main.py

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras import datasets, layers, models

(training_images, training_labels), (testing_images, testing_labels) = datasets.cifar10.load_data()
training_images, testing_images = training_images / 255, testing_images / 255

class_names = ['Plane', 'Car', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck']

for i in range(16):
    plt.subplot(4, 4, i+1)
    plt.xticks([])
    plt.yticks([])
    plt.imshow(training_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[training_labels[i][0]])

# plt.show()

training_images = training_images
training_labels = training_labels
testing_images = testing_images[:4000]
testing_labels = testing_labels[:4000]

model = models.Sequential()
model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64, (3,3,), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64, (3,3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

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

model.fit(training_images, training_labels, epochs=10, validation_data=(testing_images, testing_labels))

loss, accuracy = model.evaluate(testing_images, testing_labels)
print(f'Loss: {loss}')
print(f'Accuracy: {accuracy*100}%')

model.save('image_classifier.model')