-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathp267_AlexNet.py
101 lines (82 loc) · 3.6 KB
/
p267_AlexNet.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
import tensorflow as tf
learning_rate = 1e-4
training_iters = 200
display_step = 5
n_classes = 2
n_fc1 = 4096
n_fc2 = 2048
x = tf.placeholder(tf.float32, [None, 227, 227, 3])
y = tf.placeholder(tf.int32, [None, n_classes])
W_conv = {
'conv1': tf.Variable(tf.truncated_normal([11, 11, 3, 96], stddev=0.0001)),
'conv2': tf.Variable(tf.truncated_normal([5, 5, 96, 256], stddev=0.01)),
'conv3': tf.Variable(tf.truncated_normal([3, 3, 256, 384], stddev=0.01)),
'conv4': tf.Variable(tf.truncated_normal([3, 3, 384, 384], stddev=0.01)),
'conv5': tf.Variable(tf.truncated_normal([3, 3, 384, 256], stddev=0.01)),
'fc1': tf.Variable(tf.truncated_normal([13*13*256, n_fc1], stddev=0.1)),
'fc2': tf.Variable(tf.truncated_normal([n_fc1, n_fc2], stddev=0.1)),
'fc3': tf.Variable(tf.truncated_normal([n_fc2, n_classes], stddev=0.1)),
}
b_conv = {
'conv1': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[96])),
'conv2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[256])),
'conv3': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[384])),
'conv4': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[384])),
'conv5': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[256])),
'fc1': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc1])),
'fc2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc2])),
'fc3': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_classes])),
}
x_image = x
#第一层总卷积层
#卷积层 1
conv1 = tf.nn.conv2d(x_image, W_conv['conv1'], strides=[1, 4, 4, 1], padding='VALID')
conv1 = tf.nn.bias_add(conv1, b_conv['conv1'])
con1 = tf.nn.relu(conv1)
#池化层 1
pool1 = tf.nn.avg_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID')
#LRN层，Local Response Normalization
norm1 = tf.nn.lrn(pool1, 5, bias=1.0, alpha=0.01/9.0, beta=0.75)
#第二层卷积层
#卷积层 2
conv2 = tf.nn.conv2d(norm1, W_conv['conv2'], strides=[1, 1, 1, 1], padding="SAME")
conv2 = tf.nn.bias_add(conv2, b_conv['conv2'])
conv2 = tf.nn.relu(conv2)
#池化层 2
pool2 = tf.nn.avg_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID')
# LRN 层 Local Response Normalization
norm2 = tf.nn.lrn(pool2, 5, bias=1.0, alpha=0.001/9.0, beta=0.75)
#第三层卷积层
#卷积层 3
conv3 = tf.nn.conv2d(norm2, W_conv['conv3'], strides=[1, 1, 1, 1], padding='SAME')
conv3 = tf.nn.bias_add(conv3, b_conv['conv3'])
conv3 = tf.nn.relu(conv3)
#第四层卷积层
#卷积层 4
conv4 = tf.nn.conv2d(conv3, W_conv['conv4'], strides=[1, 1, 1, 1], padding="SAME")
conv4 = tf.nn.bias_add(conv4, b_conv['conv4'])
conv4 = tf.nn.relu(conv4)
#第五层卷积层
#卷积层 5
conv5 = tf.nn.conv2d(conv4, W_conv['conv5'], strides=[1, 1, 1, 1], padding='SAME')
conv5 = tf.nn.bias_add(conv5, b_conv['conv5'])
conv5 = tf.nn.relu(conv5)
#池化层 5
pool5 = tf.nn.avg_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID')
#第六层全连接层 1
reshape = tf.reshape(pool5, [-1, 13 * 13 * 256])
#全连接层
fc1 = tf.add(tf.matmul(reshape, W_conv['fc1']), b_conv['fc1'])
fc1 = tf.nn.dropout(fc1, 0.5)
#第七层全连接层 2
fc2 = tf.add(tf.matmul(fc1, W_conv['fc2']), b_conv['fc2'])
fc2 = tf.nn.relu(fc2)
fc2 = tf.nn.dropout(fc2, 0.5)
#第八层全连接层 3，即分类
fc3 = tf.add(tf.matmul(fc2, W_conv['fc3']), b_conv['fc3'])
#定义损失
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(fc3, y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
#评估模型
correct_pred = tf.equal(tf.argmax(fc3, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))