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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run ML model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"import numpy as np\n",
"from sklearn.svm import SVC\n",
"\n",
"# Load the data\n",
"data = np.load('/home/shanin/Desktop/SHANIN/MAIN/ALL_CODE/Face_Recognition/Face_Embedding.npz')\n",
"EMBEDDED_X = data['embeddings']\n",
"Y = data['labels']\n",
"\n",
"# Encode the labels\n",
"encoder = LabelEncoder()\n",
"encoder.fit(Y)\n",
"Y = encoder.transform(Y)\n",
"\n",
"# Train the SVM model on the entire dataset\n",
"model = SVC(kernel='rbf', probability=True) # kernal='linear'\n",
"model.fit(EMBEDDED_X, Y)\n",
"\n",
"# Save the trained model\n",
"import pickle\n",
"with open('/home/shanin/Desktop/SHANIN/MAIN/ALL_CODE/Face_Recognition/Face_Model.pkl', 'wb') as f:\n",
" pickle.dump(model, f)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "shanin",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.9"
}
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"nbformat": 4,
"nbformat_minor": 2
}
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