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{
"cells": [
{
"cell_type": "markdown",
"id": "8bc02404-8cd1-46d9-8237-2d035ebb3e79",
"metadata": {},
"source": [
"# **[Project] Cancer Subtype Classification**"
]
},
{
"cell_type": "markdown",
"id": "0c5076f4",
"metadata": {},
"source": [
"# Introduction"
]
},
{
"cell_type": "markdown",
"id": "8a599748",
"metadata": {},
"source": [
"The [TCGA Kidney Cancers Dataset](https://archive.ics.uci.edu/dataset/892/tcga+kidney+cancers) is a bulk RNA-seq dataset that contains transcriptome profiles (i.e., gene expression quantification data) of patients diagnosed with three different subtypes of kidney cancers.\n",
"This dataset can be used to make predictions about the specific subtype of kidney cancers given the normalized transcriptome profile data.\n",
"\n",
"The normalized transcriptome profile data is given as **TPM** and **FPKM** for each gene.\n",
"\n",
"> TPM (Transcripts Per Million) and FPKM (Fragments Per Kilobase Million) are two common methods for quantifying gene expression in RNA sequencing data.\n",
"> They both aim to account for the differences in sequencing depth and transcript length when estimating gene expression levels.\n",
">\n",
"> **TPM** (Transcripts Per Million):\n",
"> - TPM is a measure of gene expression that normalizes for both library size (sequencing depth) and transcript length.\n",
"> - The main idea behind TPM is to express the abundance of a transcript relative to the total number of transcripts in a sample, scaled to one million.\n",
">\n",
"> **FPKM** (Fragments Per Kilobase Million):\n",
"> - FPKM is another method for quantifying gene expression, which is commonly used in older RNA-seq analysis pipelines. It's similar in concept to TPM but differs in the way it's calculated.\n",
"> - FPKM also normalizes for library size and transcript length, but it measures gene expression as the number of fragments (i.e., reads) per kilobase of exon model per million reads.\n",
">\n",
"> TPM is generally considered more robust to variations in library size, making it a preferred choice in many modern RNA-seq analysis workflows.\n",
"\n",
"We provide one dataset for each kidney cancer subtype:\n",
"\n",
"- [TCGA-KICH](https://portal.gdc.cancer.gov/projects/TCGA-KICH): kidney chromophobe (renal clear cell carcinoma)\n",
"- [TCGA-KIRC](https://portal.gdc.cancer.gov/projects/TCGA-KIRC): kidney renal clear cell carcinoma\n",
"- [TCGA-KIRP](https://portal.gdc.cancer.gov/projects/TCGA-KIRP): kidney renal papillary cell carcinoma\n",
"\n",
"> This and _much_ more data is openly available on the [NCI Genomic Data Commons (GDC) Data Portal](https://portal.gdc.cancer.gov/)."
]
},
{
"cell_type": "markdown",
"id": "16712787",
"metadata": {},
"source": [
"# Data access"
]
},
{
"cell_type": "markdown",
"id": "6421ef6c",
"metadata": {},
"source": [
"There are two ways to access the data: via the TNT homepage or the GDC Data Portal."
]
},
{
"cell_type": "markdown",
"id": "b977e8b8",
"metadata": {},
"source": [
"## Download from the TNT homepage (_recommended_)"
]
},
{
"cell_type": "markdown",
"id": "800fa7bd",
"metadata": {},
"source": [
"The download from the TNT homepage is straightforward:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "dda97b16",
"metadata": {
"tags": []
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (2666948873.py, line 6)",
"output_type": "error",
"traceback": [
"\u001b[0;36m Cell \u001b[0;32mIn[1], line 6\u001b[0;36m\u001b[0m\n\u001b[0;31m from IPython.display import clear_output(wait=True)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"! wget http://www.tnt.uni-hannover.de/edu/vorlesungen/AMLG/data/project-cancer-classification.tar.gz\n",
"! tar -xzvf project-cancer-classification.tar.gz\n",
"! mv -v project-cancer-classification/ data/\n",
"! rm -v project-cancer-classification.tar.gz"
]
},
{
"cell_type": "markdown",
"id": "bc2db880",
"metadata": {},
"source": [
"In the `data/` folder you will now find many files in the [TSV format](https://en.wikipedia.org/wiki/Tab-separated_values) ([CSV](https://en.wikipedia.org/wiki/Comma-separated_values)-like with tabs as delimiter) containing the normalized transcriptome profile data.\n",
"\n",
"To start, you can read a TSV file into a [pandas](https://pandas.pydata.org) [`DataFrame`](pandas dataframe to dict) using the [`pandas.read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html#pandas-read-csv) function with the `sep` parameter set to `\\t`:"
]
},
{
"cell_type": "markdown",
"id": "ed50d396-fe33-47a7-ad19-8eb975ef0fa5",
"metadata": {},
"source": [
"## Lesen der DNA-Sequenz Dateien und speichern in einer Datei"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "2adae4ff",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Es wurden 1034 Dateien eingelesen.\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import pickle\n",
"\n",
"\n",
"import os\n",
"#'./data/tcga-kirp-geq'\n",
"\n",
"labels = [\"kirp\", \"kirc\", \"kich\"] # Setzen Sie hier Ihren Ordnerpfad ein\n",
"n_files = 0\n",
"y = list()\n",
"x = list()\n",
"\n",
"rick = list()\n",
"data = []\n",
"\n",
"for l in labels:\n",
" root_folder = f\"./data/tcga-{l}-geq\"\n",
" for root, dirs, files in os.walk(root_folder):\n",
" for file in files:\n",
" if file.endswith('.tsv'):\n",
" n_files += 1\n",
" # Vollständiger Pfad zur Datei\n",
" file_path = os.path.join(root, file)\n",
" # Hier können Sie etwas mit der Datei machen, z.B. einlesen\n",
" df = pd.read_csv(filepath_or_buffer=file_path, sep=\"\\t\", header=1)\n",
" df = df['tpm_unstranded']\n",
"\n",
" df = df[4:]\n",
" df = np.array(df)\n",
" rick.append(df)\n",
" \n",
" data.append([df, l])\n",
"\n",
"print(f\"Es wurden {n_files} Dateien eingelesen.\")\n",
"#tsv_file_path = \"data/tcga-kich-geq/0ba21ef5-0829-422e-a674-d3817498c333/4868e8fc-e045-475a-a81d-ef43eabb7066.rna_seq.augmented_star_gene_counts.tsv\"\n",
"\n",
"# Read the TSV file into a DataFrame\n",
"#df = pd.read_csv(filepath_or_buffer=tsv_file_path, sep=\"\\t\", header=1)\n",
"\n",
"# Display the first few rows of the DataFrame\n",
"#print(df.head(n=20))\n",
"#rick = np.array(rick)\n",
"\n",
"# Speichern der 'kirp' Liste in einer Pickle-Datei\n",
"#with open('rick.pickle', 'wb') as f:\n",
"# pickle.dump(rick, f)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dfe4f964-6068-46da-8103-194525086f01",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>genome_frequencies</th>\n",
" <th>cancer_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[20.331, 0.0, 25.1806, 1.1301, 0.4836, 7.3269,...</td>\n",
" <td>kirp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>[37.0405, 0.5002, 77.4246, 4.2188, 1.0408, 29....</td>\n",
" <td>kirp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>[45.4456, 0.0903, 74.9545, 4.843, 1.5188, 11.8...</td>\n",
" <td>kirp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[15.2345, 0.3393, 62.0003, 2.4412, 0.932, 2.66...</td>\n",
" <td>kirp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[35.0709, 0.2333, 62.8022, 2.8872, 1.0547, 18....</td>\n",
" <td>kirp</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" genome_frequencies cancer_type\n",
"0 [20.331, 0.0, 25.1806, 1.1301, 0.4836, 7.3269,... kirp\n",
"1 [37.0405, 0.5002, 77.4246, 4.2188, 1.0408, 29.... kirp\n",
"2 [45.4456, 0.0903, 74.9545, 4.843, 1.5188, 11.8... kirp\n",
"3 [15.2345, 0.3393, 62.0003, 2.4412, 0.932, 2.66... kirp\n",
"4 [35.0709, 0.2333, 62.8022, 2.8872, 1.0547, 18.... kirp"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_Frame = pd.DataFrame(data, columns=[\"genome_frequencies\", \"cancer_type\"])\n",
"data_Frame.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0f5cc92a-4485-4184-845e-116ea9a9776d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Speichern der Daten in einer lokalen Datei\n",
"with open('rick.pickle', 'wb') as f:\n",
" pickle.dump(data_Frame, f)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b7b79958-baba-4630-9def-cf47afe43d9f",
"metadata": {
"tags": []
},
"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": "code",
"execution_count": 3,
"id": "f6608b92-8ace-4a52-a3dc-70c578e56f0d",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>genome_frequencies</th>\n",
" <th>cancer_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[20.331, 0.0, 25.1806, 1.1301, 0.4836, 7.3269,...</td>\n",
" <td>kirp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>[37.0405, 0.5002, 77.4246, 4.2188, 1.0408, 29....</td>\n",
" <td>kirp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>[45.4456, 0.0903, 74.9545, 4.843, 1.5188, 11.8...</td>\n",
" <td>kirp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[15.2345, 0.3393, 62.0003, 2.4412, 0.932, 2.66...</td>\n",
" <td>kirp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[35.0709, 0.2333, 62.8022, 2.8872, 1.0547, 18....</td>\n",
" <td>kirp</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" genome_frequencies cancer_type\n",
"0 [20.331, 0.0, 25.1806, 1.1301, 0.4836, 7.3269,... kirp\n",
"1 [37.0405, 0.5002, 77.4246, 4.2188, 1.0408, 29.... kirp\n",
"2 [45.4456, 0.0903, 74.9545, 4.843, 1.5188, 11.8... kirp\n",
"3 [15.2345, 0.3393, 62.0003, 2.4412, 0.932, 2.66... kirp\n",
"4 [35.0709, 0.2333, 62.8022, 2.8872, 1.0547, 18.... kirp"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_Frame.head()"
]
},
{
"cell_type": "markdown",
"id": "c60cbf60-d904-4ee0-8f70-588bb109368b",
"metadata": {},
"source": [
"# Data preprocessing"
]
},
{
"cell_type": "markdown",
"id": "583e39c8-13ba-422e-9c39-9cf1c8d63d5b",
"metadata": {},
"source": [
"## Training set & validation set"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "38695a70-86e9-4dd0-b622-33e3762372eb",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DataSet shape: (1034, 2)\n",
"Training set\n",
"------------\n",
"Dataframe shape: (827, 2)\n",
"Dataframe head:\n",
" genome_frequencies cancer_type\n",
"518 [25.0645, 0.1125, 56.3997, 3.3108, 1.6061, 12.... kirc\n",
"355 [32.6449, 2.1789, 63.4954, 6.3228, 2.109, 40.9... kirc\n",
"528 [46.024, 0.0, 85.8077, 7.2567, 2.1301, 9.6509,... kirc\n",
"445 [153.0064, 1.6403, 99.3267, 7.3736, 1.3668, 10... kirc\n",
"986 [65.5167, 18.2363, 77.2126, 5.0375, 2.4628, 21... kich\n",
"\n",
"Validation set\n",
"--------------\n",
"Dataframe shape: (207, 2)\n",
"Dataframe head:\n",
" genome_frequencies cancer_type\n",
"294 [50.8994, 0.4635, 131.5049, 5.7193, 3.103, 15.... kirp\n",
"453 [35.857, 0.1018, 94.5681, 5.2997, 1.9388, 17.6... kirc\n",
"638 [11.3865, 0.2313, 28.5961, 3.0169, 0.7851, 8.2... kirc\n",
"139 [41.6119, 0.2207, 55.4377, 4.4395, 0.884, 3.56... kirp\n",
"539 [63.1646, 18.8107, 63.2703, 4.6696, 0.9466, 5.... kirc\n"
]
}
],
"source": [
"import os\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"train_df, val_df = train_test_split(data_Frame, train_size=0.8, random_state=42)\n",
"\n",
"print(f\"DataSet shape: {data_Frame.shape}\")\n",
"print(f\"Training set{os.linesep}------------\")\n",
"print(f\"Dataframe shape: {train_df.shape}\")\n",
"print(f\"Dataframe head:{os.linesep}{train_df.head()}\")\n",
"print(\"\")\n",
"print(f\"Validation set{os.linesep}--------------\")\n",
"print(f\"Dataframe shape: {val_df.shape}\")\n",
"print(f\"Dataframe head:{os.linesep}{val_df.head()}\")"
]
},
{
"cell_type": "markdown",
"id": "4903244b-548f-4672-967d-1c62825b6fce",
"metadata": {},
"source": [
"## Building a custom PyTorch dataset"
]
},
{
"cell_type": "markdown",
"id": "7e333251-c4e7-41f0-a086-12a3d95b723f",
"metadata": {},
"source": [
"## Öffnen der Datei mit den Gesammelten Sequenzen"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e2f78725-cda6-4e8d-9029-a4a31f6f9ab7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from torch.utils.data import Dataset\n",
"import torch\n",
"import pandas as pd\n",
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"class GenomeDataset(Dataset):\n",
" def __init__(self, dataframe):\n",
" self.dataframe = dataframe\n",
"\n",
" # Umwandlung der Genome Frequenzen in Tensoren\n",
" self.genome_frequencies = torch.tensor(dataframe['genome_frequencies'].tolist(), dtype=torch.float32)\n",
"\n",
" # Umwandlung der Krebsarten in numerische Werte\n",
" self.label_encoder = LabelEncoder()\n",
" self.cancer_types = torch.tensor(self.label_encoder.fit_transform(dataframe['cancer_type']), dtype=torch.long)\n",
"\n",
" def __getitem__(self, index):\n",
" # Rückgabe eines Tupels aus Genome Frequenzen und dem entsprechenden Krebstyp\n",
" return self.genome_frequencies[index], self.cancer_types[index]\n",
"\n",
" def __len__(self):\n",
" return len(self.dataframe)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "aaa2c50c-c79e-4bca-812f-1a06c9f485d5",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_343/2483914749.py:11: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:245.)\n",
" self.genome_frequencies = torch.tensor(dataframe['genome_frequencies'].tolist(), dtype=torch.float32)\n"
]
}
],
"source": [
"# Beispielhafte Verwendung\n",
"# Angenommen, df_train und df_valid sind Ihre Trainings- und Validierungsdaten\n",
"train_dataset = GenomeDataset(train_df)\n",
"valid_dataset = GenomeDataset(val_df)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a7fb59af-bd06-42d4-acce-03266a85bf36",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Genome frequency from dataframe:\n",
"[2.50645e+01 1.12500e-01 5.63997e+01 ... 0.00000e+00 1.29000e-02\n",
" 2.47100e-01]\n",
"\n",
"Cancer type from dataframe: kirc\n",
"\n",
"Genome frequency from dataset:\n",
"tensor([2.5065e+01, 1.1250e-01, 5.6400e+01, ..., 0.0000e+00, 1.2900e-02,\n",
" 2.4710e-01])\n",
"\n",
"Cancer type from dataset: 1\n"
]
}
],
"source": [
"# Inspect the first item from the training dataframe\n",
"train_df_head = train_df.head(n=1)\n",
"train_df_genome_frequence =train_df_head.iloc[0][\"genome_frequencies\"]\n",
"train_df_cancer_type = train_df_head.iloc[0][\"cancer_type\"]\n",
"print(f\"Genome frequency from dataframe:{os.linesep}{train_df_genome_frequence}{os.linesep}\")\n",
"print(f\"Cancer type from dataframe: {train_df_cancer_type}{os.linesep}\")\n",
"\n",
"# Inspect the first item from the training dataset\n",
"datapoint_features, datapoint_label = train_dataset[0]\n",
"print(f\"Genome frequency from dataset:{os.linesep}{datapoint_features}{os.linesep}\")\n",
"print(f\"Cancer type from dataset: {datapoint_label}\")"
]
},
{
"cell_type": "markdown",
"id": "418bc6a0-2ddb-4596-87d1-3e670195297c",
"metadata": {
"tags": []
},
"source": [
"## Hauptkomponentenanalyse (PCA)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e6672e50-47e6-48fc-9e1e-cac0f0a606f1",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Angenommen, X ist Ihr Datensatz\n",
"# X = ...\n",
"X = rick\n",
"\n",
"# Standardisieren der Daten\n",
"scaler = StandardScaler()\n",
"X_scaled = scaler.fit_transform(X)\n",
"\n",
"# Erstellen des PCA-Objekts\n",
"pca = PCA(n_components=150) # Angenommen, Sie möchten 150 Hauptkomponenten behalten\n",
"\n",
"# Durchführen der PCA\n",
"X_pca = pca.fit_transform(X_scaled)\n",
"\n",
"# Die resultierenden Hauptkomponenten\n",
"print(\"Transformierte Daten:\", X_pca)\n",
"\n",
"# Variance Ratio für jede Komponente\n",
"print(\"Varianz erklärt durch jede Komponente:\", pca.explained_variance_ratio_)\n"
]
},
{
"cell_type": "markdown",
"id": "9199fdeb-0d48-44c2-8bec-db2a7d7cbd4d",
"metadata": {},
"source": [
"# Neuronales Netz Definition"
]
},
{
"cell_type": "markdown",
"id": "e53132b9-6222-4739-be49-7628e5a37709",
"metadata": {},
"source": [
"### Simples Neuronales Netz"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "76b8eec8-d24b-4696-82bf-ebb286e7d1e7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.utils.data import DataLoader\n",
"\n",
"# Definition des Modells\n",
"class SimpleNN(nn.Module):\n",
" def __init__(self, input_size, hidden_size, num_classes):\n",
" super(SimpleNN, self).__init__()\n",
" self.fc1 = nn.Linear(input_size, hidden_size)\n",
" self.relu = nn.ReLU()\n",
" self.fc2 = nn.Linear(hidden_size, num_classes)\n",
"\n",
" def forward(self, x):\n",
" out = self.fc1(x)\n",
" out = self.relu(out)\n",
" out = self.fc2(out)\n",
" return out"
]
},
{
"cell_type": "markdown",
"id": "e2e9e0dd-3d4f-4999-9e65-704266d5e4a2",
"metadata": {
"tags": []
},
"source": [
"### Komplexes Neuronales Netz"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "944d463e-12ed-4447-8587-ee9c60ce3eb6",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"class ComplexNN(nn.Module):\n",
" def __init__(self, input_size, hidden_size, num_classes):\n",
" super(ComplexNN, self).__init__()\n",
" # Definieren der Schichten\n",
" self.fc1 = nn.Linear(input_size, 1024) # Eingabeschicht\n",
" self.fc2 = nn.Linear(1024, 512) # Versteckte Schicht\n",
" self.fc3 = nn.Linear(512, 256) # Weitere versteckte Schicht\n",
" self.fc4 = nn.Linear(256, num_classes) # Ausgabeschicht\n",
" self.dropout = nn.Dropout(p=0.5) # Dropout\n",
"\n",
" def forward(self, x):\n",
" # Definieren des Vorwärtsdurchlaufs\n",
" x = F.relu(self.fc1(x))\n",
" x = self.dropout(x)\n",
" x = F.relu(self.fc2(x))\n",
" x = self.dropout(x)\n",
" x = F.relu(self.fc3(x))\n",
" x = torch.sigmoid(self.fc4(x)) # Oder F.log_softmax(x, dim=1) für Mehrklassenklassifikation\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "60789428-7d6e-4737-a83a-1138f6a650f7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Annahme: input_size ist die Länge Ihrer Genome-Frequenzen und num_classes ist die Anzahl der Krebsarten\n",
"#model = SimpleNN(input_size=60660, hidden_size=5000, num_classes=3)\n",
"model = ComplexNN(input_size=60660, hidden_size=5000, num_classes=3)\n",
"\n",
"# Daten-Loader\n",
"train_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\n",
"valid_loader = DataLoader(dataset=valid_dataset, batch_size=64, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "de6e81de-0096-443a-a0b6-90cddecf5f88",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Verlustfunktion und Optimierer\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
"num_epochs = 70"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "a5deb2ed-c685-4d80-bc98-d6dd27334d82",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch [1/70], Trainingsverlust: 1.1040, Validierungsverlust: 1.0986\n",
"Epoch [2/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [3/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [4/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [5/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [6/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [7/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [8/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [9/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [10/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [11/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [12/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [13/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [14/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [15/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [16/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\n",
"Epoch [17/70], Trainingsverlust: 1.0986, Validierungsverlust: 1.0986\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[39], line 13\u001b[0m\n\u001b[1;32m 11\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(outputs, labels)\n\u001b[1;32m 12\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m---> 13\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 14\u001b[0m train_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Durchschnittlicher Trainingsverlust\u001b[39;00m\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/optim/optimizer.py:280\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs),\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 280\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 283\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/optim/optimizer.py:33\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 32\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m---> 33\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 35\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(prev_grad)\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/optim/adam.py:141\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 130\u001b[0m beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[1;32m 133\u001b[0m group,\n\u001b[1;32m 134\u001b[0m params_with_grad,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 138\u001b[0m max_exp_avg_sqs,\n\u001b[1;32m 139\u001b[0m state_steps)\n\u001b[0;32m--> 141\u001b[0m \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 142\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 143\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 150\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 152\u001b[0m \u001b[43m \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 154\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 155\u001b[0m \u001b[43m \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 156\u001b[0m \u001b[43m \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 157\u001b[0m \u001b[43m \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 158\u001b[0m \u001b[43m \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 159\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 160\u001b[0m \u001b[43m \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 161\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/optim/adam.py:281\u001b[0m, in \u001b[0;36madam\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 279\u001b[0m func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[0;32m--> 281\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 282\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 283\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 284\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 285\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 286\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 287\u001b[0m \u001b[43m \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 288\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 289\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 290\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 291\u001b[0m \u001b[43m \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 292\u001b[0m \u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 293\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 294\u001b[0m \u001b[43m \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 295\u001b[0m \u001b[43m \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 296\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 297\u001b[0m \u001b[43m \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/optim/adam.py:393\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m denom \u001b[38;5;241m=\u001b[39m (exp_avg_sq\u001b[38;5;241m.\u001b[39msqrt() \u001b[38;5;241m/\u001b[39m bias_correction2_sqrt)\u001b[38;5;241m.\u001b[39madd_(eps)\n\u001b[0;32m--> 393\u001b[0m \u001b[43mparam\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maddcdiv_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp_avg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdenom\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mstep_size\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"# Listen, um Verluste zu speichern\n",
"train_losses = []\n",
"valid_losses = []\n",
"\n",
"for epoch in range(num_epochs):\n",
" model.train()\n",
" train_loss = 0.0\n",
" for i, (inputs, labels) in enumerate(train_loader):\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",
" # Durchschnittlicher Trainingsverlust\n",
" train_loss /= len(train_loader)\n",
" train_losses.append(train_loss)\n",
"\n",
" # Validierungsverlust\n",
" model.eval()\n",
" valid_loss = 0.0\n",
" with torch.no_grad():\n",
" for inputs, labels in valid_loader:\n",
" outputs = model(inputs)\n",
" loss = criterion(outputs, labels)\n",
" valid_loss += loss.item()\n",
"\n",
" # Durchschnittlicher Validierungsverlust\n",
" valid_loss /= len(valid_loader)\n",
" valid_losses.append(valid_loss)\n",
"\n",
" print(f'Epoch [{epoch+1}/{num_epochs}], Trainingsverlust: {train_loss:.4f}, Validierungsverlust: {valid_loss:.4f}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "baf1caa8-d3d9-48e8-9339-81194521528d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.plot(train_losses, label='Trainingsverlust')\n",
"plt.plot(valid_losses, label='Validierungsverlust')\n",
"plt.xlabel('Epochen')\n",
"plt.ylabel('Verlust')\n",
"plt.title('Trainings- und Validierungsverlust über die Zeit')\n",
"plt.legend()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e339354-a7cc-4e8a-9323-4be41ef62117",
"metadata": {},
"outputs": [],
"source": [
"# Laden der 'kirp' Liste aus der Pickle-Datei\n",
"with open('rick.pickle', 'rb') as f:\n",
" rick = pickle.load(f)\n"
]
},
{
"cell_type": "markdown",
"id": "be10a487-728e-4953-a081-9103d485378c",
"metadata": {},
"source": [
"## Hauptkomponentenanalyse (PCA)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "088db0b3-8c33-41ff-a543-1b1e50c5e589",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transformierte Daten: [[-6.02552113e+01 4.57642675e+01 1.11957079e+02 ... 2.58331825e+00\n",
" 9.99342571e-01 -2.77477317e-01]\n",
" [-1.64705386e+01 9.03712725e+00 1.04837673e+01 ... 4.06859167e+00\n",
" 2.01083350e+00 1.49404086e+00]\n",
" [ 7.52348753e+00 -1.55853934e+01 -4.76301782e+01 ... -7.87604764e+00\n",
" -7.56801224e-02 8.37028680e+00]\n",
" ...\n",
" [-2.72012678e+01 4.44526098e+00 2.60063820e+01 ... 3.08321694e-01\n",
" 2.28939485e+00 -7.14920382e+00]\n",
" [-3.48027066e+01 2.27021639e+01 5.51486742e+01 ... -1.77955416e+01\n",
" 6.24722406e+00 2.32101665e+01]\n",
" [-3.98223613e+01 1.88534866e+01 5.32794498e+01 ... -1.45806809e+00\n",
" 1.18270903e+01 -2.84291311e+00]]\n",
"Varianz erklärt durch jede Komponente: [0.15056597 0.0997506 0.06070173 0.03658789 0.03530275 0.0263503\n",
" 0.02322747 0.01705354 0.01534278 0.01281486 0.01116959 0.0107472\n",
" 0.00989894 0.00906208 0.00871621 0.00813403 0.0074718 0.00708769\n",
" 0.00667045 0.00633275 0.00579241 0.00556758 0.00532382 0.00519289\n",
" 0.00476404 0.00472014 0.00457837 0.00414668 0.00399478 0.00380604\n",
" 0.00362433 0.00349278 0.00336446 0.00323228 0.00310834 0.00300595\n",
" 0.00297408 0.00285178 0.00280688 0.00273987 0.00268256 0.00263102\n",
" 0.00250513 0.00248987 0.0024505 0.0023979 0.00235971 0.00218554\n",
" 0.00217143 0.00212775 0.00210793 0.00205678 0.00202224 0.00200579\n",
" 0.00194754 0.00189606 0.00187714 0.00184969 0.00180133 0.00178537\n",
" 0.00176576 0.00172542 0.00168211 0.00167483 0.00162565 0.00159444\n",
" 0.00158667 0.00155982 0.00155534 0.00151929 0.00149558 0.00147549\n",
" 0.00146982 0.00146262 0.00143338 0.00142085 0.00140628 0.00139744\n",
" 0.00136563 0.00136169 0.00134972 0.00132027 0.00129168 0.00127963\n",
" 0.00126629 0.0012562 0.00123608 0.00122899 0.0012035 0.0011899\n",
" 0.00118094 0.00117162 0.00116552 0.00114295 0.00112631 0.00111896\n",
" 0.00110193 0.00109004 0.00108523 0.00106574 0.00106381 0.001051\n",
" 0.00104179 0.00103669 0.00103248 0.00101669 0.00100527 0.00099315\n",
" 0.00097478 0.00096486 0.00096244 0.00094792 0.00094463 0.00093107\n",
" 0.00092485 0.00090851 0.00089848 0.00089134 0.00087855 0.00087068\n",
" 0.00086397 0.00085563 0.00084342 0.00083406 0.00083064 0.00081791\n",
" 0.00080368 0.00080183 0.00079167 0.00079072 0.00078868 0.00078028\n",
" 0.00077115 0.00076662 0.00076043 0.00075196 0.0007447 0.0007332\n",
" 0.0007252 0.00072345 0.00071902 0.00070594 0.00070125 0.00069603\n",
" 0.00069029 0.00068619 0.00068012 0.00067224 0.00066615 0.00066017]\n"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Angenommen, X ist Ihr Datensatz\n",
"# X = ...\n",
"X = rick\n",
"\n",
"# Standardisieren der Daten\n",
"scaler = StandardScaler()\n",
"X_scaled = scaler.fit_transform(X)\n",
"\n",
"# Erstellen des PCA-Objekts\n",
"pca = PCA(n_components=150) # Angenommen, Sie möchten 150 Hauptkomponenten behalten\n",
"\n",
"# Durchführen der PCA\n",
"X_pca = pca.fit_transform(X_scaled)\n",
"\n",
"# Die resultierenden Hauptkomponenten\n",
"print(\"Transformierte Daten:\", X_pca)\n",
"\n",
"# Variance Ratio für jede Komponente\n",
"print(\"Varianz erklärt durch jede Komponente:\", pca.explained_variance_ratio_)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b11bbe20-0494-4e7a-83ff-3cb0bfa82f3b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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