127 lines
3.1 KiB
Plaintext
127 lines
3.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "086b9495",
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"metadata": {},
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"source": [
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"<div style=\"\n",
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" border: 2px solid #4CAF50; \n",
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" padding: 15px; \n",
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" background-color: #f4f4f4; \n",
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" border-radius: 10px; \n",
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" align-items: center;\">\n",
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"\n",
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"<h1 style=\"margin: 0; color: #4CAF50;\">Neural Networks: Convolutional Neural Networks (2)</h1>\n",
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"<h2 style=\"margin: 5px 0; color: #555;\">DSAI</h2>\n",
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"<h3 style=\"margin: 5px 0; color: #555;\">Jakob Eggl</h3>\n",
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"\n",
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"<div style=\"flex-shrink: 0;\">\n",
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" <img src=\"https://www.htl-grieskirchen.at/wp/wp-content/uploads/2022/11/logo_bildschirm-1024x503.png\" alt=\"Logo\" style=\"width: 250px; height: auto;\"/>\n",
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"</div>\n",
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"<p1> © 2025/26 Jakob Eggl. Nutzung oder Verbreitung nur mit ausdrücklicher Genehmigung des Autors.</p1>\n",
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"</div>\n",
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"<div style=\"flex: 1;\">\n",
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"</div> "
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]
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},
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{
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"cell_type": "markdown",
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"id": "640a7aba",
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"metadata": {},
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"source": [
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"Nachdem wir jetzt wissen, wie ein *Convolutional Neuronal Network* (**CNN**) funktioniert, wollen wir nun nochmal die Datasets **MNIST** und **Fashion-MNIST** ausprobieren."
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]
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},
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{
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"cell_type": "markdown",
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"id": "8d8125aa",
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"metadata": {},
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"source": [
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"Erstelle somit ein Neuronales Netzwerk, welches auf der CNN Architektur basiert und auf den Datasets **MNIST** und **Fashion-MNIST** trainiert wird."
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b5f313f",
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"metadata": {},
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"source": [
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"# Lösung"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6c231b6a",
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "2a0a6fdf",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.optim as optim\n",
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"from torch.utils.data import DataLoader, Dataset, random_split\n",
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"from torchvision import datasets, transforms\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.metrics import confusion_matrix\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "e942400c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"cpu\n"
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]
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}
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],
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"source": [
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"device = torch.device('cuda') if torch.cuda.is_available() else torch.device('mps') if torch.backends.mps.is_available() else torch.device('cpu')\n",
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"print(device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "86c04c79",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "dsai",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.23"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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