CNN Advanced

 from sys import path
 path.append('/home/ustcjing/models/tutorials/image/cifar10/')
 import cifar10,cifar10_input
 import tensorflow as tf
 import math
 import numpy as np
 import time

 max_steps=300
 batch_size=128
 data_dir='/tmp/cifar10_data/cifar-10-batches-bin'

 def variable_with_weight_loss(shape,stddev,w1):
     var=tf.Variable(tf.truncated_normal(shape,stddev=stddev))
     if w1 is not None:
         weight_loss=tf.multiply(tf.nn.l2_loss(var),w1,name='weight_loss')
         tf.add_to_collection('losses','weight_loss')

     return var

 cifar10.maybe_download_and_extract()
 images_train,labels_train=cifar10_input.distorted_inputs(data_dir=data_dir,batch_size=batch_size)
 images_test,labels_test=cifar10_input.inputs(eval_data=True,data_dir=data_dir,batch_size=batch_size)

 image_holder=tf.placeholder(tf.float32,[batch_size,24,24,3])
 label_holder=tf.placeholder(tf.int32,[batch_size])

 weight1=variable_with_weight_loss(shape=[5,5,3,64],stddev=5e-2,w1=0.0)
 kernel1=tf.nn.conv2d(image_holder,weight1,[1,1,1,1],padding='SAME')
 bias1=tf.Variable(tf.constant(0.0,shape=[64]))
 conv1=tf.nn.relu(tf.nn.bias_add(kernel1,bias1))
 pool1=tf.nn.max_pool(conv1,ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME')
 norm1=tf.nn.lrn(pool1,4,bias=1.0,alpha=0.001/9.0,beta=0.75)

 weight2=variable_with_weight_loss(shape=[5,5,64,64],stddev=5e-2,w1=0.0)
 kernel2=tf.nn.conv2d(norm1,weight2,[1,1,1,1],padding='SAME')
 bias2=tf.Variable(tf.constant(0.1,shape=[64]))
 conv2=tf.nn.relu(tf.nn.bias_add(kernel2,bias2))
 norm2=tf.nn.lrn(conv2,4,bias=1.0,alpha=0.001/9.0,beta=0.75)
 pool2=tf.nn.max_pool(norm2,ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME')

 reshape=tf.reshape(pool2,[batch_size,-1])
 dim=reshape.get_shape()[1].value
 weight3=variable_with_weight_loss(shape=[dim,384],stddev=0.04,w1=0.004)
 bias3=tf.variable(tf.constant(0.1,shape=[384]))
 local3=tf.nn.relu(tf.matmul(reshape,weight3)+bias3)

 weight4=variable_with_weight_loss(shape=[384,192],stddev=0.04,w1=0.004)
 bias4=tf.Variable9tf.constant(0.1,shape=[192])
 local4=tf.nn.relu(tf.matmul(local3,weight4)+bias4)

 weight5=variable_with_weight_loss(shape=[192,10],stddev=1/192.0,w1=0.0)
 bias5=tf.Variable(tf.constant(0.0,shape=[10]))
 logits=tf.add(tf.matmul(local4,weight5),bias5)

 def loss(logits,labels):
     labels=tf.cast(labels,tf.int64)
     cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=labels,name='cross_entropy_per_example')
     cross_entropy_mean=tf.reduce_mean(cross_entropy,name='cross_entropy')
     tf.add_to_collection('losses',cross_entropy_mean)
     return tf.add_n(tf.get_collection('losses'),name='total_loss')

 loss=loss(logits,label_holder)
 train_op=tf.train.AdamOptimizer(1e-3).minimize(loss)
 top_k_op=tf.nn.in_top_k(logits,label_holder,1)
 sess=tf.InteractiveSession()
 tf.initialize_all_variables().run()
 tf.train.start_queue_runners()

 for step in range(max_steps):
     start_time=time.time()
     image_batch,label_batch=sess.run([images_train,labels_train])
     loss_value=sess.run([train_op,loss],feed_dict={image_holder:image_batch,label_holder:label_batch})
     duration=time.time()-start_time
     if step%10==0:
         examples_per_sec=batch_size/duration
         sec_per_batch=float(duration)
         format_str=('step %d,loss=%.2f (%.1f examples/sec;%.3f sec/batch)')
         print(format_str % (step,loss_value,examples_per_sec,sec_per_batch))

 num_examples=1000
 num_iter=int(math.ceil(num_examples / batch_size))
 true_count=0;
 total_sample_count=num_iter*batch_size
 step=0
 while step<num_iter:
     image_batch,label_batch=sess.run([images_test,labels_test])
     predictions=sess.run([top_k_op],feed_dict={image_holder:image_batch,label_holder:label_batch})

     true_count+=np.sum(predictions)
     step+=1

 precision=true_count/total_sample_count
 print('precision @ 1=%.3f' % precision)