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ML_U3_2.ipynb
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195
ML_U3_2.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Maschinelles Lernen (ML) - Übung 3\n",
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"# Perzeptronen und mehrschichtige Perzeptronen\n",
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"# 3.2 Loss Function, Backpropagation und Gradient Descent\n",
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"\n",
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"In dieser Aufgabe wird die Aktualisierungsfunktion (Lernalgorithmus) aus der Vorlesung durch das Verfahren der Fehlerrückführung (engl. backpropagation) und des Gradientenverfahrens (engl. gradient descent) ersetzt.\n",
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"\n",
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"In dieser Übung sollen Sie das bereits ausgearbeitete Perzeptron anpassen."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# matplotlib: Modul zum Plotten von Daten\n",
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"from matplotlib import pyplot as plt \n",
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"\n",
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"# numpy: Mathematikbibliothek\n",
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"import numpy as np \n",
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"import pandas as pd\n",
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"import time"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Aufgabe 1:** Ersetzen Sie die bisherige Aktivierung in der Methode *predict* durch die Sigmoidfunktion.\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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"metadata": {},
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"source": [
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"**Aufgabe 2:** Implementieren Sie das beschriebene Gradientenlernverfahren mit Backpropagation in die Methode *fit*."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Aufgabe 3:** Schauen Sie sich die Ausgabe des Perzeptrons (perceptron.predict(.)) auf einem der bisher verwendeten Datensätze (z.B. *AND*, Iris) an. Was fällt gegenüber einem Perzeptron mit der Signum-Funktion als Aktivierung auf? Was bedeutet das für den Einsatz als binärer Klassifikator?"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"class Perceptron(object):\n",
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" def __init__(self, number_of_inputs, epochs, eta):\n",
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" \"\"\"\n",
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" Beispielaufruf des Konstruktors:\n",
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" >>> Perceptron(2, 100, 0.1)\n",
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" \"\"\"\n",
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" ### Dein Code kommt hierhin:\n",
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" \n",
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" ##########################\n",
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" pass\n",
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" \n",
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" def predict(self, inputs):\n",
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" \"\"\"\n",
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" Beispiel des Funktionsaufrufes:\n",
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" >>> inputs = np.array([0, 1])\n",
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" >>> h = perceptron.predict(inputs) \n",
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" \"\"\"\n",
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" \n",
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" # Dein Code kommt hierhin: \n",
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" \n",
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" \n",
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" ##########################\n",
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" \n",
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" \n",
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" pass\n",
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"\n",
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" def fit(self, training_inputs, labels):\n",
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" \"\"\"\n",
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" Beispiel des Funktionsaufrufs:\n",
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" >>> perceptron.fit(train_input, labels)\n",
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" \"\"\"\n",
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" \n",
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" # Dein Code kommt hierhin:\n",
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" \n",
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"\n",
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" ##########################\n",
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" pass\n",
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" \n",
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" def status(self):\n",
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" \"\"\"\n",
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" Die Methode status(...) gibt die aktuellen Gewichte aus.\n",
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"\n",
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" Beispiel des Funktionsaufrufes und der Ausgabe:\n",
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" >>> perceptron.status()\n",
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" Perceptron weights: [0. 1. 1.]\n",
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" \"\"\"\n",
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" print(\"Perceptron weights: \", self.weights)\n",
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" \n",
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" def getWeights(self):\n",
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" return self.weights"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# AND-Datensatz\n",
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"train_input = np.array([\n",
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" [0, 0],\n",
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" [0, 1],\n",
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" [1, 0],\n",
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" [1, 1]\n",
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" ])\n",
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"\n",
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"labels_AND = np.array([0, 0, 0, 1])\n",
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"\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Beispiel mit OR\n",
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"labels_OR = np.array([0, 1, 1, 1])\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Hier wird der Iris-Datensatz geladen und vorbereitet (siehe Übung 2)\n",
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"\n",
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"# Datensatz laden\n",
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"names = [\"sepal-length\", \"sepal-width\", \"petal-length\", \"petal-width\", \"class\"]\n",
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"iris_data = pd.read_csv(\"iris.csv\", names = names)\n",
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"\n",
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"# Klassen auswählen (Bei Bedarf ändern)\n",
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"iris_data = iris_data.loc[lambda x: x['class'] != 'Iris-setosa']\n",
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"\n",
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"# Merkmale auswählen (Bei Bedarf ändern)\n",
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"iris_features = ['petal-length', 'petal-width']\n",
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"X = iris_data[iris_features]\n",
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"# Pandas-Datenformat in reine Liste umwandeln\n",
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"X = X.values\n",
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"\n",
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"# Label vorbereiten\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"lb_make = LabelEncoder()\n",
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"iris_data[\"class_code\"] = lb_make.fit_transform(iris_data[\"class\"])\n",
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"y = iris_data.class_code\n",
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"y = y.values\n",
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" \n",
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"# Trainings- und Testdatensplit\n",
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"from sklearn.model_selection import train_test_split\n",
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"X_train, X_test, y_train, y_test = (\n",
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" train_test_split(X, y, test_size=.2, random_state=np.random.seed(42)))"
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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": 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": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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2
Readme.txt
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2
Readme.txt
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Name: Paul Lödige
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Matrikel: 15405036
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151
iris.csv
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iris.csv
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5.1,3.5,1.4,0.2,Iris-setosa
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4.9,3.0,1.4,0.2,Iris-setosa
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4.7,3.2,1.3,0.2,Iris-setosa
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4.6,3.1,1.5,0.2,Iris-setosa
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5.0,3.6,1.4,0.2,Iris-setosa
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5.4,3.9,1.7,0.4,Iris-setosa
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4.6,3.4,1.4,0.3,Iris-setosa
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5.0,3.4,1.5,0.2,Iris-setosa
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4.4,2.9,1.4,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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5.4,3.7,1.5,0.2,Iris-setosa
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4.8,3.4,1.6,0.2,Iris-setosa
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4.8,3.0,1.4,0.1,Iris-setosa
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4.3,3.0,1.1,0.1,Iris-setosa
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5.8,4.0,1.2,0.2,Iris-setosa
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5.7,4.4,1.5,0.4,Iris-setosa
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5.4,3.9,1.3,0.4,Iris-setosa
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5.1,3.5,1.4,0.3,Iris-setosa
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5.7,3.8,1.7,0.3,Iris-setosa
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5.1,3.8,1.5,0.3,Iris-setosa
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5.4,3.4,1.7,0.2,Iris-setosa
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5.1,3.7,1.5,0.4,Iris-setosa
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4.6,3.6,1.0,0.2,Iris-setosa
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5.1,3.3,1.7,0.5,Iris-setosa
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4.8,3.4,1.9,0.2,Iris-setosa
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5.0,3.0,1.6,0.2,Iris-setosa
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5.0,3.4,1.6,0.4,Iris-setosa
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5.2,3.5,1.5,0.2,Iris-setosa
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5.2,3.4,1.4,0.2,Iris-setosa
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4.7,3.2,1.6,0.2,Iris-setosa
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4.8,3.1,1.6,0.2,Iris-setosa
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5.4,3.4,1.5,0.4,Iris-setosa
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5.2,4.1,1.5,0.1,Iris-setosa
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5.5,4.2,1.4,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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5.0,3.2,1.2,0.2,Iris-setosa
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5.5,3.5,1.3,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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4.4,3.0,1.3,0.2,Iris-setosa
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5.1,3.4,1.5,0.2,Iris-setosa
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5.0,3.5,1.3,0.3,Iris-setosa
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4.5,2.3,1.3,0.3,Iris-setosa
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4.4,3.2,1.3,0.2,Iris-setosa
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5.0,3.5,1.6,0.6,Iris-setosa
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5.1,3.8,1.9,0.4,Iris-setosa
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4.8,3.0,1.4,0.3,Iris-setosa
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5.1,3.8,1.6,0.2,Iris-setosa
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4.6,3.2,1.4,0.2,Iris-setosa
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5.3,3.7,1.5,0.2,Iris-setosa
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5.0,3.3,1.4,0.2,Iris-setosa
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7.0,3.2,4.7,1.4,Iris-versicolor
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6.4,3.2,4.5,1.5,Iris-versicolor
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6.9,3.1,4.9,1.5,Iris-versicolor
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5.5,2.3,4.0,1.3,Iris-versicolor
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6.5,2.8,4.6,1.5,Iris-versicolor
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5.7,2.8,4.5,1.3,Iris-versicolor
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6.3,3.3,4.7,1.6,Iris-versicolor
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4.9,2.4,3.3,1.0,Iris-versicolor
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6.6,2.9,4.6,1.3,Iris-versicolor
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5.2,2.7,3.9,1.4,Iris-versicolor
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5.0,2.0,3.5,1.0,Iris-versicolor
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5.9,3.0,4.2,1.5,Iris-versicolor
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6.0,2.2,4.0,1.0,Iris-versicolor
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6.1,2.9,4.7,1.4,Iris-versicolor
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5.6,2.9,3.6,1.3,Iris-versicolor
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6.7,3.1,4.4,1.4,Iris-versicolor
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5.6,3.0,4.5,1.5,Iris-versicolor
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5.8,2.7,4.1,1.0,Iris-versicolor
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6.2,2.2,4.5,1.5,Iris-versicolor
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5.6,2.5,3.9,1.1,Iris-versicolor
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5.9,3.2,4.8,1.8,Iris-versicolor
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6.1,2.8,4.0,1.3,Iris-versicolor
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6.3,2.5,4.9,1.5,Iris-versicolor
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6.1,2.8,4.7,1.2,Iris-versicolor
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6.4,2.9,4.3,1.3,Iris-versicolor
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6.6,3.0,4.4,1.4,Iris-versicolor
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6.8,2.8,4.8,1.4,Iris-versicolor
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||||
6.7,3.0,5.0,1.7,Iris-versicolor
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||||
6.0,2.9,4.5,1.5,Iris-versicolor
|
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5.7,2.6,3.5,1.0,Iris-versicolor
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||||
5.5,2.4,3.8,1.1,Iris-versicolor
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||||
5.5,2.4,3.7,1.0,Iris-versicolor
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5.8,2.7,3.9,1.2,Iris-versicolor
|
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6.0,2.7,5.1,1.6,Iris-versicolor
|
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5.4,3.0,4.5,1.5,Iris-versicolor
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6.0,3.4,4.5,1.6,Iris-versicolor
|
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6.7,3.1,4.7,1.5,Iris-versicolor
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6.3,2.3,4.4,1.3,Iris-versicolor
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5.6,3.0,4.1,1.3,Iris-versicolor
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||||
5.5,2.5,4.0,1.3,Iris-versicolor
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5.5,2.6,4.4,1.2,Iris-versicolor
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6.1,3.0,4.6,1.4,Iris-versicolor
|
||||
5.8,2.6,4.0,1.2,Iris-versicolor
|
||||
5.0,2.3,3.3,1.0,Iris-versicolor
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||||
5.6,2.7,4.2,1.3,Iris-versicolor
|
||||
5.7,3.0,4.2,1.2,Iris-versicolor
|
||||
5.7,2.9,4.2,1.3,Iris-versicolor
|
||||
6.2,2.9,4.3,1.3,Iris-versicolor
|
||||
5.1,2.5,3.0,1.1,Iris-versicolor
|
||||
5.7,2.8,4.1,1.3,Iris-versicolor
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||||
6.3,3.3,6.0,2.5,Iris-virginica
|
||||
5.8,2.7,5.1,1.9,Iris-virginica
|
||||
7.1,3.0,5.9,2.1,Iris-virginica
|
||||
6.3,2.9,5.6,1.8,Iris-virginica
|
||||
6.5,3.0,5.8,2.2,Iris-virginica
|
||||
7.6,3.0,6.6,2.1,Iris-virginica
|
||||
4.9,2.5,4.5,1.7,Iris-virginica
|
||||
7.3,2.9,6.3,1.8,Iris-virginica
|
||||
6.7,2.5,5.8,1.8,Iris-virginica
|
||||
7.2,3.6,6.1,2.5,Iris-virginica
|
||||
6.5,3.2,5.1,2.0,Iris-virginica
|
||||
6.4,2.7,5.3,1.9,Iris-virginica
|
||||
6.8,3.0,5.5,2.1,Iris-virginica
|
||||
5.7,2.5,5.0,2.0,Iris-virginica
|
||||
5.8,2.8,5.1,2.4,Iris-virginica
|
||||
6.4,3.2,5.3,2.3,Iris-virginica
|
||||
6.5,3.0,5.5,1.8,Iris-virginica
|
||||
7.7,3.8,6.7,2.2,Iris-virginica
|
||||
7.7,2.6,6.9,2.3,Iris-virginica
|
||||
6.0,2.2,5.0,1.5,Iris-virginica
|
||||
6.9,3.2,5.7,2.3,Iris-virginica
|
||||
5.6,2.8,4.9,2.0,Iris-virginica
|
||||
7.7,2.8,6.7,2.0,Iris-virginica
|
||||
6.3,2.7,4.9,1.8,Iris-virginica
|
||||
6.7,3.3,5.7,2.1,Iris-virginica
|
||||
7.2,3.2,6.0,1.8,Iris-virginica
|
||||
6.2,2.8,4.8,1.8,Iris-virginica
|
||||
6.1,3.0,4.9,1.8,Iris-virginica
|
||||
6.4,2.8,5.6,2.1,Iris-virginica
|
||||
7.2,3.0,5.8,1.6,Iris-virginica
|
||||
7.4,2.8,6.1,1.9,Iris-virginica
|
||||
7.9,3.8,6.4,2.0,Iris-virginica
|
||||
6.4,2.8,5.6,2.2,Iris-virginica
|
||||
6.3,2.8,5.1,1.5,Iris-virginica
|
||||
6.1,2.6,5.6,1.4,Iris-virginica
|
||||
7.7,3.0,6.1,2.3,Iris-virginica
|
||||
6.3,3.4,5.6,2.4,Iris-virginica
|
||||
6.4,3.1,5.5,1.8,Iris-virginica
|
||||
6.0,3.0,4.8,1.8,Iris-virginica
|
||||
6.9,3.1,5.4,2.1,Iris-virginica
|
||||
6.7,3.1,5.6,2.4,Iris-virginica
|
||||
6.9,3.1,5.1,2.3,Iris-virginica
|
||||
5.8,2.7,5.1,1.9,Iris-virginica
|
||||
6.8,3.2,5.9,2.3,Iris-virginica
|
||||
6.7,3.3,5.7,2.5,Iris-virginica
|
||||
6.7,3.0,5.2,2.3,Iris-virginica
|
||||
6.3,2.5,5.0,1.9,Iris-virginica
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6.5,3.0,5.2,2.0,Iris-virginica
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6.2,3.4,5.4,2.3,Iris-virginica
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5.9,3.0,5.1,1.8,Iris-virginica
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Reference in New Issue
Block a user