train.py

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

# Data Preprocessing
(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']

# Triming dataset
training_images = training_images[:20000]
training_labels = training_labels[:20000]
testing_images = testing_images[:4000]
testing_labels = testing_labels[:4000]

# Model architecure
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'])

# Train
model.fit(training_images, training_labels, epochs=15, validation_data=(testing_images, testing_labels))

# Evaluation
loss, accuracy = model.evaluate(testing_images, testing_labels)
print("Accuracy: ", accuracy *100)
print("Loss: ", loss *100)

model.save('image_classification.model')