JFO_lab_skrzyzowanie/neural_network.py

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import random


mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

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

model.fit(x_train, y_train, epochs = 3)
model.save('handwritten.model')

model = tf.keras.models.load_model('handwritten.model')
numery_paczek=[]
def recognition():
    digits=[]
    try:
        for i in range(0,3):
            image_number = random.randint(1, 19)
            img = cv2.imread(f"digits/digit{image_number}.png")[:,:,0]
            img = np.invert(np.array([img]))
            prediction = model.predict(img)
            print(f"This digit is probably a {np.argmax(prediction)}")
            digits.append(np.argmax(prediction))
            plt.imshow(img[0], cmap = plt.cm.binary)
            plt.show()
    except:
        print("Error!")

    liczba = int(str(digits[0]) + str(digits[1])+str(digits[2]))
    if liczba in numery_paczek or liczba<100:
        recognition()
    else:
        numery_paczek.append(liczba)
    print(liczba)
    loss, accuracy = model.evaluate(x_test, y_test)
    print(loss)
    print(accuracy)
    print(numery_paczek)
    ostatnia = liczba %10
    return ostatnia