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3springs · Oct 13, 2020 · 894a1cc · 894a1cc
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diff --git a/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.ipynb b/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.ipynb
@@ -1824,45 +1824,12 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-10-13T05:58:42.602Z"
+     "end_time": "2020-10-13T06:00:52.239227Z",
+     "start_time": "2020-10-13T05:59:23.760111Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "90b0f467dd314fb4830cf6a3e19bf5a0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1,    10] loss: 0.0537\n",
-      "[1,    20] loss: 0.00856\n",
-      "[1,    30] loss: 0.0023\n",
-      "[1,    40] loss: 0.000716\n",
-      "[1,    50] loss: 0.00039\n",
-      "[1,    60] loss: 0.000216\n",
-      "[1,    70] loss: 0.000143\n"
-     ]
-    }
-   ],
-   "source": [
-    "learning_rate = 1e-3\n",
-    "convnet2 = BetterCNN().to(device)\n",
-    "optimizer = torch.optim.Adam(convnet2.parameters(), lr=learning_rate)\n",
-    "model = train(convnet2, x_train, y_train, criterion, optimizer, n_epochs=10)\n",
-    "test(model, x_test, y_test)"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -1876,8 +1843,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-13T05:57:46.993651Z",
-     "start_time": "2020-10-13T05:57:00.198Z"
+     "end_time": "2020-10-13T06:00:52.240942Z",
+     "start_time": "2020-10-13T05:59:24.354Z"
     }
    },
    "outputs": [],

diff --git a/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.py b/notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.py
@@ -433,11 +433,7 @@ def forward(self, x):
 #
 # </details>
 
-learning_rate = 1e-3
-convnet2 = BetterCNN().to(device)
-optimizer = torch.optim.Adam(convnet2.parameters(), lr=learning_rate)
-model = train(convnet2, x_train, y_train, criterion, optimizer, n_epochs=10)
-test(model, x_test, y_test)
+
 
 # Finally ! After changing the optimizer, creating a better CNN architecture and train for a couple of epochs we got an accuracy of over 99% on unseen data.