a first idea that the second model is performing better than the first one. This is confirmed by the test accuracy of model2 which is 73%, higher than the one of model1, 61%. So, the addes layer to the cnn net and the other modifications improved the accuracy of the classification.\n",
+    " Models comparaison: During the training, it's noticibale that the validation loss values are less than the one in the first model. It gives a first idea that the second model is performing better than the first one. This is confirmed by the test accuracy of model2 which is 73%, higher than the one of model1, 61%. So, the added layer to the cnn net and the other modifications improved the accuracy of the classification.\n",
     "\n",
     "---"
    ]
@@ -1034,7 +1054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 42,
    "id": "ef623c26",
    "metadata": {},
    "outputs": [
@@ -1051,7 +1071,7 @@
        "251342"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1081,7 +1101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 43,
    "id": "c4c65d4b",
    "metadata": {},
    "outputs": [
@@ -1098,7 +1118,7 @@
        "76650"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1111,6 +1131,16 @@
     "print_size_of_model(quantized_model, \"int8\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "the size of the model decreased considerably : 70%\n",
+    "\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7b108e17",
@@ -1121,7 +1151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1131,10 +1161,10 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 36\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m     data, target \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39mcuda(), target\u001b[39m.\u001b[39mcuda()\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39m# forward pass: compute predicted outputs by passing inputs to the model\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m output \u001b[39m=\u001b[39m quantized_model(data)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m \u001b[39m# calculate the batch loss\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m loss \u001b[39m=\u001b[39m criterion(output, target)\n",
+      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 37\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m     data, target \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39mcuda(), target\u001b[39m.\u001b[39mcuda()\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39m# forward pass: compute predicted outputs by passing inputs to the model\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m output \u001b[39m=\u001b[39m quantized_model(data)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m \u001b[39m# calculate the batch loss\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m loss \u001b[39m=\u001b[39m criterion(output, target)\n",
       "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
       "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\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",
-      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 36\u001b[0m line \u001b[0;36m2\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv2(x)))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m x \u001b[39m=\u001b[39m x\u001b[39m.\u001b[39mview(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m16\u001b[39m \u001b[39m*\u001b[39m \u001b[39m5\u001b[39m \u001b[39m*\u001b[39m \u001b[39m5\u001b[39m)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m x \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfc1(x))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m x \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc2(x))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc3(x)\n",
+      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 37\u001b[0m line \u001b[0;36m2\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv2(x)))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m x \u001b[39m=\u001b[39m x\u001b[39m.\u001b[39mview(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m16\u001b[39m \u001b[39m*\u001b[39m \u001b[39m5\u001b[39m \u001b[39m*\u001b[39m \u001b[39m5\u001b[39m)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m x \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfc1(x))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m x \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc2(x))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X50sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc3(x)\n",
       "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
       "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\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\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torch\\ao\\nn\\quantized\\dynamic\\modules\\linear.py:54\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     51\u001b[0m         Y \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mops\u001b[39m.\u001b[39mquantized\u001b[39m.\u001b[39mlinear_dynamic(\n\u001b[0;32m     52\u001b[0m             x, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_packed_params\u001b[39m.\u001b[39m_packed_params)\n\u001b[0;32m     53\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m---> 54\u001b[0m         Y \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39;49mops\u001b[39m.\u001b[39;49mquantized\u001b[39m.\u001b[39;49mlinear_dynamic(\n\u001b[0;32m     55\u001b[0m             x, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_packed_params\u001b[39m.\u001b[39;49m_packed_params, reduce_range\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[0;32m     56\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_packed_params\u001b[39m.\u001b[39mdtype \u001b[39m==\u001b[39m torch\u001b[39m.\u001b[39mfloat16:\n\u001b[0;32m     57\u001b[0m     Y \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mops\u001b[39m.\u001b[39mquantized\u001b[39m.\u001b[39mlinear_dynamic_fp16(\n\u001b[0;32m     58\u001b[0m         x, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_packed_params\u001b[39m.\u001b[39m_packed_params)\n",
@@ -1210,7 +1240,7 @@
    "metadata": {},
    "source": [
     "---\n",
-    "comments here\n",
+    "error that I couldn't solve\n",
     "\n",
     "---"
    ]
@@ -1225,7 +1255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1279,7 +1309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1297,10 +1327,10 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 40\u001b[0m line \u001b[0;36m3\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=28'>29</a>\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=29'>30</a>\u001b[0m \u001b[39m# Forward pass: compute predicted outputs by passing inputs to the model\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m output \u001b[39m=\u001b[39m model_qat(data)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=31'>32</a>\u001b[0m \u001b[39m# Calculate the batch loss\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m loss \u001b[39m=\u001b[39m criterion(output, target)\n",
+      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 42\u001b[0m line \u001b[0;36m3\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=28'>29</a>\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=29'>30</a>\u001b[0m \u001b[39m# Forward pass: compute predicted outputs by passing inputs to the model\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m output \u001b[39m=\u001b[39m model_qat(data)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=31'>32</a>\u001b[0m \u001b[39m# Calculate the batch loss\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m loss \u001b[39m=\u001b[39m criterion(output, target)\n",
       "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
       "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\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",
-      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 40\u001b[0m line \u001b[0;36m3\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mquant(x)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconv1(x)))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv2(x)))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=36'>37</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv3(x)))\n",
+      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 42\u001b[0m line \u001b[0;36m3\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mquant(x)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconv1(x)))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv2(x)))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X54sZmlsZQ%3D%3D?line=36'>37</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv3(x)))\n",
       "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
       "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\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\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torch\\nn\\modules\\conv.py:460\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    459\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 460\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_conv_forward(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n",
@@ -1398,9 +1428,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 44\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m     data, target \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39mcuda(), target\u001b[39m.\u001b[39mcuda()\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m \u001b[39m# forward pass: compute predicted outputs by passing inputs to the model\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m output \u001b[39m=\u001b[39m Aware_model(data)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m \u001b[39m# calculate the batch loss\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m loss \u001b[39m=\u001b[39m criterion(output, target)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\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",
+      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 44\u001b[0m line \u001b[0;36m3\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mquant(x)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconv1(x)))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv2(x)))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=36'>37</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv3(x)))\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\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\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torch\\nn\\modules\\conv.py:460\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    459\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 460\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_conv_forward(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torch\\nn\\modules\\conv.py:456\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m    452\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode \u001b[39m!=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mzeros\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m    453\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39mconv2d(F\u001b[39m.\u001b[39mpad(\u001b[39minput\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode),\n\u001b[0;32m    454\u001b[0m                     weight, bias, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstride,\n\u001b[0;32m    455\u001b[0m                     _pair(\u001b[39m0\u001b[39m), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdilation, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgroups)\n\u001b[1;32m--> 456\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mconv2d(\u001b[39minput\u001b[39;49m, weight, bias, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstride,\n\u001b[0;32m    457\u001b[0m                 \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpadding, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdilation, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgroups)\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same"
+     ]
+    }
+   ],
    "source": [
     "# track test loss\n",
     "test_loss = 0.0\n",
@@ -1490,7 +1539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 48,
    "id": "b4d13080",
    "metadata": {},
    "outputs": [
@@ -1498,8 +1547,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\Users\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
-      "  warnings.warn(\n",
       "c:\\Users\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
       "  warnings.warn(msg)\n"
      ]
@@ -1602,7 +1649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -1618,7 +1665,7 @@
        "102523238"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1642,7 +1689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1658,7 +1705,7 @@
        "96379996"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1670,19 +1717,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
-      "  warnings.warn(\n",
-      "c:\\Users\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
-      "  warnings.warn(msg)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -1756,10 +1793,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "id": "be2d31f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -1848,19 +1896,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 54,
    "id": "572d824c",
    "metadata": {},
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'matplotlib'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 49\u001b[0m line \u001b[0;36m5\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X66sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mos\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X66sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtime\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X66sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X66sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#X66sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\n",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n",
+      "train Loss: 0.6142 Acc: 0.6325\n",
+      "val Loss: 0.2380 Acc: 0.9216\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.4751 Acc: 0.7692\n",
+      "val Loss: 0.2016 Acc: 0.9542\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.4424 Acc: 0.7863\n",
+      "val Loss: 0.4686 Acc: 0.7974\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.5572 Acc: 0.7650\n",
+      "val Loss: 0.3039 Acc: 0.8758\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.4906 Acc: 0.7906\n",
+      "val Loss: 0.1446 Acc: 0.9477\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.3384 Acc: 0.8590\n",
+      "val Loss: 0.1722 Acc: 0.9412\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.3790 Acc: 0.8077\n",
+      "val Loss: 0.1833 Acc: 0.9477\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.2732 Acc: 0.8718\n",
+      "val Loss: 0.1769 Acc: 0.9281\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.3956 Acc: 0.8248\n",
+      "val Loss: 0.2052 Acc: 0.9412\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.3519 Acc: 0.8333\n",
+      "val Loss: 0.1860 Acc: 0.9281\n",
+      "\n",
+      "Training complete in 2m 33s\n",
+      "Best val Acc: 0.954248\n"
      ]
     }
    ],
@@ -2061,6 +2156,711 @@
     "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "CODE MODIFICATION + \"eval_model\" function\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n",
+      "train Loss: 0.5791 Acc: 0.6496\n",
+      "val Loss: 0.2132 Acc: 0.9085\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.5581 Acc: 0.7308\n",
+      "val Loss: 0.1951 Acc: 0.9216\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.4265 Acc: 0.7991\n",
+      "val Loss: 0.1586 Acc: 0.9542\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.4847 Acc: 0.7991\n",
+      "val Loss: 0.1387 Acc: 0.9412\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.5067 Acc: 0.7692\n",
+      "val Loss: 0.1500 Acc: 0.9542\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.4839 Acc: 0.8077\n",
+      "val Loss: 0.2437 Acc: 0.9150\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.4148 Acc: 0.8333\n",
+      "val Loss: 0.1465 Acc: 0.9542\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.3734 Acc: 0.8419\n",
+      "val Loss: 0.1605 Acc: 0.9412\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.3514 Acc: 0.8462\n",
+      "val Loss: 0.1513 Acc: 0.9542\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.2932 Acc: 0.8675\n",
+      "val Loss: 0.1620 Acc: 0.9477\n",
+      "\n",
+      "Training complete in 1m 55s\n",
+      "Best val Acc: 0.954248\n",
+      "Test Accuracy of  ants: 100% (11/11)\n",
+      "Test Accuracy of  bees: 66% ( 6/ 9)\n",
+      "\n",
+      "Test Accuracy (Overall): 85% (17/20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import torchvision\n",
+    "from torch.optim import lr_scheduler\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "# Data augmentation and normalization for training\n",
+    "# Just normalization for validation\n",
+    "data_transforms = {\n",
+    "    \"train\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.RandomResizedCrop(\n",
+    "                224\n",
+    "            ),  # ImageNet models were trained on 224x224 images\n",
+    "            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n",
+    "            transforms.ToTensor(),  # convert it to a PyTorch tensor\n",
+    "            transforms.Normalize(\n",
+    "                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
+    "            ),  # ImageNet models expect this norm\n",
+    "        ]\n",
+    "    ),\n",
+    "    \"val\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.Resize(256),\n",
+    "            transforms.CenterCrop(224),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "        ]\n",
+    "    ),\n",
+    "    \"test\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.Resize(256),\n",
+    "            transforms.CenterCrop(224),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "        ]\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "data_dir = \"hymenoptera_data\"\n",
+    "# Create train and validation datasets and loaders\n",
+    "image_datasets = {\n",
+    "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
+    "    for x in [\"train\", \"val\",\"test\"]\n",
+    "}\n",
+    "dataloaders = {\n",
+    "    x: torch.utils.data.DataLoader(\n",
+    "        image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n",
+    "    )\n",
+    "    for x in [\"train\", \"val\",\"test\"]\n",
+    "}\n",
+    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\",\"test\"]}\n",
+    "class_names = image_datasets[\"train\"].classes\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "# Helper function for displaying images\n",
+    "def imshow(inp, title=None):\n",
+    "    \"\"\"Imshow for Tensor.\"\"\"\n",
+    "    inp = inp.numpy().transpose((1, 2, 0))\n",
+    "    mean = np.array([0.485, 0.456, 0.406])\n",
+    "    std = np.array([0.229, 0.224, 0.225])\n",
+    "\n",
+    "    # Un-normalize the images\n",
+    "    inp = std * inp + mean\n",
+    "    # Clip just in case\n",
+    "    inp = np.clip(inp, 0, 1)\n",
+    "    plt.imshow(inp)\n",
+    "    if title is not None:\n",
+    "        plt.title(title)\n",
+    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# Get a batch of training data\n",
+    "# inputs, classes = next(iter(dataloaders['train']))\n",
+    "\n",
+    "# Make a grid from batch\n",
+    "# out = torchvision.utils.make_grid(inputs)\n",
+    "\n",
+    "# imshow(out, title=[class_names[x] for x in classes])\n",
+    "# training\n",
+    "\n",
+    "\n",
+    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+    "    since = time.time()\n",
+    "\n",
+    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "    best_acc = 0.0\n",
+    "\n",
+    "    epoch_time = []  # we'll keep track of the time needed for each epoch\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        epoch_start = time.time()\n",
+    "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
+    "        print(\"-\" * 10)\n",
+    "\n",
+    "        # Each epoch has a training and validation phase\n",
+    "        for phase in [\"train\", \"val\"]:\n",
+    "            if phase == \"train\":\n",
+    "                scheduler.step()\n",
+    "                model.train()  # Set model to training mode\n",
+    "            else:\n",
+    "                model.eval()  # Set model to evaluate mode\n",
+    "\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "\n",
+    "            # Iterate over data.\n",
+    "            for inputs, labels in dataloaders[phase]:\n",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "\n",
+    "                # zero the parameter gradients\n",
+    "                optimizer.zero_grad()\n",
+    "\n",
+    "                # Forward\n",
+    "                # Track history if only in training phase\n",
+    "                with torch.set_grad_enabled(phase == \"train\"):\n",
+    "                    outputs = model(inputs)\n",
+    "                    _, preds = torch.max(outputs, 1)\n",
+    "                    loss = criterion(outputs, labels)\n",
+    "\n",
+    "                    # backward + optimize only if in training phase\n",
+    "                    if phase == \"train\":\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "\n",
+    "                # Statistics\n",
+    "                running_loss += loss.item() * inputs.size(0)\n",
+    "                running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+    "\n",
+    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
+    "\n",
+    "            # Deep copy the model\n",
+    "            if phase == \"val\" and epoch_acc > best_acc:\n",
+    "                best_acc = epoch_acc\n",
+    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "\n",
+    "        # Add the epoch time\n",
+    "        t_epoch = time.time() - epoch_start\n",
+    "        epoch_time.append(t_epoch)\n",
+    "        print()\n",
+    "\n",
+    "    time_elapsed = time.time() - since\n",
+    "    print(\n",
+    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
+    "            time_elapsed // 60, time_elapsed % 60\n",
+    "        )\n",
+    "    )\n",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    # Load best model weights\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model, epoch_time\n",
+    "\n",
+    "\n",
+    "# Download a pre-trained ResNet18 model and freeze its weights\n",
+    "model = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "# Replace the final fully connected layer\n",
+    "# Parameters of newly constructed modules have requires_grad=True by default\n",
+    "num_ftrs = model.fc.in_features\n",
+    "model.fc = nn.Linear(num_ftrs, 2)\n",
+    "# Send the model to the GPU\n",
+    "model = model.to(device)\n",
+    "# Set the loss function\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "# Observe that only the parameters of the final layer are being optimized\n",
+    "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+    "model, epoch_time = train_model(\n",
+    "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+    ")\n",
+    "\n",
+    "\n",
+    "#### Fonction eval_model ########\n",
+    "\n",
+    "\n",
+    "def eval_model(model,criterion,optimizer):\n",
+    "    was_training = model.training\n",
+    "    model.eval()\n",
+    "    num_images = 20\n",
+    "    images_so_far = 0\n",
+    "    \n",
+    "    class_correct = list(0.0 for i in range(2))\n",
+    "    class_total = list(0.0 for i in range(2))\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for i, (inputs, labels) in enumerate(dataloaders['test']):\n",
+    "            inputs = inputs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "\n",
+    "            outputs = model(inputs)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "                    \n",
+    "            correct_tensor = preds.eq(labels.data.view_as(preds))\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(2):\n",
+    "                label = labels.data[i]\n",
+    "                class_correct[label] += correct[i].item()\n",
+    "                class_total[label] += 1\n",
+    "\n",
+    "        model.train(mode=was_training)\n",
+    "    \n",
+    "    for i in range(2):\n",
+    "        if class_total[i] > 0:\n",
+    "            print(\n",
+    "                \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+    "                % (\n",
+    "                    class_names[i],\n",
+    "                    100 * class_correct[i] / class_total[i],\n",
+    "                    np.sum(class_correct[i]),\n",
+    "                    np.sum(class_total[i]),\n",
+    "                )\n",
+    "            )\n",
+    "        else:\n",
+    "            print(\"Test Accuracy of %5s: N/A (no training examples)\" % (class_names[i]))\n",
+    "\n",
+    "    print(\n",
+    "        \"\\nTest Accuracy (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",
+    "\n",
+    "\n",
+    "##test the model on the dataset used for test part \n",
+    "\n",
+    "eval_model(model,criterion,optimizer_conv)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "Nous avons une précision de 85 %. Nous nottons que la précision pour la classe abeille est de 66% ceci peut s'expliquer par un jeu de test compliqué par rapprot aux images des données d'apprentissage.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "Modification of the model with 2 layers:\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n",
+      "train Loss: 0.7307 Acc: 0.4829\n",
+      "val Loss: 0.6812 Acc: 0.5948\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.6871 Acc: 0.5897\n",
+      "val Loss: 0.6866 Acc: 0.5490\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.6926 Acc: 0.5385\n",
+      "val Loss: 0.6810 Acc: 0.6144\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.6906 Acc: 0.5171\n",
+      "val Loss: 0.6819 Acc: 0.5882\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.6996 Acc: 0.5598\n",
+      "val Loss: 0.6848 Acc: 0.5556\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.7023 Acc: 0.4573\n",
+      "val Loss: 0.6866 Acc: 0.5817\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.6935 Acc: 0.4957\n",
+      "val Loss: 0.6859 Acc: 0.5817\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.6969 Acc: 0.4573\n",
+      "val Loss: 0.6867 Acc: 0.5882\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.6872 Acc: 0.5385\n",
+      "val Loss: 0.6855 Acc: 0.6013\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.6930 Acc: 0.5000\n",
+      "val Loss: 0.6835 Acc: 0.6144\n",
+      "\n",
+      "Training complete in 2m 4s\n",
+      "Best val Acc: 0.614379\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "# Replace the final fully connected layer\n",
+    "# Parameters of newly constructed modules have requires_grad=True by default\n",
+    "num_ftrs = model.fc.in_features\n",
+    "model.fc = nn.Sequential(\n",
+    "          nn.Linear(num_ftrs, 10),\n",
+    "          nn.ReLU(),\n",
+    "          nn.Dropout(0.4),\n",
+    "          nn.Linear(10, 2),\n",
+    "          nn.Dropout(0.4)\n",
+    "        )\n",
+    "# Send the model to the GPU\n",
+    "model = model.to(device)\n",
+    "# Set the loss function\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "# Observe that only the parameters of the final layer are being optimized\n",
+    "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+    "model, epoch_time = train_model(\n",
+    "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "Testing the model with 2 layers on testing data\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy of  ants: 10% ( 1/10)\n",
+      "Test Accuracy of  bees: 100% (10/10)\n",
+      "\n",
+      "Test Accuracy (Overall): 55% (11/20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "eval_model(model,criterion,optimizer_conv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "we have a very low accuracy on the ants class, this is a problem that needs investigation in code and model\n",
+    "but the bees class have a 100% acuracy \n",
+    "\n",
+    "The overall accuracy of this model is lower than the previous model, this means that this architecture is less suited.\n",
+    "We should keep the first model, having higner accuracy\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "Post Quantization of the model\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  fp32  \t Size (KB): 44799.29\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "44799290"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\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",
+    "    print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n",
+    "    os.remove(\"temp.p\")\n",
+    "    return size\n",
+    "\n",
+    "\n",
+    "print_size_of_model(model, \"fp32\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  int8  \t Size (KB): 44785.382\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "44785382"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch.quantization\n",
+    "\n",
+    "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+    "print_size_of_model(quantized_model, \"int8\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "We notice that the size didn' decrease much with the quantization\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NotImplementedError",
+     "evalue": "Could not run 'quantized::linear_dynamic' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'quantized::linear_dynamic' is only available for these backends: [CPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].\n\nCPU: registered at ..\\aten\\src\\ATen\\native\\quantized\\cpu\\qlinear_dynamic.cpp:662 [kernel]\nBackendSelect: fallthrough registered at ..\\aten\\src\\ATen\\core\\BackendSelectFallbackKernel.cpp:3 [backend fallback]\nPython: registered at ..\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:153 [backend fallback]\nFuncTorchDynamicLayerBackMode: registered at ..\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:498 [backend fallback]\nFunctionalize: registered at ..\\aten\\src\\ATen\\FunctionalizeFallbackKernel.cpp:290 [backend fallback]\nNamed: registered at ..\\aten\\src\\ATen\\core\\NamedRegistrations.cpp:7 [backend fallback]\nConjugate: registered at ..\\aten\\src\\ATen\\ConjugateFallback.cpp:17 [backend fallback]\nNegative: registered at ..\\aten\\src\\ATen\\native\\NegateFallback.cpp:19 [backend fallback]\nZeroTensor: registered at ..\\aten\\src\\ATen\\ZeroTensorFallback.cpp:86 [backend fallback]\nADInplaceOrView: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:86 [backend fallback]\nAutogradOther: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:53 [backend fallback]\nAutogradCPU: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:57 [backend fallback]\nAutogradCUDA: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:65 [backend fallback]\nAutogradXLA: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:69 [backend fallback]\nAutogradMPS: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:77 [backend fallback]\nAutogradXPU: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:61 [backend fallback]\nAutogradHPU: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:90 [backend fallback]\nAutogradLazy: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:73 [backend fallback]\nAutogradMeta: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:81 [backend fallback]\nTracer: registered at ..\\torch\\csrc\\autograd\\TraceTypeManual.cpp:296 [backend fallback]\nAutocastCPU: fallthrough registered at ..\\aten\\src\\ATen\\autocast_mode.cpp:382 [backend fallback]\nAutocastCUDA: fallthrough registered at ..\\aten\\src\\ATen\\autocast_mode.cpp:249 [backend fallback]\nFuncTorchBatched: registered at ..\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:710 [backend fallback]\nFuncTorchVmapMode: fallthrough registered at ..\\aten\\src\\ATen\\functorch\\VmapModeRegistrations.cpp:28 [backend fallback]\nBatched: registered at ..\\aten\\src\\ATen\\LegacyBatchingRegistrations.cpp:1075 [backend fallback]\nVmapMode: fallthrough registered at ..\\aten\\src\\ATen\\VmapModeRegistrations.cpp:33 [backend fallback]\nFuncTorchGradWrapper: registered at ..\\aten\\src\\ATen\\functorch\\TensorWrapper.cpp:203 [backend fallback]\nPythonTLSSnapshot: registered at ..\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:161 [backend fallback]\nFuncTorchDynamicLayerFrontMode: registered at ..\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:494 [backend fallback]\nPreDispatch: registered at ..\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:165 [backend fallback]\nPythonDispatcher: registered at ..\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:157 [backend fallback]\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 72\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y140sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m eval_model(quantized_model,criterion,optimizer_conv)\n",
+      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 72\u001b[0m line \u001b[0;36m2\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y140sZmlsZQ%3D%3D?line=198'>199</a>\u001b[0m inputs \u001b[39m=\u001b[39m inputs\u001b[39m.\u001b[39mto(device)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y140sZmlsZQ%3D%3D?line=199'>200</a>\u001b[0m labels \u001b[39m=\u001b[39m labels\u001b[39m.\u001b[39mto(device)\n\u001b[1;32m--> <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y140sZmlsZQ%3D%3D?line=201'>202</a>\u001b[0m outputs \u001b[39m=\u001b[39m model(inputs)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y140sZmlsZQ%3D%3D?line=202'>203</a>\u001b[0m _, preds \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mmax(outputs, \u001b[39m1\u001b[39m)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y140sZmlsZQ%3D%3D?line=204'>205</a>\u001b[0m correct_tensor \u001b[39m=\u001b[39m preds\u001b[39m.\u001b[39meq(labels\u001b[39m.\u001b[39mdata\u001b[39m.\u001b[39mview_as(preds))\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\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\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torchvision\\models\\resnet.py:285\u001b[0m, in \u001b[0;36mResNet.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 285\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_forward_impl(x)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torchvision\\models\\resnet.py:280\u001b[0m, in \u001b[0;36mResNet._forward_impl\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    278\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mavgpool(x)\n\u001b[0;32m    279\u001b[0m x \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mflatten(x, \u001b[39m1\u001b[39m)\n\u001b[1;32m--> 280\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfc(x)\n\u001b[0;32m    282\u001b[0m \u001b[39mreturn\u001b[39;00m x\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\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\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torch\\nn\\modules\\container.py:215\u001b[0m, in \u001b[0;36mSequential.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    213\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m):\n\u001b[0;32m    214\u001b[0m     \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m:\n\u001b[1;32m--> 215\u001b[0m         \u001b[39minput\u001b[39m \u001b[39m=\u001b[39m module(\u001b[39minput\u001b[39;49m)\n\u001b[0;32m    216\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39minput\u001b[39m\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\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\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torch\\ao\\nn\\quantized\\dynamic\\modules\\linear.py:54\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     51\u001b[0m         Y \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mops\u001b[39m.\u001b[39mquantized\u001b[39m.\u001b[39mlinear_dynamic(\n\u001b[0;32m     52\u001b[0m             x, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_packed_params\u001b[39m.\u001b[39m_packed_params)\n\u001b[0;32m     53\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m---> 54\u001b[0m         Y \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39;49mops\u001b[39m.\u001b[39;49mquantized\u001b[39m.\u001b[39;49mlinear_dynamic(\n\u001b[0;32m     55\u001b[0m             x, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_packed_params\u001b[39m.\u001b[39;49m_packed_params, reduce_range\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[0;32m     56\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_packed_params\u001b[39m.\u001b[39mdtype \u001b[39m==\u001b[39m torch\u001b[39m.\u001b[39mfloat16:\n\u001b[0;32m     57\u001b[0m     Y \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mops\u001b[39m.\u001b[39mquantized\u001b[39m.\u001b[39mlinear_dynamic_fp16(\n\u001b[0;32m     58\u001b[0m         x, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_packed_params\u001b[39m.\u001b[39m_packed_params)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torch\\_ops.py:692\u001b[0m, in \u001b[0;36mOpOverloadPacket.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    687\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m    688\u001b[0m     \u001b[39m# overloading __call__ to ensure torch.ops.foo.bar()\u001b[39;00m\n\u001b[0;32m    689\u001b[0m     \u001b[39m# is still callable from JIT\u001b[39;00m\n\u001b[0;32m    690\u001b[0m     \u001b[39m# We save the function ptr as the `op` attribute on\u001b[39;00m\n\u001b[0;32m    691\u001b[0m     \u001b[39m# OpOverloadPacket to access it here.\u001b[39;00m\n\u001b[1;32m--> 692\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_op(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs \u001b[39mor\u001b[39;00m {})\n",
+      "\u001b[1;31mNotImplementedError\u001b[0m: Could not run 'quantized::linear_dynamic' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'quantized::linear_dynamic' is only available for these backends: [CPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].\n\nCPU: registered at ..\\aten\\src\\ATen\\native\\quantized\\cpu\\qlinear_dynamic.cpp:662 [kernel]\nBackendSelect: fallthrough registered at ..\\aten\\src\\ATen\\core\\BackendSelectFallbackKernel.cpp:3 [backend fallback]\nPython: registered at ..\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:153 [backend fallback]\nFuncTorchDynamicLayerBackMode: registered at ..\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:498 [backend fallback]\nFunctionalize: registered at ..\\aten\\src\\ATen\\FunctionalizeFallbackKernel.cpp:290 [backend fallback]\nNamed: registered at ..\\aten\\src\\ATen\\core\\NamedRegistrations.cpp:7 [backend fallback]\nConjugate: registered at ..\\aten\\src\\ATen\\ConjugateFallback.cpp:17 [backend fallback]\nNegative: registered at ..\\aten\\src\\ATen\\native\\NegateFallback.cpp:19 [backend fallback]\nZeroTensor: registered at ..\\aten\\src\\ATen\\ZeroTensorFallback.cpp:86 [backend fallback]\nADInplaceOrView: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:86 [backend fallback]\nAutogradOther: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:53 [backend fallback]\nAutogradCPU: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:57 [backend fallback]\nAutogradCUDA: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:65 [backend fallback]\nAutogradXLA: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:69 [backend fallback]\nAutogradMPS: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:77 [backend fallback]\nAutogradXPU: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:61 [backend fallback]\nAutogradHPU: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:90 [backend fallback]\nAutogradLazy: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:73 [backend fallback]\nAutogradMeta: registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:81 [backend fallback]\nTracer: registered at ..\\torch\\csrc\\autograd\\TraceTypeManual.cpp:296 [backend fallback]\nAutocastCPU: fallthrough registered at ..\\aten\\src\\ATen\\autocast_mode.cpp:382 [backend fallback]\nAutocastCUDA: fallthrough registered at ..\\aten\\src\\ATen\\autocast_mode.cpp:249 [backend fallback]\nFuncTorchBatched: registered at ..\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:710 [backend fallback]\nFuncTorchVmapMode: fallthrough registered at ..\\aten\\src\\ATen\\functorch\\VmapModeRegistrations.cpp:28 [backend fallback]\nBatched: registered at ..\\aten\\src\\ATen\\LegacyBatchingRegistrations.cpp:1075 [backend fallback]\nVmapMode: fallthrough registered at ..\\aten\\src\\ATen\\VmapModeRegistrations.cpp:33 [backend fallback]\nFuncTorchGradWrapper: registered at ..\\aten\\src\\ATen\\functorch\\TensorWrapper.cpp:203 [backend fallback]\nPythonTLSSnapshot: registered at ..\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:161 [backend fallback]\nFuncTorchDynamicLayerFrontMode: registered at ..\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:494 [backend fallback]\nPreDispatch: registered at ..\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:165 [backend fallback]\nPythonDispatcher: registered at ..\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:157 [backend fallback]\n"
+     ]
+    }
+   ],
+   "source": [
+    "eval_model(quantized_model,criterion,optimizer_conv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "Aware Quantization\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class QuantizedResNet18(nn.Module):\n",
+    "    def __init__(self, model_fp32):\n",
+    "\n",
+    "        super(QuantizedResNet18, self).__init__()\n",
+    "        # QuantStub converts tensors from floating point to quantized.\n",
+    "        # This will only be used for inputs.\n",
+    "        self.quant = torch.quantization.QuantStub()\n",
+    "        # DeQuantStub converts tensors from quantized to floating point.\n",
+    "        # This will only be used for outputs.\n",
+    "        self.dequant = torch.quantization.DeQuantStub()\n",
+    "        # FP32 model\n",
+    "        self.model_fp32 = model_fp32\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # manually specify where tensors will be converted from floating\n",
+    "        # point to quantized in the quantized model\n",
+    "        x = self.quant(x)\n",
+    "        x = self.model_fp32(x)\n",
+    "        # manually specify where tensors will be converted from quantized\n",
+    "        # to floating point in the quantized model\n",
+    "        x = self.dequant(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 75\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y133sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m optimizer_conv \u001b[39m=\u001b[39m optim\u001b[39m.\u001b[39mSGD(model\u001b[39m.\u001b[39mfc\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/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y133sZmlsZQ%3D%3D?line=16'>17</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/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y133sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m model, epoch_time \u001b[39m=\u001b[39m train_model(\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y133sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m     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/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y133sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m )\n",
+      "\u001b[1;32mc:\\Users\\LENOVO\\Desktop\\deeplearning\\td-2-deep-learning\\TD2 Deep Learning.ipynb Cell 75\u001b[0m line \u001b[0;36m1\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y133sZmlsZQ%3D%3D?line=118'>119</a>\u001b[0m \u001b[39m# Forward\u001b[39;00m\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y133sZmlsZQ%3D%3D?line=119'>120</a>\u001b[0m \u001b[39m# Track history if only in training phase\u001b[39;00m\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y133sZmlsZQ%3D%3D?line=120'>121</a>\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mset_grad_enabled(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/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y133sZmlsZQ%3D%3D?line=121'>122</a>\u001b[0m     outputs \u001b[39m=\u001b[39m model(inputs)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y133sZmlsZQ%3D%3D?line=122'>123</a>\u001b[0m     _, preds \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mmax(outputs, \u001b[39m1\u001b[39m)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/LENOVO/Desktop/deeplearning/td-2-deep-learning/TD2%20Deep%20Learning.ipynb#Y133sZmlsZQ%3D%3D?line=123'>124</a>\u001b[0m     loss \u001b[39m=\u001b[39m criterion(outputs, labels)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\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\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torchvision\\models\\resnet.py:285\u001b[0m, in \u001b[0;36mResNet.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 285\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_forward_impl(x)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torchvision\\models\\resnet.py:268\u001b[0m, in \u001b[0;36mResNet._forward_impl\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    266\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_forward_impl\u001b[39m(\u001b[39mself\u001b[39m, x: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[0;32m    267\u001b[0m     \u001b[39m# See note [TorchScript super()]\u001b[39;00m\n\u001b[1;32m--> 268\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconv1(x)\n\u001b[0;32m    269\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbn1(x)\n\u001b[0;32m    270\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrelu(x)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\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[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\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\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torch\\nn\\modules\\conv.py:460\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    459\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 460\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_conv_forward(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n",
+      "File \u001b[1;32mc:\\Users\\LENOVO\\anaconda3\\envs\\new\\lib\\site-packages\\torch\\nn\\modules\\conv.py:456\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m    452\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode \u001b[39m!=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mzeros\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m    453\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39mconv2d(F\u001b[39m.\u001b[39mpad(\u001b[39minput\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode),\n\u001b[0;32m    454\u001b[0m                     weight, bias, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstride,\n\u001b[0;32m    455\u001b[0m                     _pair(\u001b[39m0\u001b[39m), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdilation, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgroups)\n\u001b[1;32m--> 456\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mconv2d(\u001b[39minput\u001b[39;49m, weight, bias, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstride,\n\u001b[0;32m    457\u001b[0m                 \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpadding, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdilation, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgroups)\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same"
+     ]
+    }
+   ],
+   "source": [
+    "import copy\n",
+    "import torch.quantization.quantize_fx as quantize_fx\n",
+    "model = torchvision.models.resnet18(pretrained=True)\n",
+    "\n",
+    "model_fp=QuantizedResNet18(model)\n",
+    "\n",
+    "model_fp.train()\n",
+    "model_to_quantize = copy.deepcopy(model_fp)\n",
+    "model.qconfig = torch.quantization.get_default_qat_qconfig(\"qnnpack\")\n",
+    "model_qat = torch.quantization.prepare_qat(model_fp, inplace=False)\n",
+    "# quantization aware training goes here\n",
+    "model_qat = torch.quantization.convert(model_qat.eval(), inplace=False)\n",
+    "n_epochs=30\n",
+    "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
+    "optimizer = optim.SGD(model_qat.parameters(), lr=0.01)  # specify optimizer\n",
+    "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+    "model, epoch_time = train_model(\n",
+    "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "04a263f0",
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diff --git a/hymenoptera_data/test/bees/beess.jfif b/hymenoptera_data/test/bees/beess.jfif
new file mode 100644
index 0000000000000000000000000000000000000000..6236b14ddd53e3f02e930cdfd83ea3a748477bfc
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-- 
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