280 lines
75 KiB
Plaintext
280 lines
75 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "8025eedb-9738-417a-8958-c072ec247caa",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"from sklearn.datasets import load_digits\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
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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": 2,
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"id": "d09c0f17-f3fb-4586-abe3-53b28a15f0f7",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Размер признаков: (1797, 64)\n",
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"Размер меток: (1797,)\n",
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"Классы: [0 1 2 3 4 5 6 7 8 9]\n"
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]
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}
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],
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"source": [
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"digits = load_digits()\n",
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"\n",
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"X = digits.data\n",
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"y = digits.target\n",
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"\n",
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"print(\"Размер признаков:\", X.shape)\n",
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"print(\"Размер меток:\", y.shape)\n",
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"print(\"Классы:\", digits.target_names)"
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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": 3,
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"id": "310b576e-bf54-4eb5-8415-10e58239c814",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x400 with 10 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(10, 4))\n",
|
||
"\n",
|
||
"for i in range(10):\n",
|
||
" plt.subplot(2, 5, i + 1)\n",
|
||
" plt.imshow(digits.images[i], cmap=\"gray\")\n",
|
||
" plt.title(f\"Цифра: {digits.target[i]}\")\n",
|
||
" plt.axis(\"off\")\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "099b2978-bcda-4212-8b84-4946643c3507",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Обучающая выборка: (1347, 64)\n",
|
||
"Тестовая выборка: (450, 64)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
||
" X,\n",
|
||
" y,\n",
|
||
" test_size=0.25,\n",
|
||
" random_state=42,\n",
|
||
" stratify=y\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"Обучающая выборка:\", X_train.shape)\n",
|
||
"print(\"Тестовая выборка:\", X_test.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "855c359a-4690-4477-825e-ae5b80c1e842",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Масштабирование данных выполнено\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"scaler = StandardScaler()\n",
|
||
"\n",
|
||
"X_train_scaled = scaler.fit_transform(X_train)\n",
|
||
"X_test_scaled = scaler.transform(X_test)\n",
|
||
"\n",
|
||
"print(\"Масштабирование данных выполнено\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "9757faf9-fcce-48be-918b-cb6e0326aaa6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Модель обучена\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model = MLPClassifier(\n",
|
||
" hidden_layer_sizes=(64, 32),\n",
|
||
" activation=\"relu\",\n",
|
||
" solver=\"adam\",\n",
|
||
" max_iter=500,\n",
|
||
" random_state=42\n",
|
||
")\n",
|
||
"\n",
|
||
"model.fit(X_train_scaled, y_train)\n",
|
||
"\n",
|
||
"print(\"Модель обучена\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "c24d097d-a94f-4a2e-a508-6dc864b2e55a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Точность модели: 0.9666666666666667\n",
|
||
"\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" 0 1.00 0.98 0.99 45\n",
|
||
" 1 0.93 0.93 0.93 46\n",
|
||
" 2 0.96 1.00 0.98 44\n",
|
||
" 3 0.98 0.98 0.98 46\n",
|
||
" 4 0.96 0.96 0.96 45\n",
|
||
" 5 0.98 0.98 0.98 46\n",
|
||
" 6 1.00 0.96 0.98 45\n",
|
||
" 7 0.94 1.00 0.97 45\n",
|
||
" 8 0.95 0.93 0.94 43\n",
|
||
" 9 0.98 0.96 0.97 45\n",
|
||
"\n",
|
||
" accuracy 0.97 450\n",
|
||
" macro avg 0.97 0.97 0.97 450\n",
|
||
"weighted avg 0.97 0.97 0.97 450\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"y_pred = model.predict(X_test_scaled)\n",
|
||
"\n",
|
||
"accuracy = accuracy_score(y_test, y_pred)\n",
|
||
"\n",
|
||
"print(\"Точность модели:\", accuracy)\n",
|
||
"print()\n",
|
||
"print(classification_report(y_test, y_pred))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "3250779c-02d3-4630-bf4a-b5da28ebc737",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x600 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"cm = confusion_matrix(y_test, y_pred)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(8, 6))\n",
|
||
"sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
|
||
"plt.title(\"Матрица ошибок\")\n",
|
||
"plt.xlabel(\"Предсказанный класс\")\n",
|
||
"plt.ylabel(\"Истинный класс\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "10949b2e-ed0a-426b-979f-f3639e23d58b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x400 with 10 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(10, 4))\n",
|
||
"\n",
|
||
"for i in range(10):\n",
|
||
" plt.subplot(2, 5, i + 1)\n",
|
||
" plt.imshow(X_test[i].reshape(8, 8), cmap=\"gray\")\n",
|
||
" plt.title(f\"Ответ: {y_test[i]}\\nМодель: {y_pred[i]}\")\n",
|
||
" plt.axis(\"off\")\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.14.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|