{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Laden der Rohdaten" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pickle\n", "\n", "# Laden der 'kirp' Liste aus der Pickle-Datei\n", "with open('rick.pickle', 'rb') as f:\n", " data_frame = pickle.load(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# PCA Klasse zu Reduktion der Dimensionen" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from torch.utils.data import Dataset\n", "import torch\n", "import pandas as pd\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.decomposition import PCA\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split\n", "from typing import List, Tuple\n", "\n", "\n", "class SplittedDataset(Dataset):\n", " def __init__(self, dataframe):\n", " self.dataframe = dataframe\n", " self.genome_frequencies = torch.tensor(dataframe['pca_frequencies'].tolist(), dtype=torch.float32)\n", " self.cancer_types = torch.tensor(dataframe['encoded_cancer_type'].tolist(), dtype=torch.long)\n", "\n", " def __getitem__(self, index):\n", " return self.genome_frequencies[index], self.cancer_types[index]\n", "\n", " def __len__(self):\n", " return len(self.dataframe)\n", "\n", "\n", "class GenomeDataset(Dataset):\n", " \"\"\"\n", " Eine benutzerdefinierte Dataset-Klasse, die für die Handhabung von Genomdaten konzipiert ist.\n", " Diese Klasse wendet eine Principal Component Analysis (PCA) auf die Frequenzen der Genome an\n", " und teilt den Datensatz in Trainings- und Validierungsteile auf.\n", "\n", " Attributes:\n", " dataframe (pd.DataFrame): Ein Pandas DataFrame, der die initialen Daten enthält.\n", " train_df (pd.DataFrame): Ein DataFrame, der den Trainingsdatensatz nach der Anwendung von PCA und der Aufteilung enthält.\n", " val_df (pd.DataFrame): Ein DataFrame, der den Validierungsdatensatz nach der Anwendung von PCA und der Aufteilung enthält.\n", "\n", " Methods:\n", " __init__(self, dataframe, n_pca_components=1034, train_size=0.8, split_random_state=42):\n", " Konstruktor für die GenomeDataset Klasse.\n", " _do_PCA(self, frequencies, n_components=1034):\n", " Wendet PCA auf die gegebenen Frequenzen an.\n", " _split_dataset(self, train_size=0.8, random_state=42):\n", " Teilt den DataFrame in Trainings- und Validierungsdatensätze auf.\n", " __getitem__(self, index):\n", " Gibt ein Tupel aus transformierten Frequenzen und dem zugehörigen Krebstyp für einen gegebenen Index zurück.\n", " __len__(self):\n", " Gibt die Gesamtlänge der kombinierten Trainings- und Validierungsdatensätze zurück.\n", " \"\"\"\n", "\n", " def __init__(self, dataframe: pd.DataFrame, n_pca_components: int = 1034, train_size: float = 0.8, split_random_state: int = 42):\n", " \"\"\"\n", " Konstruktor für die GenomeDataset Klasse.\n", "\n", " Parameters:\n", " dataframe (pd.DataFrame): Der DataFrame, der die Genome Frequenzen und Krebsarten enthält.\n", " n_pca_components (int): Die Anzahl der PCA-Komponenten, auf die reduziert werden soll. Standardwert ist 1034.\n", " train_size (float): Der Anteil der Daten, der als Trainingsdaten verwendet werden soll. Standardwert ist 0.8.\n", " split_random_state (int): Der Zufalls-Saatwert, der für die Aufteilung des Datensatzes verwendet wird. Standardwert ist 42.\n", " \"\"\"\n", " self.dataframe = dataframe\n", "\n", " # Umwandlung der Krebsarten in numerische Werte\n", " self.label_encoder = LabelEncoder()\n", " self.dataframe['encoded_cancer_type'] = self.label_encoder.fit_transform(dataframe['cancer_type'])\n", "\n", " # Anwenden der PCA auf die Frequenzen\n", " self.dataframe['pca_frequencies'] = self._do_PCA(self.dataframe['genome_frequencies'].tolist(), n_pca_components)\n", "\n", " # Teilen des DataFrame in Trainings- und Validierungsdatensatz\n", " self._split_dataset(train_size=train_size, random_state=split_random_state)\n", "\n", " def transform_datapoint(self, datapoint: List[float]) -> List[float]:\n", " \"\"\"\n", " Transformiert einen einzelnen Datenpunkt durch Standardisierung und Anwendung der PCA.\n", "\n", " Diese Methode nimmt einen rohen Datenpunkt (eine Liste von Frequenzen), standardisiert ihn mit dem \n", " zuvor angepassten Scaler und wendet dann die PCA-Transformation an, um ihn in den reduzierten \n", " Feature-Raum zu überführen, der für das Training des Modells verwendet wurde.\n", "\n", " Parameters:\n", " datapoint (List[float]): Ein roher Datenpunkt, bestehend aus einer Liste von Frequenzen.\n", "\n", " Returns:\n", " List[float]: Der transformierte Datenpunkt, nach Anwendung der Standardisierung und der PCA.\n", " \"\"\"\n", " # Standardisierung des Datenpunkts\n", " scaled_data_point = self.scaler.transform([datapoint])\n", "\n", " # PCA-Transformation des standardisierten Datenpunkts\n", " pca_transformed_point = self.pca.transform(scaled_data_point)\n", "\n", " return pca_transformed_point.tolist()\n", "\n", " def _do_PCA(self, frequencies: List[List[float]], n_components: int = 1034) -> List[List[float]]:\n", " \"\"\"\n", " Wendet PCA auf die gegebenen Frequenzen an.\n", "\n", " Parameters:\n", " frequencies (List[List[float]]): Die Liste der Frequenzen, auf die die PCA angewendet werden soll.\n", " n_components (int): Die Anzahl der Komponenten für die PCA. Standardwert ist 1034.\n", "\n", " Returns:\n", " List[List[float]]: Eine Liste von Listen, die die transformierten Frequenzen nach der PCA darstellt.\n", " \"\"\"\n", "\n", " # Standardisieren der Frequenzen\n", " self.scaler = StandardScaler()\n", " scaled_frequencies = self.scaler.fit_transform(frequencies)\n", "\n", " # PCA-Instanz erstellen und auf die gewünschte Anzahl von Komponenten reduzieren\n", " self.pca = PCA(n_components=n_components)\n", "\n", " # PCA auf die Frequenzen anwenden\n", " pca_result = self.pca.fit_transform(scaled_frequencies)\n", "\n", " return pca_result.tolist()\n", "\n", " def _split_dataset(self, train_size: float = 0.8, random_state: int = 42):\n", " \"\"\"\n", " Teilt den DataFrame in Trainings- und Validierungsdatensätze auf.\n", "\n", " Parameters:\n", " train_size (float): Der Anteil der Daten, der als Trainingsdaten verwendet werden soll.\n", " random_state (int): Der Zufalls-Saatwert, der für die Aufteilung des Datensatzes verwendet wird.\n", " \"\"\"\n", "\n", " # Teilen des DataFrame in Trainings- und Validierungsdatensatz\n", " train_df, val_df = train_test_split(self.dataframe, train_size=train_size, random_state=random_state)\n", " self.train_df = SplittedDataset(train_df)\n", " self.val_df = SplittedDataset(val_df)\n", "\n", "\n", " def __getitem__(self, index: int) -> Tuple[torch.Tensor, int]:\n", " \"\"\"\n", " Gibt ein Tupel aus transformierten Frequenzen und dem entsprechenden Krebstyp für einen gegebenen Index zurück.\n", "\n", " Parameters:\n", " index (int): Der Index des zu abrufenden Datenelements.\n", "\n", " Returns:\n", " Tuple[torch.Tensor, int]: Ein Tupel, bestehend aus einem Tensor der transformierten Frequenzen und dem zugehörigen Krebstyp.\n", " \"\"\"\n", "\n", " print(self.train_df.shape)\n", " print(self.val_df.shape)\n", " \n", " if index < len(self.train_df):\n", " row = self.train_df.iloc[index]\n", " else:\n", " row = self.val_df.iloc[len(self.train_df) - index]\n", "\n", " pca_frequencies_tensor = torch.tensor(row['pca_frequencies'], dtype=torch.float32)\n", " cancer_type = row['encoded_cancer_type']\n", "\n", " return pca_frequencies_tensor, cancer_type\n", "\n", " def __len__(self) -> int:\n", " \"\"\"\n", " Gibt die Gesamtlänge der kombinierten Trainings- und Validierungsdatensätze zurück.\n", "\n", " Returns:\n", " int: Die Länge der kombinierten Datensätze.\n", " \"\"\"\n", " \n", " return len(self.train_df) + len(self.val_df)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Definition des neuronalen Netzes" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from torch.utils.data import DataLoader\n", "from multiprocessing import cpu_count\n", "import os\n", "import neat\n", "import numpy as np\n", "\n", "class CancerClassifierNEAT():\n", " def __init__(self, dataframe: pd.DataFrame, n_pca_components: int = 64) -> None:\n", " self.num_generations = None\n", " self.n_pca_components = n_pca_components\n", " # Initialisierung der config Datei\n", " local_dir = os.getcwd()\n", " config_path = os.path.join(local_dir, 'config')\n", " config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path)\n", " # Initialisierung der Population\n", " self.pop = neat.Population(config)\n", " stats = neat.StatisticsReporter()\n", " self.pop.add_reporter(stats)\n", " self.pop.add_reporter(neat.StdOutReporter(True))\n", " # Erstellen des Datensatzes\n", " genome_dataset = GenomeDataset(data_frame, n_pca_components=n_pca_components)\n", " self.train_dataset = genome_dataset.train_df\n", " self.valid_dataset = genome_dataset.val_df\n", " # Erstellen der Datensatzloader\n", " self.train_loader = DataLoader(dataset=self.train_dataset, batch_size=1, shuffle=True)\n", " self.valid_loader = DataLoader(dataset=self.valid_dataset, batch_size=1, shuffle=False)\n", "\n", " self.training_accuracies = []\n", " self.validation_accuracies = []\n", "\n", " def fitness(self, genome: neat.DefaultGenome, config) -> float:\n", " net = neat.nn.FeedForwardNetwork.create(genome, config)\n", "\n", " correct_predictions = 0\n", " total_predictions = 0\n", "\n", " for i, (inputs, labels) in enumerate(self.train_loader):\n", " inputs_list = inputs.view(-1).tolist()\n", " #print(inputs_list)\n", " # Netz aktivieren\n", " outputs = net.activate(inputs_list)\n", " #print(outputs)\n", " # Berechnen der Genauigkeit\n", " predicted = np.argmax(np.array(outputs))\n", " correct_predictions += (predicted == labels).sum().item()\n", " total_predictions += labels.size(0)\n", "\n", " train_accuracy = correct_predictions / total_predictions\n", "\n", " return train_accuracy\n", "\n", " def eval_genomes(self, genomes, config):\n", " for _, genome in genomes:\n", " genome.fitness = self.fitness(genome, config)\n", "\n", " def run(self, num_generations: int = 500) -> neat.DefaultGenome:\n", " #parallel = neat.ParallelEvaluator(cpu_count(), self.fitness)\n", " #winner = self.pop.run(parallel.evaluate, num_generations)\n", " winner = self.pop.run(self.eval_genomes, num_generations)\n", "\n", " return winner" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " ****** Running generation 0 ****** \n", "\n", "Population's average fitness: 0.31162 stdev: 0.16467\n", "Best fitness: 0.68803 - size: (13, 431) - species 47 - id 47\n", "Average adjusted fitness: 0.216\n", "Mean genetic distance 3.635, standard deviation 0.325\n", "Population of 500 members in 100 species:\n", " ID age size fitness adj fit stag\n", " ==== === ==== ======= ======= ====\n", " 1 0 5 0.3 0.213 0\n", " 2 0 5 0.4 0.294 0\n", " 3 0 5 0.3 0.189 0\n", " 4 0 5 0.1 0.005 0\n", " 5 0 5 0.4 0.299 0\n", " 6 0 5 0.3 0.210 0\n", " 7 0 5 0.4 0.326 0\n", " 8 0 5 0.2 0.125 0\n", " 9 0 5 0.6 0.463 0\n", " 10 0 5 0.2 0.149 0\n", " 11 0 5 0.2 0.134 0\n", " 12 0 5 0.5 0.447 0\n", " 13 0 5 0.6 0.497 0\n", " 14 0 5 0.6 0.489 0\n", " 15 0 5 0.3 0.213 0\n", " 16 0 5 0.1 0.018 0\n", " 17 0 5 0.3 0.173 0\n", " 18 0 5 0.3 0.197 0\n", " 19 0 5 0.2 0.110 0\n", " 20 0 5 0.2 0.109 0\n", " 21 0 5 0.1 0.034 0\n", " 22 0 5 0.4 0.324 0\n", " 23 0 5 0.2 0.109 0\n", " 24 0 5 0.2 0.060 0\n", " 25 0 5 0.2 0.149 0\n", " 26 0 5 0.3 0.213 0\n", " 27 0 5 0.3 0.244 0\n", " 28 0 5 0.1 0.018 0\n", " 29 0 5 0.1 0.007 0\n", " 30 0 5 0.3 0.215 0\n", " 31 0 5 0.1 0.006 0\n", " 32 0 5 0.6 0.475 0\n", " 33 0 5 0.4 0.276 0\n", " 34 0 5 0.2 0.115 0\n", " 35 0 5 0.1 0.050 0\n", " 36 0 5 0.6 0.481 0\n", " 37 0 5 0.4 0.341 0\n", " 38 0 5 0.3 0.247 0\n", " 39 0 5 0.5 0.430 0\n", " 40 0 5 0.1 0.021 0\n", " 41 0 5 0.1 0.044 0\n", " 42 0 5 0.2 0.093 0\n", " 43 0 5 0.2 0.088 0\n", " 44 0 5 0.3 0.189 0\n", " 45 0 5 0.1 0.033 0\n", " 46 0 5 0.2 0.074 0\n", " 47 0 5 0.7 0.593 0\n", " 48 0 5 0.4 0.319 0\n", " 49 0 5 0.3 0.201 0\n", " 50 0 5 0.5 0.385 0\n", " 51 0 5 0.1 0.051 0\n", " 52 0 5 0.6 0.496 0\n", " 53 0 5 0.6 0.463 0\n", " 54 0 5 0.1 0.031 0\n", " 55 0 5 0.2 0.122 0\n", " 56 0 5 0.1 0.036 0\n", " 57 0 5 0.1 0.004 0\n", " 58 0 5 0.3 0.197 0\n", " 59 0 5 0.1 0.027 0\n", " 60 0 5 0.1 0.022 0\n", " 61 0 5 0.1 0.000 0\n", " 62 0 5 0.3 0.192 0\n", " 63 0 5 0.7 0.572 0\n", " 64 0 5 0.4 0.274 0\n", " 65 0 5 0.2 0.138 0\n", " 66 0 5 0.1 0.008 0\n", " 67 0 5 0.3 0.179 0\n", " 68 0 5 0.2 0.097 0\n", " 69 0 5 0.3 0.252 0\n", " 70 0 5 0.3 0.214 0\n", " 71 0 5 0.6 0.499 0\n", " 72 0 5 0.2 0.140 0\n", " 73 0 5 0.1 0.000 0\n", " 74 0 5 0.3 0.219 0\n", " 75 0 5 0.3 0.204 0\n", " 76 0 5 0.5 0.405 0\n", " 77 0 5 0.4 0.300 0\n", " 78 0 5 0.3 0.213 0\n", " 79 0 5 0.6 0.492 0\n", " 80 0 5 0.1 0.017 0\n", " 81 0 5 0.6 0.501 0\n", " 82 0 5 0.3 0.213 0\n", " 83 0 5 0.5 0.450 0\n", " 84 0 5 0.6 0.493 0\n", " 85 0 5 0.2 0.111 0\n", " 86 0 5 0.5 0.421 0\n", " 87 0 5 0.5 0.397 0\n", " 88 0 5 0.6 0.502 0\n", " 89 0 5 0.3 0.156 0\n", " 90 0 5 0.4 0.342 0\n", " 91 0 5 0.1 0.000 0\n", " 92 0 5 0.3 0.209 0\n", " 93 0 5 0.1 0.017 0\n", " 94 0 5 0.3 0.160 0\n", " 95 0 5 0.2 0.097 0\n", " 96 0 5 0.5 0.380 0\n", " 97 0 5 0.2 0.081 0\n", " 98 0 5 0.5 0.416 0\n", " 99 0 5 0.2 0.087 0\n", " 100 0 5 0.3 0.222 0\n", "Total extinctions: 0\n", "Generation time: 56.773 sec\n", "\n", " ****** Running generation 1 ****** \n", "\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m ea \u001b[39m=\u001b[39m CancerClassifierNEAT(data_frame)\n\u001b[0;32m----> 2\u001b[0m ea\u001b[39m.\u001b[39;49mrun()\n", "Cell \u001b[0;32mIn[3], line 59\u001b[0m, in \u001b[0;36mCancerClassifierNEAT.run\u001b[0;34m(self, num_generations)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrun\u001b[39m(\u001b[39mself\u001b[39m, num_generations: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m \u001b[39m500\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m neat\u001b[39m.\u001b[39mDefaultGenome:\n\u001b[1;32m 57\u001b[0m \u001b[39m#parallel = neat.ParallelEvaluator(cpu_count(), self.fitness)\u001b[39;00m\n\u001b[1;32m 58\u001b[0m \u001b[39m#winner = self.pop.run(parallel.evaluate, num_generations)\u001b[39;00m\n\u001b[0;32m---> 59\u001b[0m winner \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpop\u001b[39m.\u001b[39;49mrun(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49meval_genomes, num_generations)\n\u001b[1;32m 61\u001b[0m \u001b[39mreturn\u001b[39;00m winner\n", "File \u001b[0;32m~/.local/lib/python3.8/site-packages/neat/population.py:89\u001b[0m, in \u001b[0;36mPopulation.run\u001b[0;34m(self, fitness_function, n)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreporters\u001b[39m.\u001b[39mstart_generation(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgeneration)\n\u001b[1;32m 88\u001b[0m \u001b[39m# Evaluate all genomes using the user-provided function.\u001b[39;00m\n\u001b[0;32m---> 89\u001b[0m fitness_function(\u001b[39mlist\u001b[39;49m(iteritems(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpopulation)), \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconfig)\n\u001b[1;32m 91\u001b[0m \u001b[39m# Gather and report statistics.\u001b[39;00m\n\u001b[1;32m 92\u001b[0m best \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n", "Cell \u001b[0;32mIn[3], line 54\u001b[0m, in \u001b[0;36mCancerClassifierNEAT.eval_genomes\u001b[0;34m(self, genomes, config)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39meval_genomes\u001b[39m(\u001b[39mself\u001b[39m, genomes, config):\n\u001b[1;32m 53\u001b[0m \u001b[39mfor\u001b[39;00m _, genome \u001b[39min\u001b[39;00m genomes:\n\u001b[0;32m---> 54\u001b[0m genome\u001b[39m.\u001b[39mfitness \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfitness(genome, config)\n", "Cell \u001b[0;32mIn[3], line 41\u001b[0m, in \u001b[0;36mCancerClassifierNEAT.fitness\u001b[0;34m(self, genome, config)\u001b[0m\n\u001b[1;32m 38\u001b[0m inputs_list \u001b[39m=\u001b[39m inputs\u001b[39m.\u001b[39mview(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m)\u001b[39m.\u001b[39mtolist()\n\u001b[1;32m 39\u001b[0m \u001b[39m#print(inputs_list)\u001b[39;00m\n\u001b[1;32m 40\u001b[0m \u001b[39m# Netz aktivieren\u001b[39;00m\n\u001b[0;32m---> 41\u001b[0m outputs \u001b[39m=\u001b[39m net\u001b[39m.\u001b[39;49mactivate(inputs_list)\n\u001b[1;32m 42\u001b[0m \u001b[39m#print(outputs)\u001b[39;00m\n\u001b[1;32m 43\u001b[0m \u001b[39m# Berechnen der Genauigkeit\u001b[39;00m\n\u001b[1;32m 44\u001b[0m predicted \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39margmax(np\u001b[39m.\u001b[39marray(outputs))\n", "File \u001b[0;32m~/.local/lib/python3.8/site-packages/neat/nn/feed_forward.py:22\u001b[0m, in \u001b[0;36mFeedForwardNetwork.activate\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 20\u001b[0m node_inputs \u001b[39m=\u001b[39m []\n\u001b[1;32m 21\u001b[0m \u001b[39mfor\u001b[39;00m i, w \u001b[39min\u001b[39;00m links:\n\u001b[0;32m---> 22\u001b[0m node_inputs\u001b[39m.\u001b[39;49mappend(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mvalues[i] \u001b[39m*\u001b[39;49m w)\n\u001b[1;32m 23\u001b[0m s \u001b[39m=\u001b[39m agg_func(node_inputs)\n\u001b[1;32m 24\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvalues[node] \u001b[39m=\u001b[39m act_func(bias \u001b[39m+\u001b[39m response \u001b[39m*\u001b[39m s)\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "ea = CancerClassifierNEAT(data_frame)\n", "ea.run()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.display import clear_output\n", "import matplotlib.pyplot as plt\n", "\n", "# Listen, um Verluste zu speichern\n", "train_losses = []\n", "valid_losses = []\n", "train_accuracies = []\n", "valid_accuracies = []\n", "\n", "for epoch in range(num_epochs):\n", " model.train()\n", " train_loss = 0.0\n", " correct_predictions = 0\n", " total_predictions = 0\n", "\n", " for i, (inputs, labels) in enumerate(train_loader):\n", " inputs, labels = inputs.to(device), labels.to(device)\n", " optimizer.zero_grad()\n", " outputs = model(inputs)\n", " loss = criterion(outputs, labels)\n", " loss.backward()\n", " optimizer.step()\n", " train_loss += loss.item()\n", "\n", " # Berechnen der Genauigkeit\n", " _, predicted = torch.max(outputs, 1)\n", " correct_predictions += (predicted == labels).sum().item()\n", " total_predictions += labels.size(0)\n", "\n", " # Durchschnittlicher Trainingsverlust und Genauigkeit\n", " train_loss /= len(train_loader)\n", " train_accuracy = correct_predictions / total_predictions\n", " train_losses.append(train_loss)\n", " train_accuracies.append(train_accuracy)\n", "\n", " # Validierungsverlust und Genauigkeit\n", " model.eval()\n", " valid_loss = 0.0\n", " correct_predictions = 0\n", " total_predictions = 0\n", "\n", " with torch.no_grad():\n", " for inputs, labels in valid_loader:\n", " inputs, labels = inputs.to(device), labels.to(device)\n", " outputs = model(inputs)\n", " loss = criterion(outputs, labels)\n", " valid_loss += loss.item()\n", "\n", " # Berechnen der Genauigkeit\n", " _, predicted = torch.max(outputs, 1)\n", " correct_predictions += (predicted == labels).sum().item()\n", " total_predictions += labels.size(0)\n", "\n", " # Durchschnittlicher Validierungsverlust und Genauigkeit\n", " valid_loss /= len(valid_loader)\n", " valid_accuracy = correct_predictions / total_predictions\n", " valid_losses.append(valid_loss)\n", " valid_accuracies.append(valid_accuracy)\n", "\n", "\n", " # Aktualisieren des Graphen\n", " clear_output(wait=True)\n", " fig, ax1 = plt.subplots()\n", "\n", " # Zeichnen der Verlustkurven\n", " ax1.plot(train_losses, label='Trainingsverlust', color='r')\n", " ax1.plot(valid_losses, label='Validierungsverlust', color='b')\n", " ax1.set_xlabel('Epochen')\n", " ax1.set_ylabel('Verlust', color='g')\n", " ax1.tick_params(axis='y', labelcolor='g')\n", "\n", " # Zweite y-Achse für die Genauigkeit\n", " ax2 = ax1.twinx()\n", " ax2.plot(train_accuracies, label='Trainingsgenauigkeit', color='r', linestyle='dashed')\n", " ax2.plot(valid_accuracies, label='Validierungsgenauigkeit', color='b', linestyle='dashed')\n", " ax2.set_ylabel('Genauigkeit', color='g')\n", " ax2.tick_params(axis='y', labelcolor='g')\n", "\n", " # Titel und Legende\n", " plt.title('Trainings- und Validierungsverlust und -genauigkeit über die Zeit')\n", " fig.tight_layout()\n", " ax1.legend(loc='lower left')\n", " ax2.legend(loc='lower right')\n", "\n", " plt.show()\n", "\n", " print(f'Epoch [{epoch+1}/{num_epochs}], Trainingsverlust: {train_loss:.4f}, Validierungsverlust: {valid_loss:.4f}')" ] } ], "metadata": { "kernelspec": { "display_name": "rl", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.18" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }