train_CNN.py

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
import matplotlib.pyplot as plt

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

train_dir = 'GTSRB/train_images'

img_width, img_height = 32, 32

input_shape = (img_width, img_height, 3)

epochs = 22

batch_size = 16

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(3))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = datagen.flow_from_directory(train_dir, target_size=(img_width, img_height),
                                               batch_size=batch_size, class_mode='categorical')

validation_generator = datagen.flow_from_directory(train_dir, target_size=(img_height, img_width),
                                                    batch_size=batch_size, class_mode='categorical') # set as validation data

history = model.fit(train_generator, epochs=epochs, batch_size=batch_size, validation_data=validation_generator, validation_steps=epochs)

model.save("GTSRB_CNN.h5")

plt.plot(history.history['loss'], label='Ошибка на обучающем наборе')
plt.plot(history.history['val_loss'], label='Ошибка на проверочном наборе')
plt.xlabel('Эпоха обучения')
plt.ylabel('Ошибка')
plt.legend()
plt.show()