JFO_lab_skrzyzowanie/neural_network.py

54 lines
1.6 KiB
Python

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)
return liczba
recognition()