From 5bcb893326707eed75161a12646c44b0462b8feb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Mat=C3=ADas=20Duhalde?= <matias.duhalde@uc.cl>
Date: Fri, 17 Nov 2023 17:18:27 +0100
Subject: [PATCH] feat: ex1 base
---
TD2 Deep Learning.ipynb | 38 +++++++++++++++++++++++++++++++++++++-
1 file changed, 37 insertions(+), 1 deletion(-)
diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 2ecfce9..cda6a88 100644
--- a/TD2 Deep Learning.ipynb
+++ b/TD2 Deep Learning.ipynb
@@ -434,6 +434,42 @@
"Compare the results obtained with this new network to those obtained previously."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Build the new network\n",
+ "\n",
+ "class NewNet(nn.Module):\n",
+ " def __init__(self):\n",
+ " super(NewNet, self).__init__()\n",
+ " # First convolutional layer (kernel size = 3, padding = 1, output = 16)\n",
+ " self.conv1 = nn.Conv2d(3, 16, 3, padding=1)\n",
+ " # Second convolutional layer (kernel size = 3, padding = 1, output = 32)\n",
+ " self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n",
+ " # Third convolutional layer (kernel size = 3, padding = 1, output = 64)\n",
+ " self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n",
+ "\n",
+ " def forward(self, x):\n",
+ " x = self.pool(F.relu(self.conv1(x)))\n",
+ " x = self.pool(F.relu(self.conv2(x)))\n",
+ " x = x.view(-1, 16 * 5 * 5)\n",
+ " x = F.relu(self.fc1(x))\n",
+ " x = F.relu(self.fc2(x))\n",
+ " x = self.fc3(x)\n",
+ " return x\n",
+ "\n",
+ "\n",
+ "# create a complete CNN\n",
+ "new_model = NewNet()\n",
+ "print(new_model)\n",
+ "# move tensors to GPU if CUDA is available\n",
+ "if train_on_gpu:\n",
+ " new_model.cuda()\n"
+ ]
+ },
{
"cell_type": "markdown",
"id": "bc381cf4",
@@ -940,7 +976,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.11.2"
},
"vscode": {
"interpreter": {
--
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