форкнуто от main/is_dnn
Родитель
5f3950f2dd
Сommit
6e3643d715
@ -0,0 +1,879 @@
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{
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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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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "HUUZx52sc1LD"
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},
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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"
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],
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"metadata": {
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"id": "3Y-Ux1dadqdA"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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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()"
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],
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"metadata": {
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"id": "w25XE8ADdqP5"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# создание своего разбиения датасета\n",
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"from sklearn.model_selection import train_test_split\n",
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"# объединяем в один набор\n",
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"X = np.concatenate((X_train, X_test))\n",
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"y = np.concatenate((y_train, y_test))\n",
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"# 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",
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"random_state = 19) #(5*4-1)"
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],
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"metadata": {
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"id": "QcXt9zqCdqDH"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# вывод размерностей\n",
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"print('Shape of X train:', X_train.shape)\n",
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"print('Shape of y train:', y_train.shape)"
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],
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"metadata": {
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"id": "9Cd705vod51B",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# 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",
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" plt.title(f'Label: {y_train[i]}')\n",
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" plt.axis('off')\n",
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"plt.tight_layout()\n",
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"plt.show()\n",
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"\n"
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],
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"metadata": {
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"id": "vLYfI---d5rm",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# 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",
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"X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255\n",
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"print('Shape of transformed X train:', X_train.shape)"
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],
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"metadata": {
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"id": "d0oyu59gd5fz",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# 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",
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"y_test = to_categorical(y_test)\n",
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"\n",
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"print('Shape of transformed y train:', y_train.shape)\n",
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"num_classes = y_train.shape[1]"
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],
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"metadata": {
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"id": "Q227fINPeD1A",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"from keras.models import Sequential\n",
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"from keras.layers import Dense"
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],
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"metadata": {
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"id": "TzaA61smeDoO"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# 1. создаем модель - объявляем ее объектом класса Sequential\n",
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"model = Sequential()\n",
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"# 2. добавляем выходной слой(скрытые слои отсутствуют)\n",
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"model.add(Dense(units=num_classes, activation='softmax'))\n",
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"# 3. компилируем модель\n",
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"model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
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],
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"metadata": {
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"id": "Liq39zruhz0d"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# вывод информации об архитектуре модели\n",
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"print(model.summary())"
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],
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"metadata": {
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"id": "jMGGsq7piZOu",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Обучаем модель\n",
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"H = model.fit(X_train, y_train, validation_split=0.1, epochs=50)"
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],
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"metadata": {
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"id": "n_pCdxphiedM",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# вывод графика ошибки по эпохам\n",
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"plt.plot(H.history['loss'])\n",
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"plt.plot(H.history['val_loss'])\n",
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"plt.grid()\n",
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"plt.xlabel('Epochs')\n",
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"plt.ylabel('loss')\n",
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"plt.legend(['train_loss', 'val_loss'])\n",
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"plt.title('Loss by epochs')\n",
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"plt.show()"
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],
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"metadata": {
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"id": "Sz_YOlsVivoR",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Оценка качества работы модели на тестовых данных\n",
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"scores = model.evaluate(X_test, y_test)\n",
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"print('Loss on test data:', scores[0])\n",
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"print('Accuracy on test data:', scores[1])"
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],
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"metadata": {
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"id": "hpJALaZGnyWF",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# сохранение модели на диск\n",
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"model.save('/content/drive/MyDrive/Colab Notebooks/models/model_zero_hide.keras')"
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],
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"metadata": {
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"id": "Z6eSmpwXn1zM"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"model100 = Sequential()\n",
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"model100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n",
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"model100.add(Dense(units=num_classes, activation='softmax'))\n",
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"\n",
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"model100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
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],
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"metadata": {
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"id": "G1qGmPNF9afO"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# вывод информации об архитектуре модели\n",
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"print(model100.summary())"
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],
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"metadata": {
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"id": "2WtfjJKY9abn",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Обучаем модель\n",
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"H = model100.fit(X_train, y_train, validation_split=0.1, epochs=50)"
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],
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"metadata": {
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"id": "rPuWd80o9aYD",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# вывод графика ошибки по эпохам\n",
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"plt.plot(H.history['loss'])\n",
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"plt.plot(H.history['val_loss'])\n",
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"plt.grid()\n",
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"plt.xlabel('Epochs')\n",
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"plt.ylabel('loss')\n",
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"plt.legend(['train_loss', 'val_loss'])\n",
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"plt.title('Loss by epochs')\n",
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"plt.show()"
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],
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"metadata": {
|
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"id": "JLrW7S1g9aUe",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Оценка качества работы модели на тестовых данных\n",
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"scores = model100.evaluate(X_test, y_test)\n",
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"print('Loss on test data:', scores[0])\n",
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"print('Accuracy on test data:', scores[1])"
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],
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"metadata": {
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"id": "8jdS02JZ9aRc",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# сохранение модели на диск\n",
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"model100.save('/content/drive/MyDrive/Colab Notebooks/models/model100in_1hide.keras')"
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],
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"metadata": {
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"id": "_bR3qoBy9aNy"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"model300 = Sequential()\n",
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"model300.add(Dense(units=300,input_dim=num_pixels, activation='sigmoid'))\n",
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"model300.add(Dense(units=num_classes, activation='softmax'))\n",
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"\n",
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"model300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
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],
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"metadata": {
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"id": "V4m3nGORGnPC"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# вывод информации об архитектуре модели\n",
|
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"print(model300.summary())"
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],
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"metadata": {
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"id": "yETaYKzdA9fp",
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"collapsed": true
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Обучаем модель\n",
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"H = model300.fit(X_train, y_train, validation_split=0.1, epochs=50)"
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],
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"metadata": {
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"id": "SFPh0Lw-A9Zq",
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"collapsed": true
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||||
},
|
||||
"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
|
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"# вывод графика ошибки по эпохам\n",
|
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"plt.plot(H.history['loss'])\n",
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"plt.plot(H.history['val_loss'])\n",
|
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"plt.grid()\n",
|
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"plt.xlabel('Epochs')\n",
|
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"plt.ylabel('loss')\n",
|
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"plt.legend(['train_loss', 'val_loss'])\n",
|
||||
"plt.title('Loss by epochs')\n",
|
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"plt.show()"
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||||
],
|
||||
"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",
|
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"print('Loss on test data:', scores[0])\n",
|
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"print('Accuracy on test data:', scores[1])"
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||||
],
|
||||
"metadata": {
|
||||
"id": "WOJyUHP79Z86"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"source": [
|
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"# сохранение модели на диск\n",
|
||||
"model300.save('/content/drive/MyDrive/Colab Notebooks/models/model300in_1hide.keras')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "XsWc7S4aCyiE"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
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||||
},
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{
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||||
"cell_type": "code",
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||||
"source": [
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"model500 = Sequential()\n",
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||||
"model500.add(Dense(units=500,input_dim=num_pixels, activation='sigmoid'))\n",
|
||||
"model500.add(Dense(units=num_classes, activation='softmax'))\n",
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||||
"\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": []
|
||||
}
|
||||
]
|
||||
}
|
||||
Загрузка…
Ссылка в новой задаче