tensorflow实战_CNN对cifar-10图片识别

原理： 

#数据集
import tensorflow as tf
import cifar10_input
import numpy as np
import time
#https://blog.csdn.net/zeuseign/article/details/72773342
#https://blog.csdn.net/sinat_29957455/article/details/80615738


#train  500000
#test数据集：10000*3072（32*32*3）
#https://blog.csdn.net/RicardoSuzaku/article/details/77640647




#https://blog.csdn.net/u014281392/article/details/74881967
max_steps = 8000
batch_size = 128
data_dir = 'data/cifar-10-batches-bin'    # 数据所在路径

# 权重初始化+L2损失(正则)#https://blog.csdn.net/sinat_29957455/article/details/80615738
def variable_with_weight_loss(shape,std,w1):
	var = tf.Variable(tf.truncated_normal(shape,stddev=std),dtype=tf.float32)
	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

#损失函数
def loss_func(logits,labels):
	labels = tf.cast(labels,tf.int32)
	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(tf.reduce_sum(cross_entropy))
	tf.add_to_collection("losses",cross_entropy_mean)
	return tf.add_n(tf.get_collection("losses"),name="total_loss")

#distorted_inputs函数生成训练数据,
images_train, labels_train = cifar10_input.distorted_inputs(batch_size= batch_size, data_dir= data_dir)

# cifar10_input.inputs()返回测试数据,测试数据的label值,不是one-hot的形式,是我一个一维
images_test, labels_test = cifar10_input.inputs(batch_size= batch_size, data_dir= data_dir,eval_data= True)

#定义模型的输入和输出数据
image_holder = tf.placeholder(dtype=tf.float32,shape=[batch_size,24,24,3])
label_holder = tf.placeholder(dtype=tf.int32,shape=[batch_size])


# 设计第一层卷积
weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64], std=5e-2, w1=0)
kernel1 = tf.nn.conv2d(image_holder, weight1, [1, 1, 1, 1], padding="SAME")
bais1 = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[64]))
conv1 = tf.nn.relu(tf.nn.bias_add(kernel1, bais1))
pool1 = tf.nn.max_pool(conv1, [1, 3, 3, 1], [1, 2, 2, 1], padding="SAME")
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9, beta=0.75)

# 设计第二层卷积
weight2 = variable_with_weight_loss(shape=[5, 5, 64, 64], std=5e-2, w1=0)
kernel2 = tf.nn.conv2d(norm1, weight2, [1, 1, 1, 1], padding="SAME")
bais2 = tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[64]))
conv2 = tf.nn.relu(tf.nn.bias_add(kernel2, bais2))
norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.01 / 9, beta=0.75)
pool2 = tf.nn.max_pool(norm2, [1, 3, 3, 1], [1, 2, 2, 1], padding="SAME")

# 第一层全连接层
reshape = tf.reshape(pool2, [batch_size, -1])
dim = reshape.get_shape()[1].value
weight3 = variable_with_weight_loss([dim, 384], std=0.04, w1=0.004)
bais3 = tf.Variable(tf.constant(0.1, shape=[384], dtype=tf.float32))
local3 = tf.nn.relu(tf.matmul(reshape, weight3) + bais3)

# 第二层全连接层
weight4 = variable_with_weight_loss([384, 192], std=0.04, w1=0.004)
bais4 = tf.Variable(tf.constant(0.1, shape=[192], dtype=tf.float32))
local4 = tf.nn.relu(tf.matmul(local3, weight4) + bais4)

# 最后一层
weight5 = variable_with_weight_loss([192, 10], std=1 / 192.0, w1=0)
bais5 = tf.Variable(tf.constant(0.0, shape=[10], dtype=tf.float32))
logits = tf.add(tf.matmul(local4, weight5), bais5)


#获取损失函数
loss = loss_func(logits,label_holder)
#设置优化算法使得成本最小
train_step = tf.train.AdamOptimizer(1e-3).minimize(loss)
#获取最高类的分类准确率，取top1作为衡量标准
top_k_op = tf.nn.in_top_k(logits,label_holder,1)

#评估准确率;
# correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
# accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#创建会话
# 定义一个初始化变量op
init_op = tf.global_variables_initializer()
# 开启会话运行
with tf.Session() as sess:
	sess.run(init_op)
	#启动图片数据增强队列
	tf.train.start_queue_runners()
	#开始训练
	for step in range(max_steps):
		start_time = time.time()
		images_batch,labels_batch = sess.run([images_train,labels_train])
		_,loss_value = sess.run([train_step,loss],feed_dict={image_holder:images_batch,
															 label_holder:labels_batch})
		#获取计算时间
		duration = time.time() - start_time
		if step % 100 == 0:
			#计算每秒处理多少张图片
			per_images_second = batch_size / duration
			#获取时间
			sec_per_batch = float(duration)
			print("step:%d,duration:%.3f,per_images_second:%.2f,loss:%.3f"%(step,duration,per_images_second,loss_value))