Hand-Written-Mathmatical-Symbols/module2.py at master · L9Sneaky/Hand-Written-Mathmatical-Symbols · GitHub

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
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout,Activation, Flatten, Conv2D ,MaxPooling2D
import pickle
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os


X = pickle.load(open("X.pickle", "rb"))
y = pickle.load(open("y.pickle", "rb"))
X= X/255.0

CATAGORIES = ["alpha","beta","sigma","pi"]

model = Sequential()

model.add(Conv2D(32, (3,3) , input_shape = X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))


model.add(Conv2D(32, (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())

model.add(Dense(128))
model.add(Activation("relu"))
#model.add(Dropout(0.5))

model.add(Dense(128))
model.add(Activation("relu"))

model.add(Dense(128))
model.add(Activation("relu"))

model.add(Dense(128))
model.add(Activation("relu"))

model.add(Dense(128))
model.add(Activation("relu"))

model.add(Dense(6))
model.add(Activation('softmax'))

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

os.system('cls')
value = input("Please enter the number of times to train:\n>")
value = int(value)

model.fit(X, y, batch_size=512, epochs=value, validation_split=0.1,learning_rate=1e-3,dropout_rate=0.2)
os.system('cls')

def accur():
    history = model.fit(X, y, batch_size=32, epochs=value, validation_split=0.1)

    test_loss, test_acc = model.evaluate(X,  y, verbose=2)

    print('\nTest accuracy:', test_acc ,"\n")

    acc = history.history['accuracy']
    val_acc = history.history['val_accuracy']

    loss = history.history['loss']
    val_loss = history.history['val_loss']

    epochs_range = range(value)

    plt.figure(figsize=(8, 8))
    plt.subplot(1, 2, 1)
    plt.plot(epochs_range, acc, label='Training Accuracy')
    plt.plot(epochs_range, val_acc, label='Validation Accuracy')
    plt.legend(loc='lower right')
    plt.title('Training and Validation Accuracy')

    plt.subplot(1, 2, 2)
    plt.plot(epochs_range, loss, label='Training Loss')
    plt.plot(epochs_range, val_loss, label='Validation Loss')
    plt.legend(loc='upper right')
    plt.title('Training and Validation Loss')
    plt.show()


#bruh = 'Untitled.jpg'
#bruh2 = cv2.imread(bruh)
#bruh2 = bruh2/255.0
#bruh2 = np.expand_dims(bruh2, axis=0)
#predictions=model.predict(bruh2)
#print(np.argmax(bruh2))
#print(CATAGORIES[int(np.argmax(cv2.imread(bruh)))])

hehe = "Please enter an integer: - (0 to stats \ -1 to exit)\n>"
model.evaluate(X, y, verbose=2)
value = input()
value = int(value)
while value!=-1:
    os.system('cls')
    try :
        if value == 0:
            model.summary()
            value = input(hehe)
            value = int(value)
        else:
            bruh = 'Untitled.jpg'
            bruh2 = cv2.imread(bruh)
            bruh2 = bruh2/255.0
            bruh2 = np.expand_dims(bruh2, axis=0)
            predictions=model.predict(bruh2)
            print(CATAGORIES[int(np.argmax(predictions))])
            print(predictions(bruh2))
            print((np.argmax(predictions)))
            model.evaluate(X, y, verbose=2)
            value = input(hehe)
            value = int(value)
    except Exception as e:
       print("error")
       os.system('cls')
       model.evaluate(X, y, verbose=2)
       value = input(hehe)
       value = int(value)