diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 644c28380b694a2b027e10533a7c9ebed85c7728..5e4b88946ce8ca98df71fbaef81fef3d979522a3 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -157,12 +157,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -172,7 +172,9 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(range(1), [1])\n",
+    "plt.show()"
    ]
   },
   {
diff --git a/exercice 1 and 2.ipynb b/exercice 1 and 2.ipynb
index 7b2e09511343a305aca6967e3baaa403647521e9..871b5165bf17e3a8dac5f727786d795371795756 100644
--- a/exercice 1 and 2.ipynb	
+++ b/exercice 1 and 2.ipynb	
@@ -28,9 +28,22 @@
    "cell_type": "code",
    "execution_count": 1,
    "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 matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(range(1), [1])\n",
+    "plt.show()"
    ]
   },
   {
@@ -72,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "462666a2",
    "metadata": {},
    "outputs": [
@@ -153,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "317bf070",
    "metadata": {},
    "outputs": [
@@ -167,9 +180,9 @@
       "  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
-      "  (dropout1): Dropout(p=0.01, inplace=False)\n",
+      "  (dropout1): Dropout(p=0.4, inplace=False)\n",
       "  (fc2): Linear(in_features=512, out_features=64, bias=True)\n",
-      "  (dropout2): Dropout(p=0.01, inplace=False)\n",
+      "  (dropout2): Dropout(p=0.4, inplace=False)\n",
       "  (fc3): Linear(in_features=64, out_features=10, bias=True)\n",
       ")\n"
      ]
@@ -190,9 +203,9 @@
     "        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
     "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
     "        self.fc1 = nn.Linear(64 * 4 * 4, 512)\n",
-    "        self.dropout1 = nn.Dropout(p = 0.01) #p = probability of an element to be equal to 0.\n",
+    "        self.dropout1 = nn.Dropout(p = 0.4) #p = probability of an element to be equal to 0.\n",
     "        self.fc2 = nn.Linear(512, 64)\n",
-    "        self.dropout2 = nn.Dropout(p = 0.01)\n",
+    "        self.dropout2 = nn.Dropout(p = 0.4)\n",
     "        self.fc3 = nn.Linear(64, 10)\n",
     "\n",
     "    def forward(self, x):\n",
@@ -224,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "4b53f229",
    "metadata": {},
    "outputs": [
@@ -232,26 +245,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 \tTraining Loss: 45.488346 \tValidation Loss: 42.259593\n",
-      "Validation loss decreased (inf --> 42.259593).  Saving model ...\n",
-      "Epoch: 1 \tTraining Loss: 37.337089 \tValidation Loss: 32.943216\n",
-      "Validation loss decreased (42.259593 --> 32.943216).  Saving model ...\n",
-      "Epoch: 2 \tTraining Loss: 31.222531 \tValidation Loss: 28.891407\n",
-      "Validation loss decreased (32.943216 --> 28.891407).  Saving model ...\n",
-      "Epoch: 3 \tTraining Loss: 28.012236 \tValidation Loss: 27.241223\n",
-      "Validation loss decreased (28.891407 --> 27.241223).  Saving model ...\n",
-      "Epoch: 4 \tTraining Loss: 25.378012 \tValidation Loss: 23.604487\n",
-      "Validation loss decreased (27.241223 --> 23.604487).  Saving model ...\n",
-      "Epoch: 5 \tTraining Loss: 23.009165 \tValidation Loss: 21.749040\n",
-      "Validation loss decreased (23.604487 --> 21.749040).  Saving model ...\n",
-      "Epoch: 6 \tTraining Loss: 20.887737 \tValidation Loss: 20.786537\n",
-      "Validation loss decreased (21.749040 --> 20.786537).  Saving model ...\n",
-      "Epoch: 7 \tTraining Loss: 19.077979 \tValidation Loss: 19.665828\n",
-      "Validation loss decreased (20.786537 --> 19.665828).  Saving model ...\n",
-      "Epoch: 8 \tTraining Loss: 17.541750 \tValidation Loss: 18.931251\n",
-      "Validation loss decreased (19.665828 --> 18.931251).  Saving model ...\n",
-      "Epoch: 9 \tTraining Loss: 16.094752 \tValidation Loss: 17.626424\n",
-      "Validation loss decreased (18.931251 --> 17.626424).  Saving model ...\n"
+      "Epoch: 0 \tTraining Loss: 45.882111 \tValidation Loss: 44.668585\n",
+      "Validation loss decreased (inf --> 44.668585).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 40.603230 \tValidation Loss: 36.758304\n",
+      "Validation loss decreased (44.668585 --> 36.758304).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 35.396721 \tValidation Loss: 31.935054\n",
+      "Validation loss decreased (36.758304 --> 31.935054).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 32.144104 \tValidation Loss: 29.167652\n",
+      "Validation loss decreased (31.935054 --> 29.167652).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 29.971062 \tValidation Loss: 27.150468\n",
+      "Validation loss decreased (29.167652 --> 27.150468).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 28.108355 \tValidation Loss: 26.775549\n",
+      "Validation loss decreased (27.150468 --> 26.775549).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 26.360687 \tValidation Loss: 24.830030\n",
+      "Validation loss decreased (26.775549 --> 24.830030).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 24.820620 \tValidation Loss: 22.812824\n",
+      "Validation loss decreased (24.830030 --> 22.812824).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 23.395856 \tValidation Loss: 22.326507\n",
+      "Validation loss decreased (22.812824 --> 22.326507).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 22.100028 \tValidation Loss: 20.783471\n",
+      "Validation loss decreased (22.326507 --> 20.783471).  Saving model ...\n"
      ]
     }
    ],
@@ -342,10 +355,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "d39df818",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.plot(range(n_epochs), train_loss_list)\n",
     "plt.xlabel(\"Epoch\")\n",
@@ -364,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 9,
    "id": "e93efdfc",
    "metadata": {},
    "outputs": [
@@ -372,30 +396,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 17.135382\n",
+      "Test Loss: 20.574108\n",
       "\n",
-      "Test Accuracy of airplane: 79% (793/1000)\n",
-      "Test Accuracy of automobile: 86% (864/1000)\n",
-      "Test Accuracy of  bird: 62% (625/1000)\n",
-      "Test Accuracy of   cat: 41% (416/1000)\n",
-      "Test Accuracy of  deer: 63% (632/1000)\n",
-      "Test Accuracy of   dog: 60% (606/1000)\n",
-      "Test Accuracy of  frog: 82% (824/1000)\n",
-      "Test Accuracy of horse: 82% (823/1000)\n",
-      "Test Accuracy of  ship: 79% (798/1000)\n",
-      "Test Accuracy of truck: 77% (778/1000)\n",
+      "Test Accuracy of airplane: 63% (636/1000)\n",
+      "Test Accuracy of automobile: 72% (723/1000)\n",
+      "Test Accuracy of  bird: 49% (497/1000)\n",
+      "Test Accuracy of   cat: 46% (461/1000)\n",
+      "Test Accuracy of  deer: 56% (561/1000)\n",
+      "Test Accuracy of   dog: 58% (588/1000)\n",
+      "Test Accuracy of  frog: 69% (696/1000)\n",
+      "Test Accuracy of horse: 62% (625/1000)\n",
+      "Test Accuracy of  ship: 86% (866/1000)\n",
+      "Test Accuracy of truck: 72% (724/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 71% (7159/10000)\n"
+      "Test Accuracy (Overall): 63% (6377/10000)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "([0.793, 0.864, 0.625, 0.416, 0.632, 0.606, 0.824, 0.823, 0.798, 0.778],\n",
-       " 0.7159)"
+       "([0.636, 0.723, 0.497, 0.461, 0.561, 0.588, 0.696, 0.625, 0.866, 0.724],\n",
+       " 0.6377)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -483,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 10,
    "id": "ef623c26",
    "metadata": {},
    "outputs": [
@@ -500,7 +524,7 @@
        "2330946"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -530,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 11,
    "id": "c4c65d4b",
    "metadata": {},
    "outputs": [
@@ -547,7 +571,7 @@
        "659806"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -570,46 +594,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 17.135382\n",
+      "Test Loss: 20.574108\n",
       "\n",
-      "Test Accuracy of airplane: 79% (793/1000)\n",
-      "Test Accuracy of automobile: 86% (864/1000)\n",
-      "Test Accuracy of  bird: 62% (625/1000)\n",
-      "Test Accuracy of   cat: 41% (416/1000)\n",
-      "Test Accuracy of  deer: 63% (632/1000)\n",
-      "Test Accuracy of   dog: 60% (606/1000)\n",
-      "Test Accuracy of  frog: 82% (824/1000)\n",
-      "Test Accuracy of horse: 82% (823/1000)\n",
-      "Test Accuracy of  ship: 79% (798/1000)\n",
-      "Test Accuracy of truck: 77% (778/1000)\n",
+      "Test Accuracy of airplane: 63% (636/1000)\n",
+      "Test Accuracy of automobile: 72% (723/1000)\n",
+      "Test Accuracy of  bird: 49% (497/1000)\n",
+      "Test Accuracy of   cat: 46% (461/1000)\n",
+      "Test Accuracy of  deer: 56% (561/1000)\n",
+      "Test Accuracy of   dog: 58% (588/1000)\n",
+      "Test Accuracy of  frog: 69% (696/1000)\n",
+      "Test Accuracy of horse: 62% (625/1000)\n",
+      "Test Accuracy of  ship: 86% (866/1000)\n",
+      "Test Accuracy of truck: 72% (724/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 71% (7159/10000)\n",
-      "Test Loss: 17.135382\n",
+      "Test Accuracy (Overall): 63% (6377/10000)\n",
+      "Test Loss: 20.579821\n",
       "\n",
-      "Test Accuracy of airplane: 79% (793/1000)\n",
-      "Test Accuracy of automobile: 86% (864/1000)\n",
-      "Test Accuracy of  bird: 62% (625/1000)\n",
-      "Test Accuracy of   cat: 41% (416/1000)\n",
-      "Test Accuracy of  deer: 63% (632/1000)\n",
-      "Test Accuracy of   dog: 60% (606/1000)\n",
-      "Test Accuracy of  frog: 82% (824/1000)\n",
-      "Test Accuracy of horse: 82% (823/1000)\n",
-      "Test Accuracy of  ship: 79% (798/1000)\n",
-      "Test Accuracy of truck: 77% (778/1000)\n",
+      "Test Accuracy of airplane: 63% (637/1000)\n",
+      "Test Accuracy of automobile: 72% (725/1000)\n",
+      "Test Accuracy of  bird: 49% (493/1000)\n",
+      "Test Accuracy of   cat: 46% (462/1000)\n",
+      "Test Accuracy of  deer: 56% (560/1000)\n",
+      "Test Accuracy of   dog: 58% (586/1000)\n",
+      "Test Accuracy of  frog: 69% (698/1000)\n",
+      "Test Accuracy of horse: 62% (627/1000)\n",
+      "Test Accuracy of  ship: 86% (867/1000)\n",
+      "Test Accuracy of truck: 72% (721/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 71% (7159/10000)\n"
+      "Test Accuracy (Overall): 63% (6376/10000)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]