-
Notifications
You must be signed in to change notification settings - Fork 37
/
cnn_orl.py
145 lines (134 loc) · 7.4 KB
/
cnn_orl.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
134
135
136
137
138
139
140
141
142
143
144
145
'''
author：zhipeng li
#2019 年3 月22日
'''
import numpy as np
import tensorflow as tf
import random,os,cv2,glob
import time
batch_size = 50
def loadImageSet(folder=u'data_faces', sampleCount=5): #加载图像集，随机选择sampleCount张图片用于训练
trainData = []; testData = [];yTrain2=[];yTest2=[]
#print(yTest)
for k in range(40):
yTrain1 = np.zeros(40)
yTest1 = np.zeros(40)
folder2 = os.path.join(folder, 's%d' % (k+1))
"""
a=glob.glob(os.path.join(folder2, '*.pgm'))
for d in a:
#print(d)
img=cv2.imread(d)#imread读取图像返回的是三维数组,返回值是3个数组：I( : , : ,1) I( : , : ,2) I( : , : ,3) 这3个数组中应该存放的是RGB的值
#print(img)#112*92*3
cv2.imshow('image', img)
"""
#data 每次10*112*92
data = [ cv2.imread(d,0) for d in glob.glob(os.path.join(folder2, '*.pgm'))]#cv2.imread()第二个参数为0的时候读入为灰度图，即使原图是彩色图也会转成灰度图#glob.glob匹配所有的符合条件的文件，并将其以list的形式返回
sample = random.sample(range(10), 5)#random.sample()可以从指定的序列中，随机的截取指定长度的片断，不作原地修改
#print(sample)
#sample=[0,1,2,3,4]
#print(sample)
trainData.extend([data[i].ravel() for i in range(10) if i in sample])#ravel将多维数组降位一维####40*5*112*92
testData.extend([data[i].ravel() for i in range(10) if i not in sample])#40*5*112*92
yTrain1[k]=1
yTest1[k]=1
#yTest.extend([]* (10-sampleCount))
#yTrain.extend([k]* sampleCount)
yTrain = np.matrix(yTrain1)
yTrain= np.tile(yTrain1,5)# np.tile(a,(2,1))就是把a先沿x轴（就这样称呼吧）复制1倍，即没有复制，仍然是 [0,1,2]。 再把结果沿y方向复制2倍
yTest=np.tile(yTest1,5)#沿着x轴复制5倍，增加列数
"""
yTrain.shape=5,40
yTest.shape=5,40
"""
yTrain2.extend(yTrain)
yTest2.extend(yTest)
return np.array(trainData), np.array(yTrain2), np.array(testData), np.array(yTest2)
def weight_variable(shape):
inital=tf.truncated_normal(shape,stddev=0.1) #产生随机变量tf.truncated_normal(shape, mean, stddev) :shape表示生成张量的维度，mean是均值，stddev是标准差。这个函数产生正太分布，均值和标准差自己设定
return tf.Variable(inital)
def bias_variable(shape):
inital=tf.constant(0.1,shape=shape)
return tf.Variable(inital)
def conv2d(x,W):
#stride[1,x_movment,y_movment,1]
# Must have strides[0] = strides[3] = 1
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding="SAME")
def max_pool_2x2(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")
def main():
#loadImageSet()
xTrain_, yTrain, xTest_, yTest = loadImageSet()# 200*10304
yTrain.shape=200,40
yTest.shape=200,40
def return_next_batch(batch_size, pos):
start = pos * batch_size
end = start + batch_size
return xTrain_[start:end], yTrain[start:end]
###定义变量占位符
x = tf.placeholder(tf.float32, [None, 10304])#112X92
y_=tf.placeholder(tf.float32,[None,40])
keep_prob=tf.placeholder(tf.float32)
x_image=tf.reshape(x,[-1,112,92,1])# -1表示任意数量的样本数，chanel黑白为1
print(x_image.shape)
####conv1 layer###
W_conv1=weight_variable([32,32,1,32])#patch 32x32,in size=1,0utsize=32
b_conv1=bias_variable([32])
h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)#output size 112x92x32
h_pool1=max_pool_2x2(h_conv1) #output size 56x46x32
#####conv2 layer###
W_conv2 = weight_variable([5, 5, 32, 64]) # patch 5x5,in size=32,0utsize=64
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 56x46x64
h_pool2 = max_pool_2x2(h_conv2) # output size 28x23x64
print(h_conv2)
##func1 layer##
W_fc1=weight_variable([28*23*64,800])
b_fc1=bias_variable([800])
# [n_samples, 28, 23, 64] ->> [n_samples, 28*23*64]
h_pool2_fat=tf.reshape(h_pool2,[-1,28*23*64])
h_fc1=tf.nn.sigmoid(tf.matmul(h_pool2_fat,W_fc1)+b_fc1)
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob) ###dropout防止过拟合###\
##func2 layer##
W_fc2 = weight_variable([800, 40])
b_fc2 = bias_variable([40])
prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
###定义损失函数和优化器
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(prediction)))
train_step = tf.train.AdagradOptimizer(1e-3).minimize(cross_entropy)
def compute_accuracy(v_xs, v_ys):
y_pre = sess.run(prediction, feed_dict={x: v_xs, keep_prob: 1})
correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
result = sess.run(accuracy, feed_dict={x: v_xs, y_: v_ys, keep_prob: 1})
return result
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
sess.run(tf.global_variables_initializer())
for i in range(10000):
for j in range(4):
batch_x, batch_y =return_next_batch(batch_size, j)
batch_x=batch_x/255 #归一化
# training train_step 和 loss 都是由 placeholder 定义的运算，所以这里要用 feed 传入参数
sess.run(train_step, feed_dict={x: np.matrix( batch_x), y_: np.matrix(batch_y),keep_prob:0.5})
if i%50==0:
correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y_, 1))
# 计算正确预测项的比例，因为tf.equal返回的是布尔值，使用tf.cast可以把布尔值转换成浮点数，tf.reduce_mean是求平均值
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# 在session中启动accuracy，输入是orl中的测试集
print("识别率:%.2f%%" % ((sess.run(accuracy, feed_dict={x: np.matrix(xTest_ / 255), y_: np.matrix(yTest),
keep_prob: 1})) * 100))
# 计算正确预测项的比例，因为tf.equal返回的是布尔值，使用tf.cast可以把布尔值转换成浮点数，tf.reduce_mean是求平均值
# 在session中启动accuracy，输入是orl中的测试集
#print(sess.run(cross_entropy, feed_dict={x: np.matrix((batch_x)), y_: np.matrix(batch_y)}))
#if(sess.run(cross_entropy, feed_dict={x: np.matrix((batch_x)), y_: np.matrix(batch_y)})==0):
# break
# 评估模型，tf.argmax能给出某个tensor对象在某一维上数据最大值的索引。因为标签是由0,1组成了one-hot vector，返回的索引就是数值为1的位置
#correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y_, 1))
# 计算正确预测项的比例，因为tf.equal返回的是布尔值，使用tf.cast可以把布尔值转换成浮点数，tf.reduce_mean是求平均值
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# 在session中启动accuracy，输入是orl中的测试集
print("识别率:%.2f%%" %((sess.run(accuracy, feed_dict={x:np.matrix(xTest_/255), y_: np.matrix(yTest),keep_prob: 1}))*100))
if __name__ == '__main__':
main()