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