форкнуто от main/is_dnn
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3 Коммитов
| Автор | SHA1 | Дата |
|---|---|---|
 Сергей Филатов
|
b344e76a21 | 1 день назад |
Lipisin
|
047d249b1f | 2 недель назад |
|
Lipisin
|
339fe963d0 | 1 месяц назад |
|
До Ширина: | Высота: | Размер: 7.9 KiB |
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До Ширина: | Высота: | Размер: 6.5 KiB |
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До Ширина: | Высота: | Размер: 6.3 KiB |
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До Ширина: | Высота: | Размер: 6.3 KiB |
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До Ширина: | Высота: | Размер: 6.3 KiB |
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До Ширина: | Высота: | Размер: 25 KiB |
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До Ширина: | Высота: | Размер: 25 KiB |
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До Ширина: | Высота: | Размер: 25 KiB |
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До Ширина: | Высота: | Размер: 24 KiB |
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До Ширина: | Высота: | Размер: 29 KiB |
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До Ширина: | Высота: | Размер: 29 KiB |
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До Ширина: | Высота: | Размер: 7.0 KiB |
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До Ширина: | Высота: | Размер: 6.8 KiB |
@ -1,879 +0,0 @@
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{
|
||||
"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
|
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"colab": {
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"provenance": []
|
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},
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"kernelspec": {
|
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"name": "python3",
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"display_name": "Python 3"
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},
|
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"language_info": {
|
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"name": "python"
|
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}
|
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},
|
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"cells": [
|
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
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"id": "HUUZx52sc1LD"
|
||||
},
|
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"outputs": [],
|
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"source": [
|
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"import os\n",
|
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"os.chdir('/content/drive/MyDrive/Colab Notebooks')"
|
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]
|
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},
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{
|
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"cell_type": "code",
|
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"source": [
|
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"# импорт модулей\n",
|
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"from tensorflow import keras\n",
|
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"import matplotlib.pyplot as plt\n",
|
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"import numpy as np\n",
|
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"import sklearn"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "3Y-Ux1dadqdA"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
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{
|
||||
"cell_type": "code",
|
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"source": [
|
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"# загрузка датасета\n",
|
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"from keras.datasets import mnist\n",
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"(X_train, y_train), (X_test, y_test) = mnist.load_data()"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "w25XE8ADdqP5"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
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},
|
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{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# создание своего разбиения датасета\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"# объединяем в один набор\n",
|
||||
"X = np.concatenate((X_train, X_test))\n",
|
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"y = np.concatenate((y_train, y_test))\n",
|
||||
"# разбиваем по вариантам\n",
|
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"X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
|
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"test_size = 10000,\n",
|
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"train_size = 60000,\n",
|
||||
"random_state = 19) #(5*4-1)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "QcXt9zqCdqDH"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
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"# вывод размерностей\n",
|
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"print('Shape of X train:', X_train.shape)\n",
|
||||
"print('Shape of y train:', y_train.shape)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "9Cd705vod51B",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Выводим 4 изображения\n",
|
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"plt.figure(figsize=(10, 3))\n",
|
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"for i in range(4):\n",
|
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" plt.subplot(1, 4, i + 1)\n",
|
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" plt.imshow(X_train[i], cmap='gray')\n",
|
||||
" plt.title(f'Label: {y_train[i]}')\n",
|
||||
" plt.axis('off')\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()\n",
|
||||
"\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "vLYfI---d5rm",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
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{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# развернем каждое изображение 28*28 в вектор 784\n",
|
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"num_pixels = X_train.shape[1] * X_train.shape[2]\n",
|
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"X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255\n",
|
||||
"X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255\n",
|
||||
"print('Shape of transformed X train:', X_train.shape)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "d0oyu59gd5fz",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# переведем метки в one-hot\n",
|
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"from keras.utils import to_categorical\n",
|
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"\n",
|
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"y_train = to_categorical(y_train)\n",
|
||||
"y_test = to_categorical(y_test)\n",
|
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"\n",
|
||||
"print('Shape of transformed y train:', y_train.shape)\n",
|
||||
"num_classes = y_train.shape[1]"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Q227fINPeD1A",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"from keras.models import Sequential\n",
|
||||
"from keras.layers import Dense"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "TzaA61smeDoO"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# 1. создаем модель - объявляем ее объектом класса Sequential\n",
|
||||
"model = Sequential()\n",
|
||||
"# 2. добавляем выходной слой(скрытые слои отсутствуют)\n",
|
||||
"model.add(Dense(units=num_classes, activation='softmax'))\n",
|
||||
"# 3. компилируем модель\n",
|
||||
"model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Liq39zruhz0d"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод информации об архитектуре модели\n",
|
||||
"print(model.summary())"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "jMGGsq7piZOu",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Обучаем модель\n",
|
||||
"H = model.fit(X_train, y_train, validation_split=0.1, epochs=50)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "n_pCdxphiedM",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод графика ошибки по эпохам\n",
|
||||
"plt.plot(H.history['loss'])\n",
|
||||
"plt.plot(H.history['val_loss'])\n",
|
||||
"plt.grid()\n",
|
||||
"plt.xlabel('Epochs')\n",
|
||||
"plt.ylabel('loss')\n",
|
||||
"plt.legend(['train_loss', 'val_loss'])\n",
|
||||
"plt.title('Loss by epochs')\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Sz_YOlsVivoR",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Оценка качества работы модели на тестовых данных\n",
|
||||
"scores = model.evaluate(X_test, y_test)\n",
|
||||
"print('Loss on test data:', scores[0])\n",
|
||||
"print('Accuracy on test data:', scores[1])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "hpJALaZGnyWF",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# сохранение модели на диск\n",
|
||||
"model.save('/content/drive/MyDrive/Colab Notebooks/models/model_zero_hide.keras')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Z6eSmpwXn1zM"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"model100 = Sequential()\n",
|
||||
"model100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n",
|
||||
"model100.add(Dense(units=num_classes, activation='softmax'))\n",
|
||||
"\n",
|
||||
"model100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "G1qGmPNF9afO"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод информации об архитектуре модели\n",
|
||||
"print(model100.summary())"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "2WtfjJKY9abn",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Обучаем модель\n",
|
||||
"H = model100.fit(X_train, y_train, validation_split=0.1, epochs=50)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "rPuWd80o9aYD",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод графика ошибки по эпохам\n",
|
||||
"plt.plot(H.history['loss'])\n",
|
||||
"plt.plot(H.history['val_loss'])\n",
|
||||
"plt.grid()\n",
|
||||
"plt.xlabel('Epochs')\n",
|
||||
"plt.ylabel('loss')\n",
|
||||
"plt.legend(['train_loss', 'val_loss'])\n",
|
||||
"plt.title('Loss by epochs')\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "JLrW7S1g9aUe",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Оценка качества работы модели на тестовых данных\n",
|
||||
"scores = model100.evaluate(X_test, y_test)\n",
|
||||
"print('Loss on test data:', scores[0])\n",
|
||||
"print('Accuracy on test data:', scores[1])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "8jdS02JZ9aRc",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# сохранение модели на диск\n",
|
||||
"model100.save('/content/drive/MyDrive/Colab Notebooks/models/model100in_1hide.keras')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "_bR3qoBy9aNy"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"model300 = Sequential()\n",
|
||||
"model300.add(Dense(units=300,input_dim=num_pixels, activation='sigmoid'))\n",
|
||||
"model300.add(Dense(units=num_classes, activation='softmax'))\n",
|
||||
"\n",
|
||||
"model300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "V4m3nGORGnPC"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод информации об архитектуре модели\n",
|
||||
"print(model300.summary())"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "yETaYKzdA9fp",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Обучаем модель\n",
|
||||
"H = model300.fit(X_train, y_train, validation_split=0.1, epochs=50)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "SFPh0Lw-A9Zq",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод графика ошибки по эпохам\n",
|
||||
"plt.plot(H.history['loss'])\n",
|
||||
"plt.plot(H.history['val_loss'])\n",
|
||||
"plt.grid()\n",
|
||||
"plt.xlabel('Epochs')\n",
|
||||
"plt.ylabel('loss')\n",
|
||||
"plt.legend(['train_loss', 'val_loss'])\n",
|
||||
"plt.title('Loss by epochs')\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "6mvOMGiLA9QE",
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Оценка качества работы модели на тестовых данных\n",
|
||||
"scores = model300.evaluate(X_test, y_test)\n",
|
||||
"print('Loss on test data:', scores[0])\n",
|
||||
"print('Accuracy on test data:', scores[1])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "WOJyUHP79Z86"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# сохранение модели на диск\n",
|
||||
"model300.save('/content/drive/MyDrive/Colab Notebooks/models/model300in_1hide.keras')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "XsWc7S4aCyiE"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"model500 = Sequential()\n",
|
||||
"model500.add(Dense(units=500,input_dim=num_pixels, activation='sigmoid'))\n",
|
||||
"model500.add(Dense(units=num_classes, activation='softmax'))\n",
|
||||
"\n",
|
||||
"model500.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "QSL-6YbkJxm0"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод информации об архитектуре модели\n",
|
||||
"print(model500.summary())"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Vs1x3ooKCybg"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Обучаем модель\n",
|
||||
"H = model500.fit(X_train, y_train, validation_split=0.1, epochs=50)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "x3kzDT5qCyYY"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод графика ошибки по эпохам\n",
|
||||
"plt.plot(H.history['loss'])\n",
|
||||
"plt.plot(H.history['val_loss'])\n",
|
||||
"plt.grid()\n",
|
||||
"plt.xlabel('Epochs')\n",
|
||||
"plt.ylabel('loss')\n",
|
||||
"plt.legend(['train_loss', 'val_loss'])\n",
|
||||
"plt.title('Loss by epochs')\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "FwSLP5I8CyU0"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Оценка качества работы модели на тестовых данных\n",
|
||||
"scores = model500.evaluate(X_test, y_test)\n",
|
||||
"print('Loss on test data:', scores[0])\n",
|
||||
"print('Accuracy on test data:', scores[1])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "5mDveUNPCyRH"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# сохранение модели на диск\n",
|
||||
"model500.save('/content/drive/MyDrive/Colab Notebooks/models/model500in_1hide.keras')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "4IEeNu1rCyNj"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"model10050 = Sequential()\n",
|
||||
"model10050.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n",
|
||||
"model10050.add(Dense(units=50,activation='sigmoid'))\n",
|
||||
"model10050.add(Dense(units=num_classes, activation='softmax'))\n",
|
||||
"\n",
|
||||
"model10050.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Ld4hMck_CyKT"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод информации об архитектуре модели\n",
|
||||
"print(model10050.summary())"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "GVZLuKvqNZEK"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Обучаем модель\n",
|
||||
"H = model10050.fit(X_train, y_train, validation_split=0.1, epochs=50)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "UP0suqUbNY9R"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод графика ошибки по эпохам\n",
|
||||
"plt.plot(H.history['loss'])\n",
|
||||
"plt.plot(H.history['val_loss'])\n",
|
||||
"plt.grid()\n",
|
||||
"plt.xlabel('Epochs')\n",
|
||||
"plt.ylabel('loss')\n",
|
||||
"plt.legend(['train_loss', 'val_loss'])\n",
|
||||
"plt.title('Loss by epochs')\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "k-DhnF0SNY3K"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Оценка качества работы модели на тестовых данных\n",
|
||||
"scores = model10050.evaluate(X_test, y_test)\n",
|
||||
"print('Loss on test data:', scores[0])\n",
|
||||
"print('Accuracy on test data:', scores[1])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "-7E0BUrMNYx9"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# сохранение модели на диск\n",
|
||||
"model10050.save('/content/drive/MyDrive/Colab Notebooks/models/model100in_1hide_50in_2hide.keras')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "yu11cXisCyCh"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"model100100 = Sequential()\n",
|
||||
"model100100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n",
|
||||
"model100100.add(Dense(units=100,activation='sigmoid'))\n",
|
||||
"model100100.add(Dense(units=num_classes, activation='softmax'))\n",
|
||||
"\n",
|
||||
"model100100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "pTTia0gmRFaV"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод информации об архитектуре модели\n",
|
||||
"print(model100100.summary())"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "XQHhKm8YRFW6"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Обучаем модель\n",
|
||||
"H = model100100.fit(X_train, y_train, validation_split=0.1, epochs=50)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "oCgqwCmPRFTT"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"ke"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "YDdSpQO5RFPn"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Оценка качества работы модели на тестовых данных\n",
|
||||
"scores = model100100.evaluate(X_test, y_test)\n",
|
||||
"print('Loss on test data:', scores[0])\n",
|
||||
"print('Accuracy on test data:', scores[1])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "D_WHUHCwRFMS"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# сохранение модели на диск\n",
|
||||
"model100100.save('/content/drive/MyDrive/Colab Notebooks/models/model100in_1hide_100in_2hide.keras')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "fkBAnNf2RFDL"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# сохранение лучшей модели в папку best_model\n",
|
||||
"model100.save('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "mXGyPCNdS91i"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Загрузка модели с диска\n",
|
||||
"from keras.models import load_model\n",
|
||||
"model = load_model('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "ILyFn-CJp1k8"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод тестового изображения и результата распознавания\n",
|
||||
"n = 111\n",
|
||||
"result = model.predict(X_test[n:n+1])\n",
|
||||
"print('NN output:', result)\n",
|
||||
"plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n",
|
||||
"plt.show()\n",
|
||||
"print('Real mark: ', str(np.argmax(y_test[n])))\n",
|
||||
"print('NN answer: ', str(np.argmax(result)))"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "cCk7Do1mp-xb"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод тестового изображения и результата распознавания\n",
|
||||
"n = 222\n",
|
||||
"result = model.predict(X_test[n:n+1])\n",
|
||||
"print('NN output:', result)\n",
|
||||
"plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n",
|
||||
"plt.show()\n",
|
||||
"print('Real mark: ', str(np.argmax(y_test[n])))\n",
|
||||
"print('NN answer: ', str(np.argmax(result)))"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "HrL0sv-1YosF"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# загрузка собственного изображения\n",
|
||||
"from PIL import Image\n",
|
||||
"file_data = Image.open('test.png')\n",
|
||||
"file_data = file_data.convert('L') # перевод в градации серого\n",
|
||||
"test_img = np.array(file_data)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "tfARmJMip_D8"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод собственного изображения\n",
|
||||
"plt.imshow(test_img, cmap=plt.get_cmap('gray'))\n",
|
||||
"plt.show()\n",
|
||||
"# предобработка\n",
|
||||
"test_img = test_img / 255\n",
|
||||
"test_img = test_img.reshape(1, num_pixels)\n",
|
||||
"# распознавание\n",
|
||||
"result = model.predict(test_img)\n",
|
||||
"print('I think it\\'s ', np.argmax(result))"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "60zdtlMduHhT"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# загрузка собственного изображения\n",
|
||||
"from PIL import Image\n",
|
||||
"file2_data = Image.open('test_2.png')\n",
|
||||
"file2_data = file2_data.convert('L') # перевод в градации серого\n",
|
||||
"test2_img = np.array(file2_data)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "JcO7pbCSuvrv"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод собственного изображения\n",
|
||||
"plt.imshow(test2_img, cmap=plt.get_cmap('gray'))\n",
|
||||
"plt.show()\n",
|
||||
"# предобработка\n",
|
||||
"test2_img = test2_img / 255\n",
|
||||
"test2_img = test2_img.reshape(1, num_pixels)\n",
|
||||
"# распознавание\n",
|
||||
"result_2 = model.predict(test2_img)\n",
|
||||
"print('I think it\\'s ', np.argmax(result_2))"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "2E0evx2su4y1"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# загрузка собственного изображения, повернутого на 90 градусов\n",
|
||||
"from PIL import Image\n",
|
||||
"file90_data = Image.open('test90.png')\n",
|
||||
"file90_data = file90_data.convert('L') # перевод в градации серого\n",
|
||||
"test90_img = np.array(file90_data)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "ZsRQAhIIa_vD"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод собственного изображения\n",
|
||||
"plt.imshow(test90_img, cmap=plt.get_cmap('gray'))\n",
|
||||
"plt.show()\n",
|
||||
"# предобработка\n",
|
||||
"test90_img = test90_img / 255\n",
|
||||
"test90_img = test90_img.reshape(1, num_pixels)\n",
|
||||
"# распознавание\n",
|
||||
"result_3 = model.predict(test90_img)\n",
|
||||
"print('I think it\\'s ', np.argmax(result_3))"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "nQnk_zZMbM01"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# загрузка собственного изображения, повернутого на 90 градусов\n",
|
||||
"from PIL import Image\n",
|
||||
"file902_data = Image.open('test90_2.png')\n",
|
||||
"file902_data = file902_data.convert('L') # перевод в градации серого\n",
|
||||
"test902_img = np.array(file902_data)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "IXK_VfJqbhJ3"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# вывод собственного изображения\n",
|
||||
"plt.imshow(test902_img, cmap=plt.get_cmap('gray'))\n",
|
||||
"plt.show()\n",
|
||||
"# предобработка\n",
|
||||
"test902_img = test902_img / 255\n",
|
||||
"test902_img = test902_img.reshape(1, num_pixels)\n",
|
||||
"# распознавание\n",
|
||||
"result_4 = model.predict(test902_img)\n",
|
||||
"print('I think it\\'s ', np.argmax(result_4))"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "S5WcjVtMb-bp"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"from google.colab import drive\n",
|
||||
"drive.mount('/content/drive')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "n4-_iFTWXNTJ"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
}
|
||||
]
|
||||
}
|
||||
@ -1,4 +1,11 @@
|
||||
* [Задание](IS_Lab01_2023.pdf)
|
||||
## Лабораторныа работа №1
|
||||
|
||||
## Архитектура и обучение глубоких нейронных сетей
|
||||
|
||||
* [Задание](IS_Lab01_2023.pdf)
|
||||
|
||||
* [Методические указания](IS_Lab01_Metod_2023.pdf)
|
||||
|
||||
* <a href="https://youtube.com/playlist?list=PLfdZ2TeaMzfzlpZ60rbaYU_epH5XPNbWU" target="_blank"><s>Какие нейроны, что вообще происходит?</s> Рекомендуется к просмотру для понимания (4 видео)</a>
|
||||
|
||||
* <a href="https://www.youtube.com/watch?v=FwFduRA_L6Q" target="_blank">Почувствуйте себя пионером нейронных сетей в области распознавания образов</a>
|
||||
@ -1,577 +0,0 @@
|
||||
# Отчет по лабораторной работе №1
|
||||
Аникеев Андрей, Чагин Сергей, А-02-22
|
||||
|
||||
## 1. В среде Google Colab создание нового блокнота.
|
||||
```
|
||||
import os
|
||||
os.chdir('/content/drive/MyDrive/Colab Notebooks')
|
||||
```
|
||||
|
||||
1.1 Импорт необходимых модулей.
|
||||
```
|
||||
from tensorflow import keras
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import sklearn
|
||||
```
|
||||
|
||||
## 2. Загрузка датасета.
|
||||
```
|
||||
from keras.datasets import mnist
|
||||
(X_train, y_train), (X_test, y_test) = mnist.load_data()
|
||||
```
|
||||
|
||||
## 3. Разбиение набора данных на обучающий и тестовый.
|
||||
```
|
||||
from sklearn.model_selection import train_test_split
|
||||
```
|
||||
3.1 Объединение в один набор.
|
||||
```
|
||||
X = np.concatenate((X_train, X_test))
|
||||
y = np.concatenate((y_train, y_test))
|
||||
```
|
||||
3.2 Разбиение по вариантам. (5 бригада -> k=4*5-1)
|
||||
```
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size = 10000,train_size = 60000, random_state = 19)
|
||||
```
|
||||
|
||||
3.3 Вывод размерностей.
|
||||
```
|
||||
print('Shape of X train:', X_train.shape)
|
||||
print('Shape of y train:', y_train.shape)
|
||||
```
|
||||
|
||||
> Shape of X train: (60000, 28, 28)
|
||||
> Shape of y train: (60000,)
|
||||
|
||||
## 4. Вывод обучающих данных.
|
||||
4.1 Выведем первые четыре элемента обучающих данных.
|
||||
```
|
||||
plt.figure(figsize=(10, 3))
|
||||
for i in range(4):
|
||||
plt.subplot(1, 4, i + 1)
|
||||
plt.imshow(X_train[i], cmap='gray')
|
||||
plt.title(f'Label: {y_train[i]}')
|
||||
plt.axis('off')
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
## 5. Предобработка данных.
|
||||
5.1 Развернем каждое изображение в вектор.
|
||||
```
|
||||
num_pixels = X_train.shape[1] * X_train.shape[2]
|
||||
X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255
|
||||
X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255
|
||||
print('Shape of transformed X train:', X_train.shape)
|
||||
```
|
||||
|
||||
> Shape of transformed X train: (60000, 784)
|
||||
|
||||
5.2 Переведем метки в one-hot.
|
||||
```
|
||||
from keras.utils import to_categorical
|
||||
|
||||
y_train = to_categorical(y_train)
|
||||
y_test = to_categorical(y_test)
|
||||
|
||||
print('Shape of transformed y train:', y_train.shape)
|
||||
num_classes = y_train.shape[1]
|
||||
```
|
||||
|
||||
> Shape of transformed y train: (60000, 10)
|
||||
|
||||
## 6. Реализация и обучение однослойной нейронной сети.
|
||||
```
|
||||
from keras.models import Sequential
|
||||
from keras.layers import Dense
|
||||
```
|
||||
|
||||
6.1. Создаем модель - объявляем ее объектом класса Sequential, добавляем выходной слой.
|
||||
```
|
||||
model = Sequential()
|
||||
model.add(Dense(units=num_classes, activation='softmax'))
|
||||
```
|
||||
6.2. Компилируем модель.
|
||||
```
|
||||
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
|
||||
print(model.summary())
|
||||
```
|
||||
|
||||
>Model: "sequential_6"
|
||||
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
||||
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
||||
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
||||
>│ dense_18 (Dense) │ ? │ 0 (unbuilt) │
|
||||
>└─────────────────────────────────┴────────────────────────┴───────────────┘
|
||||
>Total params: 0 (0.00 B)
|
||||
>Trainable params: 0 (0.00 B)
|
||||
>Non-trainable params: 0 (0.00 B)
|
||||
|
||||
6.3 Обучаем модель.
|
||||
```
|
||||
H = model.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
||||
```
|
||||
|
||||
6.4 Выводим график функции ошибки
|
||||
```
|
||||
plt.plot(H.history['loss'])
|
||||
plt.plot(H.history['val_loss'])
|
||||
plt.grid()
|
||||
plt.xlabel('Epochs')
|
||||
plt.ylabel('loss')
|
||||
plt.legend(['train_loss', 'val_loss'])
|
||||
plt.title('Loss by epochs')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
## 7. Применение модели к тестовым данным.
|
||||
```
|
||||
scores = model.evaluate(X_test, y_test)
|
||||
print('Loss on test data:', scores[0])
|
||||
print('Accuracy on test data:', scores[1])
|
||||
```
|
||||
|
||||
>accuracy: 0.9213 - loss: 0.2825
|
||||
>Loss on test data: 0.28365787863731384
|
||||
>Accuracy on test data: 0.9225000143051147
|
||||
|
||||
## 8. Добавление одного скрытого слоя.
|
||||
8.1 При 100 нейронах в скрытом слое.
|
||||
```
|
||||
model100 = Sequential()
|
||||
model100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))
|
||||
model100.add(Dense(units=num_classes, activation='softmax'))
|
||||
|
||||
model100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']
|
||||
|
||||
print(model100.summary())
|
||||
```
|
||||
|
||||
>Model: "sequential_10"
|
||||
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
||||
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
||||
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
||||
>│ dense_19 (Dense) │ (None, 100) │ 78,500 │
|
||||
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
||||
>│ dense_20 (Dense) │ (None, 10) │ 1,010 │
|
||||
>└─────────────────────────────────┴────────────────────────┴───────────────┘
|
||||
>Total params: 79,510 (310.59 KB)
|
||||
>Trainable params: 79,510 (310.59 KB)
|
||||
>Non-trainable params: 0 (0.00 B)
|
||||
|
||||
8.2 Обучение модели.
|
||||
```
|
||||
H = model100.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
||||
```
|
||||
|
||||
8.3 График функции ошибки.
|
||||
```
|
||||
plt.plot(H.history['loss'])
|
||||
plt.plot(H.history['val_loss'])
|
||||
plt.grid()
|
||||
plt.xlabel('Epochs')
|
||||
plt.ylabel('loss')
|
||||
plt.legend(['train_loss', 'val_loss'])
|
||||
plt.title('Loss by epochs')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
```
|
||||
scores = model100.evaluate(X_test, y_test)
|
||||
print('Loss on test data:', scores[0])
|
||||
print('Accuracy on test data:', scores[1])
|
||||
```
|
||||
|
||||
>accuracy: 0.9465 - loss: 0.1946
|
||||
>Loss on test data: 0.19745595753192902
|
||||
>Accuracy on test data: 0.9442999958992004
|
||||
|
||||
8.4 При 300 нейронах в скрытом слое.
|
||||
```
|
||||
model300 = Sequential()
|
||||
model300.add(Dense(units=300,input_dim=num_pixels, activation='sigmoid'))
|
||||
model300.add(Dense(units=num_classes, activation='softmax'))
|
||||
|
||||
model300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
|
||||
|
||||
print(model300.summary())
|
||||
```
|
||||
|
||||
>Model: "sequential_14"
|
||||
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
||||
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
||||
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
||||
>│ dense_27 (Dense) │ (None, 300) │ 235,500 │
|
||||
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
||||
>│ dense_28 (Dense) │ (None, 10) │ 3,010 │
|
||||
>└─────────────────────────────────┴────────────────────────┴───────────────┘
|
||||
>Total params: 238,510 (931.68 KB)
|
||||
>Trainable params: 238,510 (931.68 KB)
|
||||
>Non-trainable params: 0 (0.00 B)
|
||||
|
||||
8.5 Обучение модели.
|
||||
```
|
||||
H = model300.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
||||
```
|
||||
|
||||
8.6 Вывод графиков функции ошибки.
|
||||
```
|
||||
plt.plot(H.history['loss'])
|
||||
plt.plot(H.history['val_loss'])
|
||||
plt.grid()
|
||||
plt.xlabel('Epochs')
|
||||
plt.ylabel('loss')
|
||||
plt.legend(['train_loss', 'val_loss'])
|
||||
plt.title('Loss by epochs')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
```
|
||||
scores = model300.evaluate(X_test, y_test)
|
||||
print('Loss on test data:', scores[0])
|
||||
print('Accuracy on test data:', scores[1])
|
||||
```
|
||||
|
||||
>accuracy: 0.9361 - loss: 0.2237
|
||||
>Loss on test data: 0.22660093009471893
|
||||
>Accuracy on test data: 0.9348000288009644
|
||||
|
||||
8.7 При 500 нейронах в скрытом слое.
|
||||
```
|
||||
model500 = Sequential()
|
||||
model500.add(Dense(units=500,input_dim=num_pixels, activation='sigmoid'))
|
||||
model500.add(Dense(units=num_classes, activation='softmax'))
|
||||
|
||||
model500.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
|
||||
|
||||
print(model500.summary())
|
||||
```
|
||||
|
||||
>Model: "sequential_16"
|
||||
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
||||
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
||||
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
||||
>│ dense_31 (Dense) │ (None, 500) │ 392,500 │
|
||||
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
||||
>│ dense_32 (Dense) │ (None, 10) │ 5,010 │
|
||||
>└─────────────────────────────────┴────────────────────────┴───────────────┘
|
||||
>Total params: 397,510 (1.52 MB)
|
||||
>Trainable params: 397,510 (1.52 MB)
|
||||
>Non-trainable params: 0 (0.00 B)
|
||||
|
||||
8.8 Обучение модели.
|
||||
```
|
||||
H = model500.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
||||
```
|
||||
|
||||
8.9 Вывод графиков функции ошибки.
|
||||
```
|
||||
plt.plot(H.history['loss'])
|
||||
plt.plot(H.history['val_loss'])
|
||||
plt.grid()
|
||||
plt.xlabel('Epochs')
|
||||
plt.ylabel('loss')
|
||||
plt.legend(['train_loss', 'val_loss'])
|
||||
plt.title('Loss by epochs')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
```
|
||||
scores = model500.evaluate(X_test, y_test)
|
||||
print('Loss on test data:', scores[0])
|
||||
print('Accuracy on test data:', scores[1])
|
||||
```
|
||||
|
||||
>accuracy: 0.9306 - loss: 0.2398
|
||||
>Loss on test data: 0.24357788264751434
|
||||
>Accuracy on test data: 0.9304999709129333
|
||||
|
||||
Как мы видим, лучшая метрика получилась при архитектуре со 100 нейронами в скрытом слое:
|
||||
Ошибка на тестовых данных: 0.19745595753192902
|
||||
Точность тестовых данных: 0.9442999958992004
|
||||
|
||||
## 9. Добавление второго скрытого слоя.
|
||||
9.1 При 50 нейронах во втором скрытом слое.
|
||||
```
|
||||
model10050 = Sequential()
|
||||
model10050.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))
|
||||
model10050.add(Dense(units=50,activation='sigmoid'))
|
||||
model10050.add(Dense(units=num_classes, activation='softmax'))
|
||||
|
||||
model10050.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
|
||||
|
||||
print(model10050.summary())
|
||||
```
|
||||
|
||||
>Model: "sequential_17"
|
||||
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
||||
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
||||
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
||||
>│ dense_33 (Dense) │ (None, 100) │ 78,500 │
|
||||
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
||||
>│ dense_34 (Dense) │ (None, 50) │ 5,050 │
|
||||
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
||||
>│ dense_35 (Dense) │ (None, 10) │ 510 │
|
||||
>└─────────────────────────────────┴────────────────────────┴───────────────┘
|
||||
>Total params: 84,060 (328.36 KB)
|
||||
>Trainable params: 84,060 (328.36 KB)
|
||||
>Non-trainable params: 0 (0.00 B)
|
||||
|
||||
9.2 Обучаем модель.
|
||||
```
|
||||
H = model10050.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
||||
```
|
||||
|
||||
9.3 Выводим график функции ошибки.
|
||||
```
|
||||
plt.plot(H.history['loss'])
|
||||
plt.plot(H.history['val_loss'])
|
||||
plt.grid()
|
||||
plt.xlabel('Epochs')
|
||||
plt.ylabel('loss')
|
||||
plt.legend(['train_loss', 'val_loss'])
|
||||
plt.title('Loss by epochs')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
```
|
||||
scores = model10050.evaluate(X_test, y_test)
|
||||
print('Loss on test data:', scores[0])
|
||||
print('Accuracy on test data:', scores[1])
|
||||
```
|
||||
|
||||
>accuracy: 0.9439 - loss: 0.1962
|
||||
>Loss on test data: 0.1993969976902008
|
||||
>Accuracy on test data: 0.9438999891281128
|
||||
|
||||
9.4 При 100 нейронах во втором скрытом слое.
|
||||
```
|
||||
model100100 = Sequential()
|
||||
model100100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))
|
||||
model100100.add(Dense(units=100,activation='sigmoid'))
|
||||
model100100.add(Dense(units=num_classes, activation='softmax'))
|
||||
|
||||
model100100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
|
||||
|
||||
print(model100100.summary())
|
||||
```
|
||||
|
||||
>Model: "sequential_18"
|
||||
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
||||
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
||||
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
||||
>│ dense_36 (Dense) │ (None, 100) │ 78,500 │
|
||||
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
||||
>│ dense_37 (Dense) │ (None, 100) │ 10,100 │
|
||||
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
||||
>│ dense_38 (Dense) │ (None, 10) │ 1,010 │
|
||||
>└─────────────────────────────────┴────────────────────────┴───────────────┘
|
||||
>Total params: 89,610 (350.04 KB)
|
||||
>Trainable params: 89,610 (350.04 KB)
|
||||
>Non-trainable params: 0 (0.00 B)
|
||||
|
||||
9.5 Обучаем модель.
|
||||
```
|
||||
H = model100100.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
||||
```
|
||||
|
||||
9.6 Выводим график функции ошибки.
|
||||
```
|
||||
plt.plot(H.history['loss'])
|
||||
plt.plot(H.history['val_loss'])
|
||||
plt.grid()
|
||||
plt.xlabel('Epochs')
|
||||
plt.ylabel('loss')
|
||||
plt.legend(['train_loss', 'val_loss'])
|
||||
plt.title('Loss by epochs')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
```
|
||||
scores = model100100.evaluate(X_test, y_test)
|
||||
print('Loss on test data:', scores[0])
|
||||
print('Accuracy on test data:', scores[1])
|
||||
```
|
||||
|
||||
>accuracy: 0.9449 - loss: 0.1931
|
||||
>Loss on test data: 0.19571688771247864
|
||||
>Accuracy on test data: 0.9435999989509583
|
||||
|
||||
## 10. Результаты исследования архитектур нейронной сети.
|
||||
|
||||
| Количество скрытых слоев | Количество нейронов в первом скрытом слое | Количество нейронов во втором скрытом слое | Значение метрики качества классификации |
|
||||
|--------------------------|-------------------------------------------|--------------------------------------------|------------------------------------------|
|
||||
| 0 | - | - | 0.9225000143051147 |
|
||||
| 1 | 100 | - | 0.9442999958992004 |
|
||||
| 1 | 300 | - | 0.9348000288009644 |
|
||||
| 1 | 500 | - | 0.9304999709129333 |
|
||||
| 2 | 100 | 50 | 0.9438999891281128 |
|
||||
| 2 | 100 | 100 | 0.9435999989509583 |
|
||||
|
||||
Анализ результатов показал, что наивысшую точность (около 94.5%) демонстрируют модели со сравнительно простой архитектурой: однослойная сеть со 100 нейронами и двухслойная конфигурация (100 и 50 нейронов). Усложнение модели за счет увеличения количества слоев или нейронов не привело к улучшению качества, а в некоторых случаях даже вызвало его снижение. Это свидетельствует о том, что для относительно простого набора данных MNIST более сложные архитектуры склонны к переобучению, в то время как простые модели лучше обобщают закономерности.
|
||||
|
||||
## 11. Сохранение наилучшей модели на диск.
|
||||
```
|
||||
model100.save('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')
|
||||
```
|
||||
|
||||
11.1 Загрузка лучшей модели с диска.
|
||||
```
|
||||
from keras.models import load_model
|
||||
model = load_model('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')
|
||||
```
|
||||
|
||||
## 12. Вывод тестовых изображений и результатов распознаваний.
|
||||
```
|
||||
n = 111
|
||||
result = model.predict(X_test[n:n+1])
|
||||
print('NN output:', result)
|
||||
plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))
|
||||
plt.show()
|
||||
print('Real mark: ', str(np.argmax(y_test[n])))
|
||||
print('NN answer: ', str(np.argmax(result)))
|
||||
```
|
||||
|
||||
>NN output: [[1.1728607e-03 5.4896927e-06 3.3185919e-05 2.6362878e-04 4.8558863e-06
|
||||
>9.9795568e-01 1.9454242e-07 1.6833146e-05 4.9621973e-04 5.1067746e-05]]
|
||||
|
||||
>Real mark: 5
|
||||
>NN answer: 5
|
||||
|
||||
```
|
||||
n = 222
|
||||
result = model.predict(X_test[n:n+1])
|
||||
print('NN output:', result)
|
||||
plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))
|
||||
plt.show()
|
||||
print('Real mark: ', str(np.argmax(y_test[n])))
|
||||
print('NN answer: ', str(np.argmax(result)))
|
||||
```
|
||||
|
||||
>NN output: [[1.02687673e-05 2.02151591e-06 2.86183599e-03 8.74871985e-05
|
||||
>1.51387369e-02 6.32769879e-05 3.97122385e-05 4.11829986e-02 1.06158564e-04 9.40507472e-01]]
|
||||
|
||||
>Real mark: 9
|
||||
>NN answer: 9
|
||||
|
||||
## 13. Тестирование на собственных изображениях.
|
||||
|
||||
13.1 Загрузка 1 собственного изображения.
|
||||
```
|
||||
from PIL import Image
|
||||
file_data = Image.open('test.png')
|
||||
file_data = file_data.convert('L') # перевод в градации серого
|
||||
test_img = np.array(file_data)
|
||||
```
|
||||
|
||||
13.2 Вывод собственного изображения.
|
||||
```
|
||||
plt.imshow(test_img, cmap=plt.get_cmap('gray'))
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
13.3 Предобработка.
|
||||
```
|
||||
test_img = test_img / 255
|
||||
test_img = test_img.reshape(1, num_pixels)
|
||||
```
|
||||
|
||||
13.4 Распознавание.
|
||||
```
|
||||
result = model.predict(test_img)
|
||||
print('I think it\'s ', np.argmax(result))
|
||||
```
|
||||
>I think it's 5
|
||||
|
||||
13.5 Тест 2 изображения.
|
||||
```
|
||||
from PIL import Image
|
||||
file2_data = Image.open('test_2.png')
|
||||
file2_data = file2_data.convert('L') # перевод в градации серого
|
||||
test2_img = np.array(file2_data)
|
||||
```
|
||||
|
||||
```
|
||||
plt.imshow(test2_img, cmap=plt.get_cmap('gray'))
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
```
|
||||
test2_img = test2_img / 255
|
||||
test2_img = test2_img.reshape(1, num_pixels)
|
||||
```
|
||||
|
||||
```
|
||||
result_2 = model.predict(test2_img)
|
||||
print('I think it\'s ', np.argmax(result_2))
|
||||
```
|
||||
|
||||
>I think it's 2
|
||||
|
||||
Сеть корректно распознала цифры на изображениях.
|
||||
|
||||
## 14. Тестирование на повернутых изображениях.
|
||||
```
|
||||
from PIL import Image
|
||||
file90_data = Image.open('test90.png')
|
||||
file90_data = file90_data.convert('L') # перевод в градации серого
|
||||
test90_img = np.array(file90_data)
|
||||
|
||||
plt.imshow(test90_img, cmap=plt.get_cmap('gray'))
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
```
|
||||
test90_img = test90_img / 255
|
||||
test90_img = test90_img.reshape(1, num_pixels)
|
||||
|
||||
result_3 = model.predict(test90_img)
|
||||
print('I think it\'s ', np.argmax(result_3))
|
||||
```
|
||||
|
||||
>I think it's 7
|
||||
|
||||
```
|
||||
from PIL import Image
|
||||
file902_data = Image.open('test90_2.png')
|
||||
file902_data = file902_data.convert('L') # перевод в градации серого
|
||||
test902_img = np.array(file902_data)
|
||||
|
||||
plt.imshow(test902_img, cmap=plt.get_cmap('gray'))
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
|
||||
```
|
||||
test902_img = test902_img / 255
|
||||
test902_img = test902_img.reshape(1, num_pixels)
|
||||
|
||||
result_4 = model.predict(test902_img)
|
||||
print('I think it\'s ', np.argmax(result_4))
|
||||
```
|
||||
|
||||
>I think it's 7
|
||||
|
||||
Сеть не распознала цифры на изображениях корректно.
|
||||
@ -0,0 +1,11 @@
|
||||
## Лабораторныа работа №2
|
||||
|
||||
## Обнаружение аномалий
|
||||
|
||||
* [Задание](IS_Lab02_2023.pdf)
|
||||
|
||||
* [Методические указания](IS_Lab02_Metod_2023.pdf)
|
||||
|
||||
* [Наборы данных](data)
|
||||
|
||||
* [Библиотека для автокодировщиков](lab02_lib.py)
|
||||
@ -0,0 +1,9 @@
|
||||
## Лабораторныа работа №3
|
||||
|
||||
## Распознавание изображений
|
||||
|
||||
* [Задание](IS_Lab03_2023.pdf)
|
||||
|
||||
* [Методические указания](IS_Lab03_Metod_2023.pdf)
|
||||
|
||||
* <a href="https://youtube.com/playlist?list=PLZDCDMGmelH-pHt-Ij0nImVrOmj8DYKbB" target="_blank">Плейлист с видео о сверточных сетях</a>
|
||||
Загрузка…
Ссылка в новой задаче