-
Notifications
You must be signed in to change notification settings - Fork 1
/
train_model.py
133 lines (110 loc) · 4.73 KB
/
train_model.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
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
125
126
127
128
129
130
131
132
133
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 21 21:15:53 2020
@author: 王玺
"""
from dataSet import DataSet
from keras.models import Sequential,load_model
from keras.layers import Dense,Activation,Convolution2D,MaxPooling2D,Flatten,Dropout
import numpy as np
#建立一个基于CNN的人脸识别模型
class Model(object):
FILE_PATH = "C:\\Face recognition\\face.model.h5" #模型进行存储和读取的地方
IMAGE_SIZE = 128 #模型接受的人脸图片一定得是128*128的
def __init__(self):
self.model = None
#读取实例化后的DataSet类作为进行训练的数据源
def read_trainData(self,dataset):
self.dataset = dataset
#建立CNN模型
def build_model(self):
self.model = Sequential()
self.model.add(
Convolution2D(
filters=32,
kernel_size=(3, 3),
padding='same',
data_format='channels_last',
input_shape=self.dataset.X_train.shape[1:]
)
)
self.model.add(Activation('relu'))
self.model.add(
Convolution2D(
filters=32,
kernel_size=(3, 3),
padding='same',
data_format='channels_last',
input_shape=self.dataset.X_train.shape[1:]
)
)
self.model.add(Activation('relu'))
self.model.add(
MaxPooling2D(
pool_size=(2, 2),
strides=(2, 2),
padding='same'
)
)
self.model.add(Dropout(0.25))
self.model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
self.model.add(Activation('relu'))
self.model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))
self.model.add(Dropout(0.25))
self.model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
self.model.add(Activation('relu'))
self.model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))
self.model.add(Dropout(0.25))
self.model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
self.model.add(Activation('relu'))
self.model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))
self.model.add(Dropout(0.25))
self.model.add(Flatten())
self.model.add(Dense(512))
self.model.add(Activation('relu'))
self.model.add(Dropout(0.5))
self.model.add(Dropout(0.5))
self.model.add(Dropout(0.5))
self.model.add(Dense(self.dataset.num_classes))
self.model.add(Activation('softmax'))
self.model.summary()
def train_model(self):
self.model.compile(
optimizer='adam', #有很多可选的optimizer,例如RMSprop,Adagrad
loss='categorical_crossentropy', #可以选用squared_hinge作为loss
metrics=['accuracy'])
#epochs、batch_size为可调的参数,epochs为训练多少轮、batch_size为每次训练多少个样本
self.model.fit(self.dataset.X_train,self.dataset.Y_train,epochs=5,batch_size=16)
def evaluate_model(self):
print('\nTesting---------------')
loss, accuracy = self.model.evaluate(self.dataset.X_test, self.dataset.Y_test)
print('test loss:', loss)
print('test accuracy:', accuracy)
def save(self, file_path=FILE_PATH):
print('Model Saved.')
self.model.save(file_path)
def load(self, file_path=FILE_PATH):
print('Model Loaded.')
self.model = load_model(file_path)
#需要确保输入的img得是灰化之后(channel =1 )且 大小为IMAGE_SIZE的人脸图片
def predict(self,img):
img = img.reshape((1, self.IMAGE_SIZE, self.IMAGE_SIZE,1))
img = img.astype('float32')
img = img/255.0
result = self.model.predict_proba(img) #测算一下该img属于某个label的概率
max_index = np.argmax(result) #找出概率最高的
return max_index,result[0][max_index] #第一个参数为概率最高的label的index,第二个参数为对应概率
if __name__ == '__main__':
dataset = DataSet('./data/')
model = Model()
model.read_trainData(dataset)
model.build_model()
model.train_model()
model.evaluate_model()
model.save()