From b43f84d739cbf99e5f9a5f4655cba8192ceecad2 Mon Sep 17 00:00:00 2001
From: Jeanne Caty <jeanne.caty@ecl22.ec-lyon.fr>
Date: Thu, 5 Dec 2024 18:08:54 +0100
Subject: [PATCH] Update TD2 Deep Learning.ipynb
---
TD2 Deep Learning.ipynb | 132 ++++++++++++++++++++++++++++++++++++----
1 file changed, 120 insertions(+), 12 deletions(-)
diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 00e4fdc..65bffee 100644
--- a/TD2 Deep Learning.ipynb
+++ b/TD2 Deep Learning.ipynb
@@ -38,7 +38,7 @@
"metadata": {},
"outputs": [],
"source": [
- "%pip install torch torchvision"
+ "%pip install torch torchvision\n"
]
},
{
@@ -52,10 +52,72 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "b1950f0a",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[ 0.6418, -0.1700, 0.6304, 0.3113, 0.1379, 1.0753, -0.3344, 1.4741,\n",
+ " -0.8532, 0.3268],\n",
+ " [ 0.0866, 2.1869, -1.1434, 0.6156, -0.4647, -0.5759, -0.6658, 0.3761,\n",
+ " -1.1228, 0.1785],\n",
+ " [-0.4243, -1.0760, 0.3367, 1.0454, 0.4841, 0.8989, 0.4678, -0.1791,\n",
+ " 2.0395, -0.9181],\n",
+ " [-0.8864, 0.1093, -0.5295, 0.4361, 0.7182, 0.2051, 0.0169, -0.8813,\n",
+ " 0.3688, 1.3760],\n",
+ " [-0.5702, -0.1380, 1.2921, 1.0146, 0.1518, 1.2922, 0.6758, -0.1043,\n",
+ " 1.1502, 0.4387],\n",
+ " [ 0.5554, 0.5230, 0.8402, -1.3635, -0.3173, 0.3087, 0.7613, -2.0258,\n",
+ " 0.1627, 0.9464],\n",
+ " [ 0.7489, -0.2015, 0.8231, 1.2146, 0.9384, 0.6064, 0.6412, 0.0341,\n",
+ " 1.0204, -0.0650],\n",
+ " [ 0.2682, 1.0076, -1.5576, -0.3533, -0.1235, 2.4824, 1.1433, 0.5006,\n",
+ " 0.0619, 0.2076],\n",
+ " [ 1.1212, 1.7072, -0.4859, 0.3846, -0.6056, 1.4281, 0.5899, 0.1623,\n",
+ " 1.9957, 0.3370],\n",
+ " [ 0.9890, -1.0958, -0.5957, -0.4671, -0.2902, -0.0344, -0.8092, -0.5606,\n",
+ " -0.5501, -1.3897],\n",
+ " [ 0.8188, -0.6413, 0.2777, 0.7005, -0.2147, -0.0083, 0.6479, -1.1746,\n",
+ " -1.2938, 2.7529],\n",
+ " [-0.2992, 1.4363, 0.4173, 2.1196, 0.1661, -0.6726, 1.1396, -0.0788,\n",
+ " -0.9447, 0.5560],\n",
+ " [ 1.8925, -1.7427, -1.7261, -0.3420, 1.3588, -0.4280, -0.2186, -0.1555,\n",
+ " -0.4196, -0.6740],\n",
+ " [-0.9354, -0.1141, 0.3492, 0.5924, -1.0574, -1.2510, 0.9382, 0.2804,\n",
+ " -1.0396, -1.6244]])\n",
+ "AlexNet(\n",
+ " (features): Sequential(\n",
+ " (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+ " (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+ " (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (7): ReLU(inplace=True)\n",
+ " (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (9): ReLU(inplace=True)\n",
+ " (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (11): ReLU(inplace=True)\n",
+ " (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+ " )\n",
+ " (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
+ " (classifier): Sequential(\n",
+ " (0): Dropout(p=0.5, inplace=False)\n",
+ " (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
+ " (2): ReLU(inplace=True)\n",
+ " (3): Dropout(p=0.5, inplace=False)\n",
+ " (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
+ " (5): ReLU(inplace=True)\n",
+ " (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
+ " )\n",
+ ")\n"
+ ]
+ }
+ ],
"source": [
"import torch\n",
"\n",
@@ -95,10 +157,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "6e18f2fd",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CUDA is not available. Training on CPU ...\n"
+ ]
+ }
+ ],
"source": [
"import torch\n",
"\n",
@@ -121,10 +191,33 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"id": "462666a2",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data\\cifar-10-python.tar.gz\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 170M/170M [00:48<00:00, 3.52MB/s] \n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Extracting data\\cifar-10-python.tar.gz to data\n",
+ "Files already downloaded and verified\n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"from torchvision import datasets, transforms\n",
@@ -193,10 +286,25 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "317bf070",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Net(\n",
+ " (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
+ " (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+ " (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
+ " (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
+ " (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
+ " (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
+ ")\n"
+ ]
+ }
+ ],
"source": [
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
@@ -883,7 +991,7 @@
},
{
"cell_type": "markdown",
- "id": "bbd48800",
+ "id": "ca7cda74",
"metadata": {},
"source": [
"Experiments:\n",
@@ -926,7 +1034,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.8.5 ('base')",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -940,7 +1048,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.13"
},
"vscode": {
"interpreter": {
--
GitLab