TensorFlow實戰6——TensorFlow實現VGGNet_16_D

  1 #coding = utf-8
  2 from datetime import datetime
  3 import tensorflow as tf
  4 import time
  5 import math
  6 
  7 def conv_op(input_op, name, kh, kw, n_out, dh, dw, p):
  8     n_in = input_op.get_shape()[-1].value
  9 
 10     '''建立卷積層並把本層的參數存入參數列表
 11     input_op：tensor
 12     name:層名
 13     kh:kernel height
 14     kw:kernel width
 15     n_out:卷積核數量即輸出通道數
 16     dh：步長的高
 17     dw: 步長的寬
 18     p： 參數列表'''
 19     with tf.name_scope(name) as scope:
 20 
 21         kernel = tf.get_variable(scope+"w",
 22             shape=[kh, kw, n_in, n_out], dtype=tf.float32,
 23             initializer=tf.contrib.layers.xavier_initializer_conv2d())
 24 
 25         conv = tf.nn.conv2d(input_op, kernel, (1, dh, dw, 1),
 26                             padding = 'SAME')
 27         bias_init_val = tf.constant(0.0, shape=[n_out], dtype=tf.float32)
 28         biases = tf.Variable(bias_init_val, trainable=True, name='b')
 29         z = tf.nn.bias_add(conv, biases)
 30         activation = tf.nn.relu(z, name=scope)
 31         p += [kernel, biases]
 32         return activation
 33 
 34 
 35 def fc_op(input_op, name, n_out, p):
 36     '''定義建立全鏈接層函數：'''
 37     n_in = input_op.get_shape()[-1].value#獲取輸入input_op的通道數
 38 
 39     with tf.name_scope(name) as scope:
 40         #建立全鏈接層參數，維度爲2：n_in, n_out
 41         kernel = tf.get_variable(scope+"w",
 42                                 shape=[n_in, n_out], dtype=tf.float32,
 43                                 initializer=tf.contrib.layers.xavier_initializer())
 44         #偏置初始化爲0.1，避免dead neoron
 45         biases = tf.Variable(tf.constant(0.1, shape=[n_out],
 46                                          dtype=tf.float32), name="b")
 47         #激活：relu非線性變換獲得
 48         activation = tf.nn.relu_layer(input_op, kernel, biases, name=scope)
 49         #添加全鏈接參數kernel和biases到參數列表p
 50         p += [kernel, biases]
 51 
 52         return activation
 53 
 54 
 55 def mpool_op(input_op, name, kh, kw, dh, dw):
 56     '''定義maxpool層
 57     input：input_op
 58     池化尺寸：khxkw
 59     步長：dhxdw
 60     padding：SAME
 61     padding="SAME" 輸出尺寸爲W/S(其中W爲輸入尺寸,S爲stride)
 62     padding="VALID" 輸出尺寸爲(W-F+1)/S(其中F爲卷積核尺寸)
 63     '''
 64 
 65     return tf.nn.max_pool(input_op,
 66                     ksize=[1, kh, kw, 1],
 67                     strides=[1, dh, dw, 1],
 68                     padding='SAME',
 69                     name=name)
 70 
 71 def inference_op(input_op, keep_prob):
 72 
 73     p = []
 74     '''第一層卷積神經網絡：2個卷積層和一個最大池化層,
 75     2個卷積層卷積核大小爲3x3，卷積核數量(輸出通道數)：64，步長爲1x1
 76     輸出尺寸：112x112x64'''
 77     conv1_1 = conv_op(input_op, name="conv1_1", kh=3, kw=3, n_out=64, dh=1,
 78                       dw=1, p=p)
 79     conv1_2 = conv_op(conv1_1, name="conv1_2", kh=3, kw=3, n_out=64, dh=1,
 80                       dw=1, p=p)
 81     pool1 = mpool_op(conv1_2, name="pool1", kh=2, kw=2, dw=2, dh=2)
 82 
 83     '''第二層卷積神經網絡:2個卷積層和一個最大池化層，
 84     2個卷積層卷積核大小爲：3x3，輸出通道數爲：128
 85     輸出尺寸爲56x56x128'''
 86     conv2_1 = conv_op(pool1, name="conv2_1", kh=3, kw=3, n_out=128, dh=1,
 87                       dw=1, p=p)
 88     conv2_2 = conv_op(conv2_1, name="conv2_2", kh=3, kw=3, n_out=128, dh=1,
 89                       dw=1, p=p)
 90     pool2 = mpool_op(conv2_2, name="pool2", kh=2, kw=2, dh=2, dw=2)
 91 
 92     '''第三層卷積神經網絡：3個卷積層和一個最大池化層。
 93     3個卷積層的卷積核大小均爲：3X3，輸出通道數爲:256
 94     輸出尺寸爲28x28x256'''
 95     conv3_1 = conv_op(pool2, name="conv3_1", kh=3, kw=3, n_out=256, dh=1,
 96                       dw=1, p=p)
 97     conv3_2 = conv_op(conv3_1, name="conv3_2", kh=3, kw=3, n_out=256, dh=1,
 98                       dw=1, p=p)
 99     conv3_3 = conv_op(conv3_2, name="conv3_3", kh=3, kw=3, n_out=256, dh=1,
100                       dw=1, p=p)
101     pool3 = mpool_op(conv3_3, name="pool3", kh=2, kw=2, dh=2, dw=2)
102     '''第四層卷積神經網絡：3個卷積層和一個最大池化層。
103     3個卷積層的卷積核大小均爲3x3，輸出通道爲512，
104     輸出尺寸爲14x14x512'''
105     conv4_1 = conv_op(pool3, name="conv3_1", kh=3, kw=3, n_out=512, dh=1,
106                       dw=1, p=p)
107     conv4_2 = conv_op(conv4_1, name="conv4_2", kh=3, kw=3, n_out=512, dh=1,
108                       dw=1, p=p)
109     conv4_3 = conv_op(conv4_2, name="conv4_3", kh=3, kw=3, n_out=512, dh=1,
110                       dw=1, p=p)
111     pool4 =mpool_op(conv4_3, name="pool4", kh=2, kw=2, dh=2, dw=2)
112 
113     '''第五層卷積神經網絡：3個卷積層和一個最大池化層。
114        3個卷積層的卷積核大小均爲3x3，輸出通道仍然爲512，
115        輸出尺寸爲7x7x512'''
116     conv5_1 = conv_op(pool4, name="conv5_1", kh=3, kw=3, n_out=512, dh=1,
117                       dw=1, p=p)
118     conv5_2 = conv_op(conv5_1, name="conv4_2", kh=3, kw=3, n_out=512, dh=1,
119                       dw=1, p=p)
120     conv5_3 = conv_op(conv5_2, name="conv4_3", kh=3, kw=3, n_out=512, dh=1,
121                       dw=1, p=p)
122     pool5 =mpool_op(conv5_3, name="pool5", kh=2, kw=2, dh=2, dw=2)
123 
124 
125     '''輸出結果扁平化，用tf.reshape將各個樣本轉化爲7x7x512=25088的一維向量'''
126     shp = pool5.get_shape()
127     flattened_shape = shp[1].value*shp[2].value*shp[3].value
128     resh1 = tf.reshape(pool5, [-1, flattened_shape], name="resh1")
129 
130     '''隱含節點數爲4096的Fully connection，activation function：ReLu,
131     再鏈接Dropout層，訓練時節點保留率爲0.5，預測時爲1.0'''
132     fc6 = fc_op(resh1, name="fc6", n_out=4096, p=p)
133     fc6_drop = tf.nn.dropout(fc6, keep_prob, name="fc_drop")
134 
135     fc7 = fc_op(fc6_drop, name="fc7", n_out=4096, p=p)
136     fc7_drop = tf.nn.dropout(fc7, keep_prob, name="fc7_drop")
137 
138     '''輸出節點爲1000的全鏈接層，並使用softmax進行分類獲得分類輸出機率'''
139     fc8 = fc_op(fc7_drop, name="fc8", n_out=1000, p=p)
140     softmax = tf.nn.softmax(fc8)
141     #tf.argmax()求出輸出機率的最大類別
142     predictions = tf.argmax(softmax, 1)
143     return  predictions, softmax, fc8, p
144 
145 
146 def time_tensorflow_run(session, target, feed, info_string):
147 
148     n_steps_burn_in = 10
149     total_duration = 0.0
150     total_duration_squared = 0.0
151     for i in range(num_batches+n_steps_burn_in):
152         start_time = time.time()
153         _ = session.run(target, feed_dict=feed)
154         duration = time.time()-start_time
155 
156         if i>=n_steps_burn_in:
157             if not i%10:
158                 print('%s: step %d, duration = %.3f' %
159                       (datetime.now(), i-n_steps_burn_in, duration))
160                 total_duration += duration
161                 total_duration_squared +=duration*duration
162 
163     mn = total_duration/num_batches
164     vr = total_duration_squared/num_batches-mn*mn
165     sd = math.sqrt(vr)
166 
167     print('%s: %s across %d steps, %.3f +/- %3.3f sec/batch' %
168           (datetime.now(), info_string, num_batches, mn, sd))
169 
170 def run_benchmark():
171 
172     with tf.Graph().as_default():
173         '''定義圖片尺寸224，利用tf.random_normal函數生成標準差爲0.1的正態分佈
174         的隨機數來構建224x224的隨機圖片'''
175         image_size = 64#GPU空間不夠,改成64
176         images = tf.Variable(tf.random_normal([batch_size,
177                                                image_size,
178                                                image_size, 3],
179                                                dtype=tf.float32,
180                                                stddev=1e-1))
181         #構建keep_prob的placeholder
182         keep_prob = tf.placeholder(tf.float32)
183         #調用inference_op構建VGGNET-16網絡結構，賦值給相應參數
184         predictions, softmax, fc8, p = inference_op(images, keep_prob)
185         init = tf.global_variables_initializer()
186         sess = tf.Session()
187         sess.run(init)
188         #設置keep_prob爲1.0，運用time_tensorflow_run來評測forward運算隨機
189         time_tensorflow_run(sess, predictions, {keep_prob:1.0}, "Forward")
190         #計算fc8輸出的loss
191         objective = tf.nn.l2_loss(fc8)
192         #求相對於loss的全部模型的梯度
193         grad = tf.gradients(objective, p)
194         #評測backward運算時間
195         time_tensorflow_run(sess, grad, {keep_prob:0.5}, "Forward-backward")
196 
197 
198 batch_size = 32
199 num_batches = 100
200 run_benchmark()

 1 2017-12-21 20:27:00.788000: step 0, duration = 0.055
 2 2017-12-21 20:27:01.343000: step 10, duration = 0.055
 3 2017-12-21 20:27:01.897000: step 20, duration = 0.055
 4 2017-12-21 20:27:02.451000: step 30, duration = 0.055
 5 2017-12-21 20:27:03.008000: step 40, duration = 0.055
 6 2017-12-21 20:27:03.566000: step 50, duration = 0.056
 7 2017-12-21 20:27:04.123000: step 60, duration = 0.056
 8 2017-12-21 20:27:04.679000: step 70, duration = 0.056
 9 2017-12-21 20:27:05.236000: step 80, duration = 0.056
10 2017-12-21 20:27:05.793000: step 90, duration = 0.056
11 2017-12-21 20:27:06.295000: Forward across 100 steps, 0.006 +/- 0.017 sec/batch
12 
13 2017-12-21 20:27:09.294000: step 0, duration = 0.188
14 2017-12-21 20:27:11.173000: step 10, duration = 0.187
15 2017-12-21 20:27:13.053000: step 20, duration = 0.188
16 2017-12-21 20:27:14.934000: step 30, duration = 0.188
17 2017-12-21 20:27:16.814000: step 40, duration = 0.188
18 2017-12-21 20:27:18.695000: step 50, duration = 0.188
19 2017-12-21 20:27:20.575000: step 60, duration = 0.188
20 2017-12-21 20:27:22.456000: step 70, duration = 0.188
21 2017-12-21 20:27:24.337000: step 80, duration = 0.188
22 2017-12-21 20:27:26.217000: step 90, duration = 0.187
23 2017-12-21 20:27:27.911000: Forward-backward across 100 steps, 0.019 +/- 0.056 sec/batch