489 lines
13 KiB
Plaintext
489 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# German traffic sign classification\n",
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"\n",
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"### Aufgaben:\n",
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"1. Suchen Sie sich 10-20 unterschiedliche Schilder aus, welche Sie mit Ihrem Modell klassifizieren wollen. Trainieren Sie dann ein Modell Ihrer Wahl. Wählen Sie danach eine der folgenden zusätzlichen Aufgaben aus:\n",
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" - Erstellen Sie ein Convolutional Neural Network und ein Fully Connected Neural Network und vergleichen Sie die Leistung beider Modelle mit Ihrem Modell.\n",
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" - Trainieren Sie ein Modell mit farbigen und ein Modell mit grauen Bildern (Farbbilder umgewandelt in Graustufen) und vergleichen Sie die Performance von beiden Modellen mit Ihrem Modell.\n",
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" - Erstellen Sie zwei weitere Modelle und vergleichen Sie deren Performance. Das zweite Modell soll alle 43 Verkehrsschilder klassifizieren und das dritte Modell nur ein Verkehrsschild erkennen können.\n",
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" - Benutzen Sie das Framework [Optuna](https://optuna.readthedocs.io/en/stable/index.html), um die Hyperparameter (Anzahl Layer, Anzahl Neuronen pro Layer, …) Ihres Modells zu optimieren. Welchen Vorteil bringt es [Optuna](https://optuna.readthedocs.io/en/stable/index.html) zu nutzen?\n",
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" - Trainieren Sie ein Modell mit einer hohen Accuracy und ein Modell mit einer geringen Latenz. Wie stark unterscheidet sich die Latenz und Accuracy von beiden Modellen. Die Latenz ist die Zeit, die das Modell braucht, ein Verkehrsschild zu klassifizieren.\n",
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" - Machen Sie eigene Bilder von Schildern und testen Sie Ihr Modell mit den eigenen Bildern. Variieren Sie Lichtverhältnisse, Wetter, … Die Bilder in den Trainingsdaten haben eine Auflösung von 32 x 32 Pixeln.\n",
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"2. Sie bekommen ein paar Tage vor der Abgabe einen Testdatensatz. Testet einmal eure erstellten Modelle mit den Testdaten und prüft, wie gut die Modelle unbekannte Daten klassifizieren können.\n",
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" **Verändern Sie danach <u>nicht</u> mehr die Modelle, um ein besseres Ergebnis zu bekommen**. Das Ziel ist es zu prüfen, wie gut eure Modelle in der Realität funktionieren würden.\n"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Import libraries and datasets"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import pickle\n",
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"import random\n",
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"import os"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n",
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"is_executing": true
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}
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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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"outputs": [],
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"source": [
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"with open(os.path.join(\"dataset\", \"train.p\"), mode='rb') as training_data:\n",
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" train = pickle.load(training_data)\n",
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"with open(os.path.join(\"dataset\", \"valid.p\"), mode='rb') as validation_data:\n",
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" valid = pickle.load(validation_data)\n",
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"\n",
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"X_train, y_train = train['features'], train['labels']\n",
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"X_valid, y_valid = valid['features'], valid['labels']"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 4,
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"outputs": [],
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"source": [
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"# TODO: Select 10-20 different signs from the dataset\n"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Shuffle and configure the datasets"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 5,
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"outputs": [],
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"source": [
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"from sklearn.utils import shuffle\n",
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"X_train, y_train = shuffle(X_train, y_train)\n",
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"X_valid, y_valid = shuffle(X_valid, y_valid)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 6,
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"outputs": [],
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"source": [
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"# Normalize image to [0, 1]\n",
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"X_train_norm = X_train / 255\n",
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"X_valid_norm = X_valid / 255"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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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"outputs": [],
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"source": [
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"# Check that the images have been correctly converted and normalised\n",
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"i = random.randint(1, len(X_train_norm))\n",
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"plt.grid(False)\n",
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"plt.imshow(X_train[i])\n",
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"plt.figure()\n",
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"plt.grid(False)\n",
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"plt.imshow(X_train_norm[i].squeeze(), cmap = 'gray') # cmap"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n",
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"is_executing": true
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Create the Convolutional Neural Network with keras\n",
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"For example a CNN that recognises handwritten numbers: [https://adamharley.com/nn_vis/cnn/2d.html](https://adamharley.com/nn_vis/cnn/2d.html)\n",
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"Here a fully connected neural network that also recognise handwritten numbers: [https://adamharley.com/nn_vis/mlp/2d.html](https://adamharley.com/nn_vis/mlp/2d.html)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"Use CNN.add() to add a [Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers) to your model. Here is a list of layers that might be useful:\n",
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"- [Convolution Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D): layers.Conv2D()\n",
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"- [Average Pooling](https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D): layers.AveragePooling2D()\n",
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"- [Max Pooling](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D): layers.MaxPool2D()\n",
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"- [Dropout](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout): layers.Dropout()\n",
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"- [Flattens](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten) the input. 2D -> 1D: layers.Flatten()\n",
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"- [Densely-connected NN layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense): layers.Dense()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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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"outputs": [],
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"source": [
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"from tensorflow.keras import datasets, layers, models\n",
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"model = models.Sequential()\n",
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"\n",
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"# Only in the first layer you have to select the input_shape of the data (image).\n",
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"# TODO: Replace the question marks:\n",
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"# model.add(layers.Conv2D( filters = ? , kernel_size = ( ? , ? ), padding = ? , activation = ? , input_shape = ( ? , ? , ?)))\n",
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"\n",
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"# TODO: Add layers to the model:\n",
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"\n",
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"# Prints a summary of your network\n",
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"model.summary()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Compile your model\n",
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"When you want, you can change the [optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) or the [loss function](https://www.tensorflow.org/api_docs/python/tf/keras/losses)."
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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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"outputs": [],
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"source": [
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"model.compile(optimizer = 'Adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Train your model\n",
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"The documentation of the fit method: [https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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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"outputs": [],
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"source": [
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"# TODO: Choose the batch size and the epochs\n",
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"history = model.fit(x = X_train_norm,\n",
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" y = y_train,\n",
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" batch_size = ...,\n",
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" epochs = ...,\n",
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" verbose = 1,\n",
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" validation_data = (X_valid_norm, y_valid))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Save your model\n",
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"Create a folder for your models"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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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"outputs": [],
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"source": [
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"model.save('saved_model/my_model.h5')"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Analyse the results"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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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"outputs": [],
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"source": [
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"history.history.keys()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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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"outputs": [],
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"source": [
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"accuracy = history.history['accuracy']\n",
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"val_accuracy = history.history['val_accuracy']\n",
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"loss = history.history['loss']\n",
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"val_loss = history.history['val_loss']"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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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"outputs": [],
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"source": [
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"epochs = range(len(accuracy))\n",
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"plt.plot(epochs, loss, 'b', label = 'Training loss')\n",
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"plt.plot(epochs, val_loss, 'r', label = 'Validation loss')\n",
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"plt.title('Training and Validation loss')"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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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"outputs": [],
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"source": [
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"plt.plot(epochs, accuracy, 'ro', label = 'Training accuracy')\n",
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"plt.plot(epochs, val_accuracy, 'r', label = 'Validation accuracy')\n",
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"plt.title('Training and Validation accuracy')"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Load your model"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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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"outputs": [],
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"source": [
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"model = tf.keras.models.load_model('saved_model/my_model.h5')"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n",
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"is_executing": true
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Test your model with the test dataset\n",
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"If you don't have the test dataset use the validation dataset."
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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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"outputs": [],
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"source": [
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"score = model.evaluate(X_valid_norm, y_valid)\n",
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"print('Test Accuracy: {}'.format(score[1]))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n",
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"is_executing": true
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}
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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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"outputs": [],
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"source": [
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"from sklearn.metrics import confusion_matrix\n",
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"predicted_classes = np.argmax(model.predict(X_valid_norm), axis=-1)\n",
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"y_true = y_valid\n",
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"\n",
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"cm = confusion_matrix(y_true, predicted_classes)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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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"outputs": [],
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"source": [
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"plt.figure(figsize = (25, 25))\n",
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"sns.heatmap(cm, annot = True)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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} |