"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#plots\n",
+ "def plot_cv_histories(cv_histories, metric): \n",
+ " plt.figure(figsize=(10,6)) \n",
+ " \n",
+ " for i, hist in enumerate(cv_histories): \n",
+ " plt.plot(hist[metric], label=f\"Fold {i+1} Train\", alpha=0.7) \n",
+ " plt.plot(hist[f\"val_{metric}\"], label=f\"Fold {i+1} Val\", linestyle=\"--\", alpha=0.7) \n",
+ " plt.xlabel(\"Epochs\") \n",
+ " plt.ylabel(metric.capitalize()) \n",
+ " plt.title(f\"Cross-Validation {metric.capitalize()} Verläufe\") \n",
+ " plt.legend() \n",
+ " plt.grid(True) \n",
+ " plt.show()\n",
+ " \n",
+ "plot_cv_histories(cv_histories, \"loss\") \n",
+ "plot_cv_histories(cv_histories, \"accuracy\") \n",
+ "plot_cv_histories(cv_histories, \"auc\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/model_training/CNN/CNN_crossVal_HybridFusion.ipynb b/model_training/CNN/CNN_crossVal_HybridFusion.ipynb
new file mode 100644
index 0000000..9fef5ac
--- /dev/null
+++ b/model_training/CNN/CNN_crossVal_HybridFusion.ipynb
@@ -0,0 +1,1750 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "47f6de7b",
+ "metadata": {},
+ "source": [
+ "Bibliotheken importieren"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "99294260",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd \n",
+ "import numpy as np \n",
+ "import matplotlib.pyplot as plt\n",
+ "import random \n",
+ "import joblib \n",
+ "from pathlib import Path \n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "\n",
+ "from sklearn.model_selection import GroupKFold \n",
+ "from sklearn.preprocessing import StandardScaler \n",
+ "\n",
+ "import tensorflow as tf \n",
+ "from tensorflow.keras import Input, layers, models, regularizers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "52b4ca8c",
+ "metadata": {},
+ "source": [
+ "Seed festlegen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "6e49d281",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SEED = 42 \n",
+ "np.random.seed(SEED) \n",
+ "tf.random.set_seed(SEED) \n",
+ "random.seed(SEED)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae1a715f",
+ "metadata": {},
+ "source": [
+ "Daten laden"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "870f01c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_path = Path(r\"~/data-paulusjafahrsimulator-gpu/new_datasets/50s_25Hz_dataset.parquet\") \n",
+ "\n",
+ "data = pd.read_parquet(path=data_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bedbc23b",
+ "metadata": {},
+ "source": [
+ "Labels erstellen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "38848515",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "low_all = data[((data[\"PHASE\"] == \"baseline\") | \n",
+ " ((data[\"STUDY\"] == \"n-back\") & (data[\"PHASE\"] != \"baseline\") & (data[\"LEVEL\"].isin([1,4]))))].copy() \n",
+ "\n",
+ "high_all = pd.concat([ \n",
+ " data[(data[\"STUDY\"]==\"n-back\") & (data[\"LEVEL\"].isin([2,3,5,6])) & (data[\"PHASE\"].isin([\"train\",\"test\"]))], \n",
+ " data[(data[\"STUDY\"]==\"k-drive\") & (data[\"PHASE\"]!=\"baseline\")] \n",
+ "]).copy() \n",
+ "\n",
+ "low_all[\"label\"] = 0 \n",
+ "high_all[\"label\"] = 1 \n",
+ "\n",
+ "data = pd.concat([low_all, high_all], ignore_index=True).drop_duplicates() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0b282acf",
+ "metadata": {},
+ "source": [
+ "Features und Labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "5edb00a0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AUs: 20\n",
+ "Eye Features: 15\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Face AUs\n",
+ "au_columns = [col for col in data.columns if \"face\" in col.lower()]\n",
+ "\n",
+ "#Eye Features\n",
+ "eye_columns = [\n",
+ " 'Fix_count_short_66_150',\n",
+ " 'Fix_count_medium_300_500',\n",
+ " 'Fix_count_long_gt_1000',\n",
+ " 'Fix_count_100',\n",
+ " 'Fix_mean_duration',\n",
+ " 'Fix_median_duration',\n",
+ " 'Sac_count',\n",
+ " 'Sac_mean_amp',\n",
+ " 'Sac_mean_dur',\n",
+ " 'Sac_median_dur',\n",
+ " 'Blink_count',\n",
+ " 'Blink_mean_dur',\n",
+ " 'Blink_median_dur',\n",
+ " 'Pupil_mean',\n",
+ " 'Pupil_IPA'\n",
+ "]\n",
+ "\n",
+ "print(\"AUs:\", len(au_columns)) \n",
+ "print(\"Eye Features:\", len(eye_columns))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "af6892fa",
+ "metadata": {},
+ "source": [
+ "Daten vorbereiten"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "5e3d1741",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Label-Verteilung: [3287 4685]\n",
+ "Anteil Klasse 1: 0.5876818866031109\n",
+ "NaNs in AUs: 0\n",
+ "NaNs in Eye: 0\n",
+ "NaNs in y: 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = data.dropna(subset=eye_columns + au_columns + [\"label\"])\n",
+ "\n",
+ "X_au = data[au_columns].values[..., np.newaxis]\n",
+ "X_eye = data[eye_columns].values\n",
+ "y = data[\"label\"].values\n",
+ "groups = data[\"subjectID\"].values\n",
+ "\n",
+ "#Klassenverteilung prüfen \n",
+ "print(\"Label-Verteilung:\", np.bincount(y)) \n",
+ "print(\"Anteil Klasse 1:\", y.mean())\n",
+ "\n",
+ "#NaN-Check \n",
+ "print(\"NaNs in AUs:\", np.isnan(X_au).sum()) \n",
+ "print(\"NaNs in Eye:\", np.isnan(X_eye).sum()) \n",
+ "print(\"NaNs in y:\", np.isnan(y).sum())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1164b8b2",
+ "metadata": {},
+ "source": [
+ "Train/Test-Split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6955ad52",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#X_au_train, X_au_test, X_eye_train, X_eye_test, y_train, y_test, groups_train, groups_test = train_test_split( \n",
+ "# X_au, X_eye, y, groups, \n",
+ "# test_size=0.2, \n",
+ "# random_state=42, \n",
+ "# stratify=y \n",
+ "#)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a539b83b",
+ "metadata": {},
+ "source": [
+ "CNN-Modell Hybride Fusion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "e4a7f496",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_hybrid_model(n_aus, n_eye, lr=1e-4):\n",
+ "\n",
+ " #CNN-Zweig für FACE AUs\n",
+ " input_au = Input(shape=(n_aus, 1), name=\"au_input\")\n",
+ " x = layers.Conv1D(32, 3, activation=\"relu\")(input_au)\n",
+ " x = layers.BatchNormalization()(x)\n",
+ " x = layers.MaxPooling1D(2)(x)\n",
+ "\n",
+ " x = layers.Conv1D(64, 3, activation=\"relu\")(x)\n",
+ " x = layers.BatchNormalization()(x)\n",
+ " x = layers.GlobalAveragePooling1D()(x)\n",
+ "\n",
+ " #MLP-Zweig für Eye-Features\n",
+ " input_eye = Input(shape=(n_eye,), name=\"eye_input\")\n",
+ " e = layers.Dense(32, activation=\"relu\")(input_eye)\n",
+ " e = layers.Dropout(0.3)(e)\n",
+ " e = layers.Dense(16, activation=\"relu\")(e)\n",
+ "\n",
+ " #Fusion \n",
+ " fused = layers.concatenate([x, e])\n",
+ "\n",
+ " #Gemeinsamer Klassifikator\n",
+ " z = layers.Dense(32, activation=\"relu\")(fused)\n",
+ " z = layers.Dropout(0.4)(z)\n",
+ " output = layers.Dense(1, activation=\"sigmoid\")(z)\n",
+ "\n",
+ " model = models.Model(inputs=[input_au, input_eye], outputs=output)\n",
+ "\n",
+ " model.compile(\n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=lr),\n",
+ " loss=\"binary_crossentropy\",\n",
+ " metrics=[\"accuracy\", tf.keras.metrics.AUC(name=\"auc\")]\n",
+ " )\n",
+ "\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5905871b",
+ "metadata": {},
+ "source": [
+ "Cross-Validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "90658000",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "--- Fold 1 ---\n",
+ "Train-Subjects: [ 0 3 4 7 8 9 14 16 17 18 20 21 22 26 27 28]\n",
+ "Val-Subjects: [ 2 5 12 13]\n",
+ "Train Eye Mittelwerte (erste 5 Features): [-1.15772575e-15 -3.28654014e-15 -2.79364390e-16 2.88001922e-15\n",
+ " -2.53018766e-15]\n",
+ "Train Eye Std (erste 5 Features): [1. 1. 1. 1. 1.]\n",
+ "Val Eye Mittelwerte (erste 5 Features): [-0.10344007 0.02468102 0.02777478 -0.1121909 0.03594133]\n",
+ "Val Eye Std (erste 5 Features): [0.83938855 1.20780657 1.35451015 0.71699883 1.09413923]\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+ "I0000 00:00:1771848221.859708 31811 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
+ "I0000 00:00:1771848221.905617 31811 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
+ "I0000 00:00:1771848221.907114 31811 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
+ "I0000 00:00:1771848221.912465 31811 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
+ "I0000 00:00:1771848221.913779 31811 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
+ "I0000 00:00:1771848221.915060 31811 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
+ "I0000 00:00:1771848222.079870 31811 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
+ "I0000 00:00:1771848222.081303 31811 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
+ "2026-02-23 13:03:42.082529: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:47] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
+ "I0000 00:00:1771848222.082626 31811 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
+ "2026-02-23 13:03:42.084810: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 37496 MB memory: -> device: 0, name: NVIDIA A100 80GB PCIe MIG 3g.40gb, pci bus id: 0000:1e:00.0, compute capability: 8.0\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "Model: \"functional\"\n",
+ "
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+ ],
+ "text/plain": [
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+ "metadata": {},
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+ },
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+ "data": {
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+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ au_input │ (None, 20, 1) │ 0 │ - │\n",
+ "│ (InputLayer) │ │ │ │\n",
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+ "│ conv1d (Conv1D) │ (None, 18, 32) │ 128 │ au_input[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalization │ (None, 18, 32) │ 128 │ conv1d[0][0] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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+ "│ (MaxPooling1D) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ eye_input │ (None, 15) │ 0 │ - │\n",
+ "│ (InputLayer) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv1d_1 (Conv1D) │ (None, 7, 64) │ 6,208 │ max_pooling1d[0]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense (Dense) │ (None, 32) │ 512 │ eye_input[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None, 7, 64) │ 256 │ conv1d_1[0][0] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout (Dropout) │ (None, 32) │ 0 │ dense[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ global_average_poo… │ (None, 64) │ 0 │ batch_normalizat… │\n",
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+ "│ concatenate │ (None, 80) │ 0 │ global_average_p… │\n",
+ "│ (Concatenate) │ │ │ dense_1[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_2 (Dense) │ (None, 32) │ 2,592 │ concatenate[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_1 (Dropout) │ (None, 32) │ 0 │ dense_2[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_3 (Dense) │ (None, 1) │ 33 │ dropout_1[0][0] │\n",
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+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv1d (\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 │ au_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │ conv1d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ max_pooling1d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ eye_input │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv1d_1 (\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 │ max_pooling1d[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ eye_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv1d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m528\u001b[0m │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ concatenate │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m80\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ global_average_p… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ dense_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,592\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │ dropout_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ " Total params: 10,385 (40.57 KB)\n",
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+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m10,385\u001b[0m (40.57 KB)\n"
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+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 10,193 (39.82 KB)\n",
+ "
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+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,193\u001b[0m (39.82 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "data": {
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+ " Non-trainable params: 192 (768.00 B)\n",
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+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+ "I0000 00:00:1771848224.515104 32002 service.cc:146] XLA service 0x7fe4e8001f50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
+ "I0000 00:00:1771848224.515186 32002 service.cc:154] StreamExecutor device (0): NVIDIA A100 80GB PCIe MIG 3g.40gb, Compute Capability 8.0\n",
+ "2026-02-23 13:03:44.563108: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+ "2026-02-23 13:03:44.784726: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8907\n",
+ "I0000 00:00:1771848226.525473 32002 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fold 1 - Val Loss: 0.1029, Val Acc: 0.9710, Val AUC: 0.9921\n",
+ "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step \n",
+ "Konfusionsmatrix Fold 1:\n",
+ "[[609 29]\n",
+ " [ 17 933]]\n",
+ "\n",
+ "\n",
+ "--- Fold 2 ---\n",
+ "Train-Subjects: [ 2 3 4 5 7 8 9 12 13 14 18 20 22 26 27 28]\n",
+ "Val-Subjects: [ 0 16 17 21]\n",
+ "Train Eye Mittelwerte (erste 5 Features): [ 9.62913006e-18 -5.50192472e-16 3.91726244e-15 7.87695052e-16\n",
+ " -2.19998632e-16]\n",
+ "Train Eye Std (erste 5 Features): [1. 1. 1. 1. 1.]\n",
+ "Val Eye Mittelwerte (erste 5 Features): [0.15155672 0.0254572 0.16134084 0.19681455 0.13300908]\n",
+ "Val Eye Std (erste 5 Features): [1.19005297 0.94363323 1.23775801 1.17691797 1.23677154]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "Model: \"functional_1\"\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ au_input │ (None, 20, 1) │ 0 │ - │\n",
+ "│ (InputLayer) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv1d_2 (Conv1D) │ (None, 18, 32) │ 128 │ au_input[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None, 18, 32) │ 128 │ conv1d_2[0][0] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ max_pooling1d_1 │ (None, 9, 32) │ 0 │ batch_normalizat… │\n",
+ "│ (MaxPooling1D) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ eye_input │ (None, 15) │ 0 │ - │\n",
+ "│ (InputLayer) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv1d_3 (Conv1D) │ (None, 7, 64) │ 6,208 │ max_pooling1d_1[… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_4 (Dense) │ (None, 32) │ 512 │ eye_input[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None, 7, 64) │ 256 │ conv1d_3[0][0] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_2 (Dropout) │ (None, 32) │ 0 │ dense_4[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ global_average_poo… │ (None, 64) │ 0 │ batch_normalizat… │\n",
+ "│ (GlobalAveragePool… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_5 (Dense) │ (None, 16) │ 528 │ dropout_2[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ concatenate_1 │ (None, 80) │ 0 │ global_average_p… │\n",
+ "│ (Concatenate) │ │ │ dense_5[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_6 (Dense) │ (None, 32) │ 2,592 │ concatenate_1[0]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_3 (Dropout) │ (None, 32) │ 0 │ dense_6[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_7 (Dense) │ (None, 1) │ 33 │ dropout_3[0][0] │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
+ "
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+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃\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┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ au_input │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv1d_2 (\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 │ au_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │ conv1d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ max_pooling1d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ eye_input │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv1d_3 (\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 │ max_pooling1d_1[\u001b[38;5;34m…\u001b[0m │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ eye_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv1d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m528\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ concatenate_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m80\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ global_average_p… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ dense_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,592\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │ dropout_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "Fold 2 - Val Loss: 0.2018, Val Acc: 0.9279, Val AUC: 0.9726\n",
+ "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n",
+ "Konfusionsmatrix Fold 2:\n",
+ "[[576 92]\n",
+ " [ 23 905]]\n",
+ "\n",
+ "\n",
+ "--- Fold 3 ---\n",
+ "Train-Subjects: [ 0 2 3 4 5 7 12 13 16 17 20 21 22 26 27 28]\n",
+ "Val-Subjects: [ 8 9 14 18]\n",
+ "Train Eye Mittelwerte (erste 5 Features): [ 8.75039067e-16 7.17018311e-16 2.97785829e-15 -4.25220995e-16\n",
+ " 1.96998367e-15]\n",
+ "Train Eye Std (erste 5 Features): [1. 1. 1. 1. 1.]\n",
+ "Val Eye Mittelwerte (erste 5 Features): [-0.05250125 0.03135972 -0.06767335 -0.09485742 -0.08292531]\n",
+ "Val Eye Std (erste 5 Features): [0.67752758 1.02595343 0.77415946 0.73257356 0.86742107]\n"
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+ "Fold 3 - Val Loss: 0.0876, Val Acc: 0.9594, Val AUC: 0.9951\n",
+ "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
+ "Konfusionsmatrix Fold 3:\n",
+ "[[605 51]\n",
+ " [ 14 931]]\n",
+ "\n",
+ "\n",
+ "--- Fold 4 ---\n",
+ "Train-Subjects: [ 0 2 3 5 7 8 9 12 13 14 16 17 18 20 21 27]\n",
+ "Val-Subjects: [ 4 22 26 28]\n",
+ "Train Eye Mittelwerte (erste 5 Features): [-1.96526513e-15 -2.17596585e-15 3.76557886e-15 -1.38721376e-15\n",
+ " -4.37308956e-16]\n",
+ "Train Eye Std (erste 5 Features): [1. 1. 1. 1. 1.]\n",
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+ "Val Eye Std (erste 5 Features): [1.22147796 1.02366192 0.98843159 1.1614222 0.78466949]\n"
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+ "│ (Concatenate) │ │ │ dense_13[0][0] │\n",
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+ "│ dense_14 (Dense) │ (None, 32) │ 2,592 │ concatenate_3[0]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_7 (Dropout) │ (None, 32) │ 0 │ dense_14[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_15 (Dense) │ (None, 1) │ 33 │ dropout_7[0][0] │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
+ "
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+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃\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┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ au_input │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ 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 │ au_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │ conv1d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ max_pooling1d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ eye_input │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ 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 │ max_pooling1d_3[\u001b[38;5;34m…\u001b[0m │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_12 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ eye_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv1d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_13 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m528\u001b[0m │ dropout_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ concatenate_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m80\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ global_average_p… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ dense_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_14 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,592\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_15 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │ dropout_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 10,385 (40.57 KB)\n",
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+ ],
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+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m10,385\u001b[0m (40.57 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 10,193 (39.82 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,193\u001b[0m (39.82 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 192 (768.00 B)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fold 4 - Val Loss: 0.0672, Val Acc: 0.9723, Val AUC: 0.9953\n",
+ "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step\n",
+ "Konfusionsmatrix Fold 4:\n",
+ "[[634 28]\n",
+ " [ 16 908]]\n",
+ "\n",
+ "\n",
+ "--- Fold 5 ---\n",
+ "Train-Subjects: [ 0 2 4 5 8 9 12 13 14 16 17 18 21 22 26 28]\n",
+ "Val-Subjects: [ 3 7 20 27]\n",
+ "Train Eye Mittelwerte (erste 5 Features): [-1.93565842e-15 1.41728133e-15 2.43938879e-15 -2.23281429e-15\n",
+ " 2.74988006e-15]\n",
+ "Train Eye Std (erste 5 Features): [1. 1. 1. 1. 1.]\n",
+ "Val Eye Mittelwerte (erste 5 Features): [-0.1010507 -0.09647137 -0.11200891 -0.08795535 0.00787404]\n",
+ "Val Eye Std (erste 5 Features): [1.02817401 0.78631459 0.58448027 1.15757787 0.99451225]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "Model: \"functional_4\"\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"functional_4\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ au_input │ (None, 20, 1) │ 0 │ - │\n",
+ "│ (InputLayer) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv1d_8 (Conv1D) │ (None, 18, 32) │ 128 │ au_input[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None, 18, 32) │ 128 │ conv1d_8[0][0] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ max_pooling1d_4 │ (None, 9, 32) │ 0 │ batch_normalizat… │\n",
+ "│ (MaxPooling1D) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ eye_input │ (None, 15) │ 0 │ - │\n",
+ "│ (InputLayer) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv1d_9 (Conv1D) │ (None, 7, 64) │ 6,208 │ max_pooling1d_4[… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_16 (Dense) │ (None, 32) │ 512 │ eye_input[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None, 7, 64) │ 256 │ conv1d_9[0][0] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_8 (Dropout) │ (None, 32) │ 0 │ dense_16[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ global_average_poo… │ (None, 64) │ 0 │ batch_normalizat… │\n",
+ "│ (GlobalAveragePool… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_17 (Dense) │ (None, 16) │ 528 │ dropout_8[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ concatenate_4 │ (None, 80) │ 0 │ global_average_p… │\n",
+ "│ (Concatenate) │ │ │ dense_17[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_18 (Dense) │ (None, 32) │ 2,592 │ concatenate_4[0]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_9 (Dropout) │ (None, 32) │ 0 │ dense_18[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_19 (Dense) │ (None, 1) │ 33 │ dropout_9[0][0] │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃\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┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ au_input │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv1d_8 (\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 │ au_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │ conv1d_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ max_pooling1d_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ eye_input │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv1d_9 (\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 │ max_pooling1d_4[\u001b[38;5;34m…\u001b[0m │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_16 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ eye_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv1d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_17 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m528\u001b[0m │ dropout_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ concatenate_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m80\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ global_average_p… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ dense_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_18 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,592\u001b[0m │ concatenate_4[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_19 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │ dropout_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 10,385 (40.57 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m10,385\u001b[0m (40.57 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 10,193 (39.82 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,193\u001b[0m (39.82 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 192 (768.00 B)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fold 5 - Val Loss: 0.1100, Val Acc: 0.9638, Val AUC: 0.9897\n",
+ "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
+ "Konfusionsmatrix Fold 5:\n",
+ "[[627 36]\n",
+ " [ 22 916]]\n",
+ "\n",
+ "Aggregierte Konfusionsmatrix über alle Folds:\n",
+ "[[3051 236]\n",
+ " [ 92 4593]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "gkf = GroupKFold(n_splits=5) \n",
+ "cv_histories = [] \n",
+ "cv_results = [] \n",
+ "fold_subjects = []\n",
+ "all_conf_matrices = []\n",
+ "\n",
+ "for fold, (train_idx, val_idx) in enumerate(gkf.split(X_au, y, groups)):\n",
+ " train_subjects = np.unique(groups[train_idx]) \n",
+ " val_subjects = np.unique(groups[val_idx]) \n",
+ " fold_subjects.append({\"Fold\": fold+1, \n",
+ " \"Train_Subjects\": train_subjects, \n",
+ " \"Val_Subjects\": val_subjects}) \n",
+ " \n",
+ " print(f\"\\n--- Fold {fold+1} ---\") \n",
+ " print(\"Train-Subjects:\", train_subjects) \n",
+ " print(\"Val-Subjects:\", val_subjects) \n",
+ "\n",
+ " #Split\n",
+ " X_train_au, X_val_au = X_au[train_idx], X_au[val_idx] \n",
+ " X_train_eye, X_val_eye = X_eye[train_idx], X_eye[val_idx]\n",
+ " y_train, y_val = y[train_idx], y[val_idx] # Normalisierung pro Fold \n",
+ "\n",
+ " #Normalisierung pro Fold\n",
+ " scaler_au = StandardScaler() \n",
+ " scaler_eye = StandardScaler() \n",
+ "\n",
+ " X_train_au = scaler_au.fit_transform(X_train_au.reshape(len(X_train_au), -1)).reshape(X_train_au.shape) \n",
+ " X_val_au = scaler_au.transform(X_val_au.reshape(len(X_val_au), -1)).reshape(X_val_au.shape) \n",
+ "\n",
+ " X_train_eye = scaler_eye.fit_transform(X_train_eye) \n",
+ " X_val_eye = scaler_eye.transform(X_val_eye)\n",
+ "\n",
+ " # Plausibilitäts-Check \n",
+ " print(\"Train Eye Mittelwerte (erste 5 Features):\", X_train_eye.mean(axis=0)[:5]) \n",
+ " print(\"Train Eye Std (erste 5 Features):\", X_train_eye.std(axis=0)[:5]) \n",
+ " print(\"Val Eye Mittelwerte (erste 5 Features):\", X_val_eye.mean(axis=0)[:5]) \n",
+ " print(\"Val Eye Std (erste 5 Features):\", X_val_eye.std(axis=0)[:5])\n",
+ "\n",
+ " # Modell \n",
+ " model = build_hybrid_model( \n",
+ " n_aus=len(au_columns), \n",
+ " n_eye=len(eye_columns), \n",
+ " lr=1e-4 \n",
+ " )\n",
+ " model.summary() \n",
+ "\n",
+ " 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",
+ " ] \n",
+ "\n",
+ " history = model.fit( \n",
+ " [X_train_au, X_train_eye], y_train, \n",
+ " validation_data=([X_val_au, X_val_eye], y_val), \n",
+ " epochs=100, \n",
+ " batch_size=16, \n",
+ " callbacks=callbacks, \n",
+ " verbose=0 \n",
+ " ) \n",
+ "\n",
+ " cv_histories.append(history.history) \n",
+ " scores = model.evaluate([X_val_au, X_val_eye], y_val, verbose=0) \n",
+ " cv_results.append(scores) \n",
+ " print(f\"Fold {fold+1} - Val Loss: {scores[0]:.4f}, Val Acc: {scores[1]:.4f}, Val AUC: {scores[2]:.4f}\")\n",
+ "\n",
+ "\n",
+ " #Konfusionsmatrix \n",
+ " y_pred = (model.predict([X_val_au, X_val_eye]) > 0.5).astype(int) \n",
+ " cm = confusion_matrix(y_val, y_pred)\n",
+ " all_conf_matrices.append(cm) \n",
+ " \n",
+ " print(f\"Konfusionsmatrix Fold {fold+1}:\\n{cm}\\n\") \n",
+ " \n",
+ "# Aggregierte Matrix \n",
+ "agg_cm = sum(all_conf_matrices) \n",
+ "print(\"Aggregierte Konfusionsmatrix über alle Folds:\") \n",
+ "print(agg_cm)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d10b7e78",
+ "metadata": {},
+ "source": [
+ "Results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "9aeba7f4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== Cross-Validation Ergebnisse ===\n",
+ "Durchschnittlicher Val-Loss: 0.1139\n",
+ "Durchschnittliche Val-Accuracy: 0.9589\n",
+ "Durchschnittliche Val-AUC: 0.9890\n",
+ "\n",
+ "=== Ergebnis-Tabelle ===\n",
+ " Fold Val Loss Val Accuracy Val AUC\n",
+ "0 1 0.102893 0.971033 0.992081\n",
+ "1 2 0.201812 0.927945 0.972621\n",
+ "2 3 0.087613 0.959400 0.995120\n",
+ "3 4 0.067188 0.972257 0.995276\n",
+ "4 5 0.110020 0.963773 0.989739\n",
+ "5 Ø 0.113905 0.958882 0.988968\n",
+ "Ergebnisse gespeichert als 'hybrid_crossVal_results_V2.csv'\n"
+ ]
+ }
+ ],
+ "source": [
+ "#results\n",
+ "cv_results = np.array(cv_results) \n",
+ "print(\"\\n=== Cross-Validation Ergebnisse ===\") \n",
+ "print(f\"Durchschnittlicher Val-Loss: {cv_results[:,0].mean():.4f}\") \n",
+ "print(f\"Durchschnittliche Val-Accuracy: {cv_results[:,1].mean():.4f}\") \n",
+ "print(f\"Durchschnittliche Val-AUC: {cv_results[:,2].mean():.4f}\")\n",
+ "\n",
+ "#Ergebnis-Tabelle erstellen\n",
+ "results_table = pd.DataFrame({ \n",
+ " \"Fold\": np.arange(1, len(cv_results)+1), \n",
+ " \"Val Loss\": cv_results[:,0], \n",
+ " \"Val Accuracy\": cv_results[:,1], \n",
+ " \"Val AUC\": cv_results[:,2] }) \n",
+ "\n",
+ "# Durchschnittszeile hinzufügen \n",
+ "avg_row = pd.DataFrame({ \n",
+ " \"Fold\": [\"Ø\"], \n",
+ " \"Val Loss\": [cv_results[:,0].mean()], \n",
+ " \"Val Accuracy\": [cv_results[:,1].mean()], \n",
+ " \"Val AUC\": [cv_results[:,2].mean()] \n",
+ "}) \n",
+ "\n",
+ "results_table = pd.concat([results_table, avg_row], ignore_index=True) \n",
+ "\n",
+ "print(\"\\n=== Ergebnis-Tabelle ===\") \n",
+ "print(results_table) \n",
+ "\n",
+ "#Tabelle speichern \n",
+ "results_table.to_csv(\"hybrid_crossVal_results_V2.csv\", index=False) \n",
+ "print(\"Ergebnisse gespeichert als 'hybrid_crossVal_results_V2.csv'\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2f6de79",
+ "metadata": {},
+ "source": [
+ "Finales Modell auf alle Daten trainieren"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "6e403e5f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.6486 - auc: 0.6661 - loss: 0.6314\n",
+ "Epoch 2/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8178 - auc: 0.8857 - loss: 0.4406\n",
+ "Epoch 3/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8559 - auc: 0.9171 - loss: 0.3649\n",
+ "Epoch 4/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8746 - auc: 0.9352 - loss: 0.3196\n",
+ "Epoch 5/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8919 - auc: 0.9446 - loss: 0.2894\n",
+ "Epoch 6/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9021 - auc: 0.9507 - loss: 0.2698\n",
+ "Epoch 7/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9076 - auc: 0.9576 - loss: 0.2511\n",
+ "Epoch 8/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9144 - auc: 0.9627 - loss: 0.2370\n",
+ "Epoch 9/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9177 - auc: 0.9660 - loss: 0.2246\n",
+ "Epoch 10/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9204 - auc: 0.9700 - loss: 0.2136\n",
+ "Epoch 11/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9235 - auc: 0.9762 - loss: 0.1934\n",
+ "Epoch 12/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9312 - auc: 0.9772 - loss: 0.1891\n",
+ "Epoch 13/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9342 - auc: 0.9801 - loss: 0.1768\n",
+ "Epoch 14/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9315 - auc: 0.9818 - loss: 0.1716\n",
+ "Epoch 15/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9407 - auc: 0.9845 - loss: 0.1592\n",
+ "Epoch 16/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9401 - auc: 0.9855 - loss: 0.1552\n",
+ "Epoch 17/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9411 - auc: 0.9870 - loss: 0.1458\n",
+ "Epoch 18/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9466 - auc: 0.9884 - loss: 0.1385\n",
+ "Epoch 19/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9469 - auc: 0.9889 - loss: 0.1368\n",
+ "Epoch 20/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9505 - auc: 0.9900 - loss: 0.1280\n",
+ "Epoch 21/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9509 - auc: 0.9893 - loss: 0.1302\n",
+ "Epoch 22/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9556 - auc: 0.9914 - loss: 0.1196\n",
+ "Epoch 23/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9585 - auc: 0.9922 - loss: 0.1150\n",
+ "Epoch 24/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9561 - auc: 0.9919 - loss: 0.1157\n",
+ "Epoch 25/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9562 - auc: 0.9936 - loss: 0.1071\n",
+ "Epoch 26/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9608 - auc: 0.9942 - loss: 0.1024\n",
+ "Epoch 27/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9622 - auc: 0.9940 - loss: 0.1007\n",
+ "Epoch 28/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9610 - auc: 0.9941 - loss: 0.1012\n",
+ "Epoch 29/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9637 - auc: 0.9949 - loss: 0.0956\n",
+ "Epoch 30/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9673 - auc: 0.9952 - loss: 0.0911\n",
+ "Epoch 31/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9628 - auc: 0.9952 - loss: 0.0932\n",
+ "Epoch 32/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9677 - auc: 0.9962 - loss: 0.0867\n",
+ "Epoch 33/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9659 - auc: 0.9957 - loss: 0.0868\n",
+ "Epoch 34/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9732 - auc: 0.9970 - loss: 0.0757\n",
+ "Epoch 35/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9680 - auc: 0.9966 - loss: 0.0784\n",
+ "Epoch 36/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9712 - auc: 0.9967 - loss: 0.0771\n",
+ "Epoch 37/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9691 - auc: 0.9964 - loss: 0.0810\n",
+ "Epoch 38/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9715 - auc: 0.9967 - loss: 0.0745\n",
+ "Epoch 39/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9698 - auc: 0.9973 - loss: 0.0724\n",
+ "Epoch 40/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9746 - auc: 0.9971 - loss: 0.0718\n",
+ "Epoch 41/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9704 - auc: 0.9973 - loss: 0.0715\n",
+ "Epoch 42/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9729 - auc: 0.9972 - loss: 0.0716\n",
+ "Epoch 43/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9752 - auc: 0.9978 - loss: 0.0642\n",
+ "Epoch 44/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9732 - auc: 0.9974 - loss: 0.0690\n",
+ "Epoch 45/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9751 - auc: 0.9980 - loss: 0.0626\n",
+ "Epoch 46/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9732 - auc: 0.9973 - loss: 0.0664\n",
+ "Epoch 47/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9788 - auc: 0.9982 - loss: 0.0591\n",
+ "Epoch 48/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9788 - auc: 0.9979 - loss: 0.0623\n",
+ "Epoch 49/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9796 - auc: 0.9981 - loss: 0.0594\n",
+ "Epoch 50/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9768 - auc: 0.9975 - loss: 0.0604\n",
+ "Epoch 51/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9793 - auc: 0.9986 - loss: 0.0531\n",
+ "Epoch 52/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9810 - auc: 0.9983 - loss: 0.0540\n",
+ "Epoch 53/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9762 - auc: 0.9983 - loss: 0.0576\n",
+ "Epoch 54/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9797 - auc: 0.9984 - loss: 0.0542\n",
+ "Epoch 55/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9794 - auc: 0.9983 - loss: 0.0527\n",
+ "Epoch 56/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9806 - auc: 0.9986 - loss: 0.0494\n",
+ "Epoch 57/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9782 - auc: 0.9982 - loss: 0.0561\n",
+ "Epoch 58/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9824 - auc: 0.9989 - loss: 0.0472\n",
+ "Epoch 59/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9807 - auc: 0.9987 - loss: 0.0496\n",
+ "Epoch 60/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9828 - auc: 0.9988 - loss: 0.0472\n",
+ "Epoch 61/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9821 - auc: 0.9984 - loss: 0.0505\n",
+ "Epoch 62/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9799 - auc: 0.9985 - loss: 0.0477\n",
+ "Epoch 63/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9820 - auc: 0.9988 - loss: 0.0458\n",
+ "Epoch 64/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9811 - auc: 0.9988 - loss: 0.0473\n",
+ "Epoch 65/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9830 - auc: 0.9990 - loss: 0.0436\n",
+ "Epoch 66/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9851 - auc: 0.9992 - loss: 0.0403\n",
+ "Epoch 67/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9810 - auc: 0.9987 - loss: 0.0476\n",
+ "Epoch 68/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9853 - auc: 0.9984 - loss: 0.0427\n",
+ "Epoch 69/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9864 - auc: 0.9994 - loss: 0.0369\n",
+ "Epoch 70/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9853 - auc: 0.9991 - loss: 0.0403\n",
+ "Epoch 71/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9845 - auc: 0.9991 - loss: 0.0406\n",
+ "Epoch 72/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9840 - auc: 0.9989 - loss: 0.0412\n",
+ "Epoch 73/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9858 - auc: 0.9993 - loss: 0.0372\n",
+ "Epoch 74/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9868 - auc: 0.9994 - loss: 0.0359\n",
+ "Epoch 75/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9860 - auc: 0.9993 - loss: 0.0368\n",
+ "Epoch 76/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9862 - auc: 0.9988 - loss: 0.0378\n",
+ "Epoch 77/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9864 - auc: 0.9993 - loss: 0.0349\n",
+ "Epoch 78/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9844 - auc: 0.9990 - loss: 0.0381\n",
+ "Epoch 79/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9857 - auc: 0.9993 - loss: 0.0358\n",
+ "Epoch 80/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9860 - auc: 0.9993 - loss: 0.0353\n",
+ "Epoch 81/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9865 - auc: 0.9993 - loss: 0.0354\n",
+ "Epoch 82/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9873 - auc: 0.9993 - loss: 0.0339\n",
+ "Epoch 83/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9880 - auc: 0.9994 - loss: 0.0326\n",
+ "Epoch 84/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9895 - auc: 0.9996 - loss: 0.0290\n",
+ "Epoch 85/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9879 - auc: 0.9993 - loss: 0.0336\n",
+ "Epoch 86/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9863 - auc: 0.9994 - loss: 0.0344\n",
+ "Epoch 87/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9908 - auc: 0.9995 - loss: 0.0275\n",
+ "Epoch 88/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9890 - auc: 0.9996 - loss: 0.0288\n",
+ "Epoch 89/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9884 - auc: 0.9994 - loss: 0.0319\n",
+ "Epoch 90/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9904 - auc: 0.9996 - loss: 0.0291\n",
+ "Epoch 91/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9888 - auc: 0.9996 - loss: 0.0302\n",
+ "Epoch 92/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9915 - auc: 0.9997 - loss: 0.0246\n",
+ "Epoch 93/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9899 - auc: 0.9995 - loss: 0.0290\n",
+ "Epoch 94/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9900 - auc: 0.9996 - loss: 0.0284\n",
+ "Epoch 95/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9904 - auc: 0.9996 - loss: 0.0272\n",
+ "Epoch 96/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9892 - auc: 0.9995 - loss: 0.0285\n",
+ "Epoch 97/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9883 - auc: 0.9993 - loss: 0.0309\n",
+ "Epoch 98/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9920 - auc: 0.9996 - loss: 0.0237\n",
+ "Epoch 99/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9881 - auc: 0.9991 - loss: 0.0323\n",
+ "Epoch 100/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9912 - auc: 0.9998 - loss: 0.0228\n",
+ "Epoch 101/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9907 - auc: 0.9997 - loss: 0.0249\n",
+ "Epoch 102/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9934 - auc: 0.9998 - loss: 0.0221\n",
+ "Epoch 103/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9905 - auc: 0.9996 - loss: 0.0258\n",
+ "Epoch 104/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9915 - auc: 0.9995 - loss: 0.0246\n",
+ "Epoch 105/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9913 - auc: 0.9998 - loss: 0.0224\n",
+ "Epoch 106/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9888 - auc: 0.9996 - loss: 0.0283\n",
+ "Epoch 107/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9913 - auc: 0.9995 - loss: 0.0257\n",
+ "Epoch 108/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9922 - auc: 0.9994 - loss: 0.0244\n",
+ "Epoch 109/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9926 - auc: 0.9996 - loss: 0.0216\n",
+ "Epoch 110/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9937 - auc: 0.9998 - loss: 0.0216\n",
+ "Epoch 111/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9944 - auc: 0.9998 - loss: 0.0194\n",
+ "Epoch 112/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9899 - auc: 0.9997 - loss: 0.0247\n",
+ "Epoch 113/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9935 - auc: 0.9998 - loss: 0.0200\n",
+ "Epoch 114/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9916 - auc: 0.9998 - loss: 0.0223\n",
+ "Epoch 115/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9920 - auc: 0.9996 - loss: 0.0222\n",
+ "Epoch 116/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9931 - auc: 0.9998 - loss: 0.0201\n",
+ "Epoch 117/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9925 - auc: 0.9997 - loss: 0.0218\n",
+ "Epoch 118/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9929 - auc: 0.9997 - loss: 0.0215\n",
+ "Epoch 119/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9922 - auc: 0.9998 - loss: 0.0216\n",
+ "Epoch 120/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9926 - auc: 0.9997 - loss: 0.0222\n",
+ "Epoch 121/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9921 - auc: 0.9996 - loss: 0.0208\n",
+ "Epoch 122/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9937 - auc: 0.9998 - loss: 0.0197\n",
+ "Epoch 123/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9906 - auc: 0.9996 - loss: 0.0257\n",
+ "Epoch 124/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9930 - auc: 0.9997 - loss: 0.0221\n",
+ "Epoch 125/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9919 - auc: 0.9997 - loss: 0.0216\n",
+ "Epoch 126/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9930 - auc: 0.9998 - loss: 0.0193\n",
+ "Epoch 127/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9920 - auc: 0.9998 - loss: 0.0209\n",
+ "Epoch 128/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9926 - auc: 0.9996 - loss: 0.0197\n",
+ "Epoch 129/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9942 - auc: 0.9999 - loss: 0.0162\n",
+ "Epoch 130/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9948 - auc: 0.9998 - loss: 0.0182\n",
+ "Epoch 131/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9913 - auc: 0.9991 - loss: 0.0262\n",
+ "Epoch 132/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9945 - auc: 0.9999 - loss: 0.0164\n",
+ "Epoch 133/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9947 - auc: 0.9998 - loss: 0.0185\n",
+ "Epoch 134/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9953 - auc: 0.9999 - loss: 0.0158\n",
+ "Epoch 135/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9919 - auc: 0.9998 - loss: 0.0209\n",
+ "Epoch 136/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9941 - auc: 0.9999 - loss: 0.0168\n",
+ "Epoch 137/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9914 - auc: 0.9998 - loss: 0.0203\n",
+ "Epoch 138/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9948 - auc: 0.9997 - loss: 0.0180\n",
+ "Epoch 139/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9937 - auc: 0.9998 - loss: 0.0182\n",
+ "Epoch 140/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9936 - auc: 0.9998 - loss: 0.0185\n",
+ "Epoch 141/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9966 - auc: 0.9999 - loss: 0.0149\n",
+ "Epoch 142/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9960 - auc: 0.9999 - loss: 0.0148\n",
+ "Epoch 143/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9952 - auc: 0.9996 - loss: 0.0182\n",
+ "Epoch 144/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9944 - auc: 0.9998 - loss: 0.0176\n",
+ "Epoch 145/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9959 - auc: 0.9999 - loss: 0.0144\n",
+ "Epoch 146/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9953 - auc: 0.9997 - loss: 0.0168\n",
+ "Epoch 147/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9950 - auc: 0.9999 - loss: 0.0159\n",
+ "Epoch 148/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9945 - auc: 0.9998 - loss: 0.0157\n",
+ "Epoch 149/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9971 - auc: 0.9999 - loss: 0.0136\n",
+ "Epoch 150/150\n",
+ "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9928 - auc: 0.9996 - loss: 0.0199\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scaler_au_final = StandardScaler()\n",
+ "scaler_eye_final = StandardScaler()\n",
+ "\n",
+ "X_au_scaled = scaler_au_final.fit_transform(X_au.reshape(len(X_au), -1)).reshape(X_au.shape)\n",
+ "X_eye_scaled = scaler_eye_final.fit_transform(X_eye)\n",
+ "\n",
+ "final_model = build_hybrid_model(len(au_columns), len(eye_columns))\n",
+ "\n",
+ "final_model.fit(\n",
+ " [X_au_scaled, X_eye_scaled], y,\n",
+ " epochs=150,\n",
+ " batch_size=16,\n",
+ " verbose=1\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7c7f9cc4",
+ "metadata": {},
+ "source": [
+ "Speichern des Modells"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "2d3af5be",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['scaler_eye_V2.joblib']"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Speichern\n",
+ "final_model.save(\"hybrid_fusion_model_V2.keras\")\n",
+ "joblib.dump(scaler_au_final, \"scaler_au_V2.joblib\")\n",
+ "joblib.dump(scaler_eye_final, \"scaler_eye_V2.joblib\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c11891e0",
+ "metadata": {},
+ "source": [
+ "Plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "9f6a8584",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#plots\n",
+ "def plot_cv_histories(cv_histories, metric): \n",
+ " plt.figure(figsize=(10,6)) \n",
+ " \n",
+ " for i, hist in enumerate(cv_histories): \n",
+ " plt.plot(hist[metric], label=f\"Fold {i+1} Train\", alpha=0.7) \n",
+ " plt.plot(hist[f\"val_{metric}\"], label=f\"Fold {i+1} Val\", linestyle=\"--\", alpha=0.7) \n",
+ " plt.xlabel(\"Epochs\") \n",
+ " plt.ylabel(metric.capitalize()) \n",
+ " plt.title(f\"Cross-Validation {metric.capitalize()} Verläufe\") \n",
+ " plt.legend() \n",
+ " plt.grid(True) \n",
+ " plt.show()\n",
+ " \n",
+ "plot_cv_histories(cv_histories, \"loss\") \n",
+ "plot_cv_histories(cv_histories, \"accuracy\") \n",
+ "plot_cv_histories(cv_histories, \"auc\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/model_training/CNN/CNN_crossVal_faceAUs.ipynb b/model_training/CNN/CNN_crossVal_faceAUs.ipynb
new file mode 100644
index 0000000..18f60ba
--- /dev/null
+++ b/model_training/CNN/CNN_crossVal_faceAUs.ipynb
@@ -0,0 +1,1619 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "47f6de7b",
+ "metadata": {},
+ "source": [
+ "Bibliotheken importieren"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "99294260",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2026-02-18 15:51:45.686247: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "2026-02-18 15:51:45.789201: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "2026-02-18 15:51:45.819528: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "2026-02-18 15:51:46.036932: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+ "2026-02-18 15:51:47.239360: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd \n",
+ "import numpy as np \n",
+ "import matplotlib.pyplot as plt\n",
+ "import random \n",
+ "import joblib \n",
+ "from pathlib import Path \n",
+ "\n",
+ "\n",
+ "from sklearn.model_selection import GroupKFold \n",
+ "from sklearn.preprocessing import StandardScaler \n",
+ "\n",
+ "import tensorflow as tf \n",
+ "from tensorflow.keras import Input, layers, models, regularizers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "52b4ca8c",
+ "metadata": {},
+ "source": [
+ "Seed festlegen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "6e49d281",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SEED = 42 \n",
+ "np.random.seed(SEED) \n",
+ "tf.random.set_seed(SEED) \n",
+ "random.seed(SEED)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae1a715f",
+ "metadata": {},
+ "source": [
+ "Daten laden"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "870f01c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_path = Path(r\"~/data-paulusjafahrsimulator-gpu/new_datasets/50s_25Hz_dataset.parquet\") \n",
+ "\n",
+ "data = pd.read_parquet(path=data_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bedbc23b",
+ "metadata": {},
+ "source": [
+ "Labels erstellen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "38848515",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "low_all = data[((data[\"PHASE\"] == \"baseline\") | \n",
+ " ((data[\"STUDY\"] == \"n-back\") & (data[\"PHASE\"] != \"baseline\") & (data[\"LEVEL\"].isin([1,4]))))].copy() \n",
+ "\n",
+ "high_all = pd.concat([ \n",
+ " data[(data[\"STUDY\"]==\"n-back\") & (data[\"LEVEL\"].isin([2,3,5,6])) & (data[\"PHASE\"].isin([\"train\",\"test\"]))], \n",
+ " data[(data[\"STUDY\"]==\"k-drive\") & (data[\"PHASE\"]!=\"baseline\")] \n",
+ "]).copy() \n",
+ "\n",
+ "low_all[\"label\"] = 0 \n",
+ "high_all[\"label\"] = 1 \n",
+ "data = pd.concat([low_all, high_all], ignore_index=True).drop_duplicates() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0b282acf",
+ "metadata": {},
+ "source": [
+ "Features und Labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5edb00a0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['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",
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "#Face AUs\n",
+ "au_columns = [col for col in data.columns if \"face\" in col.lower()] \n",
+ "\n",
+ "X = data[au_columns].values[..., np.newaxis] \n",
+ "y = data[\"label\"].values \n",
+ "groups = data[\"subjectID\"].values\n",
+ "print(data.columns.tolist())\n",
+ "\n",
+ "print(\"Gefundene FACE_AU-Spalten:\", au_columns)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a539b83b",
+ "metadata": {},
+ "source": [
+ "CNN-Modell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "e4a7f496",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_model(input_shape, lr=1e-4): \n",
+ " model = models.Sequential([ \n",
+ " Input(shape=input_shape), \n",
+ " layers.Conv1D(32, kernel_size=3, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n",
+ " layers.BatchNormalization(), \n",
+ " layers.MaxPooling1D(pool_size=2),\n",
+ "\n",
+ " layers.Conv1D(64, kernel_size=3, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n",
+ " layers.BatchNormalization(), \n",
+ " layers.GlobalAveragePooling1D(), \n",
+ " \n",
+ " layers.Dense(32, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n",
+ " layers.Dropout(0.5), \n",
+ " layers.Dense(1, activation=\"sigmoid\") \n",
+ " ]) \n",
+ " \n",
+ " model.compile( \n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=lr), \n",
+ " loss=\"binary_crossentropy\", \n",
+ " metrics=[\"accuracy\", tf.keras.metrics.AUC(name=\"auc\")] \n",
+ " ) \n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5905871b",
+ "metadata": {},
+ "source": [
+ "Cross-Validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "90658000",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "--- Fold 1 ---\n",
+ "Train-Subjects: [ 0 3 4 7 9 12 13 14 16 18 20 21 22 26 27 28]\n",
+ "Val-Subjects: [ 2 5 8 17]\n",
+ "Train Mittelwerte (erste 5 Features): [[-9.48333057e-17]\n",
+ " [ 3.78136535e-17]\n",
+ " [-3.63102089e-16]\n",
+ " [-4.05458101e-16]\n",
+ " [-7.46497309e-16]]\n",
+ "Train Std (erste 5 Features): [[1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]]\n",
+ "Val Mittelwerte (erste 5 Features): [[ 0.01837255]\n",
+ " [ 0.03634784]\n",
+ " [-0.01417256]\n",
+ " [-0.00136129]\n",
+ " [ 0.01066093]]\n",
+ "Val Std (erste 5 Features): [[0.9809553 ]\n",
+ " [0.90597105]\n",
+ " [1.0661958 ]\n",
+ " [0.97687653]\n",
+ " [1.06061798]]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential_7\"\n",
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+ "│ conv1d_14 (Conv1D) │ (None, 18, 32) │ 128 │\n",
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+ "metadata": {},
+ "output_type": "display_data"
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+ "
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+ ],
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+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,641\u001b[0m (33.75 KB)\n"
+ ]
+ },
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+ "output_type": "display_data"
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+ "metadata": {},
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+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fold 1 - Val Loss: 0.2336, Val Acc: 0.9435, Val AUC: 0.9757\n",
+ "\n",
+ "--- Fold 2 ---\n",
+ "Train-Subjects: [ 0 2 3 4 5 7 8 9 16 17 18 20 21 22 26 28]\n",
+ "Val-Subjects: [12 13 14 27]\n",
+ "Train Mittelwerte (erste 5 Features): [[-1.44302026e-16]\n",
+ " [ 8.74930579e-17]\n",
+ " [ 1.50201191e-16]\n",
+ " [ 6.48301479e-16]\n",
+ " [ 6.66605748e-17]]\n",
+ "Train Std (erste 5 Features): [[1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]]\n",
+ "Val Mittelwerte (erste 5 Features): [[0.12995691]\n",
+ " [0.06355549]\n",
+ " [0.02275319]\n",
+ " [0.06616701]\n",
+ " [0.06788641]]\n",
+ "Val Std (erste 5 Features): [[0.93313441]\n",
+ " [1.00300875]\n",
+ " [0.99231989]\n",
+ " [1.03627391]\n",
+ " [1.088837 ]]\n"
+ ]
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+ "│ conv1d_16 (Conv1D) │ (None, 18, 32) │ 128 │\n",
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+ "text": [
+ "Fold 2 - Val Loss: 0.2085, Val Acc: 0.9514, Val AUC: 0.9803\n",
+ "\n",
+ "--- Fold 3 ---\n",
+ "Train-Subjects: [ 0 2 3 5 8 9 12 13 14 17 18 20 22 26 27 28]\n",
+ "Val-Subjects: [ 4 7 16 21]\n",
+ "Train Mittelwerte (erste 5 Features): [[-1.70932546e-16]\n",
+ " [ 8.53862129e-17]\n",
+ " [ 2.40045488e-16]\n",
+ " [ 1.14228078e-15]\n",
+ " [-9.88531659e-17]]\n",
+ "Train Std (erste 5 Features): [[1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]]\n",
+ "Val Mittelwerte (erste 5 Features): [[-0.16879516]\n",
+ " [-0.09483067]\n",
+ " [-0.00774855]\n",
+ " [-0.02642025]\n",
+ " [-0.01273985]]\n",
+ "Val Std (erste 5 Features): [[1.11007971]\n",
+ " [1.03956086]\n",
+ " [0.95289305]\n",
+ " [0.97649474]\n",
+ " [0.98732466]]\n"
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+ "│ conv1d_18 (Conv1D) │ (None, 18, 32) │ 128 │\n",
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+ "│ batch_normalization_18 │ (None, 18, 32) │ 128 │\n",
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+ "│ max_pooling1d_9 (MaxPooling1D) │ (None, 9, 32) │ 0 │\n",
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+ "│ global_average_pooling1d_9 │ (None, 64) │ 0 │\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "text": [
+ "Fold 3 - Val Loss: 0.2030, Val Acc: 0.9478, Val AUC: 0.9797\n",
+ "\n",
+ "--- Fold 4 ---\n",
+ "Train-Subjects: [ 2 3 4 5 7 8 12 13 14 16 17 18 20 21 26 27]\n",
+ "Val-Subjects: [ 0 9 22 28]\n",
+ "Train Mittelwerte (erste 5 Features): [[-6.20161010e-18]\n",
+ " [-2.54131196e-17]\n",
+ " [ 3.29511093e-16]\n",
+ " [ 5.47831361e-16]\n",
+ " [-1.38162773e-16]]\n",
+ "Train Std (erste 5 Features): [[1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]]\n",
+ "Val Mittelwerte (erste 5 Features): [[ 0.01788626]\n",
+ " [ 0.0311636 ]\n",
+ " [-0.04004633]\n",
+ " [-0.01924637]\n",
+ " [ 0.01714694]]\n",
+ "Val Std (erste 5 Features): [[0.98096724]\n",
+ " [1.05932267]\n",
+ " [0.95190592]\n",
+ " [1.01373334]\n",
+ " [0.99555169]]\n"
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+ "│ conv1d_20 (\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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+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_10 (\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv1d_21 (\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
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+ "
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+ ],
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+ ]
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+ },
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+ "data": {
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+ "
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+ ],
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+ ]
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+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
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+ "text": [
+ "Fold 4 - Val Loss: 0.2266, Val Acc: 0.9356, Val AUC: 0.9743\n",
+ "\n",
+ "--- Fold 5 ---\n",
+ "Train-Subjects: [ 0 2 4 5 7 8 9 12 13 14 16 17 21 22 27 28]\n",
+ "Val-Subjects: [ 3 18 20 26]\n",
+ "Train Mittelwerte (erste 5 Features): [[-9.87688921e-18]\n",
+ " [ 1.07120426e-16]\n",
+ " [-9.48417331e-17]\n",
+ " [ 1.12171797e-15]\n",
+ " [-3.25397992e-16]]\n",
+ "Train Std (erste 5 Features): [[1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]]\n",
+ "Val Mittelwerte (erste 5 Features): [[-0.00321515]\n",
+ " [-0.03717355]\n",
+ " [ 0.03970031]\n",
+ " [-0.01854768]\n",
+ " [-0.07944637]]\n",
+ "Val Std (erste 5 Features): [[0.98009046]\n",
+ " [0.98567844]\n",
+ " [1.03448059]\n",
+ " [0.99467548]\n",
+ " [0.86050472]]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential_11\"\n",
+ "
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+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ conv1d_22 (Conv1D) │ (None, 18, 32) │ 128 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_22 │ (None, 18, 32) │ 128 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_11 (MaxPooling1D) │ (None, 9, 32) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv1d_23 (Conv1D) │ (None, 7, 64) │ 6,208 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_23 │ (None, 7, 64) │ 256 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling1d_11 │ (None, 64) │ 0 │\n",
+ "│ (GlobalAveragePooling1D) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_22 (Dense) │ (None, 32) │ 2,080 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_11 (Dropout) │ (None, 32) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_23 (Dense) │ (None, 1) │ 33 │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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+ "│ conv1d_22 (\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_22 │ (\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",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_11 (\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv1d_23 (\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_23 │ (\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",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling1d_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_22 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_11 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_23 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
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+ " Trainable params: 8,641 (33.75 KB)\n",
+ "
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+ ],
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+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,641\u001b[0m (33.75 KB)\n"
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+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fold 5 - Val Loss: 0.1719, Val Acc: 0.9532, Val AUC: 0.9869\n"
+ ]
+ }
+ ],
+ "source": [
+ "gkf = GroupKFold(n_splits=5) \n",
+ "cv_histories = [] \n",
+ "cv_results = [] \n",
+ "fold_subjects = []\n",
+ "\n",
+ "for fold, (train_idx, val_idx) in enumerate(gkf.split(X, y, groups)):\n",
+ " train_subjects = np.unique(groups[train_idx]) \n",
+ " val_subjects = np.unique(groups[val_idx]) \n",
+ " fold_subjects.append({\"Fold\": fold+1, \n",
+ " \"Train_Subjects\": train_subjects, \n",
+ " \"Val_Subjects\": val_subjects}) \n",
+ " \n",
+ " print(f\"\\n--- Fold {fold+1} ---\") \n",
+ " print(\"Train-Subjects:\", train_subjects) \n",
+ " print(\"Val-Subjects:\", val_subjects) \n",
+ "\n",
+ " #Split\n",
+ " X_train, X_val = X[train_idx], X[val_idx] \n",
+ " y_train, y_val = y[train_idx], y[val_idx] # Normalisierung pro Fold \n",
+ "\n",
+ " #Normalisierung pro Fold\n",
+ " scaler = StandardScaler() \n",
+ " X_train = scaler.fit_transform(X_train.reshape(len(X_train), -1)).reshape(X_train.shape) \n",
+ " X_val = scaler.transform(X_val.reshape(len(X_val), -1)).reshape(X_val.shape) \n",
+ "\n",
+ " # Plausibilitäts-Check \n",
+ " print(\"Train Mittelwerte (erste 5 Features):\", X_train.mean(axis=0)[:5]) \n",
+ " print(\"Train Std (erste 5 Features):\", X_train.std(axis=0)[:5]) \n",
+ " print(\"Val Mittelwerte (erste 5 Features):\", X_val.mean(axis=0)[:5]) \n",
+ " print(\"Val Std (erste 5 Features):\", X_val.std(axis=0)[:5]) \n",
+ "\n",
+ " # Modell \n",
+ " model = build_model(input_shape=(len(au_columns),1), lr=1e-4) \n",
+ " model.summary() \n",
+ "\n",
+ " 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",
+ " ] \n",
+ "\n",
+ " history = model.fit( \n",
+ " X_train, y_train, \n",
+ " validation_data=(X_val, y_val), \n",
+ " epochs=100, \n",
+ " batch_size=16, \n",
+ " callbacks=callbacks, \n",
+ " verbose=0 \n",
+ " ) \n",
+ "\n",
+ " cv_histories.append(history.history) \n",
+ " scores = model.evaluate(X_val, y_val, verbose=0) \n",
+ " cv_results.append(scores) \n",
+ " print(f\"Fold {fold+1} - Val Loss: {scores[0]:.4f}, Val Acc: {scores[1]:.4f}, Val AUC: {scores[2]:.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d10b7e78",
+ "metadata": {},
+ "source": [
+ "Results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "9aeba7f4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== Cross-Validation Ergebnisse ===\n",
+ "Durchschnittlicher Val-Loss: 0.2087\n",
+ "Durchschnittliche Val-Accuracy: 0.9463\n",
+ "Durchschnittliche Val-AUC: 0.9794\n",
+ "\n",
+ "=== Ergebnis-Tabelle ===\n",
+ " Fold Val Loss Val Accuracy Val AUC\n",
+ "0 1 0.233584 0.943534 0.975684\n",
+ "1 2 0.208549 0.951427 0.980279\n",
+ "2 3 0.203029 0.947784 0.979743\n",
+ "3 4 0.226566 0.935601 0.974336\n",
+ "4 5 0.171882 0.953248 0.986887\n",
+ "5 Ø 0.208722 0.946319 0.979386\n",
+ "Ergebnisse gespeichert als 'cnn_crossVal_results.csv'\n"
+ ]
+ }
+ ],
+ "source": [
+ "#results\n",
+ "cv_results = np.array(cv_results) \n",
+ "print(\"\\n=== Cross-Validation Ergebnisse ===\") \n",
+ "print(f\"Durchschnittlicher Val-Loss: {cv_results[:,0].mean():.4f}\") \n",
+ "print(f\"Durchschnittliche Val-Accuracy: {cv_results[:,1].mean():.4f}\") \n",
+ "print(f\"Durchschnittliche Val-AUC: {cv_results[:,2].mean():.4f}\")\n",
+ "\n",
+ "#Ergebnis-Tabelle erstellen\n",
+ "results_table = pd.DataFrame({ \n",
+ " \"Fold\": np.arange(1, len(cv_results)+1), \n",
+ " \"Val Loss\": cv_results[:,0], \n",
+ " \"Val Accuracy\": cv_results[:,1], \n",
+ " \"Val AUC\": cv_results[:,2] }) \n",
+ "\n",
+ "# Durchschnittszeile hinzufügen \n",
+ "avg_row = pd.DataFrame({ \n",
+ " \"Fold\": [\"Ø\"], \n",
+ " \"Val Loss\": [cv_results[:,0].mean()], \n",
+ " \"Val Accuracy\": [cv_results[:,1].mean()], \n",
+ " \"Val AUC\": [cv_results[:,2].mean()] \n",
+ "}) \n",
+ "\n",
+ "results_table = pd.concat([results_table, avg_row], ignore_index=True) \n",
+ "\n",
+ "print(\"\\n=== Ergebnis-Tabelle ===\") \n",
+ "print(results_table) \n",
+ "\n",
+ "#Tabelle speichern \n",
+ "results_table.to_csv(\"cnn_crossVal_results.csv\", index=False) \n",
+ "print(\"Ergebnisse gespeichert als 'cnn_crossVal_results.csv'\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2f6de79",
+ "metadata": {},
+ "source": [
+ "Finales Modell auf alle Daten trainieren"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "6e403e5f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential_14\"\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"sequential_14\"\u001b[0m\n"
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+ },
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+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ conv1d_28 (Conv1D) │ (None, 18, 32) │ 128 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_28 │ (None, 18, 32) │ 128 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_14 (MaxPooling1D) │ (None, 9, 32) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv1d_29 (Conv1D) │ (None, 7, 64) │ 6,208 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_29 │ (None, 7, 64) │ 256 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling1d_14 │ (None, 64) │ 0 │\n",
+ "│ (GlobalAveragePooling1D) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_28 (Dense) │ (None, 32) │ 2,080 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_14 (Dropout) │ (None, 32) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_29 (Dense) │ (None, 1) │ 33 │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+ "
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+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃\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",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ conv1d_28 (\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_28 │ (\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",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_14 (\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv1d_29 (\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_29 │ (\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",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling1d_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_28 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_14 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_29 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 8,833 (34.50 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,833\u001b[0m (34.50 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 8,641 (33.75 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,641\u001b[0m (33.75 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 192 (768.00 B)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.6497 - auc: 0.7074 - loss: 0.7224\n",
+ "Epoch 2/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8300 - auc: 0.8909 - loss: 0.5339\n",
+ "Epoch 3/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8763 - auc: 0.9222 - loss: 0.4463\n",
+ "Epoch 4/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8935 - auc: 0.9344 - loss: 0.4032\n",
+ "Epoch 5/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8982 - auc: 0.9404 - loss: 0.3786\n",
+ "Epoch 6/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9120 - auc: 0.9506 - loss: 0.3505\n",
+ "Epoch 7/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9157 - auc: 0.9560 - loss: 0.3320\n",
+ "Epoch 8/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9192 - auc: 0.9580 - loss: 0.3230\n",
+ "Epoch 9/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9208 - auc: 0.9599 - loss: 0.3145\n",
+ "Epoch 10/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9257 - auc: 0.9658 - loss: 0.2972\n",
+ "Epoch 11/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9255 - auc: 0.9682 - loss: 0.2881\n",
+ "Epoch 12/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9281 - auc: 0.9702 - loss: 0.2824\n",
+ "Epoch 13/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9285 - auc: 0.9720 - loss: 0.2749\n",
+ "Epoch 14/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9313 - auc: 0.9726 - loss: 0.2703\n",
+ "Epoch 15/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9315 - auc: 0.9745 - loss: 0.2622\n",
+ "Epoch 16/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9360 - auc: 0.9758 - loss: 0.2558\n",
+ "Epoch 17/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9343 - auc: 0.9759 - loss: 0.2543\n",
+ "Epoch 18/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9353 - auc: 0.9764 - loss: 0.2510\n",
+ "Epoch 19/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9374 - auc: 0.9787 - loss: 0.2431\n",
+ "Epoch 20/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9376 - auc: 0.9803 - loss: 0.2374\n",
+ "Epoch 21/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9378 - auc: 0.9794 - loss: 0.2362\n",
+ "Epoch 22/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9402 - auc: 0.9805 - loss: 0.2327\n",
+ "Epoch 23/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9399 - auc: 0.9803 - loss: 0.2312\n",
+ "Epoch 24/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9389 - auc: 0.9794 - loss: 0.2315\n",
+ "Epoch 25/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9391 - auc: 0.9804 - loss: 0.2282\n",
+ "Epoch 26/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9371 - auc: 0.9811 - loss: 0.2240\n",
+ "Epoch 27/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9401 - auc: 0.9807 - loss: 0.2237\n",
+ "Epoch 28/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9433 - auc: 0.9823 - loss: 0.2187\n",
+ "Epoch 29/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9432 - auc: 0.9834 - loss: 0.2133\n",
+ "Epoch 30/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9425 - auc: 0.9833 - loss: 0.2117\n",
+ "Epoch 31/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9429 - auc: 0.9834 - loss: 0.2102\n",
+ "Epoch 32/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9452 - auc: 0.9849 - loss: 0.2049\n",
+ "Epoch 33/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9401 - auc: 0.9836 - loss: 0.2082\n",
+ "Epoch 34/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9436 - auc: 0.9838 - loss: 0.2064\n",
+ "Epoch 35/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9468 - auc: 0.9845 - loss: 0.2015\n",
+ "Epoch 36/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9469 - auc: 0.9849 - loss: 0.1997\n",
+ "Epoch 37/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9449 - auc: 0.9837 - loss: 0.2039\n",
+ "Epoch 38/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9461 - auc: 0.9850 - loss: 0.1981\n",
+ "Epoch 39/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9447 - auc: 0.9845 - loss: 0.1975\n",
+ "Epoch 40/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9482 - auc: 0.9854 - loss: 0.1958\n",
+ "Epoch 41/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9447 - auc: 0.9854 - loss: 0.1952\n",
+ "Epoch 42/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9475 - auc: 0.9848 - loss: 0.1960\n",
+ "Epoch 43/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9459 - auc: 0.9859 - loss: 0.1919\n",
+ "Epoch 44/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9488 - auc: 0.9860 - loss: 0.1897\n",
+ "Epoch 45/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9465 - auc: 0.9856 - loss: 0.1911\n",
+ "Epoch 46/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9493 - auc: 0.9869 - loss: 0.1859\n",
+ "Epoch 47/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9489 - auc: 0.9863 - loss: 0.1861\n",
+ "Epoch 48/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9485 - auc: 0.9862 - loss: 0.1857\n",
+ "Epoch 49/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9470 - auc: 0.9862 - loss: 0.1862\n",
+ "Epoch 50/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9490 - auc: 0.9866 - loss: 0.1827\n",
+ "Epoch 51/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9495 - auc: 0.9862 - loss: 0.1830\n",
+ "Epoch 52/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9498 - auc: 0.9871 - loss: 0.1788\n",
+ "Epoch 53/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9490 - auc: 0.9872 - loss: 0.1791\n",
+ "Epoch 54/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9475 - auc: 0.9863 - loss: 0.1823\n",
+ "Epoch 55/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9500 - auc: 0.9865 - loss: 0.1793\n",
+ "Epoch 56/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9470 - auc: 0.9875 - loss: 0.1765\n",
+ "Epoch 57/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9481 - auc: 0.9870 - loss: 0.1778\n",
+ "Epoch 58/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9482 - auc: 0.9877 - loss: 0.1740\n",
+ "Epoch 59/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9485 - auc: 0.9877 - loss: 0.1746\n",
+ "Epoch 60/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9498 - auc: 0.9878 - loss: 0.1726\n",
+ "Epoch 61/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9493 - auc: 0.9880 - loss: 0.1708\n",
+ "Epoch 62/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9482 - auc: 0.9882 - loss: 0.1700\n",
+ "Epoch 63/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9500 - auc: 0.9882 - loss: 0.1695\n",
+ "Epoch 64/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9502 - auc: 0.9884 - loss: 0.1673\n",
+ "Epoch 65/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9484 - auc: 0.9880 - loss: 0.1695\n",
+ "Epoch 66/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9515 - auc: 0.9884 - loss: 0.1678\n",
+ "Epoch 67/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9505 - auc: 0.9891 - loss: 0.1654\n",
+ "Epoch 68/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9486 - auc: 0.9888 - loss: 0.1651\n",
+ "Epoch 69/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9501 - auc: 0.9885 - loss: 0.1656\n",
+ "Epoch 70/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9514 - auc: 0.9879 - loss: 0.1663\n",
+ "Epoch 71/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9516 - auc: 0.9888 - loss: 0.1629\n",
+ "Epoch 72/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9503 - auc: 0.9882 - loss: 0.1653\n",
+ "Epoch 73/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9513 - auc: 0.9890 - loss: 0.1614\n",
+ "Epoch 74/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9508 - auc: 0.9890 - loss: 0.1611\n",
+ "Epoch 75/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9511 - auc: 0.9890 - loss: 0.1604\n",
+ "Epoch 76/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9519 - auc: 0.9889 - loss: 0.1611\n",
+ "Epoch 77/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9506 - auc: 0.9891 - loss: 0.1595\n",
+ "Epoch 78/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9519 - auc: 0.9892 - loss: 0.1577\n",
+ "Epoch 79/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9511 - auc: 0.9892 - loss: 0.1590\n",
+ "Epoch 80/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9507 - auc: 0.9893 - loss: 0.1581\n",
+ "Epoch 81/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9518 - auc: 0.9896 - loss: 0.1560\n",
+ "Epoch 82/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9519 - auc: 0.9890 - loss: 0.1579\n",
+ "Epoch 83/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9511 - auc: 0.9894 - loss: 0.1554\n",
+ "Epoch 84/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9527 - auc: 0.9890 - loss: 0.1571\n",
+ "Epoch 85/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9506 - auc: 0.9893 - loss: 0.1563\n",
+ "Epoch 86/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9504 - auc: 0.9893 - loss: 0.1565\n",
+ "Epoch 87/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9533 - auc: 0.9902 - loss: 0.1519\n",
+ "Epoch 88/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9520 - auc: 0.9893 - loss: 0.1542\n",
+ "Epoch 89/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9510 - auc: 0.9895 - loss: 0.1535\n",
+ "Epoch 90/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9524 - auc: 0.9898 - loss: 0.1507\n",
+ "Epoch 91/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9512 - auc: 0.9899 - loss: 0.1533\n",
+ "Epoch 92/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9521 - auc: 0.9905 - loss: 0.1496\n",
+ "Epoch 93/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9537 - auc: 0.9897 - loss: 0.1500\n",
+ "Epoch 94/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9521 - auc: 0.9901 - loss: 0.1505\n",
+ "Epoch 95/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9522 - auc: 0.9901 - loss: 0.1500\n",
+ "Epoch 96/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9521 - auc: 0.9898 - loss: 0.1509\n",
+ "Epoch 97/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9517 - auc: 0.9902 - loss: 0.1485\n",
+ "Epoch 98/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9537 - auc: 0.9900 - loss: 0.1488\n",
+ "Epoch 99/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9525 - auc: 0.9906 - loss: 0.1476\n",
+ "Epoch 100/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9527 - auc: 0.9905 - loss: 0.1472\n",
+ "Epoch 101/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9506 - auc: 0.9899 - loss: 0.1490\n",
+ "Epoch 102/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9528 - auc: 0.9908 - loss: 0.1462\n",
+ "Epoch 103/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9536 - auc: 0.9911 - loss: 0.1439\n",
+ "Epoch 104/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9537 - auc: 0.9904 - loss: 0.1457\n",
+ "Epoch 105/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9513 - auc: 0.9902 - loss: 0.1464\n",
+ "Epoch 106/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9533 - auc: 0.9908 - loss: 0.1434\n",
+ "Epoch 107/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9522 - auc: 0.9897 - loss: 0.1479\n",
+ "Epoch 108/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9522 - auc: 0.9901 - loss: 0.1469\n",
+ "Epoch 109/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9907 - loss: 0.1432\n",
+ "Epoch 110/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9525 - auc: 0.9907 - loss: 0.1448\n",
+ "Epoch 111/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9545 - auc: 0.9907 - loss: 0.1431\n",
+ "Epoch 112/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9523 - auc: 0.9910 - loss: 0.1411\n",
+ "Epoch 113/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9533 - auc: 0.9911 - loss: 0.1419\n",
+ "Epoch 114/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9529 - auc: 0.9911 - loss: 0.1413\n",
+ "Epoch 115/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9539 - auc: 0.9910 - loss: 0.1422\n",
+ "Epoch 116/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9518 - auc: 0.9907 - loss: 0.1431\n",
+ "Epoch 117/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9913 - loss: 0.1407\n",
+ "Epoch 118/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9528 - auc: 0.9915 - loss: 0.1398\n",
+ "Epoch 119/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9517 - auc: 0.9908 - loss: 0.1413\n",
+ "Epoch 120/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9534 - auc: 0.9912 - loss: 0.1399\n",
+ "Epoch 121/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9912 - loss: 0.1379\n",
+ "Epoch 122/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9538 - auc: 0.9908 - loss: 0.1404\n",
+ "Epoch 123/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9532 - auc: 0.9912 - loss: 0.1407\n",
+ "Epoch 124/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9532 - auc: 0.9916 - loss: 0.1377\n",
+ "Epoch 125/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9527 - auc: 0.9913 - loss: 0.1379\n",
+ "Epoch 126/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9554 - auc: 0.9913 - loss: 0.1377\n",
+ "Epoch 127/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9525 - auc: 0.9915 - loss: 0.1381\n",
+ "Epoch 128/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9529 - auc: 0.9910 - loss: 0.1384\n",
+ "Epoch 129/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9914 - loss: 0.1372\n",
+ "Epoch 130/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9534 - auc: 0.9913 - loss: 0.1375\n",
+ "Epoch 131/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9525 - auc: 0.9915 - loss: 0.1372\n",
+ "Epoch 132/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9540 - auc: 0.9916 - loss: 0.1359\n",
+ "Epoch 133/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9912 - loss: 0.1374\n",
+ "Epoch 134/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9915 - loss: 0.1361\n",
+ "Epoch 135/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9531 - auc: 0.9919 - loss: 0.1341\n",
+ "Epoch 136/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9543 - auc: 0.9914 - loss: 0.1362\n",
+ "Epoch 137/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9547 - auc: 0.9914 - loss: 0.1344\n",
+ "Epoch 138/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9539 - auc: 0.9915 - loss: 0.1354\n",
+ "Epoch 139/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9523 - auc: 0.9911 - loss: 0.1366\n",
+ "Epoch 140/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9543 - auc: 0.9915 - loss: 0.1336\n",
+ "Epoch 141/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9536 - auc: 0.9918 - loss: 0.1330\n",
+ "Epoch 142/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9547 - auc: 0.9918 - loss: 0.1325\n",
+ "Epoch 143/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9918 - loss: 0.1347\n",
+ "Epoch 144/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9529 - auc: 0.9915 - loss: 0.1357\n",
+ "Epoch 145/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9552 - auc: 0.9920 - loss: 0.1318\n",
+ "Epoch 146/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9545 - auc: 0.9918 - loss: 0.1316\n",
+ "Epoch 147/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9533 - auc: 0.9917 - loss: 0.1322\n",
+ "Epoch 148/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9540 - auc: 0.9920 - loss: 0.1309\n",
+ "Epoch 149/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9537 - auc: 0.9918 - loss: 0.1321\n",
+ "Epoch 150/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9539 - auc: 0.9919 - loss: 0.1319\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scaler_final = StandardScaler() \n",
+ "X_scaled = scaler_final.fit_transform(X.reshape(len(X), -1)).reshape(X.shape) \n",
+ "\n",
+ "final_model = build_model(input_shape=(len(au_columns),1), lr=1e-4) \n",
+ "final_model.summary() \n",
+ "\n",
+ "final_model.fit( \n",
+ " X_scaled, y, \n",
+ " epochs=150, \n",
+ " batch_size=16, \n",
+ " verbose=1 \n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7c7f9cc4",
+ "metadata": {},
+ "source": [
+ "Speichern des Modells"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "2d3af5be",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Finales Modell und Scaler gespeichert als 'cnn_crossVal.keras' und 'scaler_crossVal.joblib'\n"
+ ]
+ }
+ ],
+ "source": [
+ "# --- Speichern --- \n",
+ "final_model.save(\"cnn_crossVal.keras\") \n",
+ "joblib.dump(scaler_final, \"scaler_crossVal.joblib\") \n",
+ "\n",
+ "print(\"Finales Modell und Scaler gespeichert als 'cnn_crossVal.keras' und 'scaler_crossVal.joblib'\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c11891e0",
+ "metadata": {},
+ "source": [
+ "Plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "9f6a8584",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#plots\n",
+ "def plot_cv_histories(cv_histories, metric): \n",
+ " plt.figure(figsize=(10,6)) \n",
+ " \n",
+ " for i, hist in enumerate(cv_histories): \n",
+ " plt.plot(hist[metric], label=f\"Fold {i+1} Train\", alpha=0.7) \n",
+ " plt.plot(hist[f\"val_{metric}\"], label=f\"Fold {i+1} Val\", linestyle=\"--\", alpha=0.7) \n",
+ " plt.xlabel(\"Epochs\") \n",
+ " plt.ylabel(metric.capitalize()) \n",
+ " plt.title(f\"Cross-Validation {metric.capitalize()} Verläufe\") \n",
+ " plt.legend() \n",
+ " plt.grid(True) \n",
+ " plt.show()\n",
+ " \n",
+ "plot_cv_histories(cv_histories, \"loss\") \n",
+ "plot_cv_histories(cv_histories, \"accuracy\") \n",
+ "plot_cv_histories(cv_histories, \"auc\")"
+ ]
+ }
+ ],
+ "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": "3.12.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/model_training/CNN/CNN_crossVal_faceAUs_eyeFeatures.ipynb b/model_training/CNN/CNN_crossVal_faceAUs_eyeFeatures.ipynb
new file mode 100644
index 0000000..8f49aab
--- /dev/null
+++ b/model_training/CNN/CNN_crossVal_faceAUs_eyeFeatures.ipynb
@@ -0,0 +1,1668 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "47f6de7b",
+ "metadata": {},
+ "source": [
+ "Bibliotheken importieren"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "99294260",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd \n",
+ "import numpy as np \n",
+ "import matplotlib.pyplot as plt\n",
+ "import random \n",
+ "import joblib \n",
+ "from pathlib import Path \n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "\n",
+ "from sklearn.model_selection import GroupKFold \n",
+ "from sklearn.preprocessing import StandardScaler \n",
+ "\n",
+ "import tensorflow as tf \n",
+ "from tensorflow.keras import Input, layers, models, regularizers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "52b4ca8c",
+ "metadata": {},
+ "source": [
+ "Seed festlegen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "6e49d281",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SEED = 42 \n",
+ "np.random.seed(SEED) \n",
+ "tf.random.set_seed(SEED) \n",
+ "random.seed(SEED)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae1a715f",
+ "metadata": {},
+ "source": [
+ "Daten laden"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "870f01c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_path = Path(r\"~/data-paulusjafahrsimulator-gpu/new_datasets/50s_25Hz_dataset.parquet\") \n",
+ "\n",
+ "data = pd.read_parquet(path=data_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bedbc23b",
+ "metadata": {},
+ "source": [
+ "Labels erstellen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "38848515",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "low_all = data[((data[\"PHASE\"] == \"baseline\") | \n",
+ " ((data[\"STUDY\"] == \"n-back\") & (data[\"PHASE\"] != \"baseline\") & (data[\"LEVEL\"].isin([1,4]))))].copy() \n",
+ "\n",
+ "high_all = pd.concat([ \n",
+ " data[(data[\"STUDY\"]==\"n-back\") & (data[\"LEVEL\"].isin([2,3,5,6])) & (data[\"PHASE\"].isin([\"train\",\"test\"]))], \n",
+ " data[(data[\"STUDY\"]==\"k-drive\") & (data[\"PHASE\"]!=\"baseline\")] \n",
+ "]).copy() \n",
+ "\n",
+ "low_all[\"label\"] = 0 \n",
+ "high_all[\"label\"] = 1 \n",
+ "data = pd.concat([low_all, high_all], ignore_index=True).drop_duplicates() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0b282acf",
+ "metadata": {},
+ "source": [
+ "Features und Labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "5edb00a0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['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",
+ "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",
+ "Gefundene Eye Features: ['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']\n",
+ "Anzahl FACE_AUs: 20\n",
+ "Anzahl EYE Features: 15\n",
+ "Gesamtzahl Features: 35\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Face AUs\n",
+ "au_columns = [col for col in data.columns if \"face\" in col.lower()] \n",
+ "\n",
+ "#Eye Features\n",
+ "eye_columns = [ \n",
+ " 'Fix_count_short_66_150', \n",
+ " 'Fix_count_medium_300_500', \n",
+ " 'Fix_count_long_gt_1000', \n",
+ " 'Fix_count_100', \n",
+ " 'Fix_mean_duration', \n",
+ " 'Fix_median_duration', \n",
+ " 'Sac_count', \n",
+ " 'Sac_mean_amp', \n",
+ " 'Sac_mean_dur', \n",
+ " 'Sac_median_dur', \n",
+ " 'Blink_count', \n",
+ " 'Blink_mean_dur', \n",
+ " 'Blink_median_dur', \n",
+ " 'Pupil_mean', \n",
+ " 'Pupil_IPA' \n",
+ "]\n",
+ "\n",
+ "#Early Fusion\n",
+ "feature_columns = au_columns + eye_columns\n",
+ "\n",
+ "X = data[feature_columns].values[..., np.newaxis] \n",
+ "y = data[\"label\"].values \n",
+ "\n",
+ "groups = data[\"subjectID\"].values\n",
+ "print(data.columns.tolist())\n",
+ "\n",
+ "print(\"Gefundene FACE_AU-Spalten:\", au_columns)\n",
+ "print(\"Gefundene Eye Features:\" , eye_columns)\n",
+ "\n",
+ "print(\"Anzahl FACE_AUs:\", len(au_columns)) \n",
+ "print(\"Anzahl EYE Features:\", len(eye_columns)) \n",
+ "print(\"Gesamtzahl Features:\", len(feature_columns))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a539b83b",
+ "metadata": {},
+ "source": [
+ "CNN-Modell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "e4a7f496",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_model(input_shape, lr=1e-4): \n",
+ " model = models.Sequential([ \n",
+ " Input(shape=input_shape), \n",
+ " layers.Conv1D(32, kernel_size=3, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n",
+ " layers.BatchNormalization(), \n",
+ " layers.MaxPooling1D(pool_size=2),\n",
+ "\n",
+ " layers.Conv1D(64, kernel_size=3, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n",
+ " layers.BatchNormalization(), \n",
+ " layers.GlobalAveragePooling1D(), \n",
+ " \n",
+ " layers.Dense(32, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n",
+ " layers.Dropout(0.5), \n",
+ " layers.Dense(1, activation=\"sigmoid\") \n",
+ " ]) \n",
+ " \n",
+ " model.compile( \n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=lr), \n",
+ " loss=\"binary_crossentropy\", \n",
+ " metrics=[\"accuracy\", tf.keras.metrics.AUC(name=\"auc\")] \n",
+ " ) \n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5905871b",
+ "metadata": {},
+ "source": [
+ "Cross-Validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "90658000",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "--- Fold 1 ---\n",
+ "Train-Subjects: [ 0 3 4 7 9 12 13 14 16 18 20 21 22 26 27 28]\n",
+ "Val-Subjects: [ 2 5 8 17]\n",
+ "Train Mittelwerte (erste 5 Features): [[1.54526039e-14]\n",
+ " [2.33341760e-14]\n",
+ " [8.12769382e-15]\n",
+ " [1.14457302e-15]\n",
+ " [1.91277926e-15]]\n",
+ "Train Std (erste 5 Features): [[1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]]\n",
+ "Val Mittelwerte (erste 5 Features): [[ 0.01837255]\n",
+ " [ 0.03634784]\n",
+ " [-0.01417256]\n",
+ " [-0.00136129]\n",
+ " [ 0.01066093]]\n",
+ "Val Std (erste 5 Features): [[0.9809553 ]\n",
+ " [0.90597105]\n",
+ " [1.0661958 ]\n",
+ " [0.97687653]\n",
+ " [1.06061798]]\n"
+ ]
+ },
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+ "data": {
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+ "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n",
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+ "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fold 1 - Val Loss: nan, Val Acc: 0.4232, Val AUC: 0.5000\n",
+ "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
+ "Konfusionsmatrix Fold 1:\n",
+ "[[697 0]\n",
+ " [950 0]]\n",
+ "\n",
+ "\n",
+ "--- Fold 2 ---\n",
+ "Train-Subjects: [ 0 2 3 4 5 7 8 9 16 17 18 20 21 22 26 28]\n",
+ "Val-Subjects: [12 13 14 27]\n",
+ "Train Mittelwerte (erste 5 Features): [[-8.59518586e-15]\n",
+ " [ 3.94281794e-14]\n",
+ " [ 3.17014434e-15]\n",
+ " [ 3.62114400e-15]\n",
+ " [-6.06451111e-16]]\n",
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+ " [0.02275319]\n",
+ " [0.06616701]\n",
+ " [0.06788641]]\n",
+ "Val Std (erste 5 Features): [[0.93313441]\n",
+ " [1.00300875]\n",
+ " [0.99231989]\n",
+ " [1.03627391]\n",
+ " [1.088837 ]]\n"
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "text": [
+ "Fold 2 - Val Loss: nan, Val Acc: 0.4238, Val AUC: 0.5000\n",
+ "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
+ "Konfusionsmatrix Fold 2:\n",
+ "[[698 0]\n",
+ " [949 0]]\n",
+ "\n",
+ "\n",
+ "--- Fold 3 ---\n",
+ "Train-Subjects: [ 0 2 3 5 8 9 12 13 14 17 18 20 22 26 27 28]\n",
+ "Val-Subjects: [ 4 7 16 21]\n",
+ "Train Mittelwerte (erste 5 Features): [[-7.83948585e-16]\n",
+ " [ 5.02170375e-14]\n",
+ " [ 1.79313660e-14]\n",
+ " [ 1.59491872e-14]\n",
+ " [-5.69915048e-15]]\n",
+ "Train Std (erste 5 Features): [[1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]]\n",
+ "Val Mittelwerte (erste 5 Features): [[-0.16879516]\n",
+ " [-0.09483067]\n",
+ " [-0.00774855]\n",
+ " [-0.02642025]\n",
+ " [-0.01273985]]\n",
+ "Val Std (erste 5 Features): [[1.11007971]\n",
+ " [1.03956086]\n",
+ " [0.95289305]\n",
+ " [0.97649474]\n",
+ " [0.98732466]]\n"
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+ "│ conv1d_6 (Conv1D) │ (None, 33, 32) │ 128 │\n",
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+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_3 (MaxPooling1D) │ (None, 16, 32) │ 0 │\n",
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+ "│ conv1d_6 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling1d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
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+ "metadata": {},
+ "output_type": "display_data"
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+ "name": "stdout",
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+ "text": [
+ "Fold 3 - Val Loss: nan, Val Acc: 0.4232, Val AUC: 0.5000\n",
+ "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step \n",
+ "Konfusionsmatrix Fold 3:\n",
+ "[[697 0]\n",
+ " [950 0]]\n",
+ "\n",
+ "\n",
+ "--- Fold 4 ---\n",
+ "Train-Subjects: [ 2 3 4 5 7 8 12 13 14 16 17 18 20 21 26 27]\n",
+ "Val-Subjects: [ 0 9 22 28]\n",
+ "Train Mittelwerte (erste 5 Features): [[-6.62156695e-15]\n",
+ " [ 2.37570538e-14]\n",
+ " [ 1.27947811e-14]\n",
+ " [-6.40683620e-15]\n",
+ " [-1.10846197e-15]]\n",
+ "Train Std (erste 5 Features): [[1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]]\n",
+ "Val Mittelwerte (erste 5 Features): [[ 0.01788626]\n",
+ " [ 0.0311636 ]\n",
+ " [-0.04004633]\n",
+ " [-0.01924637]\n",
+ " [ 0.01714694]]\n",
+ "Val Std (erste 5 Features): [[0.98096724]\n",
+ " [1.05932267]\n",
+ " [0.95190592]\n",
+ " [1.01373334]\n",
+ " [0.99555169]]\n"
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+ "│ dense_8 (Dense) │ (None, 32) │ 2,080 │\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_4 (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv1d_9 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,208\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
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+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
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+ },
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+ "
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+ ]
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+ "
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+ ],
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fold 4 - Val Loss: nan, Val Acc: 0.4235, Val AUC: 0.5000\n",
+ "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
+ "Konfusionsmatrix Fold 4:\n",
+ "[[697 0]\n",
+ " [949 0]]\n",
+ "\n",
+ "\n",
+ "--- Fold 5 ---\n",
+ "Train-Subjects: [ 0 2 4 5 7 8 9 12 13 14 16 17 21 22 27 28]\n",
+ "Val-Subjects: [ 3 18 20 26]\n",
+ "Train Mittelwerte (erste 5 Features): [[ 1.55435437e-15]\n",
+ " [ 2.67469531e-14]\n",
+ " [ 8.25766087e-15]\n",
+ " [-5.59537572e-15]\n",
+ " [ 4.44628562e-17]]\n",
+ "Train Std (erste 5 Features): [[1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]\n",
+ " [1.]]\n",
+ "Val Mittelwerte (erste 5 Features): [[-0.00321515]\n",
+ " [-0.03717355]\n",
+ " [ 0.03970031]\n",
+ " [-0.01854768]\n",
+ " [-0.07944637]]\n",
+ "Val Std (erste 5 Features): [[0.98009046]\n",
+ " [0.98567844]\n",
+ " [1.03448059]\n",
+ " [0.99467548]\n",
+ " [0.86050472]]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential_5\"\n",
+ "
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+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ conv1d_10 (Conv1D) │ (None, 33, 32) │ 128 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_10 │ (None, 33, 32) │ 128 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_5 (MaxPooling1D) │ (None, 16, 32) │ 0 │\n",
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+ "│ conv1d_11 (Conv1D) │ (None, 14, 64) │ 6,208 │\n",
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+ "│ batch_normalization_11 │ (None, 14, 64) │ 256 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling1d_5 │ (None, 64) │ 0 │\n",
+ "│ (GlobalAveragePooling1D) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_10 (Dense) │ (None, 32) │ 2,080 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_5 (Dropout) │ (None, 32) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_11 (Dense) │ (None, 1) │ 33 │\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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+ "│ conv1d_10 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_5 (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv1d_11 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,208\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling1d_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_10 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_11 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ ],
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+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,833\u001b[0m (34.50 KB)\n"
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+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 8,641 (33.75 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,641\u001b[0m (33.75 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fold 5 - Val Loss: nan, Val Acc: 0.4220, Val AUC: 0.5000\n",
+ "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
+ "Konfusionsmatrix Fold 5:\n",
+ "[[695 0]\n",
+ " [952 0]]\n",
+ "\n",
+ "Aggregierte Konfusionsmatrix über alle Folds:\n",
+ "[[3484 0]\n",
+ " [4750 0]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "gkf = GroupKFold(n_splits=5) \n",
+ "cv_histories = [] \n",
+ "cv_results = [] \n",
+ "fold_subjects = []\n",
+ "all_conf_matrices = []\n",
+ "\n",
+ "for fold, (train_idx, val_idx) in enumerate(gkf.split(X, y, groups)):\n",
+ " train_subjects = np.unique(groups[train_idx]) \n",
+ " val_subjects = np.unique(groups[val_idx]) \n",
+ " fold_subjects.append({\"Fold\": fold+1, \n",
+ " \"Train_Subjects\": train_subjects, \n",
+ " \"Val_Subjects\": val_subjects}) \n",
+ " \n",
+ " print(f\"\\n--- Fold {fold+1} ---\") \n",
+ " print(\"Train-Subjects:\", train_subjects) \n",
+ " print(\"Val-Subjects:\", val_subjects) \n",
+ "\n",
+ " #Split\n",
+ " X_train, X_val = X[train_idx], X[val_idx] \n",
+ " y_train, y_val = y[train_idx], y[val_idx] # Normalisierung pro Fold \n",
+ "\n",
+ " #Normalisierung pro Fold\n",
+ " scaler = StandardScaler() \n",
+ " X_train = scaler.fit_transform(X_train.reshape(len(X_train), -1)).reshape(X_train.shape) \n",
+ " X_val = scaler.transform(X_val.reshape(len(X_val), -1)).reshape(X_val.shape) \n",
+ "\n",
+ " # Plausibilitäts-Check \n",
+ " print(\"Train Mittelwerte (erste 5 Features):\", X_train.mean(axis=0)[:5]) \n",
+ " print(\"Train Std (erste 5 Features):\", X_train.std(axis=0)[:5]) \n",
+ " print(\"Val Mittelwerte (erste 5 Features):\", X_val.mean(axis=0)[:5]) \n",
+ " print(\"Val Std (erste 5 Features):\", X_val.std(axis=0)[:5]) \n",
+ "\n",
+ " # Modell \n",
+ " model = build_model(input_shape=(len(feature_columns),1), lr=1e-4) \n",
+ " model.summary() \n",
+ "\n",
+ " 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",
+ " ] \n",
+ "\n",
+ " history = model.fit( \n",
+ " X_train, y_train, \n",
+ " validation_data=(X_val, y_val), \n",
+ " epochs=100, \n",
+ " batch_size=16, \n",
+ " callbacks=callbacks, \n",
+ " verbose=0 \n",
+ " ) \n",
+ "\n",
+ " cv_histories.append(history.history) \n",
+ " scores = model.evaluate(X_val, y_val, verbose=0) \n",
+ " cv_results.append(scores) \n",
+ " print(f\"Fold {fold+1} - Val Loss: {scores[0]:.4f}, Val Acc: {scores[1]:.4f}, Val AUC: {scores[2]:.4f}\")\n",
+ "\n",
+ "\n",
+ " #Konfusionsmatrix \n",
+ " y_pred = (model.predict(X_val) > 0.5).astype(int) \n",
+ " cm = confusion_matrix(y_val, y_pred) \n",
+ " all_conf_matrices.append(cm) \n",
+ " \n",
+ " print(f\"Konfusionsmatrix Fold {fold+1}:\\n{cm}\\n\") \n",
+ " \n",
+ "# Aggregierte Matrix \n",
+ "agg_cm = sum(all_conf_matrices) \n",
+ "print(\"Aggregierte Konfusionsmatrix über alle Folds:\") \n",
+ "print(agg_cm)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d10b7e78",
+ "metadata": {},
+ "source": [
+ "Results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9aeba7f4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== Cross-Validation Ergebnisse ===\n",
+ "Durchschnittlicher Val-Loss: 0.2087\n",
+ "Durchschnittliche Val-Accuracy: 0.9463\n",
+ "Durchschnittliche Val-AUC: 0.9794\n",
+ "\n",
+ "=== Ergebnis-Tabelle ===\n",
+ " Fold Val Loss Val Accuracy Val AUC\n",
+ "0 1 0.233584 0.943534 0.975684\n",
+ "1 2 0.208549 0.951427 0.980279\n",
+ "2 3 0.203029 0.947784 0.979743\n",
+ "3 4 0.226566 0.935601 0.974336\n",
+ "4 5 0.171882 0.953248 0.986887\n",
+ "5 Ø 0.208722 0.946319 0.979386\n",
+ "Ergebnisse gespeichert als 'cnn_crossVal_results.csv'\n"
+ ]
+ }
+ ],
+ "source": [
+ "#results\n",
+ "cv_results = np.array(cv_results) \n",
+ "print(\"\\n=== Cross-Validation Ergebnisse ===\") \n",
+ "print(f\"Durchschnittlicher Val-Loss: {cv_results[:,0].mean():.4f}\") \n",
+ "print(f\"Durchschnittliche Val-Accuracy: {cv_results[:,1].mean():.4f}\") \n",
+ "print(f\"Durchschnittliche Val-AUC: {cv_results[:,2].mean():.4f}\")\n",
+ "\n",
+ "#Ergebnis-Tabelle erstellen\n",
+ "results_table = pd.DataFrame({ \n",
+ " \"Fold\": np.arange(1, len(cv_results)+1), \n",
+ " \"Val Loss\": cv_results[:,0], \n",
+ " \"Val Accuracy\": cv_results[:,1], \n",
+ " \"Val AUC\": cv_results[:,2] }) \n",
+ "\n",
+ "# Durchschnittszeile hinzufügen \n",
+ "avg_row = pd.DataFrame({ \n",
+ " \"Fold\": [\"Ø\"], \n",
+ " \"Val Loss\": [cv_results[:,0].mean()], \n",
+ " \"Val Accuracy\": [cv_results[:,1].mean()], \n",
+ " \"Val AUC\": [cv_results[:,2].mean()] \n",
+ "}) \n",
+ "\n",
+ "results_table = pd.concat([results_table, avg_row], ignore_index=True) \n",
+ "\n",
+ "print(\"\\n=== Ergebnis-Tabelle ===\") \n",
+ "print(results_table) \n",
+ "\n",
+ "#Tabelle speichern \n",
+ "results_table.to_csv(\"cnn_crossVal_results.csv\", index=False) \n",
+ "print(\"Ergebnisse gespeichert als 'cnn_crossVal_results.csv'\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2f6de79",
+ "metadata": {},
+ "source": [
+ "Finales Modell auf alle Daten trainieren"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6e403e5f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential_14\"\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"sequential_14\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ conv1d_28 (Conv1D) │ (None, 18, 32) │ 128 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_28 │ (None, 18, 32) │ 128 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_14 (MaxPooling1D) │ (None, 9, 32) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv1d_29 (Conv1D) │ (None, 7, 64) │ 6,208 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_29 │ (None, 7, 64) │ 256 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling1d_14 │ (None, 64) │ 0 │\n",
+ "│ (GlobalAveragePooling1D) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_28 (Dense) │ (None, 32) │ 2,080 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_14 (Dropout) │ (None, 32) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_29 (Dense) │ (None, 1) │ 33 │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+ "
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+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃\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",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ conv1d_28 (\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_28 │ (\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",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_14 (\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv1d_29 (\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_29 │ (\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",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling1d_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_28 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_14 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_29 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
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+ " Total params: 8,833 (34.50 KB)\n",
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+ " Trainable params: 8,641 (33.75 KB)\n",
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+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,641\u001b[0m (33.75 KB)\n"
+ ]
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+ "metadata": {},
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+ " Non-trainable params: 192 (768.00 B)\n",
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+ "text/plain": [
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+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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+ "text": [
+ "Epoch 1/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.6497 - auc: 0.7074 - loss: 0.7224\n",
+ "Epoch 2/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8300 - auc: 0.8909 - loss: 0.5339\n",
+ "Epoch 3/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8763 - auc: 0.9222 - loss: 0.4463\n",
+ "Epoch 4/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8935 - auc: 0.9344 - loss: 0.4032\n",
+ "Epoch 5/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8982 - auc: 0.9404 - loss: 0.3786\n",
+ "Epoch 6/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9120 - auc: 0.9506 - loss: 0.3505\n",
+ "Epoch 7/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9157 - auc: 0.9560 - loss: 0.3320\n",
+ "Epoch 8/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9192 - auc: 0.9580 - loss: 0.3230\n",
+ "Epoch 9/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9208 - auc: 0.9599 - loss: 0.3145\n",
+ "Epoch 10/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9257 - auc: 0.9658 - loss: 0.2972\n",
+ "Epoch 11/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9255 - auc: 0.9682 - loss: 0.2881\n",
+ "Epoch 12/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9281 - auc: 0.9702 - loss: 0.2824\n",
+ "Epoch 13/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9285 - auc: 0.9720 - loss: 0.2749\n",
+ "Epoch 14/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9313 - auc: 0.9726 - loss: 0.2703\n",
+ "Epoch 15/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9315 - auc: 0.9745 - loss: 0.2622\n",
+ "Epoch 16/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9360 - auc: 0.9758 - loss: 0.2558\n",
+ "Epoch 17/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9343 - auc: 0.9759 - loss: 0.2543\n",
+ "Epoch 18/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9353 - auc: 0.9764 - loss: 0.2510\n",
+ "Epoch 19/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9374 - auc: 0.9787 - loss: 0.2431\n",
+ "Epoch 20/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9376 - auc: 0.9803 - loss: 0.2374\n",
+ "Epoch 21/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9378 - auc: 0.9794 - loss: 0.2362\n",
+ "Epoch 22/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9402 - auc: 0.9805 - loss: 0.2327\n",
+ "Epoch 23/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9399 - auc: 0.9803 - loss: 0.2312\n",
+ "Epoch 24/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9389 - auc: 0.9794 - loss: 0.2315\n",
+ "Epoch 25/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9391 - auc: 0.9804 - loss: 0.2282\n",
+ "Epoch 26/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9371 - auc: 0.9811 - loss: 0.2240\n",
+ "Epoch 27/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9401 - auc: 0.9807 - loss: 0.2237\n",
+ "Epoch 28/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9433 - auc: 0.9823 - loss: 0.2187\n",
+ "Epoch 29/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9432 - auc: 0.9834 - loss: 0.2133\n",
+ "Epoch 30/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9425 - auc: 0.9833 - loss: 0.2117\n",
+ "Epoch 31/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9429 - auc: 0.9834 - loss: 0.2102\n",
+ "Epoch 32/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9452 - auc: 0.9849 - loss: 0.2049\n",
+ "Epoch 33/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9401 - auc: 0.9836 - loss: 0.2082\n",
+ "Epoch 34/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9436 - auc: 0.9838 - loss: 0.2064\n",
+ "Epoch 35/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9468 - auc: 0.9845 - loss: 0.2015\n",
+ "Epoch 36/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9469 - auc: 0.9849 - loss: 0.1997\n",
+ "Epoch 37/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9449 - auc: 0.9837 - loss: 0.2039\n",
+ "Epoch 38/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9461 - auc: 0.9850 - loss: 0.1981\n",
+ "Epoch 39/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9447 - auc: 0.9845 - loss: 0.1975\n",
+ "Epoch 40/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9482 - auc: 0.9854 - loss: 0.1958\n",
+ "Epoch 41/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9447 - auc: 0.9854 - loss: 0.1952\n",
+ "Epoch 42/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9475 - auc: 0.9848 - loss: 0.1960\n",
+ "Epoch 43/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9459 - auc: 0.9859 - loss: 0.1919\n",
+ "Epoch 44/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9488 - auc: 0.9860 - loss: 0.1897\n",
+ "Epoch 45/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9465 - auc: 0.9856 - loss: 0.1911\n",
+ "Epoch 46/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9493 - auc: 0.9869 - loss: 0.1859\n",
+ "Epoch 47/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9489 - auc: 0.9863 - loss: 0.1861\n",
+ "Epoch 48/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9485 - auc: 0.9862 - loss: 0.1857\n",
+ "Epoch 49/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9470 - auc: 0.9862 - loss: 0.1862\n",
+ "Epoch 50/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9490 - auc: 0.9866 - loss: 0.1827\n",
+ "Epoch 51/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9495 - auc: 0.9862 - loss: 0.1830\n",
+ "Epoch 52/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9498 - auc: 0.9871 - loss: 0.1788\n",
+ "Epoch 53/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9490 - auc: 0.9872 - loss: 0.1791\n",
+ "Epoch 54/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9475 - auc: 0.9863 - loss: 0.1823\n",
+ "Epoch 55/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9500 - auc: 0.9865 - loss: 0.1793\n",
+ "Epoch 56/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9470 - auc: 0.9875 - loss: 0.1765\n",
+ "Epoch 57/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9481 - auc: 0.9870 - loss: 0.1778\n",
+ "Epoch 58/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9482 - auc: 0.9877 - loss: 0.1740\n",
+ "Epoch 59/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9485 - auc: 0.9877 - loss: 0.1746\n",
+ "Epoch 60/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9498 - auc: 0.9878 - loss: 0.1726\n",
+ "Epoch 61/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9493 - auc: 0.9880 - loss: 0.1708\n",
+ "Epoch 62/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9482 - auc: 0.9882 - loss: 0.1700\n",
+ "Epoch 63/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9500 - auc: 0.9882 - loss: 0.1695\n",
+ "Epoch 64/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9502 - auc: 0.9884 - loss: 0.1673\n",
+ "Epoch 65/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9484 - auc: 0.9880 - loss: 0.1695\n",
+ "Epoch 66/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9515 - auc: 0.9884 - loss: 0.1678\n",
+ "Epoch 67/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9505 - auc: 0.9891 - loss: 0.1654\n",
+ "Epoch 68/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9486 - auc: 0.9888 - loss: 0.1651\n",
+ "Epoch 69/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9501 - auc: 0.9885 - loss: 0.1656\n",
+ "Epoch 70/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9514 - auc: 0.9879 - loss: 0.1663\n",
+ "Epoch 71/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9516 - auc: 0.9888 - loss: 0.1629\n",
+ "Epoch 72/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9503 - auc: 0.9882 - loss: 0.1653\n",
+ "Epoch 73/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9513 - auc: 0.9890 - loss: 0.1614\n",
+ "Epoch 74/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9508 - auc: 0.9890 - loss: 0.1611\n",
+ "Epoch 75/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9511 - auc: 0.9890 - loss: 0.1604\n",
+ "Epoch 76/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9519 - auc: 0.9889 - loss: 0.1611\n",
+ "Epoch 77/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9506 - auc: 0.9891 - loss: 0.1595\n",
+ "Epoch 78/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9519 - auc: 0.9892 - loss: 0.1577\n",
+ "Epoch 79/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9511 - auc: 0.9892 - loss: 0.1590\n",
+ "Epoch 80/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9507 - auc: 0.9893 - loss: 0.1581\n",
+ "Epoch 81/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9518 - auc: 0.9896 - loss: 0.1560\n",
+ "Epoch 82/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9519 - auc: 0.9890 - loss: 0.1579\n",
+ "Epoch 83/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9511 - auc: 0.9894 - loss: 0.1554\n",
+ "Epoch 84/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9527 - auc: 0.9890 - loss: 0.1571\n",
+ "Epoch 85/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9506 - auc: 0.9893 - loss: 0.1563\n",
+ "Epoch 86/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9504 - auc: 0.9893 - loss: 0.1565\n",
+ "Epoch 87/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9533 - auc: 0.9902 - loss: 0.1519\n",
+ "Epoch 88/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9520 - auc: 0.9893 - loss: 0.1542\n",
+ "Epoch 89/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9510 - auc: 0.9895 - loss: 0.1535\n",
+ "Epoch 90/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9524 - auc: 0.9898 - loss: 0.1507\n",
+ "Epoch 91/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9512 - auc: 0.9899 - loss: 0.1533\n",
+ "Epoch 92/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9521 - auc: 0.9905 - loss: 0.1496\n",
+ "Epoch 93/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9537 - auc: 0.9897 - loss: 0.1500\n",
+ "Epoch 94/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9521 - auc: 0.9901 - loss: 0.1505\n",
+ "Epoch 95/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9522 - auc: 0.9901 - loss: 0.1500\n",
+ "Epoch 96/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9521 - auc: 0.9898 - loss: 0.1509\n",
+ "Epoch 97/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9517 - auc: 0.9902 - loss: 0.1485\n",
+ "Epoch 98/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9537 - auc: 0.9900 - loss: 0.1488\n",
+ "Epoch 99/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9525 - auc: 0.9906 - loss: 0.1476\n",
+ "Epoch 100/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9527 - auc: 0.9905 - loss: 0.1472\n",
+ "Epoch 101/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9506 - auc: 0.9899 - loss: 0.1490\n",
+ "Epoch 102/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9528 - auc: 0.9908 - loss: 0.1462\n",
+ "Epoch 103/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9536 - auc: 0.9911 - loss: 0.1439\n",
+ "Epoch 104/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9537 - auc: 0.9904 - loss: 0.1457\n",
+ "Epoch 105/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9513 - auc: 0.9902 - loss: 0.1464\n",
+ "Epoch 106/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9533 - auc: 0.9908 - loss: 0.1434\n",
+ "Epoch 107/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9522 - auc: 0.9897 - loss: 0.1479\n",
+ "Epoch 108/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9522 - auc: 0.9901 - loss: 0.1469\n",
+ "Epoch 109/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9907 - loss: 0.1432\n",
+ "Epoch 110/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9525 - auc: 0.9907 - loss: 0.1448\n",
+ "Epoch 111/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9545 - auc: 0.9907 - loss: 0.1431\n",
+ "Epoch 112/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9523 - auc: 0.9910 - loss: 0.1411\n",
+ "Epoch 113/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9533 - auc: 0.9911 - loss: 0.1419\n",
+ "Epoch 114/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9529 - auc: 0.9911 - loss: 0.1413\n",
+ "Epoch 115/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9539 - auc: 0.9910 - loss: 0.1422\n",
+ "Epoch 116/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9518 - auc: 0.9907 - loss: 0.1431\n",
+ "Epoch 117/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9913 - loss: 0.1407\n",
+ "Epoch 118/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9528 - auc: 0.9915 - loss: 0.1398\n",
+ "Epoch 119/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9517 - auc: 0.9908 - loss: 0.1413\n",
+ "Epoch 120/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9534 - auc: 0.9912 - loss: 0.1399\n",
+ "Epoch 121/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9912 - loss: 0.1379\n",
+ "Epoch 122/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9538 - auc: 0.9908 - loss: 0.1404\n",
+ "Epoch 123/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9532 - auc: 0.9912 - loss: 0.1407\n",
+ "Epoch 124/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9532 - auc: 0.9916 - loss: 0.1377\n",
+ "Epoch 125/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9527 - auc: 0.9913 - loss: 0.1379\n",
+ "Epoch 126/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9554 - auc: 0.9913 - loss: 0.1377\n",
+ "Epoch 127/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9525 - auc: 0.9915 - loss: 0.1381\n",
+ "Epoch 128/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9529 - auc: 0.9910 - loss: 0.1384\n",
+ "Epoch 129/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9914 - loss: 0.1372\n",
+ "Epoch 130/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9534 - auc: 0.9913 - loss: 0.1375\n",
+ "Epoch 131/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9525 - auc: 0.9915 - loss: 0.1372\n",
+ "Epoch 132/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9540 - auc: 0.9916 - loss: 0.1359\n",
+ "Epoch 133/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9912 - loss: 0.1374\n",
+ "Epoch 134/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9915 - loss: 0.1361\n",
+ "Epoch 135/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9531 - auc: 0.9919 - loss: 0.1341\n",
+ "Epoch 136/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9543 - auc: 0.9914 - loss: 0.1362\n",
+ "Epoch 137/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9547 - auc: 0.9914 - loss: 0.1344\n",
+ "Epoch 138/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9539 - auc: 0.9915 - loss: 0.1354\n",
+ "Epoch 139/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9523 - auc: 0.9911 - loss: 0.1366\n",
+ "Epoch 140/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9543 - auc: 0.9915 - loss: 0.1336\n",
+ "Epoch 141/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9536 - auc: 0.9918 - loss: 0.1330\n",
+ "Epoch 142/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9547 - auc: 0.9918 - loss: 0.1325\n",
+ "Epoch 143/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9535 - auc: 0.9918 - loss: 0.1347\n",
+ "Epoch 144/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9529 - auc: 0.9915 - loss: 0.1357\n",
+ "Epoch 145/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9552 - auc: 0.9920 - loss: 0.1318\n",
+ "Epoch 146/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9545 - auc: 0.9918 - loss: 0.1316\n",
+ "Epoch 147/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9533 - auc: 0.9917 - loss: 0.1322\n",
+ "Epoch 148/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9540 - auc: 0.9920 - loss: 0.1309\n",
+ "Epoch 149/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9537 - auc: 0.9918 - loss: 0.1321\n",
+ "Epoch 150/150\n",
+ "\u001b[1m515/515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9539 - auc: 0.9919 - loss: 0.1319\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scaler_final = StandardScaler() \n",
+ "X_scaled = scaler_final.fit_transform(X.reshape(len(X), -1)).reshape(X.shape) \n",
+ "\n",
+ "final_model = build_model(input_shape=(len(au_columns),1), lr=1e-4) \n",
+ "final_model.summary() \n",
+ "\n",
+ "final_model.fit( \n",
+ " X_scaled, y, \n",
+ " epochs=150, \n",
+ " batch_size=16, \n",
+ " verbose=1 \n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7c7f9cc4",
+ "metadata": {},
+ "source": [
+ "Speichern des Modells"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2d3af5be",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Finales Modell und Scaler gespeichert als 'cnn_crossVal.keras' und 'scaler_crossVal.joblib'\n"
+ ]
+ }
+ ],
+ "source": [
+ "# --- Speichern --- \n",
+ "final_model.save(\"cnn_crossVal_FACE_EYE.keras\") \n",
+ "joblib.dump(scaler_final, \"scaler_crossVal_FACE_EYE.joblib\") \n",
+ "\n",
+ "print(\"Finales Modell und Scaler gespeichert als 'cnn_crossVal_FACE_EYE.keras' und 'scaler_crossVal_FACE_EYE.joblib'\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c11891e0",
+ "metadata": {},
+ "source": [
+ "Plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9f6a8584",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#plots\n",
+ "def plot_cv_histories(cv_histories, metric): \n",
+ " plt.figure(figsize=(10,6)) \n",
+ " \n",
+ " for i, hist in enumerate(cv_histories): \n",
+ " plt.plot(hist[metric], label=f\"Fold {i+1} Train\", alpha=0.7) \n",
+ " plt.plot(hist[f\"val_{metric}\"], label=f\"Fold {i+1} Val\", linestyle=\"--\", alpha=0.7) \n",
+ " plt.xlabel(\"Epochs\") \n",
+ " plt.ylabel(metric.capitalize()) \n",
+ " plt.title(f\"Cross-Validation {metric.capitalize()} Verläufe\") \n",
+ " plt.legend() \n",
+ " plt.grid(True) \n",
+ " plt.show()\n",
+ " \n",
+ "plot_cv_histories(cv_histories, \"loss\") \n",
+ "plot_cv_histories(cv_histories, \"accuracy\") \n",
+ "plot_cv_histories(cv_histories, \"auc\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/model_training/CNN/CNN_simple.ipynb b/model_training/CNN/CNN_simple.ipynb
new file mode 100644
index 0000000..5da621f
--- /dev/null
+++ b/model_training/CNN/CNN_simple.ipynb
@@ -0,0 +1,663 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "8698744b",
+ "metadata": {},
+ "source": [
+ "Bibliotheken importieren"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "a1be2d54",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from pathlib import Path\n",
+ "\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix, classification_report\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import Input, layers, models, regularizers\n",
+ "\n",
+ "import joblib \n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9a2832a7",
+ "metadata": {},
+ "source": [
+ "Daten laden"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "f2a8a12d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_path = Path(r\"~/data-paulusjafahrsimulator-gpu/new_datasets/50s_25Hz_dataset.parquet\") \n",
+ "\n",
+ "df = pd.read_parquet(path=data_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5192248c",
+ "metadata": {},
+ "source": [
+ "Labels erstellen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "05b15322",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Label distribution:\n",
+ "label\n",
+ "1 4750\n",
+ "0 3484\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_2528/3529185502.py:9: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " low_all[\"label\"] = 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "low_all = df[((df[\"PHASE\"] == \"baseline\") | \n",
+ " ((df[\"STUDY\"] == \"n-back\") & (df[\"PHASE\"] != \"baseline\") & (df[\"LEVEL\"].isin([1,4]))))] \n",
+ "\n",
+ "high_all = pd.concat([ \n",
+ " df[(df[\"STUDY\"]==\"n-back\") & (df[\"LEVEL\"].isin([2,3,5,6])) & (df[\"PHASE\"].isin([\"train\",\"test\"]))], \n",
+ " df[(df[\"STUDY\"]==\"k-drive\") & (df[\"PHASE\"]!=\"baseline\")] \n",
+ "]) \n",
+ "\n",
+ "low_all[\"label\"] = 0 \n",
+ "high_all[\"label\"] = 1 \n",
+ "data = pd.concat([low_all, high_all], ignore_index=True).drop_duplicates() \n",
+ "\n",
+ "print(\"Label distribution:\") \n",
+ "print(data[\"label\"].value_counts())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9b44f8a4",
+ "metadata": {},
+ "source": [
+ "Feature-Auswahl"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "5c11e2fd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['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",
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "au_columns = [col for col in data.columns if col.lower().startswith(\"face\")] \n",
+ "print(data.columns.tolist())\n",
+ "\n",
+ "print(\"Gefundene FACE_AU-Spalten:\", au_columns)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "52aaa568",
+ "metadata": {},
+ "source": [
+ "Train-/Val- und Test-Split nach Subjects"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "78023e16",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(4116, 41) (1235, 41) (2883, 41)\n"
+ ]
+ }
+ ],
+ "source": [
+ "subjects = np.random.permutation(data[\"subjectID\"].unique()) \n",
+ "n = len(subjects) \n",
+ "n_train = int(n*0.66) \n",
+ "\n",
+ "train_subjects = subjects[:n_train] \n",
+ "test_subjects = subjects[n_train:] \n",
+ "train_subs, val_subs = train_test_split(train_subjects, test_size=0.2, random_state=42) \n",
+ "\n",
+ "train_df = data[data.subjectID.isin(train_subs)] \n",
+ "val_df = data[data.subjectID.isin(val_subs)] \n",
+ "test_df = data[data.subjectID.isin(test_subjects)] \n",
+ "\n",
+ "print(train_df.shape, val_df.shape, test_df.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4fbd22c4",
+ "metadata": {},
+ "source": [
+ "Normalisierung - StandardScaler global"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "13a6ec83",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Vorher (erste 5 Werte): FACE_AU01_mean FACE_AU02_mean FACE_AU04_mean\n",
+ "697 0.792161 0.804894 0.310528\n",
+ "698 0.793914 0.806557 0.312750\n",
+ "699 0.795915 0.803190 0.293604\n",
+ "700 0.790821 0.797190 0.299714\n",
+ "701 0.790045 0.793320 0.292109\n",
+ "Nachher (erste 5 Werte): FACE_AU01_mean FACE_AU02_mean FACE_AU04_mean\n",
+ "697 0.679178 1.116911 1.874625\n",
+ "698 0.709019 1.138696 1.913728\n",
+ "699 0.743082 1.094597 1.576792\n",
+ "700 0.656358 1.016024 1.684310\n",
+ "701 0.643142 0.965348 1.550483\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X_train = train_scaled[au_columns].values[..., np.newaxis] \n",
+ "y_train = train_scaled[\"label\"].values \n",
+ "\n",
+ "X_val = val_scaled[au_columns].values[..., np.newaxis] \n",
+ "y_val = val_scaled[\"label\"].values \n",
+ "\n",
+ "X_test = test_scaled[au_columns].values[..., np.newaxis] \n",
+ "y_test = test_scaled[\"label\"].values\n",
+ "\n",
+ "#Shape-Kontrolle\n",
+ "print(\"X_train Shape:\", X_train.shape)\n",
+ "print(\"X_val Shape:\", X_val.shape)\n",
+ "print(\"X_test Shape:\", X_test.shape)\n",
+ "\n",
+ "#Label-Verteilung\n",
+ "print(\"Train Labels:\", np.bincount(y_train))\n",
+ "print(\"Val Labels:\", np.bincount(y_val))\n",
+ "print(\"Test Labels:\", np.bincount(y_test))\n",
+ "\n",
+ "#Histogramm nach Datenvorbereitung\n",
+ "col_idx = 0 #nur erste AU-Spalte\n",
+ "plt.figure(figsize=(10,4))\n",
+ "plt.hist(X_train[:, col_idx, 0], bins=30)\n",
+ "plt.title(f\"Verteilung nach CNN-Form: Feature {au_columns[col_idx]}\")\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2dddde72",
+ "metadata": {},
+ "source": [
+ "CNN Architektur - mit Keras"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "4501c65a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential_2\"\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ conv1d_6 (Conv1D) │ (None, 18, 32) │ 128 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_2 │ (None, 18, 32) │ 128 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling1d_3 (MaxPooling1D) │ (None, 9, 32) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv1d_7 (Conv1D) │ (None, 7, 64) │ 6,208 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_3 │ (None, 7, 64) │ 256 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling1d_3 │ (None, 64) │ 0 │\n",
+ "│ (GlobalAveragePooling1D) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_4 (Dense) │ (None, 64) │ 4,160 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_2 (Dropout) │ (None, 64) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_5 (Dense) │ (None, 1) │ 65 │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+ "
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+ ],
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+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃\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",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ 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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ 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",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ 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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ 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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ 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",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling1d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ 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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ 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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ 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",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
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+ "data": {
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+ " Total params: 10,945 (42.75 KB)\n",
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+ "metadata": {},
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+ " Trainable params: 10,753 (42.00 KB)\n",
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+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,753\u001b[0m (42.00 KB)\n"
+ ]
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+ "metadata": {},
+ "output_type": "display_data"
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+ " Non-trainable params: 192 (768.00 B)\n",
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+ "source": [
+ "model = models.Sequential([\n",
+ " Input(shape=(len(au_columns),1)),\n",
+ " layers.Conv1D(32, kernel_size=3, activation=\"relu\"), \n",
+ " layers.BatchNormalization(), \n",
+ " layers.MaxPooling1D(pool_size=2), \n",
+ " \n",
+ " layers.Conv1D(64, kernel_size=3, activation=\"relu\"), \n",
+ " layers.BatchNormalization(), \n",
+ " layers.GlobalAveragePooling1D(), \n",
+ " \n",
+ " layers.Dense(64, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n",
+ " layers.Dropout(0.6), # etwas stärkeres Dropout \n",
+ " layers.Dense(1, activation=\"sigmoid\")\n",
+ "])\n",
+ "\n",
+ "model.compile(\n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
+ " loss=\"binary_crossentropy\",\n",
+ " metrics=[\"accuracy\", tf.keras.metrics.AUC(name=\"auc\")]\n",
+ ")\n",
+ "\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bc203cec",
+ "metadata": {},
+ "source": [
+ "Training - mit Callbacks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "be371dbb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/100\n",
+ "\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",
+ "Epoch 2/100\n",
+ "\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",
+ "Epoch 3/100\n",
+ "\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",
+ "Epoch 4/100\n",
+ "\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",
+ "Epoch 5/100\n",
+ "\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",
+ "Epoch 6/100\n",
+ "\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",
+ "Epoch 7/100\n",
+ "\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",
+ "Epoch 8/100\n",
+ "\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",
+ "Epoch 9/100\n",
+ "\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",
+ "Epoch 10/100\n",
+ "\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",
+ "Epoch 11/100\n",
+ "\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",
+ "Epoch 12/100\n",
+ "\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"
+ ]
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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
+}
diff --git a/model_training/CNN/deployment_pipeline.ipynb b/model_training/CNN/deployment_pipeline.ipynb
new file mode 100644
index 0000000..6a0293a
--- /dev/null
+++ b/model_training/CNN/deployment_pipeline.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d48f2e13",
+ "metadata": {},
+ "source": [
+ "Importe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e34b838d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np \n",
+ "import pandas as pd \n",
+ "import joblib \n",
+ "import seaborn as sns \n",
+ "import matplotlib.pyplot as plt \n",
+ "\n",
+ "from sklearn.metrics import ( \n",
+ " confusion_matrix, \n",
+ " roc_curve, auc, \n",
+ " precision_recall_curve, \n",
+ " f1_score, \n",
+ " balanced_accuracy_score \n",
+ ")\n",
+ " \n",
+ "import tensorflow as tf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "324554b5",
+ "metadata": {},
+ "source": [
+ "Modell und Scaler laden"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4acc3d2f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = tf.keras.models.load_model(\"hybrid_fusion_model_V2.keras\") \n",
+ "scaler_au = joblib.load(\"scaler_au_V2.joblib\") \n",
+ "scaler_eye = joblib.load(\"scaler_eye_V2.joblib\")\n",
+ "\n",
+ "print(\"Modell & Scaler erfolgreich geladen.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4271cbee",
+ "metadata": {},
+ "source": [
+ "Features laden"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8342ea10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "au_columns = [...] \n",
+ "eye_columns = [...]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4a58b20c",
+ "metadata": {},
+ "source": [
+ "Preprocessing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b683be47",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def preprocess_sample(df, au_columns, eye_columns, scaler_au, scaler_eye):\n",
+ " # AUs\n",
+ " X_au = df[au_columns].values\n",
+ " X_au = scaler_au.transform(X_au).reshape(len(df), len(au_columns), 1)\n",
+ "\n",
+ " # Eye\n",
+ " X_eye = df[eye_columns].values\n",
+ " X_eye = scaler_eye.transform(X_eye)\n",
+ "\n",
+ " return X_au, X_eye"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9dc99a3d",
+ "metadata": {},
+ "source": [
+ "Predict-Funktion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "00295aa6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict_workload(df, model, au_columns, eye_columns, scaler_au, scaler_eye):\n",
+ " X_au, X_eye = preprocess_sample(df, au_columns, eye_columns, scaler_au, scaler_eye)\n",
+ "\n",
+ " probs = model.predict([X_au, X_eye]).flatten()\n",
+ " preds = (probs > 0.5).astype(int)\n",
+ " \n",
+ " return preds, probs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5753516b",
+ "metadata": {},
+ "source": [
+ "Testdaten laden"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8875b0ee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_data = pd.read_csv(\"test_data.csv\") # oder direkt aus Notebook 1 exportieren \n",
+ "\n",
+ "X_au_test = test_data[au_columns].values[..., np.newaxis] \n",
+ "X_eye_test = test_data[eye_columns].values \n",
+ "y_test = test_data[\"label\"].values \n",
+ "groups_test = test_data[\"subjectID\"].values \n",
+ "\n",
+ "X_au_test_scaled = scaler_au.transform(X_au_test.reshape(len(X_au_test), -1)).reshape(X_au_test.shape) \n",
+ "X_eye_test_scaled = scaler_eye.transform(X_eye_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "332a3a07",
+ "metadata": {},
+ "source": [
+ "Vorhersagen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b5f58ece",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_prob = model.predict([X_au_test_scaled, X_eye_test_scaled]).flatten() \n",
+ "y_pred = (y_prob > 0.5).astype(int)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3bc5c66c",
+ "metadata": {},
+ "source": [
+ "Konfusionsmatrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "40648dd7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cm = confusion_matrix(y_test, y_pred) \n",
+ "plt.figure(figsize=(6,5)) \n",
+ "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", \n",
+ " xticklabels=[\"Pred 0\", \"Pred 1\"], \n",
+ " yticklabels=[\"True 0\", \"True 1\"]) \n",
+ "plt.title(\"Konfusionsmatrix - Testdaten\") \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e79ad8a6",
+ "metadata": {},
+ "source": [
+ "ROC"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd93f15c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fpr, tpr, _ = roc_curve(y_test, y_prob) \n",
+ "roc_auc = auc(fpr, tpr) \n",
+ "\n",
+ "plt.figure(figsize=(7,6)) \n",
+ "plt.plot(fpr, tpr, label=f\"AUC = {roc_auc:.3f}\") \n",
+ "plt.plot([0,1], [0,1], \"k--\") \n",
+ "plt.xlabel(\"False Positive Rate\") \n",
+ "plt.ylabel(\"True Positive Rate\") \n",
+ "plt.title(\"ROC‑Kurve – Testdaten\") \n",
+ "plt.legend() \n",
+ "plt.grid(True) \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2eaaf2a0",
+ "metadata": {},
+ "source": [
+ "Precision-Recall"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "601e5dc9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precision, recall, _ = precision_recall_curve(y_test, y_prob) \n",
+ "plt.figure(figsize=(7,6)) \n",
+ "plt.plot(recall, precision) \n",
+ "plt.xlabel(\"Recall\") \n",
+ "plt.ylabel(\"Precision\") \n",
+ "plt.title(\"Precision‑Recall‑Kurve – Testdaten\")\n",
+ "plt.grid(True) \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "270af771",
+ "metadata": {},
+ "source": [
+ "Scores"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2e7da5b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"F1‑Score:\", f1_score(y_test, y_pred)) \n",
+ "print(\"Balanced Accuracy:\", balanced_accuracy_score(y_test, y_pred))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c6e22e1a",
+ "metadata": {},
+ "source": [
+ "Subject-Performance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "731aaf73",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_eval = pd.DataFrame({ \n",
+ " \"subject\": groups_test, \n",
+ " \"y_true\": y_test, \n",
+ " \"y_pred\": y_pred \n",
+ "}) \n",
+ "\n",
+ "subject_perf = df_eval.groupby(\"subject\").apply( \n",
+ " lambda x: balanced_accuracy_score(x[\"y_true\"], x[\"y_pred\"]) \n",
+ ") \n",
+ "\n",
+ "print(\"\\n=== Balanced Accuracy pro Proband ===\") \n",
+ "print(subject_perf.sort_values())"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}