664 lines
209 KiB
Plaintext
664 lines
209 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "8698744b",
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"metadata": {},
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"source": [
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"Bibliotheken importieren"
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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": 31,
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"id": "a1be2d54",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from pathlib import Path\n",
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"\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix, classification_report\n",
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"\n",
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"import tensorflow as tf\n",
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"from tensorflow.keras import Input, layers, models, regularizers\n",
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"\n",
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"import joblib \n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9a2832a7",
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"metadata": {},
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"source": [
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"Daten laden"
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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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"id": "f2a8a12d",
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"metadata": {},
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"outputs": [],
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"source": [
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"data_path = Path(r\"~/data-paulusjafahrsimulator-gpu/new_datasets/50s_25Hz_dataset.parquet\") \n",
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"\n",
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"df = pd.read_parquet(path=data_path)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5192248c",
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"metadata": {},
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"source": [
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"Labels erstellen"
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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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"id": "05b15322",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Label distribution:\n",
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"label\n",
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"1 4750\n",
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"0 3484\n",
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"Name: count, dtype: int64\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_2528/3529185502.py:9: SettingWithCopyWarning: \n",
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"A value is trying to be set on a copy of a slice from a DataFrame.\n",
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"Try using .loc[row_indexer,col_indexer] = value instead\n",
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"\n",
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"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
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" low_all[\"label\"] = 0\n"
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]
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}
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],
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"source": [
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"low_all = df[((df[\"PHASE\"] == \"baseline\") | \n",
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" ((df[\"STUDY\"] == \"n-back\") & (df[\"PHASE\"] != \"baseline\") & (df[\"LEVEL\"].isin([1,4]))))] \n",
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"\n",
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"high_all = pd.concat([ \n",
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" df[(df[\"STUDY\"]==\"n-back\") & (df[\"LEVEL\"].isin([2,3,5,6])) & (df[\"PHASE\"].isin([\"train\",\"test\"]))], \n",
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" df[(df[\"STUDY\"]==\"k-drive\") & (df[\"PHASE\"]!=\"baseline\")] \n",
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"]) \n",
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"\n",
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"low_all[\"label\"] = 0 \n",
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"high_all[\"label\"] = 1 \n",
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"data = pd.concat([low_all, high_all], ignore_index=True).drop_duplicates() \n",
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"\n",
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"print(\"Label distribution:\") \n",
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"print(data[\"label\"].value_counts())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9b44f8a4",
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"metadata": {},
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"source": [
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"Feature-Auswahl"
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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": 9,
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"id": "5c11e2fd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['subjectID', 'start_time', 'STUDY', 'LEVEL', 'PHASE', 'FACE_AU01_mean', 'FACE_AU02_mean', 'FACE_AU04_mean', 'FACE_AU05_mean', 'FACE_AU06_mean', 'FACE_AU07_mean', 'FACE_AU09_mean', 'FACE_AU10_mean', 'FACE_AU11_mean', 'FACE_AU12_mean', 'FACE_AU14_mean', 'FACE_AU15_mean', 'FACE_AU17_mean', 'FACE_AU20_mean', 'FACE_AU23_mean', 'FACE_AU24_mean', 'FACE_AU25_mean', 'FACE_AU26_mean', 'FACE_AU28_mean', 'FACE_AU43_mean', 'Fix_count_short_66_150', 'Fix_count_medium_300_500', 'Fix_count_long_gt_1000', 'Fix_count_100', 'Fix_mean_duration', 'Fix_median_duration', 'Sac_count', 'Sac_mean_amp', 'Sac_mean_dur', 'Sac_median_dur', 'Blink_count', 'Blink_mean_dur', 'Blink_median_dur', 'Pupil_mean', 'Pupil_IPA', 'label']\n",
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"Gefundene FACE_AU-Spalten: ['FACE_AU01_mean', 'FACE_AU02_mean', 'FACE_AU04_mean', 'FACE_AU05_mean', 'FACE_AU06_mean', 'FACE_AU07_mean', 'FACE_AU09_mean', 'FACE_AU10_mean', 'FACE_AU11_mean', 'FACE_AU12_mean', 'FACE_AU14_mean', 'FACE_AU15_mean', 'FACE_AU17_mean', 'FACE_AU20_mean', 'FACE_AU23_mean', 'FACE_AU24_mean', 'FACE_AU25_mean', 'FACE_AU26_mean', 'FACE_AU28_mean', 'FACE_AU43_mean']\n"
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]
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}
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],
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"source": [
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"au_columns = [col for col in data.columns if col.lower().startswith(\"face\")] \n",
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"print(data.columns.tolist())\n",
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"\n",
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"print(\"Gefundene FACE_AU-Spalten:\", au_columns)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "52aaa568",
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"metadata": {},
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"source": [
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"Train-/Val- und Test-Split nach Subjects"
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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": 10,
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"id": "78023e16",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(4116, 41) (1235, 41) (2883, 41)\n"
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]
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}
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],
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"source": [
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"subjects = np.random.permutation(data[\"subjectID\"].unique()) \n",
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"n = len(subjects) \n",
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"n_train = int(n*0.66) \n",
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"\n",
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"train_subjects = subjects[:n_train] \n",
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"test_subjects = subjects[n_train:] \n",
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"train_subs, val_subs = train_test_split(train_subjects, test_size=0.2, random_state=42) \n",
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"\n",
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"train_df = data[data.subjectID.isin(train_subs)] \n",
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"val_df = data[data.subjectID.isin(val_subs)] \n",
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"test_df = data[data.subjectID.isin(test_subjects)] \n",
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"\n",
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"print(train_df.shape, val_df.shape, test_df.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4fbd22c4",
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"metadata": {},
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"source": [
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"Normalisierung - StandardScaler global"
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]
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},
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{
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"cell_type": "code",
|
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"execution_count": null,
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"id": "13a6ec83",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Vorher (erste 5 Werte): FACE_AU01_mean FACE_AU02_mean FACE_AU04_mean\n",
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"697 0.792161 0.804894 0.310528\n",
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"698 0.793914 0.806557 0.312750\n",
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"699 0.795915 0.803190 0.293604\n",
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"700 0.790821 0.797190 0.299714\n",
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"701 0.790045 0.793320 0.292109\n",
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"Nachher (erste 5 Werte): FACE_AU01_mean FACE_AU02_mean FACE_AU04_mean\n",
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"697 0.679178 1.116911 1.874625\n",
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"698 0.709019 1.138696 1.913728\n",
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"699 0.743082 1.094597 1.576792\n",
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"700 0.656358 1.016024 1.684310\n",
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"701 0.643142 0.965348 1.550483\n"
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]
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},
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1000x400 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"scaler = StandardScaler() \n",
|
|
"scaler.fit(train_df[au_columns]) \n",
|
|
"\n",
|
|
"train_scaled = train_df.copy() \n",
|
|
"val_scaled = val_df.copy() \n",
|
|
"test_scaled = test_df.copy() \n",
|
|
"\n",
|
|
"train_scaled[au_columns] = scaler.transform(train_df[au_columns]) \n",
|
|
"val_scaled[au_columns] = scaler.transform(val_df[au_columns]) \n",
|
|
"test_scaled[au_columns] = scaler.transform(test_df[au_columns])\n",
|
|
"\n",
|
|
"#Normalisierung - Vorher-Nachher Vergleich\n",
|
|
"print(\"Vorher (erste 5 Werte):\", train_df[au_columns].iloc[:5, :3])\n",
|
|
"print(\"Nachher (erste 5 Werte):\", train_scaled[au_columns].iloc[:5, :3])\n",
|
|
"\n",
|
|
"#Histogramme - vor und nach Normalisierung\n",
|
|
"col = au_columns[0] # erste AU-Spalte\n",
|
|
"plt.figure(figsize=(10,4))\n",
|
|
"plt.subplot(1,2,1)\n",
|
|
"train_df[col].hist(bins=30)\n",
|
|
"plt.title(f\"Vorher: {col}\")\n",
|
|
"\n",
|
|
"plt.subplot(1,2,2)\n",
|
|
"train_scaled[col].hist(bins=30)\n",
|
|
"plt.title(f\"Nachher: {col}\")\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "367533dc",
|
|
"metadata": {},
|
|
"source": [
|
|
"Datenvorbereitung für CNN "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e5c1c8ae",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"X_train Shape: (4116, 20, 1)\n",
|
|
"X_val Shape: (1235, 20, 1)\n",
|
|
"X_test Shape: (2883, 20, 1)\n",
|
|
"Train Labels: [1741 2375]\n",
|
|
"Val Labels: [524 711]\n",
|
|
"Test Labels: [1219 1664]\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"text/plain": [
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"<Figure size 1000x400 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"X_train = train_scaled[au_columns].values[..., np.newaxis] \n",
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"y_train = train_scaled[\"label\"].values \n",
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"\n",
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"X_val = val_scaled[au_columns].values[..., np.newaxis] \n",
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"y_val = val_scaled[\"label\"].values \n",
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"\n",
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"X_test = test_scaled[au_columns].values[..., np.newaxis] \n",
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"y_test = test_scaled[\"label\"].values\n",
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"\n",
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"#Shape-Kontrolle\n",
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"print(\"X_train Shape:\", X_train.shape)\n",
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"print(\"X_val Shape:\", X_val.shape)\n",
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"print(\"X_test Shape:\", X_test.shape)\n",
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"\n",
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"#Label-Verteilung\n",
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"print(\"Train Labels:\", np.bincount(y_train))\n",
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"print(\"Val Labels:\", np.bincount(y_val))\n",
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"print(\"Test Labels:\", np.bincount(y_test))\n",
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"\n",
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"#Histogramm nach Datenvorbereitung\n",
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"col_idx = 0 #nur erste AU-Spalte\n",
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"plt.figure(figsize=(10,4))\n",
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"plt.hist(X_train[:, col_idx, 0], bins=30)\n",
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"plt.title(f\"Verteilung nach CNN-Form: Feature {au_columns[col_idx]}\")\n",
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"plt.show()\n"
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]
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},
|
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{
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"cell_type": "markdown",
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"id": "2dddde72",
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"metadata": {},
|
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"source": [
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"CNN Architektur - mit Keras"
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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": 32,
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"id": "4501c65a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
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"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ conv1d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
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"│ batch_normalization_2 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ max_pooling1d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ conv1d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">6,208</span> │\n",
|
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
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"│ batch_normalization_3 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
|
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
|
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
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"│ global_average_pooling1d_3 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
|
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling1D</span>) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
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"│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
|
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
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"│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
|
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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"</pre>\n"
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],
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
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"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ conv1d_6 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
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"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ max_pooling1d_3 (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ conv1d_7 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,208\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
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"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ global_average_pooling1d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
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"│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m65\u001b[0m │\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,945</span> (42.75 KB)\n",
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"</pre>\n"
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],
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"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m10,945\u001b[0m (42.75 KB)\n"
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]
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},
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,753</span> (42.00 KB)\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,753\u001b[0m (42.00 KB)\n"
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]
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},
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"output_type": "display_data"
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">192</span> (768.00 B)\n",
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"</pre>\n"
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],
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"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"model = models.Sequential([\n",
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" Input(shape=(len(au_columns),1)),\n",
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" layers.Conv1D(32, kernel_size=3, activation=\"relu\"), \n",
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" layers.BatchNormalization(), \n",
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" layers.MaxPooling1D(pool_size=2), \n",
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" \n",
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" layers.Conv1D(64, kernel_size=3, activation=\"relu\"), \n",
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" layers.BatchNormalization(), \n",
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" layers.GlobalAveragePooling1D(), \n",
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" \n",
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" layers.Dense(64, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n",
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" layers.Dropout(0.6), # etwas stärkeres Dropout \n",
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" layers.Dense(1, activation=\"sigmoid\")\n",
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"])\n",
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"\n",
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"model.compile(\n",
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" optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
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" loss=\"binary_crossentropy\",\n",
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" metrics=[\"accuracy\", tf.keras.metrics.AUC(name=\"auc\")]\n",
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")\n",
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"\n",
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"model.summary()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bc203cec",
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"metadata": {},
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"source": [
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"Training - mit Callbacks"
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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": 34,
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"id": "be371dbb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/100\n",
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9429 - auc: 0.9839 - loss: 0.1901 - val_accuracy: 0.9206 - val_auc: 0.9442 - val_loss: 0.3106 - learning_rate: 2.5000e-04\n",
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"Epoch 2/100\n",
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9420 - auc: 0.9863 - loss: 0.1799 - val_accuracy: 0.9271 - val_auc: 0.9437 - val_loss: 0.2897 - learning_rate: 2.5000e-04\n",
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"Epoch 3/100\n",
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9508 - auc: 0.9869 - loss: 0.1764 - val_accuracy: 0.9247 - val_auc: 0.9421 - val_loss: 0.3190 - learning_rate: 2.5000e-04\n",
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"Epoch 4/100\n",
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9443 - auc: 0.9855 - loss: 0.1819 - val_accuracy: 0.9255 - val_auc: 0.9454 - val_loss: 0.3098 - learning_rate: 2.5000e-04\n",
|
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"Epoch 5/100\n",
|
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9431 - auc: 0.9866 - loss: 0.1755 - val_accuracy: 0.9279 - val_auc: 0.9432 - val_loss: 0.3023 - learning_rate: 2.5000e-04\n",
|
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"Epoch 6/100\n",
|
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9476 - auc: 0.9865 - loss: 0.1704 - val_accuracy: 0.9247 - val_auc: 0.9445 - val_loss: 0.3061 - learning_rate: 2.5000e-04\n",
|
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"Epoch 7/100\n",
|
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9485 - auc: 0.9865 - loss: 0.1682 - val_accuracy: 0.9279 - val_auc: 0.9423 - val_loss: 0.3132 - learning_rate: 2.5000e-04\n",
|
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"Epoch 8/100\n",
|
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9484 - auc: 0.9884 - loss: 0.1624 - val_accuracy: 0.9287 - val_auc: 0.9423 - val_loss: 0.3191 - learning_rate: 1.2500e-04\n",
|
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"Epoch 9/100\n",
|
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9496 - auc: 0.9885 - loss: 0.1611 - val_accuracy: 0.9320 - val_auc: 0.9409 - val_loss: 0.3137 - learning_rate: 1.2500e-04\n",
|
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"Epoch 10/100\n",
|
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9436 - auc: 0.9880 - loss: 0.1662 - val_accuracy: 0.9279 - val_auc: 0.9424 - val_loss: 0.3159 - learning_rate: 1.2500e-04\n",
|
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"Epoch 11/100\n",
|
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9427 - auc: 0.9876 - loss: 0.1657 - val_accuracy: 0.9312 - val_auc: 0.9415 - val_loss: 0.3128 - learning_rate: 1.2500e-04\n",
|
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"Epoch 12/100\n",
|
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"\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9523 - auc: 0.9898 - loss: 0.1504 - val_accuracy: 0.9255 - val_auc: 0.9421 - val_loss: 0.3188 - learning_rate: 1.2500e-04\n"
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]
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},
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{
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"data": {
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"image/png": 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",
|
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"text/plain": [
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"<Figure size 800x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 800x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 800x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"callbacks = [ \n",
|
|
" tf.keras.callbacks.EarlyStopping( monitor=\"val_loss\", patience=10, restore_best_weights=True ), \n",
|
|
" tf.keras.callbacks.ReduceLROnPlateau( monitor=\"val_loss\", factor=0.5, patience=5, min_lr=1e-6 ), \n",
|
|
" tf.keras.callbacks.ModelCheckpoint( \"cnn_best_model.keras\", save_best_only=True, monitor=\"val_loss\" ) \n",
|
|
"]\n",
|
|
"\n",
|
|
"history = model.fit(\n",
|
|
" X_train, y_train, \n",
|
|
" validation_data=(X_val, y_val), \n",
|
|
" epochs=100, \n",
|
|
" batch_size=32, \n",
|
|
" callbacks=callbacks \n",
|
|
")\n",
|
|
"\n",
|
|
"# Trainingsverlauf plotten\n",
|
|
"def plot_history(history, metric): \n",
|
|
" plt.figure(figsize=(8,6)) \n",
|
|
" plt.plot(history.history[metric], label=f\"Train {metric}\") \n",
|
|
" plt.plot(history.history[f\"val_{metric}\"], label=f\"Val {metric}\") \n",
|
|
" plt.xlabel(\"Epochs\") \n",
|
|
" plt.ylabel(metric.capitalize()) \n",
|
|
" plt.title(f\"Training vs Validation {metric.capitalize()}\")\n",
|
|
" plt.legend()\n",
|
|
" plt.grid(True)\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"# Funktionsaufrufe NACH der Definition\n",
|
|
"plot_history(history, \"loss\")\n",
|
|
"plot_history(history, \"accuracy\")\n",
|
|
"plot_history(history, \"auc\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ff5c5462",
|
|
"metadata": {},
|
|
"source": [
|
|
"Evaluation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"id": "fd803602",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[1m91/91\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n",
|
|
"Test Accuracy: 0.9448491155046826\n",
|
|
"Test F1: 0.9538193435957014\n",
|
|
"Test AUC: 0.937195821764372\n",
|
|
"Confusion Matrix:\n",
|
|
" [[1082 137]\n",
|
|
" [ 22 1642]]\n",
|
|
" precision recall f1-score support\n",
|
|
"\n",
|
|
" 0 0.98 0.89 0.93 1219\n",
|
|
" 1 0.92 0.99 0.95 1664\n",
|
|
"\n",
|
|
" accuracy 0.94 2883\n",
|
|
" macro avg 0.95 0.94 0.94 2883\n",
|
|
"weighted avg 0.95 0.94 0.94 2883\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"y_test_pred_proba = model.predict(X_test).ravel() \n",
|
|
"y_test_pred = (y_test_pred_proba > 0.5).astype(int) \n",
|
|
"\n",
|
|
"print(\"Test Accuracy:\", accuracy_score(y_test, y_test_pred)) \n",
|
|
"print(\"Test F1:\", f1_score(y_test, y_test_pred)) \n",
|
|
"print(\"Test AUC:\", roc_auc_score(y_test, y_test_pred)) \n",
|
|
"print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_test_pred)) \n",
|
|
"print(classification_report(y_test, y_test_pred)) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4edb792b",
|
|
"metadata": {},
|
|
"source": [
|
|
"Model speichern "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"id": "1a805bf6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['scaler_cnn_v2.joblib']"
|
|
]
|
|
},
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.save(\"cnn_model_full_v2.keras\") \n",
|
|
"\n",
|
|
"# Scaler speichern \n",
|
|
"joblib.dump(scaler, \"scaler_cnn_v2.joblib\")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|