main.py

"""
@name: HorizonV8
@author: Arnav Jain
@dateOfCreation: 04/09/2021
"""

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

# loading dataset from keras
(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]])

training_images = training_images[:20000]
training_labels = training_labels[:20000]
testing_images = testing_images[:4000]
testing_labels = testing_labels[:4000]

# designing the neural network
model = models.load_model("image_classifier.model")

img = cv.imread("deer.jpg")
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)

plt.imshow(img, cmap=plt.cm.binary)

prediction = model.predict(np.array([img]) / 255)
index = np.argmax(prediction)

print(f'Prediction Is: {class_names[index]}')