1 #coding = utf-8
2
3 from datetime import datetime
4 import math
5 import time
6 import tensorflow as tf
7
8 batch_size = 32
9 num_batches = 1000
10
11 def print_activations(t):
12 '''打印每一个卷积层或池化层输出tensor的尺寸
13 t:tensor t.op.name：tensor的名称 ;
14 t.get-shape.as_list()：tensor尺寸'''
15 print(t.op.name, '', t.get_shape().as_list())
16
17 def interence(images):
18 '''input: images; return: 最后一层pool5及parameters
19 '''
20 parameters = []
21
22 with tf.name_scope('conv1') as scope:
23 #定义第一个卷积层，卷积核尺寸为11x11，颜色通道为3，卷积核数量为64
24 kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 64],
25 dtype=tf.float32, stddev=1e-1, name='weights'))
26 #对输入的images进行卷积操作，strides步长设置为4x4
27 conv = tf.nn.conv2d(images, kernel, [1, 4, 4, 1], padding='SAME')
28 #biases全部初始化为0
29 biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
30 trainable=True, name='biases')
31 #将卷积结果conv和biases
32 bias = tf.nn.bias_add(conv, biases)
33 #rele对结果进行非线性处理
34 conv1 = tf.nn.relu(bias, name=scope)
35 print_activations(conv1)
36 #将这一层的参数kernel和biases添加到parameters
37 parameters += [kernel, biases]
38 #LRN层，depth_radius=4，等都是AlexNet论文中推荐值
39 lrn1 = tf.nn.lrn(conv1, 4, bias=1.0, alpha=0.001/9, beta=0.75, name='lrn1')
40 pool1 = tf.nn.max_pool(lrn1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
41 padding='VALID', name='pool1')
42
43 print_activations(pool1)
44
45 with tf.name_scope('conv2') as scope:
46 #定义第二个卷积层，卷积核尺寸为5x5，输入通道为64，卷积核数量为192
47 kernel = tf.Variable(tf.truncated_normal([5, 5, 64, 192],
48 dtype=tf.float32, stddev=1e-1, name='weights'))
49 #对输入的images进行卷积操作，strides步长设置为1x1
50 conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding='SAME')
51 #biases全部初始化为0
52 biases = tf.Variable(tf.constant(0.0, shape=[192], dtype=tf.float32),
53 trainable=True, name='biases')
54 #将卷积结果conv和biases
55 bias = tf.nn.bias_add(conv, biases)
56 #rele对结果进行非线性处理
57 conv2 = tf.nn.relu(bias, name=scope)
58
59 #将这一层的参数kernel和biases添加到parameters
60 parameters += [kernel, biases]
61
62 print_activations(conv2)
63 #LRN层，depth_radius=4，等都是AlexNet论文中推荐值
64 lrn2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9, beta=0.75, name='lrn2')
65 pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
66 padding='VALID', name='pool2')
67
68 print_activations(pool2)
69
70 with tf.name_scope('conv3') as scope:
71 #定义第三个卷积层，卷积核尺寸为5x5，输入通道为64，卷积核数量为192
72 kernel = tf.Variable(tf.truncated_normal([3, 3, 192, 384],
73 dtype=tf.float32, stddev=1e-1, name='weights'))
74 #对输入的images进行卷积操作，strides步长设置为1x1
75 conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME')
76 #biases全部初始化为0
77 biases = tf.Variable(tf.constant(0.0, shape=[384], dtype=tf.float32),
78 trainable=True, name='biases')
79 #将卷积结果conv和biases
80 bias = tf.nn.bias_add(conv, biases)
81 #rele对结果进行非线性处理
82 conv3 = tf.nn.relu(bias, name=scope)
83
84 #将这一层的参数kernel和biases添加到parameters
85 parameters += [kernel, biases]
86
87 print_activations(conv3)
88
89
90 with tf.name_scope('conv4') as scope:
91 #定义第四个卷积层，卷积核尺寸为3x3，输入通道为384，卷积核数量为256
92 kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256],
93 dtype=tf.float32, stddev=1e-1, name='weights'))
94 #对输入的images进行卷积操作，strides步长设置为1x1
95 conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding='SAME')
96 #biases全部初始化为0
97 biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
98 trainable=True, name='biases')
99 #将卷积结果conv和biases
100 bias = tf.nn.bias_add(conv, biases)
101 #rele对结果进行非线性处理
102 conv4 = tf.nn.relu(bias, name=scope)
103
104 #将这一层的参数kernel和biases添加到parameters
105 parameters += [kernel, biases]
106
107 print_activations(conv4)
108
109 with tf.name_scope('conv5') as scope:
110 #定义第五个卷积层，卷积核尺寸为3x3，输入通道为256，卷积核数量为256
111 kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256],
112 dtype=tf.float32, stddev=1e-1, name='weights'))
113 #对输入的images进行卷积操作，strides步长设置为1x1
114 conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding='SAME')
115 #biases全部初始化为0
116 biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
117 trainable=True, name='biases')
118 #将卷积结果conv和biases
119 bias = tf.nn.bias_add(conv, biases)
120 #rele对结果进行非线性处理
121 conv5 = tf.nn.relu(bias, name=scope)
122
123 #将这一层的参数kernel和biases添加到parameters
124 parameters += [kernel, biases]
125
126 print_activations(conv5)
127
128 pool5 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
129 padding='VALID', name='pool5')
130 print_activations(pool5)
131 return pool5, parameters
132
133 def time_tensorflow_run(session, target, info_string):
134 '''评估AlexNet每轮计算时间
135 target:评测的运算算子
136 info_string：评测的名称'''
137 num_steps_burn_in = 10#预热轮数，给程序热身
138 total_duration = 0.0#总时间
139 total_duration_squared =0.0#计算方差
140
141 for i in range(num_batches + num_steps_burn_in):
142
143 start_time = time.time()
144 _ = session.run(target)
145 duration = time.time()-start_time
146 #在初始热身的num_steps_burn_in次迭代后每10轮显示当前迭代所需要的时间
147 if i >= num_steps_burn_in:
148 if not i%10:
149 print('%s: step %d, duration = %.3f' %
150 (datetime.now(), i-num_steps_burn_in, duration))
151 total_duration +=duration
152 total_duration_squared += duration*duration
153 mn = total_duration/num_batches#每轮迭代平均耗时
154 vr = total_duration_squared/num_batches-mn*mn
155 #平均耗时标准差
156 sd = math.sqrt(vr)
157
158 print('%s: %s across %d steps, %.3f +/-%.3f sec/batch' %
159 (datetime.now(), info_string, num_batches, mn, sd))
160
161 def run_benchmark():
162
163 with tf.Graph().as_default():
164 image_size = 224
165 '''batch_size:每轮迭代样本数
166 image_size:图片尺寸
167 3：图片颜色通道数'''
168 images = tf.Variable(tf.random_normal([batch_size,
169 image_size,
170 image_size, 3],
171 dtype=tf.float32,
172 stddev=1e-1))
173 pool5, parameters = interence(images)
174
175 init = tf.global_variables_initializer()
176 sess = tf.Session()
177 sess.run(init)
178
179 time_tensorflow_run(sess, pool5, "Forward")
180
181 objective = tf.nn.l2_loss(pool5)
182 grad = tf.gradients(objective, parameters)
183 time_tensorflow_run(sess, grad, "Forward-backward")
184
185 run_benchmark()
1 conv1 [32, 56, 56, 64]
2 pool1 [32, 27, 27, 64]
3 conv2 [32, 27, 27, 192]
4 pool2 [32, 13, 13, 192]
5 conv3 [32, 13, 13, 384]
6 conv4 [32, 13, 13, 256]
7 conv5 [32, 13, 13, 256]
8 pool5 [32, 6, 6, 256]
9 2017-12-20 23:31:19.926000: step 990, duration = 0.197
10 2017-12-20 23:31:19.926000: Forward-backward across 1000 steps, 0.196 +/-0.019 sec/batch
11 2017-12-20 23:31:20.122000: Forward-backward across 1000 steps, 0.196 +/-0.018 sec/batch
12 2017-12-20 23:31:20.319000: Forward-backward across 1000 steps, 0.197 +/-0.017 sec/batch
13 2017-12-20 23:31:20.515000: Forward-backward across 1000 steps, 0.197 +/-0.016 sec/batch
14 2017-12-20 23:31:20.711000: Forward-backward across 1000 steps, 0.197 +/-0.014 sec/batch
15 2017-12-20 23:31:20.907000: Forward-backward across 1000 steps, 0.197 +/-0.013 sec/batch
16 2017-12-20 23:31:21.104000: Forward-backward across 1000 steps, 0.197 +/-0.011 sec/batch
17 2017-12-20 23:31:21.299000: Forward-backward across 1000 steps, 0.197 +/-0.010 sec/batch
18 2017-12-20 23:31:21.494000: Forward-backward across 1000 steps, 0.198 +/-0.007 sec/batch
19 2017-12-20 23:31:21.690000: Forward-backward across 1000 steps, 0.198 +/-0.004 sec/batch
