tensorflow cifar10 CNN

cifar10.maybe_download_and_extract() 默认在E:/tmp/中

import sys
path = r'E:\learn\pc_code\tensorflow\example\models-master\tutorials\image\cifar10'
sys.path.append(path)
import cifar10, cifar10_input
import tensorflow as tf
import numpy as np
import time
import os
batch_size=128
max_steps=3000
data_dir= r'E:\tmp\cifar10_data\cifar-10-batches-bin'
try:
    os.makedirs(data_dir)
except: pass
#L2正则
def variable_with_weight_loss(shape, stddev, wl):
    var=tf.Variable(tf.truncated_normal(shape,stddev=stddev))
    if wl is not None:
        weight_loss=tf.multiply(tf.nn.l2_loss(var),wl,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])
#第一层
weight1=variable_with_weight_loss(shape=[5,5,3,64],stddev=0.05,wl=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(kernel1+bias1)
#步长小于尺寸，这样有重叠能丰富特征
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)
#第二层
weight2=variable_with_weight_loss(shape=[5,5,64,64],stddev=0.05,wl=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(kernel1+bias1)
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,wl=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,wl=0.004)
bias4=tf.Variable(tf.constant(0.1,shape=[192]))
local4=tf.nn.relu(tf.matmul(local3,weight4)+bias4)

weight5=variable_with_weight_loss(shape=[192,10],stddev=1.0/192,wl=0.0)
bias5=tf.Variable(tf.constant(0.0,shape=[10]))
logit5=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_sample'
    )
    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(logit5,label_holder)
train_op=tf.train.AdamOptimizer(1e-3).minimize(loss)
top_k_op = tf.nn.in_top_k(logit5,label_holder,1)
sess=tf.InteractiveSession()
tf.global_variables_initializer().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_cur=sess.run([train_op,loss],
                     feed_dict={image_holder:image_batch,label_holder:label_batch})
    duration=time.time()-start_time
    if step % 20==0:
        example_per_sec=batch_size/duration
        #sec_per_batch=float(duration)
        print('Step:',step,'example_per_sec:',example_per_sec,'Loss:',loss_cur)
num_examples=10000
import math
num_iter=int(math.ceil(num_examples/batch_size))
true_count=0
total_sample=batch_size*num_iter
for step in range(num_iter):
    image_batch,label_batch=sess.run([images_test,labels_test])
    pre=sess.run([top_k_op],feed_dict={image_holder:image_batch,label_holder:label_batch})
    true_count+=np.sum(pre)
    step+=1
print('Test accuracy:', true_count/total_sample)