-
Notifications
You must be signed in to change notification settings - Fork 0
/
recognizer.py
35 lines (24 loc) · 1.26 KB
/
recognizer.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
import cv2 as cv
import numpy as np
import tensorflow as tf
class Recognizer():
def __init__(self):
self.mnist = tf.keras.datasets.mnist
(self.x_train, self.y_train), (self.x_test, self.y_test) = self.mnist.load_data()
self.x_train = tf.keras.utils.normalize(self.x_train, axis=1)
self.x_test = tf.keras.utils.normalize(self.x_test, axis=1)
self.model = tf.keras.models.Sequential()
self.model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
self.model.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
self.model.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
self.model.add(tf.keras.layers.Dense(units=10, activation=tf.nn.softmax))
self.model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
self.model.fit(self.x_train, self.y_train, epochs=4)
loss, accuracy = self.model.evaluate(self.x_test, self.y_test)
print(f"Accuracy: {accuracy}")
print(f"Loss: {loss}")
def Guess(self):
img = cv.imread(f'./image.png')[:,:,0]
img = np.invert(np.array([img]))
prediction = self.model.predict(img)
return np.argmax(prediction)