{
"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",
"#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",
"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"
]
},
{
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"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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"┃\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_16 (\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_16 │ (\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_8 (\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_17 (\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_17 │ (\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_8 │ (\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_16 (\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_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 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_17 (\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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" Total params: 8,833 (34.50 KB)\n",
"\n"
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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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" 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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" Non-trainable params: 192 (768.00 B)\n",
"\n"
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"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n"
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"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"
]
},
{
"data": {
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"Model: \"sequential_9\"\n",
"\n"
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"text/plain": [
"\u001b[1mModel: \"sequential_9\"\u001b[0m\n"
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"data": {
"text/html": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ conv1d_18 (Conv1D) │ (None, 18, 32) │ 128 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_18 │ (None, 18, 32) │ 128 │\n",
"│ (BatchNormalization) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ max_pooling1d_9 (MaxPooling1D) │ (None, 9, 32) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ conv1d_19 (Conv1D) │ (None, 7, 64) │ 6,208 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_19 │ (None, 7, 64) │ 256 │\n",
"│ (BatchNormalization) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ global_average_pooling1d_9 │ (None, 64) │ 0 │\n",
"│ (GlobalAveragePooling1D) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_18 (Dense) │ (None, 32) │ 2,080 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_9 (Dropout) │ (None, 32) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_19 (Dense) │ (None, 1) │ 33 │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\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_18 (\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_18 │ (\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_9 (\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_19 (\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_19 │ (\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_9 │ (\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_18 (\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_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 │\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 │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
]
},
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" Total params: 8,833 (34.50 KB)\n",
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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": {},
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"data": {
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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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" 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"
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"metadata": {},
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{
"name": "stdout",
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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"
]
},
{
"data": {
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"Model: \"sequential_10\"\n",
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"data": {
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ conv1d_20 (Conv1D) │ (None, 18, 32) │ 128 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_20 │ (None, 18, 32) │ 128 │\n",
"│ (BatchNormalization) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ max_pooling1d_10 (MaxPooling1D) │ (None, 9, 32) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ conv1d_21 (Conv1D) │ (None, 7, 64) │ 6,208 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_21 │ (None, 7, 64) │ 256 │\n",
"│ (BatchNormalization) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ global_average_pooling1d_10 │ (None, 64) │ 0 │\n",
"│ (GlobalAveragePooling1D) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_20 (Dense) │ (None, 32) │ 2,080 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_10 (Dropout) │ (None, 32) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_21 (Dense) │ (None, 1) │ 33 │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\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_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",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_20 │ (\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_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",
"│ batch_normalization_21 │ (\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_10 │ (\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_20 (\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_10 (\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_21 (\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",
"\n"
],
"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",
"\n"
],
"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",
"\n"
],
"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: 0.2266, 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",
"\n"
],
"text/plain": [
"\u001b[1mModel: \"sequential_11\"\u001b[0m\n"
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"metadata": {},
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"data": {
"text/html": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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",
"\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_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"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" Total params: 8,833 (34.50 KB)\n",
"\n"
],
"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",
"\n"
],
"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",
"\n"
],
"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",
"\n"
],
"text/plain": [
"\u001b[1mModel: \"sequential_14\"\u001b[0m\n"
]
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"metadata": {},
"output_type": "display_data"
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{
"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",
"\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"
]
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" Total params: 8,833 (34.50 KB)\n",
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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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" Trainable params: 8,641 (33.75 KB)\n",
"\n"
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"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,641\u001b[0m (33.75 KB)\n"
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" 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"
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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": 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": {
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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
}