Вы не можете выбрать более 25 тем
Темы должны начинаться с буквы или цифры, могут содержать дефисы(-) и должны содержать не более 35 символов.
581 строка
23 KiB
Markdown
581 строка
23 KiB
Markdown
# Отчет по лабораторной работе №1
|
|
Пивоваров Я.В., Сидора Д.А., А-02-22
|
|
|
|
## 1. В среде Google Colab создание нового блокнота.
|
|
```
|
|
import os
|
|
os.chdir('/content/drive/MyDrive/Colab Notebooks')
|
|
```
|
|
|
|
* Импорт библиотек и модулей
|
|
```
|
|
from tensorflow import keras
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import sklearn
|
|
```
|
|
|
|
## 2. Загрузка и рассмотрение набора данных
|
|
```
|
|
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
|
|
```
|
|
* Объединение в один набор.
|
|
```
|
|
X = np.concatenate((X_train, X_test))
|
|
y = np.concatenate((y_train, y_test))
|
|
```
|
|
* Разбиение по вариантам. (4 бригада -> k=4*4-1)
|
|
```
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size = 10000,train_size = 60000, random_state = 15)
|
|
```
|
|
|
|
* 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.
|
|
* 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.
|
|
* 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)
|
|
|
|
* 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
|
|
```
|
|
|
|
* Создаем модель - объявляем ее объектом класса Sequential, добавляем выходной слой.
|
|
```
|
|
model = Sequential()
|
|
model.add(Dense(units=num_classes, activation='softmax'))
|
|
```
|
|
* Компилируем модель.
|
|
```
|
|
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
|
|
print(model.summary())
|
|
```
|
|
|
|
>Model: "sequential"
|
|
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
|
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
|
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
|
>│ dense (Dense) │ ? │ 0 (unbuilt) │
|
|
>└─────────────────────────────────┴────────────────────────┴───────────────┘
|
|
> Total params: 0 (0.00 B)
|
|
> Trainable params: 0 (0.00 B)
|
|
> Non-trainable params: 0 (0.00 B)
|
|
>None
|
|
|
|
* Обучаем модель.
|
|
```
|
|
H = model.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
|
```
|
|
|
|
* 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: 0.9313 - loss: 0.2648
|
|
>Loss on test data: 0.2729383409023285
|
|
>Accuracy on test data: 0.9290000200271606
|
|
|
|
## 8. Добавление одного скрытого слоя.
|
|
* 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_1"
|
|
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
|
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
|
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
|
>│ dense_1 (Dense) │ (None, 100) │ 78,500 │
|
|
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
|
>│ dense_2 (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)
|
|
>None
|
|
|
|
* Обучение модели.
|
|
```
|
|
H = model100.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
|
```
|
|
|
|
* 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: 0.9500 - loss: 0.1884
|
|
>Loss on test data: 0.1930633932352066
|
|
>Accuracy on test data: 0.9473999738693237
|
|
|
|
* 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_2"
|
|
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
|
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
|
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
|
>│ dense_3 (Dense) │ (None, 300) │ 235,500 │
|
|
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
|
>│ dense_4 (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)
|
|
>None
|
|
* Обучение модели.
|
|
```
|
|
H = model300.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
|
```
|
|
|
|
* 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: 0.9444 - loss: 0.2126
|
|
>Loss on test data: 0.2181043177843094
|
|
>Accuracy on test data: 0.9419999718666077
|
|
|
|
* 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_3"
|
|
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
|
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
|
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
|
>│ dense_5 (Dense) │ (None, 500) │ 392,500 │
|
|
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
|
>│ dense_6 (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)
|
|
>None
|
|
|
|
* Обучение модели.
|
|
```
|
|
H = model500.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
|
```
|
|
|
|
* 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: 0.9401 - loss: 0.2261
|
|
>Loss on test data: 0.2324201464653015
|
|
>Accuracy on test data: 0.9376000165939331
|
|
|
|
Как мы видим, лучшая метрика получилась при архитектуре со 100 нейронами в скрытом слое:
|
|
Ошибка на тестовых данных: 0.1930633932352066
|
|
Точность тестовых данных: 0.9473999738693237
|
|
|
|
## 9. Добавление второго скрытого слоя.
|
|
* 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_4"
|
|
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
|
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
|
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
|
>│ dense_7 (Dense) │ (None, 100) │ 78,500 │
|
|
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
|
>│ dense_8 (Dense) │ (None, 50) │ 5,050 │
|
|
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
|
>│ dense_9 (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)
|
|
>None
|
|
|
|
* Обучаем модель.
|
|
```
|
|
H = model10050.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
|
```
|
|
|
|
* 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: 0.9476 - loss: 0.1931
|
|
>Loss on test data: 0.1974852979183197
|
|
>Accuracy on test data: 0.9449999928474426
|
|
|
|
* 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_5"
|
|
>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
|
|
>┃ Layer (type) ┃ Output Shape ┃ Param # ┃
|
|
>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
|
|
>│ dense_10 (Dense) │ (None, 100) │ 78,500 │
|
|
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
|
>│ dense_11 (Dense) │ (None, 100) │ 10,100 │
|
|
>├─────────────────────────────────┼────────────────────────┼───────────────┤
|
|
>│ dense_12 (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)
|
|
>None
|
|
|
|
* Обучаем модель.
|
|
```
|
|
H = model100100.fit(X_train, y_train, validation_split=0.1, epochs=50)
|
|
```
|
|
|
|
* 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: 0.9485 - loss: 0.1814
|
|
>Loss on test data: 0.18734164535999298
|
|
>Accuracy on test data: 0.9470000267028809
|
|
|
|
## 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 | - | - | 0.9290000200271606 |
|
|
| 1 | 100 | - | 0.9473999738693237 |
|
|
| 1 | 300 | - | 0.9419999718666077 |
|
|
| 1 | 500 | - | 0.9376000165939331 |
|
|
| 2 | 100 | 50 | 0.9449999928474426 |
|
|
| 2 | 100 | 100 | 0.9470000267028809 |
|
|
|
|
Анализ результатов позволяет сделать вывод, что наилучшее качество классификации (порядка 94.7%) достигается при использовании моделей с относительно простой архитектурой. Наибольшую точность показали однослойная сеть со 100 нейронами и двухслойная конфигурация с 100 и 100 нейронами соответственно.
|
|
|
|
## 11. Сохранение наилучшей модели на диск.
|
|
```
|
|
model100.save('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')
|
|
```
|
|
|
|
* 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 = 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: [[3.7926259e-03 9.0994104e-07 2.0981293e-04 2.9478846e-02 2.0727816e-06
|
|
> 9.6508384e-01 7.6052487e-07 5.7595258e-05 1.0619552e-03 3.1140275e-04]]
|
|
|
|
>Real mark: 5
|
|
>NN answer: 5
|
|
|
|
```
|
|
n = 123
|
|
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: [[7.6678516e-06 2.1507578e-06 2.5754166e-04 6.3994766e-04 2.8644723e-04
|
|
> 2.3038971e-04 1.0776109e-05 2.3045135e-05 9.9186021e-01 6.6818334e-03]]
|
|
|
|
>Real mark: 8
|
|
>NN answer: 8
|
|
|
|
## 13. Тестирование на собственных изображениях.
|
|
* 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)
|
|
```
|
|
|
|
* Вывод собственного изображения.
|
|
```
|
|
plt.imshow(test_img, cmap=plt.get_cmap('gray'))
|
|
plt.show()
|
|
```
|
|
|
|
|
|
|
|
* Предобработка.
|
|
```
|
|
test_img = test_img / 255
|
|
test_img = test_img.reshape(1, num_pixels)
|
|
```
|
|
|
|
* Распознавание.
|
|
```
|
|
result = model.predict(test_img)
|
|
print('I think it\'s ', np.argmax(result))
|
|
```
|
|
>I think it's 2
|
|
|
|
* Тест 2 изображения.
|
|
```
|
|
from PIL import Image
|
|
file2_data = Image.open('test2.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 8
|
|
|
|
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 8
|
|
|
|
```
|
|
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 4
|
|
|
|
Сеть не распознала цифры на изображениях корректно. |