#1.cifar10 資料集
CIFAR-10資料集包含10個類的60000張32x32的彩色影象，每個類有6000張影象．有50000張訓練影象和10000張測試影象.
圖如下：

#2.模型訓練

import cifar10
import cifar10_input
import tensorflow as tf
import numpy as np
import time
tf.reset_default_graph()

max_steps = 3000
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


## 第一層卷積 步長為[1,1,1,1],掃過每一個畫素，然後通過池化層進行圖片縮小，
##得到灰度值最大的，特徵值最明顯的

#### 第一層卷積 ####
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")
#lRN層可以多個filter中挑選出最大的反饋，在區域性中建立競爭條件，增大反應較大值
norm1 = tf.nn.lrn(pool1,4,bias=0.1,alpha=0.001/9.0,beta=0.75)


##用封裝好的類distored_inputs產生所需要的資料和label
tf.app.flags.DEFINE_string('f', '', 'kernel') 
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,[128,24,24,3])
label_holder = tf.placeholder(tf.int32,[128])

##第二層卷積 
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")

###全連線層，將卷積層flatten 
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)

###第二層全連線層 
weight4 = variable_with_weight_loss(shape=[384,192],stddev=0.04,w1=0.004)
bias4 = tf.Variable(tf.constant(0.1,shape=[192]))
local4 = tf.nn.relu(tf.matmul(local3,weight4)+bias4)

###logits
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)

# 計算損失函式
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)
#優化器
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.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_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 = 10000
import math
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)