From cb3a7f0dfe5b0950007be1024cce5e713ea91100 Mon Sep 17 00:00:00 2001
From: lpoirson <louis14.poirson@etu.ec-lyon.fr>
Date: Tue, 28 Nov 2023 16:07:48 +0100
Subject: [PATCH] Ex4

---
 TD2_Deep_Learning.ipynb                       | 690 +++++++++---------
 hymenoptera_data/train/ants/formica.jpeg      | Bin 7858 -> 0 bytes
 hymenoptera_data/train/ants/imageNotFound.gif | Bin 5504 -> 0 bytes
 3 files changed, 337 insertions(+), 353 deletions(-)
 delete mode 100644 hymenoptera_data/train/ants/formica.jpeg
 delete mode 100644 hymenoptera_data/train/ants/imageNotFound.gif

diff --git a/TD2_Deep_Learning.ipynb b/TD2_Deep_Learning.ipynb
index 91c1d0c..39443a8 100644
--- a/TD2_Deep_Learning.ipynb
+++ b/TD2_Deep_Learning.ipynb
@@ -39,11 +39,40 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
-        "id": "330a42f5"
+        "id": "330a42f5",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "81f5caed-7e86-49a4-a07d-b65e5f4c8214"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",
+            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.16.0+cu118)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n",
+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
+            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
+            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.23.5)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n",
+            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.7.22)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"
+          ]
+        }
+      ],
       "source": [
         "!pip install torch torchvision"
       ],
@@ -62,47 +91,47 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "b1950f0a",
-        "outputId": "4f805dc9-cc98-4cf7-d70b-3819bbf8febe"
+        "outputId": "ed1990eb-9b87-4b21-ecef-e5eee4eccf69"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "tensor([[-0.5250, -1.5878,  1.2373, -0.4463, -0.9542,  1.5210,  0.3039, -0.1577,\n",
-            "          0.4926, -0.4071],\n",
-            "        [-1.3307, -0.8433, -0.7765, -0.1829,  0.7930, -0.7069, -0.5152, -0.4200,\n",
-            "          1.0203,  1.1917],\n",
-            "        [-0.1136, -1.2670,  0.5183,  0.3753,  0.3023, -0.2250,  0.2876,  1.6003,\n",
-            "         -0.0242, -0.5977],\n",
-            "        [-1.2466,  0.8898,  0.8563, -2.7605,  0.4482, -0.4284,  0.5633,  0.5803,\n",
-            "          0.4976,  1.0436],\n",
-            "        [-1.1549, -0.1155,  0.1341, -0.4466,  1.1712,  0.5158,  0.1951, -0.2732,\n",
-            "         -1.3928,  0.0689],\n",
-            "        [-0.9471, -0.3165, -0.9674, -0.5913,  1.6934, -1.0990,  0.3858,  1.2201,\n",
-            "         -1.0375, -1.1741],\n",
-            "        [ 0.5245, -1.0708, -0.1096,  0.7402,  0.1946, -0.4872, -0.8072, -1.2370,\n",
-            "          1.1297,  0.6642],\n",
-            "        [-1.1072,  0.1123, -2.3255, -0.3360,  0.6739, -0.1380,  0.1683, -1.7936,\n",
-            "         -1.9374, -0.3841],\n",
-            "        [-0.3464, -0.6614, -2.2669,  0.7528, -0.2793,  1.8053, -0.0628, -0.6133,\n",
-            "          0.8094,  0.3893],\n",
-            "        [ 2.1503,  1.5026, -0.1787, -2.3691,  0.5555, -0.2884,  0.9710,  0.3432,\n",
-            "         -0.0997,  1.4255],\n",
-            "        [-0.6184, -1.1084,  0.7993, -1.2145, -0.4672, -0.6829, -0.1484,  0.3961,\n",
-            "          1.5318, -1.8019],\n",
-            "        [-0.2495, -0.1458, -1.2062, -1.6310, -0.8753,  1.2997, -0.4681,  0.9760,\n",
-            "          1.1409,  0.4308],\n",
-            "        [-1.0811,  0.6437, -0.6477, -0.5390,  0.3499,  1.1290,  0.8379, -1.0099,\n",
-            "         -0.2674, -2.1319],\n",
-            "        [-0.3605,  0.4578, -0.0146, -0.8141,  1.2869,  0.0233, -0.4747,  0.6892,\n",
-            "          1.4487,  0.2062]])\n",
+            "tensor([[-0.1850,  0.6557, -0.7101, -0.3187, -1.2011, -0.1954, -0.0574,  0.2173,\n",
+            "         -0.8031, -1.1407],\n",
+            "        [-0.3505, -0.6793,  0.6278,  0.7995,  1.4728, -1.4100,  0.1423,  1.4640,\n",
+            "          2.1847,  0.3977],\n",
+            "        [-1.0245, -0.7064, -0.0367,  0.7646,  0.4124,  0.0961,  0.9879,  1.1582,\n",
+            "          1.2998,  2.8311],\n",
+            "        [ 0.2284,  0.9420, -1.3543,  0.3679,  1.7470, -1.3565, -0.6021,  0.8764,\n",
+            "         -0.3528,  1.6005],\n",
+            "        [ 1.1724, -0.6632, -0.5846,  0.3014,  0.2010,  0.5299, -1.4902, -1.1754,\n",
+            "         -0.3084,  1.0813],\n",
+            "        [ 1.3432, -0.3851, -0.8674,  0.7944, -0.0721,  0.5486,  0.3374,  1.1070,\n",
+            "         -0.2465, -0.6105],\n",
+            "        [-1.2024, -1.7267, -0.0465, -1.1976,  0.7334, -0.3691,  0.1552,  0.3709,\n",
+            "         -2.0797,  0.3014],\n",
+            "        [ 0.4365,  1.1905, -1.9395,  0.4570,  1.1570,  1.3329,  0.7727,  0.6412,\n",
+            "          0.9590,  0.1094],\n",
+            "        [-1.2041, -2.1140, -1.0550,  0.2697,  0.2572, -0.9096,  0.3382, -0.4136,\n",
+            "          1.0680, -0.3909],\n",
+            "        [-0.7090,  0.0662,  0.5451, -0.6964,  0.1922, -1.8023, -1.8417, -0.0817,\n",
+            "          0.4520,  0.9043],\n",
+            "        [-0.7054, -1.6090,  0.4699,  0.7582, -0.4254,  0.2804,  0.7420,  1.4684,\n",
+            "         -0.3052, -0.6173],\n",
+            "        [-0.5227, -0.7100,  0.6235, -1.9130, -1.3116, -0.2120, -0.1662, -1.5924,\n",
+            "          0.2496, -0.1803],\n",
+            "        [ 0.3525, -0.6941,  0.8807,  0.1866,  1.9066,  0.0909, -0.5595,  0.1609,\n",
+            "          1.1281,  0.1914],\n",
+            "        [ 0.5434, -1.1985, -0.6200, -0.1399,  2.2227,  0.5081,  0.4034,  1.9144,\n",
+            "          0.5388,  0.4422]])\n",
             "AlexNet(\n",
             "  (features): Sequential(\n",
             "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
@@ -177,13 +206,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "6e18f2fd",
-        "outputId": "1caad02a-d607-435d-ddbc-43782a81c17a"
+        "outputId": "a79394d9-4033-4127-bdab-88e1bffb6984"
       },
       "outputs": [
         {
@@ -219,13 +248,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "462666a2",
-        "outputId": "5c4e0742-9abb-4e19-df69-bfa90c57a472"
+        "outputId": "de744e09-23fd-4e15-e999-391722dbe55e"
       },
       "outputs": [
         {
@@ -239,7 +268,7 @@
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "100%|██████████| 170498071/170498071 [00:12<00:00, 13161528.52it/s]\n"
+            "100%|██████████| 170498071/170498071 [00:04<00:00, 35179738.42it/s]\n"
           ]
         },
         {
@@ -322,13 +351,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "317bf070",
-        "outputId": "2ed6557a-ea91-4208-bee3-6c1ac6ec828f"
+        "outputId": "bd40852c-fba8-4ab4-a281-2b31a4aefff7"
       },
       "outputs": [
         {
@@ -394,61 +423,61 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "metadata": {
         "id": "4b53f229",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "f81bfda3-b6bc-4acf-b7cc-a65eef9950c2"
+        "outputId": "325cf7ff-c9ac-40f7-96bf-f5c12b6d1698"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Epoch: 0 \tTraining Loss: 44.307754 \tValidation Loss: 39.392413\n",
-            "Validation loss decreased (inf --> 39.392413).  Saving model ...\n",
-            "Epoch: 1 \tTraining Loss: 35.753493 \tValidation Loss: 32.884607\n",
-            "Validation loss decreased (39.392413 --> 32.884607).  Saving model ...\n",
-            "Epoch: 2 \tTraining Loss: 31.538194 \tValidation Loss: 30.027051\n",
-            "Validation loss decreased (32.884607 --> 30.027051).  Saving model ...\n",
-            "Epoch: 3 \tTraining Loss: 28.993267 \tValidation Loss: 27.458260\n",
-            "Validation loss decreased (30.027051 --> 27.458260).  Saving model ...\n",
-            "Epoch: 4 \tTraining Loss: 27.132795 \tValidation Loss: 26.931607\n",
-            "Validation loss decreased (27.458260 --> 26.931607).  Saving model ...\n",
-            "Epoch: 5 \tTraining Loss: 25.574330 \tValidation Loss: 25.314186\n",
-            "Validation loss decreased (26.931607 --> 25.314186).  Saving model ...\n",
-            "Epoch: 6 \tTraining Loss: 24.262112 \tValidation Loss: 24.334575\n",
-            "Validation loss decreased (25.314186 --> 24.334575).  Saving model ...\n",
-            "Epoch: 7 \tTraining Loss: 23.174192 \tValidation Loss: 25.005386\n",
-            "Epoch: 8 \tTraining Loss: 22.125215 \tValidation Loss: 24.085739\n",
-            "Validation loss decreased (24.334575 --> 24.085739).  Saving model ...\n",
-            "Epoch: 9 \tTraining Loss: 21.263283 \tValidation Loss: 22.894592\n",
-            "Validation loss decreased (24.085739 --> 22.894592).  Saving model ...\n",
-            "Epoch: 10 \tTraining Loss: 20.459993 \tValidation Loss: 22.631767\n",
-            "Validation loss decreased (22.894592 --> 22.631767).  Saving model ...\n",
-            "Epoch: 11 \tTraining Loss: 19.646447 \tValidation Loss: 22.054595\n",
-            "Validation loss decreased (22.631767 --> 22.054595).  Saving model ...\n",
-            "Epoch: 12 \tTraining Loss: 18.954876 \tValidation Loss: 21.892770\n",
-            "Validation loss decreased (22.054595 --> 21.892770).  Saving model ...\n",
-            "Epoch: 13 \tTraining Loss: 18.268278 \tValidation Loss: 22.564975\n",
-            "Epoch: 14 \tTraining Loss: 17.624434 \tValidation Loss: 22.852805\n",
-            "Epoch: 15 \tTraining Loss: 16.975349 \tValidation Loss: 22.241853\n",
-            "Epoch: 16 \tTraining Loss: 16.397408 \tValidation Loss: 22.040380\n",
-            "Epoch: 17 \tTraining Loss: 15.835682 \tValidation Loss: 22.187388\n",
-            "Epoch: 18 \tTraining Loss: 15.328455 \tValidation Loss: 22.427778\n",
-            "Epoch: 19 \tTraining Loss: 14.776516 \tValidation Loss: 22.282838\n",
-            "Epoch: 20 \tTraining Loss: 14.260037 \tValidation Loss: 23.586892\n",
-            "Epoch: 21 \tTraining Loss: 13.701756 \tValidation Loss: 23.862957\n",
-            "Epoch: 22 \tTraining Loss: 13.271071 \tValidation Loss: 23.934974\n",
-            "Epoch: 23 \tTraining Loss: 12.851553 \tValidation Loss: 24.151106\n",
-            "Epoch: 24 \tTraining Loss: 12.386875 \tValidation Loss: 24.450667\n",
-            "Epoch: 25 \tTraining Loss: 11.917942 \tValidation Loss: 25.039863\n",
-            "Epoch: 26 \tTraining Loss: 11.517738 \tValidation Loss: 25.714219\n",
-            "Epoch: 27 \tTraining Loss: 11.058535 \tValidation Loss: 26.544897\n",
-            "Epoch: 28 \tTraining Loss: 10.693729 \tValidation Loss: 28.075849\n",
-            "Epoch: 29 \tTraining Loss: 10.271838 \tValidation Loss: 28.377825\n"
+            "Epoch: 0 \tTraining Loss: 43.764788 \tValidation Loss: 39.683365\n",
+            "Validation loss decreased (inf --> 39.683365).  Saving model ...\n",
+            "Epoch: 1 \tTraining Loss: 35.515662 \tValidation Loss: 32.618072\n",
+            "Validation loss decreased (39.683365 --> 32.618072).  Saving model ...\n",
+            "Epoch: 2 \tTraining Loss: 31.447336 \tValidation Loss: 30.040689\n",
+            "Validation loss decreased (32.618072 --> 30.040689).  Saving model ...\n",
+            "Epoch: 3 \tTraining Loss: 29.083876 \tValidation Loss: 28.342380\n",
+            "Validation loss decreased (30.040689 --> 28.342380).  Saving model ...\n",
+            "Epoch: 4 \tTraining Loss: 27.366197 \tValidation Loss: 27.522554\n",
+            "Validation loss decreased (28.342380 --> 27.522554).  Saving model ...\n",
+            "Epoch: 5 \tTraining Loss: 25.819487 \tValidation Loss: 25.206887\n",
+            "Validation loss decreased (27.522554 --> 25.206887).  Saving model ...\n",
+            "Epoch: 6 \tTraining Loss: 24.537992 \tValidation Loss: 24.429555\n",
+            "Validation loss decreased (25.206887 --> 24.429555).  Saving model ...\n",
+            "Epoch: 7 \tTraining Loss: 23.311594 \tValidation Loss: 23.779194\n",
+            "Validation loss decreased (24.429555 --> 23.779194).  Saving model ...\n",
+            "Epoch: 8 \tTraining Loss: 22.315277 \tValidation Loss: 23.360532\n",
+            "Validation loss decreased (23.779194 --> 23.360532).  Saving model ...\n",
+            "Epoch: 9 \tTraining Loss: 21.491438 \tValidation Loss: 22.892413\n",
+            "Validation loss decreased (23.360532 --> 22.892413).  Saving model ...\n",
+            "Epoch: 10 \tTraining Loss: 20.712313 \tValidation Loss: 22.644033\n",
+            "Validation loss decreased (22.892413 --> 22.644033).  Saving model ...\n",
+            "Epoch: 11 \tTraining Loss: 20.009503 \tValidation Loss: 23.014492\n",
+            "Epoch: 12 \tTraining Loss: 19.341019 \tValidation Loss: 22.925271\n",
+            "Epoch: 13 \tTraining Loss: 18.743798 \tValidation Loss: 21.667198\n",
+            "Validation loss decreased (22.644033 --> 21.667198).  Saving model ...\n",
+            "Epoch: 14 \tTraining Loss: 18.163065 \tValidation Loss: 22.132765\n",
+            "Epoch: 15 \tTraining Loss: 17.594034 \tValidation Loss: 21.829996\n",
+            "Epoch: 16 \tTraining Loss: 16.975132 \tValidation Loss: 21.899496\n",
+            "Epoch: 17 \tTraining Loss: 16.534599 \tValidation Loss: 21.848445\n",
+            "Epoch: 18 \tTraining Loss: 15.952033 \tValidation Loss: 22.044693\n",
+            "Epoch: 19 \tTraining Loss: 15.419088 \tValidation Loss: 22.427119\n",
+            "Epoch: 20 \tTraining Loss: 14.966997 \tValidation Loss: 22.158565\n",
+            "Epoch: 21 \tTraining Loss: 14.443457 \tValidation Loss: 22.078650\n",
+            "Epoch: 22 \tTraining Loss: 13.998294 \tValidation Loss: 23.254764\n",
+            "Epoch: 23 \tTraining Loss: 13.523148 \tValidation Loss: 23.506892\n",
+            "Epoch: 24 \tTraining Loss: 13.000857 \tValidation Loss: 23.579084\n",
+            "Epoch: 25 \tTraining Loss: 12.622303 \tValidation Loss: 24.378682\n",
+            "Epoch: 26 \tTraining Loss: 12.132899 \tValidation Loss: 25.271672\n",
+            "Epoch: 27 \tTraining Loss: 11.789124 \tValidation Loss: 25.790779\n",
+            "Epoch: 28 \tTraining Loss: 11.409434 \tValidation Loss: 25.123173\n",
+            "Epoch: 29 \tTraining Loss: 11.004180 \tValidation Loss: 25.883635\n"
           ]
         }
       ],
@@ -535,14 +564,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "metadata": {
         "id": "d39df818",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 472
         },
-        "outputId": "93f890dc-7788-452c-8b3d-5daf01d13eb9"
+        "outputId": "2b5d3c77-6d77-4174-fce8-e8aaf7eaffe7"
       },
       "outputs": [
         {
@@ -551,7 +580,7 @@
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ],
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {}
         }
@@ -579,33 +608,33 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "metadata": {
         "id": "e93efdfc",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "8ba64ea3-64f3-4672-8ba8-f0a2fec99ced"
+        "outputId": "def8e405-c32d-42ed-be5a-79941ef02802"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Test Loss: 21.712322\n",
+            "Test Loss: 21.221255\n",
             "\n",
-            "Test Accuracy of airplane: 57% (577/1000)\n",
-            "Test Accuracy of automobile: 73% (734/1000)\n",
-            "Test Accuracy of  bird: 52% (520/1000)\n",
-            "Test Accuracy of   cat: 48% (486/1000)\n",
-            "Test Accuracy of  deer: 52% (523/1000)\n",
-            "Test Accuracy of   dog: 44% (446/1000)\n",
-            "Test Accuracy of  frog: 68% (680/1000)\n",
-            "Test Accuracy of horse: 72% (726/1000)\n",
-            "Test Accuracy of  ship: 77% (771/1000)\n",
-            "Test Accuracy of truck: 74% (743/1000)\n",
+            "Test Accuracy of airplane: 69% (694/1000)\n",
+            "Test Accuracy of automobile: 72% (728/1000)\n",
+            "Test Accuracy of  bird: 55% (559/1000)\n",
+            "Test Accuracy of   cat: 46% (460/1000)\n",
+            "Test Accuracy of  deer: 54% (547/1000)\n",
+            "Test Accuracy of   dog: 48% (484/1000)\n",
+            "Test Accuracy of  frog: 67% (676/1000)\n",
+            "Test Accuracy of horse: 71% (715/1000)\n",
+            "Test Accuracy of  ship: 69% (692/1000)\n",
+            "Test Accuracy of truck: 71% (715/1000)\n",
             "\n",
-            "Test Accuracy (Overall): 62% (6206/10000)\n"
+            "Test Accuracy (Overall): 62% (6270/10000)\n"
           ]
         }
       ],
@@ -858,10 +887,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "NMNftJRRwzDe",
-        "outputId": "1b088533-8397-423a-e582-d6ffaeb3bd1f"
+        "outputId": "4a02df3d-600c-4e01-962a-692327efe2fe"
       },
       "id": "NMNftJRRwzDe",
-      "execution_count": null,
+      "execution_count": 9,
       "outputs": [
         {
           "output_type": "stream",
@@ -875,62 +904,63 @@
             "  (fc2): Linear(in_features=512, out_features=64, bias=True)\n",
             "  (fc3): Linear(in_features=64, out_features=10, bias=True)\n",
             ")\n",
-            "Epoch: 0 \tTraining Loss: 45.816719 \tValidation Loss: 43.966355\n",
-            "Validation loss decreased (inf --> 43.966355).  Saving model ...\n",
-            "Epoch: 1 \tTraining Loss: 38.209464 \tValidation Loss: 35.172074\n",
-            "Validation loss decreased (43.966355 --> 35.172074).  Saving model ...\n",
-            "Epoch: 2 \tTraining Loss: 31.435999 \tValidation Loss: 29.549833\n",
-            "Validation loss decreased (35.172074 --> 29.549833).  Saving model ...\n",
-            "Epoch: 3 \tTraining Loss: 28.073995 \tValidation Loss: 26.215444\n",
-            "Validation loss decreased (29.549833 --> 26.215444).  Saving model ...\n",
-            "Epoch: 4 \tTraining Loss: 25.583197 \tValidation Loss: 24.258325\n",
-            "Validation loss decreased (26.215444 --> 24.258325).  Saving model ...\n",
-            "Epoch: 5 \tTraining Loss: 23.430590 \tValidation Loss: 22.700692\n",
-            "Validation loss decreased (24.258325 --> 22.700692).  Saving model ...\n",
-            "Epoch: 6 \tTraining Loss: 21.498990 \tValidation Loss: 21.403723\n",
-            "Validation loss decreased (22.700692 --> 21.403723).  Saving model ...\n",
-            "Epoch: 7 \tTraining Loss: 19.799957 \tValidation Loss: 20.328666\n",
-            "Validation loss decreased (21.403723 --> 20.328666).  Saving model ...\n",
-            "Epoch: 8 \tTraining Loss: 18.312641 \tValidation Loss: 19.622646\n",
-            "Validation loss decreased (20.328666 --> 19.622646).  Saving model ...\n",
-            "Epoch: 9 \tTraining Loss: 16.875187 \tValidation Loss: 18.478383\n",
-            "Validation loss decreased (19.622646 --> 18.478383).  Saving model ...\n",
-            "Epoch: 10 \tTraining Loss: 15.455087 \tValidation Loss: 18.739886\n",
-            "Epoch: 11 \tTraining Loss: 14.161176 \tValidation Loss: 17.485207\n",
-            "Validation loss decreased (18.478383 --> 17.485207).  Saving model ...\n",
-            "Epoch: 12 \tTraining Loss: 12.833713 \tValidation Loss: 18.220959\n",
-            "Epoch: 13 \tTraining Loss: 11.581126 \tValidation Loss: 16.867536\n",
-            "Validation loss decreased (17.485207 --> 16.867536).  Saving model ...\n",
-            "Epoch: 14 \tTraining Loss: 10.281501 \tValidation Loss: 18.264160\n",
-            "Epoch: 15 \tTraining Loss: 9.066314 \tValidation Loss: 18.052295\n",
-            "Epoch: 16 \tTraining Loss: 7.870172 \tValidation Loss: 20.063252\n",
-            "Epoch: 17 \tTraining Loss: 6.672348 \tValidation Loss: 19.564190\n",
-            "Epoch: 18 \tTraining Loss: 5.619572 \tValidation Loss: 20.775727\n",
-            "Epoch: 19 \tTraining Loss: 4.583318 \tValidation Loss: 22.814755\n",
-            "Epoch: 20 \tTraining Loss: 3.865993 \tValidation Loss: 24.554825\n",
-            "Epoch: 21 \tTraining Loss: 3.077722 \tValidation Loss: 27.161921\n",
-            "Epoch: 22 \tTraining Loss: 2.617743 \tValidation Loss: 28.106431\n",
-            "Epoch: 23 \tTraining Loss: 2.138785 \tValidation Loss: 29.020546\n",
-            "Epoch: 24 \tTraining Loss: 1.815105 \tValidation Loss: 30.161924\n",
-            "Epoch: 25 \tTraining Loss: 1.692375 \tValidation Loss: 33.760924\n",
-            "Epoch: 26 \tTraining Loss: 1.241958 \tValidation Loss: 35.043594\n",
-            "Epoch: 27 \tTraining Loss: 1.202187 \tValidation Loss: 34.946057\n",
-            "Epoch: 28 \tTraining Loss: 1.373119 \tValidation Loss: 33.273757\n",
-            "Epoch: 29 \tTraining Loss: 1.063768 \tValidation Loss: 38.109132\n",
-            "Test Loss: 16.849309\n",
+            "Epoch: 0 \tTraining Loss: 44.598365 \tValidation Loss: 39.828695\n",
+            "Validation loss decreased (inf --> 39.828695).  Saving model ...\n",
+            "Epoch: 1 \tTraining Loss: 36.485231 \tValidation Loss: 32.274785\n",
+            "Validation loss decreased (39.828695 --> 32.274785).  Saving model ...\n",
+            "Epoch: 2 \tTraining Loss: 30.665269 \tValidation Loss: 28.728429\n",
+            "Validation loss decreased (32.274785 --> 28.728429).  Saving model ...\n",
+            "Epoch: 3 \tTraining Loss: 27.425119 \tValidation Loss: 26.196423\n",
+            "Validation loss decreased (28.728429 --> 26.196423).  Saving model ...\n",
+            "Epoch: 4 \tTraining Loss: 25.078207 \tValidation Loss: 23.950758\n",
+            "Validation loss decreased (26.196423 --> 23.950758).  Saving model ...\n",
+            "Epoch: 5 \tTraining Loss: 22.944724 \tValidation Loss: 22.399232\n",
+            "Validation loss decreased (23.950758 --> 22.399232).  Saving model ...\n",
+            "Epoch: 6 \tTraining Loss: 21.050774 \tValidation Loss: 20.558065\n",
+            "Validation loss decreased (22.399232 --> 20.558065).  Saving model ...\n",
+            "Epoch: 7 \tTraining Loss: 19.362591 \tValidation Loss: 20.036665\n",
+            "Validation loss decreased (20.558065 --> 20.036665).  Saving model ...\n",
+            "Epoch: 8 \tTraining Loss: 17.799513 \tValidation Loss: 19.358157\n",
+            "Validation loss decreased (20.036665 --> 19.358157).  Saving model ...\n",
+            "Epoch: 9 \tTraining Loss: 16.435562 \tValidation Loss: 18.018229\n",
+            "Validation loss decreased (19.358157 --> 18.018229).  Saving model ...\n",
+            "Epoch: 10 \tTraining Loss: 15.048540 \tValidation Loss: 17.323416\n",
+            "Validation loss decreased (18.018229 --> 17.323416).  Saving model ...\n",
+            "Epoch: 11 \tTraining Loss: 13.723058 \tValidation Loss: 17.753303\n",
+            "Epoch: 12 \tTraining Loss: 12.506142 \tValidation Loss: 17.225937\n",
+            "Validation loss decreased (17.323416 --> 17.225937).  Saving model ...\n",
+            "Epoch: 13 \tTraining Loss: 11.195725 \tValidation Loss: 17.772931\n",
+            "Epoch: 14 \tTraining Loss: 9.920171 \tValidation Loss: 16.975273\n",
+            "Validation loss decreased (17.225937 --> 16.975273).  Saving model ...\n",
+            "Epoch: 15 \tTraining Loss: 8.673090 \tValidation Loss: 18.099817\n",
+            "Epoch: 16 \tTraining Loss: 7.428356 \tValidation Loss: 18.768417\n",
+            "Epoch: 17 \tTraining Loss: 6.209162 \tValidation Loss: 20.619868\n",
+            "Epoch: 18 \tTraining Loss: 5.202735 \tValidation Loss: 21.190901\n",
+            "Epoch: 19 \tTraining Loss: 4.301398 \tValidation Loss: 23.800836\n",
+            "Epoch: 20 \tTraining Loss: 3.521855 \tValidation Loss: 23.808694\n",
+            "Epoch: 21 \tTraining Loss: 2.760550 \tValidation Loss: 27.276794\n",
+            "Epoch: 22 \tTraining Loss: 2.258518 \tValidation Loss: 27.840429\n",
+            "Epoch: 23 \tTraining Loss: 1.986257 \tValidation Loss: 28.886084\n",
+            "Epoch: 24 \tTraining Loss: 1.682468 \tValidation Loss: 29.863841\n",
+            "Epoch: 25 \tTraining Loss: 1.542804 \tValidation Loss: 33.225463\n",
+            "Epoch: 26 \tTraining Loss: 1.146354 \tValidation Loss: 32.458885\n",
+            "Epoch: 27 \tTraining Loss: 1.200897 \tValidation Loss: 37.082571\n",
+            "Epoch: 28 \tTraining Loss: 1.475183 \tValidation Loss: 37.265593\n",
+            "Epoch: 29 \tTraining Loss: 1.192577 \tValidation Loss: 35.028623\n",
+            "Test Loss: 17.678122\n",
             "\n",
-            "Test Accuracy of airplane: 78% (780/1000)\n",
-            "Test Accuracy of automobile: 78% (785/1000)\n",
-            "Test Accuracy of  bird: 59% (591/1000)\n",
-            "Test Accuracy of   cat: 48% (489/1000)\n",
-            "Test Accuracy of  deer: 66% (667/1000)\n",
-            "Test Accuracy of   dog: 68% (680/1000)\n",
-            "Test Accuracy of  frog: 76% (761/1000)\n",
-            "Test Accuracy of horse: 78% (781/1000)\n",
-            "Test Accuracy of  ship: 79% (797/1000)\n",
-            "Test Accuracy of truck: 83% (837/1000)\n",
+            "Test Accuracy of airplane: 78% (782/1000)\n",
+            "Test Accuracy of automobile: 88% (880/1000)\n",
+            "Test Accuracy of  bird: 55% (556/1000)\n",
+            "Test Accuracy of   cat: 53% (537/1000)\n",
+            "Test Accuracy of  deer: 69% (693/1000)\n",
+            "Test Accuracy of   dog: 55% (552/1000)\n",
+            "Test Accuracy of  frog: 80% (800/1000)\n",
+            "Test Accuracy of horse: 72% (729/1000)\n",
+            "Test Accuracy of  ship: 85% (856/1000)\n",
+            "Test Accuracy of truck: 75% (750/1000)\n",
             "\n",
-            "Test Accuracy (Overall): 71% (7168/10000)\n"
+            "Test Accuracy (Overall): 71% (7135/10000)\n"
           ]
         }
       ]
@@ -950,10 +980,10 @@
           "height": 472
         },
         "id": "HgKA8KzG-gqB",
-        "outputId": "0cebe1ba-d08d-4e2c-d578-5b182e4213e9"
+        "outputId": "179d1dd7-effe-4877-8c7d-290c2422f705"
       },
       "id": "HgKA8KzG-gqB",
-      "execution_count": null,
+      "execution_count": 10,
       "outputs": [
         {
           "output_type": "display_data",
@@ -961,7 +991,7 @@
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ],
-            "image/png": 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\n"
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\n"
           },
           "metadata": {}
         }
@@ -998,31 +1028,31 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 11,
       "metadata": {
         "id": "ef623c26",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "693c86b4-17ff-4a7d-94b3-bd8826185aa2"
+        "outputId": "a19d0a1a-baea-4f12-9503-1d06d594c215"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "model:  fp32  \t Size (KB): 46828.292\n"
+            "model:  fp32  \t Size (KB): 2331.01\n"
           ]
         },
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "46828292"
+              "2331010"
             ]
           },
           "metadata": {},
-          "execution_count": 22
+          "execution_count": 11
         }
       ],
       "source": [
@@ -1053,13 +1083,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 12,
       "metadata": {
         "id": "c4c65d4b",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "2b1c025a-b23f-4f11-aaa2-08e4f0814713"
+        "outputId": "66760d32-f474-46ef-9b80-4199b1185fc7"
       },
       "outputs": [
         {
@@ -1077,7 +1107,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 30
+          "execution_count": 12
         }
       ],
       "source": [
@@ -1157,29 +1187,29 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "HqDGkcZ5_rdn",
-        "outputId": "41447794-76fa-44ba-b8da-7192cc846608"
+        "outputId": "4ae56d5f-794e-4f62-b1a6-0f09301527de"
       },
       "id": "HqDGkcZ5_rdn",
-      "execution_count": null,
+      "execution_count": 13,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Test Loss: 16.859613\n",
+            "Test Loss: 17.683190\n",
             "\n",
-            "Test Accuracy of airplane: 78% (780/1000)\n",
-            "Test Accuracy of automobile: 78% (784/1000)\n",
-            "Test Accuracy of  bird: 59% (592/1000)\n",
-            "Test Accuracy of   cat: 50% (500/1000)\n",
-            "Test Accuracy of  deer: 66% (660/1000)\n",
-            "Test Accuracy of   dog: 67% (674/1000)\n",
-            "Test Accuracy of  frog: 75% (759/1000)\n",
-            "Test Accuracy of horse: 78% (783/1000)\n",
-            "Test Accuracy of  ship: 79% (797/1000)\n",
-            "Test Accuracy of truck: 83% (838/1000)\n",
+            "Test Accuracy of airplane: 78% (784/1000)\n",
+            "Test Accuracy of automobile: 87% (877/1000)\n",
+            "Test Accuracy of  bird: 55% (556/1000)\n",
+            "Test Accuracy of   cat: 53% (534/1000)\n",
+            "Test Accuracy of  deer: 69% (690/1000)\n",
+            "Test Accuracy of   dog: 55% (556/1000)\n",
+            "Test Accuracy of  frog: 79% (799/1000)\n",
+            "Test Accuracy of horse: 73% (731/1000)\n",
+            "Test Accuracy of  ship: 85% (856/1000)\n",
+            "Test Accuracy of truck: 75% (751/1000)\n",
             "\n",
-            "Test Accuracy (Overall): 71% (7167/10000)\n"
+            "Test Accuracy (Overall): 71% (7134/10000)\n"
           ]
         }
       ]
@@ -1287,30 +1317,30 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "TdA7xe1iDuJW",
-        "outputId": "0f669a23-45fd-472b-8aa4-eb51f3a55191"
+        "outputId": "49f08bc2-8488-4d92-e522-4e31b728a683"
       },
       "id": "TdA7xe1iDuJW",
-      "execution_count": null,
+      "execution_count": 14,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "model:  int8  \t Size (KB): 2330.882\n",
-            "Test Loss: 16.849309\n",
+            "Test Loss: 17.678122\n",
             "\n",
-            "Test Accuracy of airplane: 78% (780/1000)\n",
-            "Test Accuracy of automobile: 78% (785/1000)\n",
-            "Test Accuracy of  bird: 59% (591/1000)\n",
-            "Test Accuracy of   cat: 48% (489/1000)\n",
-            "Test Accuracy of  deer: 66% (667/1000)\n",
-            "Test Accuracy of   dog: 68% (680/1000)\n",
-            "Test Accuracy of  frog: 76% (761/1000)\n",
-            "Test Accuracy of horse: 78% (781/1000)\n",
-            "Test Accuracy of  ship: 79% (797/1000)\n",
-            "Test Accuracy of truck: 83% (837/1000)\n",
+            "Test Accuracy of airplane: 78% (782/1000)\n",
+            "Test Accuracy of automobile: 88% (880/1000)\n",
+            "Test Accuracy of  bird: 55% (556/1000)\n",
+            "Test Accuracy of   cat: 53% (537/1000)\n",
+            "Test Accuracy of  deer: 69% (693/1000)\n",
+            "Test Accuracy of   dog: 55% (552/1000)\n",
+            "Test Accuracy of  frog: 80% (800/1000)\n",
+            "Test Accuracy of horse: 72% (729/1000)\n",
+            "Test Accuracy of  ship: 85% (856/1000)\n",
+            "Test Accuracy of truck: 75% (750/1000)\n",
             "\n",
-            "Test Accuracy (Overall): 71% (7168/10000)\n"
+            "Test Accuracy (Overall): 71% (7135/10000)\n"
           ]
         }
       ]
@@ -1330,14 +1360,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 16,
       "metadata": {
         "id": "b4d13080",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 547
         },
-        "outputId": "29b337ff-b4b9-46ae-9507-c641c81ce66c"
+        "outputId": "3ba03d46-af25-49c8-8d7a-8cb7695268b2"
       },
       "outputs": [
         {
@@ -1349,7 +1379,7 @@
             "/usr/local/lib/python3.10/dist-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",
             "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
-            "100%|██████████| 97.8M/97.8M [00:00<00:00, 137MB/s]\n"
+            "100%|██████████| 97.8M/97.8M [00:01<00:00, 91.0MB/s]\n"
           ]
         },
         {
@@ -1445,10 +1475,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "ofs6wepVIxCo",
-        "outputId": "582d78de-7555-4ced-cbef-13f484377d95"
+        "outputId": "79046997-e332-43ec-cdba-34393bb10915"
       },
       "id": "ofs6wepVIxCo",
-      "execution_count": null,
+      "execution_count": 17,
       "outputs": [
         {
           "output_type": "stream",
@@ -1465,7 +1495,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 12
+          "execution_count": 17
         }
       ]
     },
@@ -1493,10 +1523,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "7W7ZK6sWJamd",
-        "outputId": "5cef4ca9-7018-4f71-b459-42ec0c9f89bd"
+        "outputId": "65ffb4f4-19d1-4eab-865b-62582b6a96d8"
       },
       "id": "7W7ZK6sWJamd",
-      "execution_count": null,
+      "execution_count": 18,
       "outputs": [
         {
           "output_type": "stream",
@@ -1513,7 +1543,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 13
+          "execution_count": 18
         }
       ]
     },
@@ -1566,10 +1596,10 @@
           "height": 423
         },
         "id": "PS0bnfqTJgJA",
-        "outputId": "a3f2116b-245a-4149-8774-2861617f48bd"
+        "outputId": "90638268-9ffa-4970-9130-c76b727a0b07"
       },
       "id": "PS0bnfqTJgJA",
-      "execution_count": null,
+      "execution_count": 19,
       "outputs": [
         {
           "output_type": "stream",
@@ -1650,26 +1680,24 @@
         "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
       ],
       "metadata": {
+        "id": "Z-_ounggKL_r",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 547
+          "height": 513
         },
-        "id": "Z-_ounggKL_r",
-        "outputId": "b942d30c-ffa7-4aba-fb1e-b14a7a5463f7"
+        "outputId": "f4187b15-9dc5-4855-d08b-5c2019966fb6"
       },
       "id": "Z-_ounggKL_r",
-      "execution_count": null,
+      "execution_count": 20,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "/usr/local/lib/python3.10/dist-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",
             "/usr/local/lib/python3.10/dist-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=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
             "  warnings.warn(msg)\n",
             "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n",
-            "100%|██████████| 44.7M/44.7M [00:00<00:00, 108MB/s]\n"
+            "100%|██████████| 44.7M/44.7M [00:00<00:00, 88.3MB/s]\n"
           ]
         },
         {
@@ -1721,30 +1749,30 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 21,
       "metadata": {
         "id": "be2d31f5",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
         },
-        "outputId": "b9cba17a-fca9-4364-c9a9-06dbdda10402"
+        "outputId": "ffd76407-93e4-40b1-81f7-7bcf08c16d42"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "--2023-11-22 15:24:10--  https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
-            "Resolving download.pytorch.org (download.pytorch.org)... 18.160.200.77, 18.160.200.126, 18.160.200.112, ...\n",
-            "Connecting to download.pytorch.org (download.pytorch.org)|18.160.200.77|:443... connected.\n",
+            "--2023-11-28 14:51:38--  https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
+            "Resolving download.pytorch.org (download.pytorch.org)... 99.86.38.106, 99.86.38.37, 99.86.38.72, ...\n",
+            "Connecting to download.pytorch.org (download.pytorch.org)|99.86.38.106|:443... connected.\n",
             "HTTP request sent, awaiting response... 200 OK\n",
             "Length: 47286322 (45M) [application/zip]\n",
             "Saving to: ‘hymenoptera_data.zip’\n",
             "\n",
-            "hymenoptera_data.zi 100%[===================>]  45.10M   196MB/s    in 0.2s    \n",
+            "hymenoptera_data.zi 100%[===================>]  45.10M  88.1MB/s    in 0.5s    \n",
             "\n",
-            "2023-11-22 15:24:10 (196 MB/s) - ‘hymenoptera_data.zip’ saved [47286322/47286322]\n",
+            "2023-11-28 14:51:39 (88.1 MB/s) - ‘hymenoptera_data.zip’ saved [47286322/47286322]\n",
             "\n",
             "Archive:  hymenoptera_data.zip\n",
             "   creating: hymenoptera_data/\n",
@@ -2160,7 +2188,7 @@
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ],
-            "image/png": 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\n"
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\n"
           },
           "metadata": {}
         }
@@ -2262,99 +2290,73 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 24,
       "metadata": {
         "id": "572d824c",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "7fdf8046-aee1-4dff-bbee-cb174d510636"
+        "outputId": "d6eb46c5-b87d-4839-f662-725e44d44fa9"
       },
       "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
-            "  warnings.warn(_create_warning_msg(\n",
-            "/usr/local/lib/python3.10/dist-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",
-            "/usr/local/lib/python3.10/dist-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=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
-            "  warnings.warn(msg)\n"
-          ]
-        },
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "Epoch 1/10\n",
-            "----------\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
-            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "train Loss: 0.6672 Acc: 0.6393\n",
-            "val Loss: 0.4174 Acc: 0.7908\n",
+            "----------\n",
+            "train Loss: 1.0511 Acc: 0.6025\n",
+            "val Loss: 0.8865 Acc: 0.6340\n",
             "\n",
             "Epoch 2/10\n",
             "----------\n",
-            "train Loss: 0.5774 Acc: 0.7459\n",
-            "val Loss: 0.1873 Acc: 0.9542\n",
+            "train Loss: 0.6231 Acc: 0.7295\n",
+            "val Loss: 0.2029 Acc: 0.9150\n",
             "\n",
             "Epoch 3/10\n",
             "----------\n",
-            "train Loss: 0.3908 Acc: 0.8525\n",
-            "val Loss: 0.2271 Acc: 0.9085\n",
+            "train Loss: 0.6095 Acc: 0.7254\n",
+            "val Loss: 0.2616 Acc: 0.9346\n",
             "\n",
             "Epoch 4/10\n",
             "----------\n",
-            "train Loss: 0.3549 Acc: 0.8156\n",
-            "val Loss: 0.1635 Acc: 0.9477\n",
+            "train Loss: 0.5578 Acc: 0.7705\n",
+            "val Loss: 0.3618 Acc: 0.8627\n",
             "\n",
             "Epoch 5/10\n",
             "----------\n",
-            "train Loss: 0.4418 Acc: 0.8197\n",
-            "val Loss: 0.3405 Acc: 0.8627\n",
+            "train Loss: 0.9787 Acc: 0.6926\n",
+            "val Loss: 0.2166 Acc: 0.9412\n",
             "\n",
             "Epoch 6/10\n",
             "----------\n",
-            "train Loss: 0.5072 Acc: 0.7787\n",
-            "val Loss: 0.4774 Acc: 0.8105\n",
+            "train Loss: 0.5505 Acc: 0.7459\n",
+            "val Loss: 0.2558 Acc: 0.9216\n",
             "\n",
             "Epoch 7/10\n",
             "----------\n",
-            "train Loss: 0.6458 Acc: 0.7746\n",
-            "val Loss: 0.1821 Acc: 0.9477\n",
+            "train Loss: 0.4467 Acc: 0.7992\n",
+            "val Loss: 0.2033 Acc: 0.9412\n",
             "\n",
             "Epoch 8/10\n",
             "----------\n",
-            "train Loss: 0.3293 Acc: 0.8443\n",
-            "val Loss: 0.1823 Acc: 0.9477\n",
+            "train Loss: 0.4655 Acc: 0.8115\n",
+            "val Loss: 0.1944 Acc: 0.9477\n",
             "\n",
             "Epoch 9/10\n",
             "----------\n",
-            "train Loss: 0.2703 Acc: 0.8852\n",
-            "val Loss: 0.2138 Acc: 0.9346\n",
+            "train Loss: 0.3452 Acc: 0.8607\n",
+            "val Loss: 0.1865 Acc: 0.9477\n",
             "\n",
             "Epoch 10/10\n",
             "----------\n",
-            "train Loss: 0.3470 Acc: 0.8648\n",
-            "val Loss: 0.2350 Acc: 0.9281\n",
+            "train Loss: 0.2652 Acc: 0.8975\n",
+            "val Loss: 0.1970 Acc: 0.9346\n",
             "\n",
-            "Training complete in 0m 33s\n",
-            "Best val Acc: 0.954248\n",
-            "Test loss: 0.1476 \n",
-            " Test acc: 0.9592\n"
+            "Training complete in 0m 37s\n",
+            "Best val Acc: 0.947712\n",
+            "Test loss: 0.0984 \n",
+            " Test acc: 0.9459\n"
           ]
         }
       ],
@@ -2421,7 +2423,7 @@
         "dossier_destination_test_bees = 'hymenoptera_data/test/bees'\n",
         "\n",
         "# Nombre de fichiers à copier\n",
-        "nombre_de_fichiers_a_copier = 10\n",
+        "nombre_de_fichiers_a_copier = 30\n",
         "\n",
         "# Sélection aléatoire de 10 fichiers parmi la liste\n",
         "fichiers_aleatoires_train_ants = random.sample(os.listdir(dossier_source_train_ants), nombre_de_fichiers_a_copier)\n",
@@ -2634,8 +2636,8 @@
     {
       "cell_type": "markdown",
       "source": [
-        "Sous Google Colab, j'ai dû télécharger et dézipper informatiquement le dossier de hymenoptera_data afin de pouvoir lancer le programme fourni dans le BE. Ainsi, je dois également créer informatiquement le dossier test sur lequel évaluer mon modèle. C'est à cela que serve les lignes de codes créant les dossier demandés, à base des dossiers val et train. On remarque que le test Accuracy est de 0.9592, et le test loss de 0.15 ce qui est très bon. Le modèle est donc très performant.\n",
-        " A noter également que j'ai choisi de prendre 10 images de chaques tests, cela peut-être modifié mais les résultats restent relativement similaires."
+        "Sous Google Colab, j'ai dû télécharger et dézipper informatiquement le dossier de hymenoptera_data afin de pouvoir lancer le programme fourni dans le BE. Ainsi, je dois également créer informatiquement le dossier test sur lequel évaluer mon modèle. C'est à cela que servent les lignes de codes créant les dossier demandés, à base des dossiers val et train. On remarque que le test Accuracy est de 0.94, et le test loss de 0.10 ce qui est très bon. Le modèle est donc très performant.\n",
+        " A noter également que j'ai choisi de prendre 30 images de chaques tests, cela peut-être modifié mais les résultats restent relativement similaires."
       ],
       "metadata": {
         "id": "se9QqWVx5WHy"
@@ -2870,12 +2872,13 @@
         "# 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",
+        "      nn.ReLU(),\n",
+        "      nn.Dropout(p=0.2),\n",
+        "      nn.Linear(num_ftrs, 256),\n",
+        "      nn.Dropout(p=0.2),\n",
+        "      nn.Linear(256,2),\n",
+        "    )\n",
+        "\n",
         "# Send the model to the GPU\n",
         "model = model.to(device)\n",
         "# Set the loss function\n",
@@ -2895,93 +2898,69 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "c1ht4_RuwAPX",
-        "outputId": "f708027e-cd27-473c-f6f0-04c20b3b1f4c"
+        "outputId": "91b1b08f-01ce-407b-8593-996fd59b421c"
       },
       "id": "c1ht4_RuwAPX",
-      "execution_count": 31,
+      "execution_count": 25,
       "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-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",
-            "/usr/local/lib/python3.10/dist-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=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
-            "  warnings.warn(msg)\n"
-          ]
-        },
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "Epoch 1/10\n",
-            "----------\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
-            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "train Loss: 0.7073 Acc: 0.5041\n",
-            "val Loss: 0.5622 Acc: 0.8693\n",
+            "----------\n",
+            "train Loss: 0.6582 Acc: 0.6516\n",
+            "val Loss: 0.3784 Acc: 0.8105\n",
             "\n",
             "Epoch 2/10\n",
             "----------\n",
-            "train Loss: 0.6581 Acc: 0.5943\n",
-            "val Loss: 0.5268 Acc: 0.9281\n",
+            "train Loss: 0.6349 Acc: 0.6557\n",
+            "val Loss: 0.3036 Acc: 0.8889\n",
             "\n",
             "Epoch 3/10\n",
             "----------\n",
-            "train Loss: 0.6319 Acc: 0.6107\n",
-            "val Loss: 0.4690 Acc: 0.7386\n",
+            "train Loss: 0.4823 Acc: 0.7377\n",
+            "val Loss: 0.2076 Acc: 0.9412\n",
             "\n",
             "Epoch 4/10\n",
             "----------\n",
-            "train Loss: 0.5654 Acc: 0.6844\n",
-            "val Loss: 0.3571 Acc: 0.9216\n",
+            "train Loss: 0.3847 Acc: 0.8074\n",
+            "val Loss: 0.2002 Acc: 0.9412\n",
             "\n",
             "Epoch 5/10\n",
             "----------\n",
-            "train Loss: 0.6048 Acc: 0.6230\n",
-            "val Loss: 0.4651 Acc: 0.9346\n",
+            "train Loss: 0.3730 Acc: 0.8279\n",
+            "val Loss: 0.3336 Acc: 0.8693\n",
             "\n",
             "Epoch 6/10\n",
             "----------\n",
-            "train Loss: 0.6198 Acc: 0.6230\n",
-            "val Loss: 0.3884 Acc: 0.9346\n",
+            "train Loss: 0.4953 Acc: 0.7664\n",
+            "val Loss: 0.2415 Acc: 0.8824\n",
             "\n",
             "Epoch 7/10\n",
             "----------\n",
-            "train Loss: 0.5611 Acc: 0.6680\n",
-            "val Loss: 0.3740 Acc: 0.9608\n",
+            "train Loss: 0.3315 Acc: 0.8525\n",
+            "val Loss: 0.2015 Acc: 0.9412\n",
             "\n",
             "Epoch 8/10\n",
             "----------\n",
-            "train Loss: 0.5563 Acc: 0.6557\n",
-            "val Loss: 0.3353 Acc: 0.9608\n",
+            "train Loss: 0.3200 Acc: 0.8893\n",
+            "val Loss: 0.1962 Acc: 0.9412\n",
             "\n",
             "Epoch 9/10\n",
             "----------\n",
-            "train Loss: 0.5847 Acc: 0.6475\n",
-            "val Loss: 0.3512 Acc: 0.9608\n",
+            "train Loss: 0.3867 Acc: 0.8115\n",
+            "val Loss: 0.1889 Acc: 0.9542\n",
             "\n",
             "Epoch 10/10\n",
             "----------\n",
-            "train Loss: 0.5557 Acc: 0.6803\n",
-            "val Loss: 0.3597 Acc: 0.9542\n",
+            "train Loss: 0.4006 Acc: 0.7869\n",
+            "val Loss: 0.2018 Acc: 0.9477\n",
             "\n",
-            "Training complete in 0m 34s\n",
-            "Best val Acc: 0.960784\n",
-            "Test loss: 0.3782 \n",
-            " Test acc: 0.9796\n"
+            "Training complete in 0m 37s\n",
+            "Best val Acc: 0.954248\n",
+            "Test loss: 0.1330 \n",
+            " Test acc: 0.9595\n"
           ]
         }
       ]
@@ -2989,14 +2968,14 @@
     {
       "cell_type": "code",
       "source": [
-        "model.cpu()\n",
-        "\n",
-        "# Quantification du modèle\n",
-        "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+        "import torch.quantization\n",
         "\n",
         "#Affichage des tailles de modèle\n",
         "print(\"Taille du modèle de base\")\n",
         "print_size_of_model(model, \"int8\")\n",
+        "\n",
+        "# Quantification du modèle\n",
+        "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
         "print(\"Taille du modèle quantifié\")\n",
         "print_size_of_model(quantized_model, \"int8\")\n",
         "\n",
@@ -3006,42 +2985,45 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 443
+          "height": 1000
         },
         "id": "yXnOvUFIHACu",
-        "outputId": "f1d8724e-d989-4a53-a160-d7b0cdd09e25"
+        "outputId": "34663b41-b060-4416-e11b-318de9fe6843"
       },
       "id": "yXnOvUFIHACu",
-      "execution_count": 32,
+      "execution_count": 27,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "Taille du modèle de base\n",
-            "model:  int8  \t Size (KB): 44797.562\n",
+            "model:  int8  \t Size (KB): 45305.978\n",
             "Taille du modèle quantifié\n",
-            "model:  int8  \t Size (KB): 44783.654\n"
+            "model:  int8  \t Size (KB): 44912.742\n"
           ]
         },
         {
           "output_type": "error",
-          "ename": "RuntimeError",
+          "ename": "NotImplementedError",
           "evalue": "ignored",
           "traceback": [
             "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-32-7f902b5b4046>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m#Evaluation du modèle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0meval_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquantized_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-            "\u001b[0;32m<ipython-input-31-8abf47fa7235>\u001b[0m in \u001b[0;36meval_model\u001b[0;34m(model, criterion)\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;31m# Track history if only in training phase\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"train\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-27-b4224cc1986d>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m#Evaluation du modèle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0meval_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquantized_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m<ipython-input-25-a52b60717672>\u001b[0m in \u001b[0;36meval_model\u001b[0;34m(model, criterion)\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;31m# Track history if only in training phase\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"train\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
             "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
             "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
             "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m         \u001b[0;31m# See note [TorchScript super()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mavgpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
             "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
             "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    454\u001b[0m                             \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 456\u001b[0;31m         return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m    457\u001b[0m                         self.padding, self.dilation, self.groups)\n\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mRuntimeError\u001b[0m: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same"
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/ao/nn/quantized/dynamic/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     52\u001b[0m                     x, self._packed_params._packed_params)\n\u001b[1;32m     53\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m                 Y = torch.ops.quantized.linear_dynamic(\n\u001b[0m\u001b[1;32m     55\u001b[0m                     x, self._packed_params._packed_params, reduce_range=True)\n\u001b[1;32m     56\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_packed_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat16\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_ops.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    690\u001b[0m         \u001b[0;31m# We save the function ptr as the `op` attribute on\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m         \u001b[0;31m# OpOverloadPacket to access it here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 692\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m     \u001b[0;31m# TODO: use this to make a __dir__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;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"
           ]
         }
       ]
@@ -3049,7 +3031,9 @@
     {
       "cell_type": "markdown",
       "source": [
-        "On remarque que le test loss est moins bon qu'avec le modèle précédent, l'accuracy n'a quant à lui pas changé."
+        "On remarque que le test loss (pour le modèle non quantifié) est légèrement moins bon que le modèle précédent (0.13 contre 0.09). Le test accuracy est légèrement meilleure, passant de 0.94 à 0.96.\n",
+        "\n",
+        "Je ne suis pas parvenu à mesurer le modèle quantifié à partir du modèle composé des couches \"relu\" et \"dropout\"... je ne suis pas parvenu à générer un résultat que ce soit en mettant le modèle quantifié sur le cpu ou le gpu."
       ],
       "metadata": {
         "id": "aolhPVBi-W89"
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