changed leaky relu syntax to remove keras bug
This commit is contained in:
parent
3169c29319
commit
537b452449
@ -20,10 +20,10 @@
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"from pathlib import Path\n",
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"import sys\n",
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"import os\n",
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"import tensorflow as tf\n",
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"\n",
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"base_dir = os.path.abspath(os.path.join(os.getcwd(), \"..\"))\n",
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"sys.path.append(base_dir)\n",
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"print(base_dir)\n",
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"\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.svm import OneClassSVM\n",
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@ -31,7 +31,7 @@
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"import matplotlib.pyplot as plt\n",
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"import tensorflow as tf\n",
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"import pickle\n",
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"from tools import evaluation_tools, scaler\n",
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"from Fahrsimulator_MSY2526_AI.model_training.tools import evaluation_tools, scaler\n",
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"from sklearn.metrics import (balanced_accuracy_score, accuracy_score, precision_score, \n",
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" recall_score, f1_score, confusion_matrix, classification_report) "
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]
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@ -44,6 +44,17 @@
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"### Load data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "30a4c885",
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"metadata": {},
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"outputs": [],
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"source": [
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"enconder_path = Path(\".keras\")\n",
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"model_path = Path(\".pkl\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -51,7 +62,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"data_path = Path(r\"/home/jovyan/data-paulusjafahrsimulator-gpu/first_AU_dataset/output_windowed.parquet\")"
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"data_path = Path(r\".parquet\")"
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]
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},
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{
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@ -61,7 +72,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_parquet(path=data_path)"
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"df = pd.read_parquet(path=data_path)\n",
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"df = df.dropna()"
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]
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},
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{
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@ -150,20 +162,29 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"au_columns = [col for col in low_all.columns if col.startswith('AU')]\n",
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"au_columns = [col for col in low_all.columns if \"face\" in col.lower()] \n",
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"\n",
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"eye_columns = [ \n",
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" 'Fix_count_short_66_150','Fix_count_medium_300_500','Fix_count_long_gt_1000', \n",
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" 'Fix_count_100','Fix_mean_duration','Fix_median_duration', \n",
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" 'Sac_count','Sac_mean_amp','Sac_mean_dur','Sac_median_dur', \n",
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" 'Blink_count','Blink_mean_dur','Blink_median_dur', \n",
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" 'Pupil_mean','Pupil_IPA' \n",
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"] \n",
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"cols = au_columns +eye_columns\n",
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"\n",
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"# Prepare training data (only normal/low data)\n",
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"train_data = low_all[low_all['subjectID'].isin(train_subjects)][['subjectID'] + au_columns].copy()\n",
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"train_data = low_all[low_all['subjectID'].isin(train_subjects)][['subjectID'] + cols].copy()\n",
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"\n",
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"# Prepare validation data (normal and anomaly) \n",
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"val_normal_data = low_all[low_all['subjectID'].isin(val_subjects)][['subjectID'] + au_columns].copy()\n",
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"val_high_data = high_all[high_all['subjectID'].isin(val_subjects)][['subjectID'] + au_columns].copy()\n",
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"val_normal_data = val_normal_data.sample(n=1000, random_state=42)\n",
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"val_high_data = val_high_data.sample(n=1000, random_state=42)\n",
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"val_normal_data = low_all[low_all['subjectID'].isin(val_subjects)][['subjectID'] + cols].copy()\n",
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"val_high_data = high_all[high_all['subjectID'].isin(val_subjects)][['subjectID'] + cols].copy()\n",
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"val_normal_data = val_normal_data.sample(n=500, random_state=42)\n",
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"val_high_data = val_high_data.sample(n=500, random_state=42)\n",
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"\n",
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"# Prepare test data (normal and anomaly) - 1000 samples each\n",
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"test_normal_data = low_all[low_all['subjectID'].isin(test_subjects)][['subjectID'] + au_columns].copy()\n",
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"test_high_data = high_all[high_all['subjectID'].isin(test_subjects)][['subjectID'] + au_columns].copy()\n",
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"test_normal_data = low_all[low_all['subjectID'].isin(test_subjects)][['subjectID'] + cols].copy()\n",
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"test_high_data = high_all[high_all['subjectID'].isin(test_subjects)][['subjectID'] + cols].copy()\n",
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"test_normal_data = test_normal_data.sample(n=500, random_state=42)\n",
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"test_high_data = test_high_data.sample(n=500, random_state=42)\n",
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"\n",
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@ -186,7 +207,7 @@
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"outputs": [],
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"source": [
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"# Cell 3: Fit normalizer on training data\n",
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"normalizer = scaler.fit_normalizer(train_data, au_columns, method='minmax', scope='global')\n",
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"normalizer = scaler.fit_normalizer(train_data, cols, method='minmax', scope='global')\n",
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"print(\"Normalizer fitted on training data\")"
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]
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},
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@ -198,11 +219,11 @@
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"outputs": [],
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"source": [
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"# Cell 4: Apply normalization to all datasets\n",
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"train_normalized = scaler.apply_normalizer(train_data, au_columns, normalizer)\n",
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"val_normal_normalized = scaler.apply_normalizer(val_normal_data, au_columns, normalizer)\n",
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"val_high_normalized = scaler.apply_normalizer(val_high_data, au_columns, normalizer)\n",
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"test_normal_normalized = scaler.apply_normalizer(test_normal_data, au_columns, normalizer)\n",
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"test_high_normalized = scaler.apply_normalizer(test_high_data, au_columns, normalizer)\n",
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"train_normalized = scaler.apply_normalizer(train_data, cols, normalizer)\n",
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"val_normal_normalized = scaler.apply_normalizer(val_normal_data, cols, normalizer)\n",
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"val_high_normalized = scaler.apply_normalizer(val_high_data, cols, normalizer)\n",
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"test_normal_normalized = scaler.apply_normalizer(test_normal_data, cols, normalizer)\n",
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"test_high_normalized = scaler.apply_normalizer(test_high_data, cols, normalizer)\n",
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"\n",
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"print(\"Normalization applied to all datasets\")"
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]
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@ -214,13 +235,11 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cell 5: Extract AU columns and create labels for grid search\n",
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"# Extract only AU columns (drop subjectID)\n",
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"X_train = train_normalized[au_columns].copy()\n",
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"X_val_normal = val_normal_normalized[au_columns].copy()\n",
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"X_val_high = val_high_normalized[au_columns].copy()\n",
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"X_test_high = test_high_normalized[au_columns].copy()\n",
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"X_test_normal = test_normal_normalized[au_columns].copy()\n",
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"X_train = train_normalized[cols].copy()\n",
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"X_val_normal = val_normal_normalized[cols].copy()\n",
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"X_val_high = val_high_normalized[cols].copy()\n",
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"X_test_high = test_high_normalized[cols].copy()\n",
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"X_test_normal = test_normal_normalized[cols].copy()\n",
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"\n",
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"\n",
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"# Create labels for grid search\n",
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@ -241,116 +260,12 @@
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"X_train.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "50fc80dc-fe16-4917-aad6-0dbaa1ce5ef9",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install keras-tuner --quiet # nur einmal nötig\n",
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"\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"from kerastuner import HyperModel\n",
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"from kerastuner.tuners import RandomSearch\n",
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"\n",
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"# 1️⃣ HyperModel definieren\n",
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"class AutoencoderHyperModel(HyperModel):\n",
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" def __init__(self, input_dim):\n",
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" self.input_dim = input_dim\n",
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"\n",
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" def build(self, hp):\n",
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" reg = hp.Float(\"l2_reg\", min_value=1e-5, max_value=0.01, sampling=\"log\")\n",
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" lr = hp.Float(\"learning_rate\", 1e-4, 1e-2, sampling=\"log\")\n",
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"\n",
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" # Encoder\n",
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" encoder = keras.Sequential([\n",
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" keras.layers.Dense(\n",
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" units=hp.Int(\"enc_units1\", min_value=10, max_value=self.input_dim, step=10),\n",
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" activation=None,\n",
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" kernel_regularizer=keras.regularizers.l2(reg)\n",
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" ),\n",
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" keras.layers.LeakyReLU(alpha=0.1),\n",
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" keras.layers.Dense(\n",
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" units=hp.Int(\"enc_units2\", min_value=5, max_value=20, step=1),\n",
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" activation=tf.keras.layers.LeakyReLU(alpha=0.1),\n",
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" kernel_regularizer=keras.regularizers.l2(reg)\n",
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" ),\n",
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" keras.layers.Dense(\n",
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" units=2, # Bottleneck\n",
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" activation='linear',\n",
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" kernel_regularizer=keras.regularizers.l2(reg)\n",
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" ),\n",
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" ])\n",
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"\n",
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" # Decoder\n",
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" decoder = keras.Sequential([\n",
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" keras.layers.Dense(\n",
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" units=hp.Int(\"dec_units1\", min_value=5, max_value=20, step=1),\n",
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" activation=tf.keras.layers.LeakyReLU(alpha=0.1),\n",
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" kernel_regularizer=keras.regularizers.l2(reg)\n",
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" ),\n",
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" keras.layers.Dense(\n",
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" units=hp.Int(\"dec_units2\", min_value=10, max_value=self.input_dim, step=10),\n",
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" activation=tf.keras.layers.LeakyReLU(alpha=0.1),\n",
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" kernel_regularizer=keras.regularizers.l2(reg)\n",
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" ),\n",
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" keras.layers.Dense(\n",
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" units=self.input_dim,\n",
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" activation='linear',\n",
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" kernel_regularizer=keras.regularizers.l2(reg)\n",
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" ),\n",
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" ])\n",
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"\n",
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" # Autoencoder\n",
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" inputs = keras.Input(shape=(self.input_dim,))\n",
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" encoded = encoder(inputs)\n",
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" decoded = decoder(encoded)\n",
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" autoencoder = keras.Model(inputs, decoded)\n",
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"\n",
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" autoencoder.compile(\n",
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" optimizer=keras.optimizers.Adam(learning_rate=lr),\n",
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" loss='mse'\n",
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" )\n",
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"\n",
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" return autoencoder\n",
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"\n",
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"# 2️⃣ RandomSearch-Tuner\n",
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"hypermodel = AutoencoderHyperModel(input_dim=X_train.shape[1])\n",
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"\n",
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"tuner = RandomSearch(\n",
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" hypermodel,\n",
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" objective='val_loss',\n",
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" max_trials=10, # Anzahl der getesteten Kombinationen\n",
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" executions_per_trial=1, # Anzahl Trainings pro Kombination\n",
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" directory='tuner_dir',\n",
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" project_name='oc_ae'\n",
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")\n",
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"\n",
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"# 3️⃣ Hyperparameter-Tuning starten\n",
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"tuner.search(\n",
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" X_train, X_train,\n",
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" epochs=100,\n",
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" batch_size=64,\n",
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" validation_data=(X_val_normal, X_val_normal),\n",
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" verbose=0\n",
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")\n",
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"\n",
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"# 4️⃣ Beste Architektur holen\n",
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"best_model = tuner.get_best_models(num_models=1)[0]\n",
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"best_hyperparameters = tuner.get_best_hyperparameters(1)[0]\n",
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"\n",
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"print(\"Beste Hyperparameter:\", best_hyperparameters.values)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "362c0a6f",
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"metadata": {},
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"source": [
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"\n",
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"Beste Hyperparameter: {'l2_reg': 1.3757411430582133e-05, 'learning_rate': 0.007321002854350309, 'enc_units1': 20, 'enc_units2': 16, 'dec_units1': 14, 'dec_units2': 10}"
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"Build model"
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]
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},
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{
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@ -360,26 +275,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# reg = 0.1\n",
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"# encoder = tf.keras.Sequential(\n",
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"# [\n",
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"# tf.keras.layers.Dense(units=X_train.shape[1], activation='relu', kernel_regularizer=tf.keras.regularizers.l2(reg)),\n",
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"# tf.keras.layers.Dense(units=10, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(reg)),\n",
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"# tf.keras.layers.Dense(units=5, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(reg)),\n",
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" \n",
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"# ]\n",
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"# )\n",
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"\n",
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"# decoder = tf.keras.Sequential(\n",
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"# [\n",
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"# tf.keras.layers.Dense(units=5,activation='relu', kernel_regularizer=tf.keras.regularizers.l2(reg)),\n",
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"# tf.keras.layers.Dense(units=10, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(reg)),\n",
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"# tf.keras.layers.Dense(units=X_train.shape[1], activation='linear', kernel_regularizer=tf.keras.regularizers.l2(reg))\n",
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"# ]\n",
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"# )\n",
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"\n",
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"\n",
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"\n",
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"reg = 1e-5\n",
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"\n",
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"# ENCODER\n",
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" activation=None,\n",
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" kernel_regularizer=tf.keras.regularizers.l2(reg)\n",
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" ),\n",
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" tf.keras.layers.LeakyReLU(alpha=0.1),\n",
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" \n",
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" tf.keras.layers.LeakyReLU(negative_slope=0.1),\n",
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"\n",
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" tf.keras.layers.Dense(\n",
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" units=12,\n",
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" activation=tf.keras.layers.LeakyReLU(negative_slope=0.1),\n",
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" activation=None,\n",
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" kernel_regularizer=tf.keras.regularizers.l2(reg)\n",
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" ),\n",
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" \n",
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" tf.keras.layers.LeakyReLU(negative_slope=0.1),\n",
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"\n",
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" tf.keras.layers.Dense(\n",
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" units=8,\n",
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" activation='linear', # Bottleneck stays linear\n",
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"decoder = tf.keras.Sequential([\n",
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" tf.keras.layers.Dense(\n",
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" units=8,\n",
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" activation=tf.keras.layers.LeakyReLU(negative_slope=0.1),\n",
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" activation=None,\n",
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" kernel_regularizer=tf.keras.regularizers.l2(reg)\n",
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" ),\n",
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" tf.keras.layers.LeakyReLU(negative_slope=0.1),\n",
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"\n",
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" tf.keras.layers.Dense(\n",
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" units=12,\n",
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" activation=tf.keras.layers.LeakyReLU(negative_slope=0.1),\n",
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" activation=None,\n",
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" kernel_regularizer=tf.keras.regularizers.l2(reg)\n",
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" ),\n",
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" tf.keras.layers.LeakyReLU(negative_slope=0.1),\n",
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"\n",
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" tf.keras.layers.Dense(\n",
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" units=X_train.shape[1],\n",
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" activation='linear',\n",
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" kernel_regularizer=tf.keras.regularizers.l2(reg)\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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@ -456,7 +356,7 @@
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"source": [
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"history = autoencoder.fit(\n",
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" X_train, X_train, # Input and target are the same for autoencoder\n",
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" epochs=200,\n",
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" epochs=50,\n",
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" batch_size=64,\n",
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" validation_data=(X_val_normal, X_val_normal),\n",
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" verbose=1\n",
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@ -470,8 +370,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"save_path = Path(\"/home/jovyan/data-paulusjafahrsimulator-gpu/saved_models/encoder_model_2_neurons_minmax.keras\")\n",
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"encoder.save(save_path)"
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"encoder.save(enconder_path)"
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]
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},
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{
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@ -489,8 +388,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"load_path = Path(\"/home/jovyan/data-paulusjafahrsimulator-gpu/saved_models/encoder_model_2_neurons_minmax.keras\")\n",
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"encoder = tf.keras.models.load_model(load_path)"
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"encoder = tf.keras.models.load_model(enconder_path)"
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]
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},
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{
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@ -541,25 +439,6 @@
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"test_predictions.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "759118d8-989d-489c-9d35-331454b4795e",
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"metadata": {},
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"outputs": [],
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"source": [
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"all_zero = {\n",
|
||||
" \"X_train_encoded\": np.all(X_train_encoded == 0),\n",
|
||||
" \"X_val_normal_encoded\": np.all(X_val_normal_encoded == 0),\n",
|
||||
" \"X_val_high_encoded\": np.all(X_val_high_encoded == 0),\n",
|
||||
" \"X_test_normal_encoded\": np.all(X_test_normal_encoded == 0),\n",
|
||||
" \"X_test_high_encoded\": np.all(X_test_high_encoded == 0),\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"print(all_zero)\n",
|
||||
"print(X_train_encoded.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -567,81 +446,49 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
|
||||
"fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
|
||||
"\n",
|
||||
"# # Subplot A: Normal\n",
|
||||
"# axes[0].scatter(\n",
|
||||
"# X_val_normal_encoded[:, 0],\n",
|
||||
"# X_val_normal_encoded[:, 1],\n",
|
||||
"# color=\"blue\",\n",
|
||||
"# label=\"Normal\"\n",
|
||||
"# )\n",
|
||||
"# axes[0].set_title(\"Val Normal (encoded)\")\n",
|
||||
"# axes[0].set_xlabel(\"latent feature 1\")\n",
|
||||
"# axes[0].set_ylabel(\"latent feature 2\")\n",
|
||||
"# axes[0].legend()\n",
|
||||
"\n",
|
||||
"# # Subplot B: High\n",
|
||||
"# axes[1].scatter(\n",
|
||||
"# X_val_high_encoded[:, 0],\n",
|
||||
"# X_val_high_encoded[:, 1],\n",
|
||||
"# color=\"orange\",\n",
|
||||
"# label=\"High\"\n",
|
||||
"# )\n",
|
||||
"# axes[1].set_title(\"ValHigh (encoded)\")\n",
|
||||
"# axes[1].set_xlabel(\"latent feature 1\")\n",
|
||||
"# axes[1].set_ylabel(\"latent feature 2\")\n",
|
||||
"# axes[1].legend()\n",
|
||||
"\n",
|
||||
"# # Subplot C: Both\n",
|
||||
"# axes[2].scatter(\n",
|
||||
"# X_val_normal_encoded[:, 0],\n",
|
||||
"# X_val_normal_encoded[:, 1],\n",
|
||||
"# color=\"blue\",\n",
|
||||
"# label=\"Normal\"\n",
|
||||
"# )\n",
|
||||
"# axes[2].scatter(\n",
|
||||
"# X_val_high_encoded[:, 0],\n",
|
||||
"# X_val_high_encoded[:, 1],\n",
|
||||
"# color=\"orange\",\n",
|
||||
"# label=\"High\"\n",
|
||||
"# )\n",
|
||||
"# axes[2].set_title(\"Normal vs High (encoded)\")\n",
|
||||
"# axes[2].set_xlabel(\"latent feature 1\")\n",
|
||||
"# axes[2].set_ylabel(\"latent feature 2\")\n",
|
||||
"# axes[2].legend()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"latent_dim = 8\n",
|
||||
"fig, axes = plt.subplots(2, 4, figsize=(20, 10))\n",
|
||||
"axes = axes.flatten() # flatten to index easily\n",
|
||||
"\n",
|
||||
"for i in range(latent_dim):\n",
|
||||
" axes[i].scatter(\n",
|
||||
" X_val_normal_encoded[:, i],\n",
|
||||
" [0]*X_val_normal_encoded.shape[0], # optional: place on a line for 1D visualization\n",
|
||||
" color='blue',\n",
|
||||
" label='Normal',\n",
|
||||
" alpha=0.6\n",
|
||||
" )\n",
|
||||
" axes[i].scatter(\n",
|
||||
" X_val_high_encoded[:, i],\n",
|
||||
" [0]*X_val_high_encoded.shape[0], # same for High\n",
|
||||
" color='orange',\n",
|
||||
" label='High',\n",
|
||||
" alpha=0.6\n",
|
||||
" )\n",
|
||||
" axes[i].set_title(f'Latent dim {i+1}')\n",
|
||||
" axes[i].set_xlabel(f'Feature {i+1}')\n",
|
||||
" axes[i].set_yticks([]) # hide y-axis as it's just a 1D comparison\n",
|
||||
" axes[i].legend()\n",
|
||||
" axes[i].grid(True)\n",
|
||||
"\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()\n",
|
||||
"# Subplot A: Normal\n",
|
||||
"axes[0].scatter(\n",
|
||||
" X_val_normal_encoded[:, 0],\n",
|
||||
" X_val_normal_encoded[:, 1],\n",
|
||||
" color=\"blue\",\n",
|
||||
" label=\"Normal\"\n",
|
||||
")\n",
|
||||
"axes[0].set_title(\"Val Normal (encoded)\")\n",
|
||||
"axes[0].set_xlabel(\"latent feature 1\")\n",
|
||||
"axes[0].set_ylabel(\"latent feature 2\")\n",
|
||||
"axes[0].legend()\n",
|
||||
"\n",
|
||||
"# Subplot B: High\n",
|
||||
"axes[1].scatter(\n",
|
||||
" X_val_high_encoded[:, 0],\n",
|
||||
" X_val_high_encoded[:, 1],\n",
|
||||
" color=\"orange\",\n",
|
||||
" label=\"High\"\n",
|
||||
")\n",
|
||||
"axes[1].set_title(\"ValHigh (encoded)\")\n",
|
||||
"axes[1].set_xlabel(\"latent feature 1\")\n",
|
||||
"axes[1].set_ylabel(\"latent feature 2\")\n",
|
||||
"axes[1].legend()\n",
|
||||
"\n",
|
||||
"# Subplot C: Both\n",
|
||||
"axes[2].scatter(\n",
|
||||
" X_val_normal_encoded[:, 0],\n",
|
||||
" X_val_normal_encoded[:, 1],\n",
|
||||
" color=\"blue\",\n",
|
||||
" label=\"Normal\"\n",
|
||||
")\n",
|
||||
"axes[2].scatter(\n",
|
||||
" X_val_high_encoded[:, 0],\n",
|
||||
" X_val_high_encoded[:, 1],\n",
|
||||
" color=\"orange\",\n",
|
||||
" label=\"High\"\n",
|
||||
")\n",
|
||||
"axes[2].set_title(\"Normal vs High (encoded)\")\n",
|
||||
"axes[2].set_xlabel(\"latent feature 1\")\n",
|
||||
"axes[2].set_ylabel(\"latent feature 2\")\n",
|
||||
"axes[2].legend()\n",
|
||||
"\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()\n",
|
||||
@ -708,11 +555,11 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Save\n",
|
||||
"with open('ocsvm_model.pkl', 'wb') as f:\n",
|
||||
"with open(model_path, \"wb\") as f:\n",
|
||||
" pickle.dump(ocsvm, f)\n",
|
||||
"\n",
|
||||
"# Load later\n",
|
||||
"with open('ocsvm_model.pkl', 'rb') as f:\n",
|
||||
"with open(model_path, \"rb\") as f:\n",
|
||||
" ocsvm_loaded = pickle.load(f)"
|
||||
]
|
||||
},
|
||||
@ -731,11 +578,6 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# X_combined = np.concatenate([X_train_encoded, X_val_normal_encoded, X_val_high_encoded], axis=0)\n",
|
||||
"# y_combined = np.concatenate([\n",
|
||||
"# np.ones(X_train_encoded.shape[0]+X_val_normal_encoded.shape[0]), # normal = 1\n",
|
||||
"# -np.ones(X_val_high_encoded.shape[0]) # anomaly = -1\n",
|
||||
"# ], axis=0)\n",
|
||||
"X_combined = np.concatenate([X_train_encoded, X_val_high_encoded], axis=0)\n",
|
||||
"y_combined = np.concatenate([\n",
|
||||
" np.ones(X_train_encoded.shape[0]), # normal = 1\n",
|
||||
@ -856,14 +698,6 @@
|
||||
"source": [
|
||||
"f1_score(y_true=np.concatenate([y_test_normal, y_test_high]), y_pred=predictions)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9e412041-7534-40fe-8486-ee97349a6168",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user