1 from sys import path
2 path.append('/home/ustcjing/models/tutorials/image/cifar10/')
3 import cifar10,cifar10_input
4 import tensorflow as tf
5 import math
6 import numpy as np
7 import time
8
9 max_steps=300
10 batch_size=128
11 data_dir='/tmp/cifar10_data/cifar-10-batches-bin'
12
13 def variable_with_weight_loss(shape,stddev,w1):
14 var=tf.Variable(tf.truncated_normal(shape,stddev=stddev))
15 if w1 is not None:
16 weight_loss=tf.multiply(tf.nn.l2_loss(var),w1,name='weight_loss')
17 tf.add_to_collection('losses','weight_loss')
18
19 return var
20
21 cifar10.maybe_download_and_extract()
22 images_train,labels_train=cifar10_input.distorted_inputs(data_dir=data_dir,batch_size=batch_size)
23 images_test,labels_test=cifar10_input.inputs(eval_data=True,data_dir=data_dir,batch_size=batch_size)
24
25 image_holder=tf.placeholder(tf.float32,[batch_size,24,24,3])
26 label_holder=tf.placeholder(tf.int32,[batch_size])
27
28 weight1=variable_with_weight_loss(shape=[5,5,3,64],stddev=5e-2,w1=0.0)
29 kernel1=tf.nn.conv2d(image_holder,weight1,[1,1,1,1],padding='SAME')
30 bias1=tf.Variable(tf.constant(0.0,shape=[64]))
31 conv1=tf.nn.relu(tf.nn.bias_add(kernel1,bias1))
32 pool1=tf.nn.max_pool(conv1,ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME')
33 norm1=tf.nn.lrn(pool1,4,bias=1.0,alpha=0.001/9.0,beta=0.75)
34
35 weight2=variable_with_weight_loss(shape=[5,5,64,64],stddev=5e-2,w1=0.0)
36 kernel2=tf.nn.conv2d(norm1,weight2,[1,1,1,1],padding='SAME')
37 bias2=tf.Variable(tf.constant(0.1,shape=[64]))
38 conv2=tf.nn.relu(tf.nn.bias_add(kernel2,bias2))
39 norm2=tf.nn.lrn(conv2,4,bias=1.0,alpha=0.001/9.0,beta=0.75)
40 pool2=tf.nn.max_pool(norm2,ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME')
41
42 reshape=tf.reshape(pool2,[batch_size,-1])
43 dim=reshape.get_shape()[1].value
44 weight3=variable_with_weight_loss(shape=[dim,384],stddev=0.04,w1=0.004)
45 bias3=tf.variable(tf.constant(0.1,shape=[384]))
46 local3=tf.nn.relu(tf.matmul(reshape,weight3)+bias3)
47
48 weight4=variable_with_weight_loss(shape=[384,192],stddev=0.04,w1=0.004)
49 bias4=tf.Variable9tf.constant(0.1,shape=[192])
50 local4=tf.nn.relu(tf.matmul(local3,weight4)+bias4)
51
52 weight5=variable_with_weight_loss(shape=[192,10],stddev=1/192.0,w1=0.0)
53 bias5=tf.Variable(tf.constant(0.0,shape=[10]))
54 logits=tf.add(tf.matmul(local4,weight5),bias5)
55
56 def loss(logits,labels):
57 labels=tf.cast(labels,tf.int64)
58 cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=labels,name='cross_entropy_per_example')
59 cross_entropy_mean=tf.reduce_mean(cross_entropy,name='cross_entropy')
60 tf.add_to_collection('losses',cross_entropy_mean)
61 return tf.add_n(tf.get_collection('losses'),name='total_loss')
62
63 loss=loss(logits,label_holder)
64 train_op=tf.train.AdamOptimizer(1e-3).minimize(loss)
65 top_k_op=tf.nn.in_top_k(logits,label_holder,1)
66 sess=tf.InteractiveSession()
67 tf.initialize_all_variables().run()
68 tf.train.start_queue_runners()
69
70 for step in range(max_steps):
71 start_time=time.time()
72 image_batch,label_batch=sess.run([images_train,labels_train])
73 loss_value=sess.run([train_op,loss],feed_dict={image_holder:image_batch,label_holder:label_batch})
74 duration=time.time()-start_time
75 if step%10==0:
76 examples_per_sec=batch_size/duration
77 sec_per_batch=float(duration)
78 format_str=('step %d,loss=%.2f (%.1f examples/sec;%.3f sec/batch)')
79 print(format_str % (step,loss_value,examples_per_sec,sec_per_batch))
80
81 num_examples=1000
82 num_iter=int(math.ceil(num_examples / batch_size))
83 true_count=0;
84 total_sample_count=num_iter*batch_size
85 step=0
86 while step<num_iter:
87 image_batch,label_batch=sess.run([images_test,labels_test])
88 predictions=sess.run([top_k_op],feed_dict={image_holder:image_batch,label_holder:label_batch})
89
90 true_count+=np.sum(predictions)
91 step+=1
92
93 precision=true_count/total_sample_count
94 print('precision @ 1=%.3f' % precision)
