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v6_03_cnn_train.py
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v6_03_cnn_train.py
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# coding: utf-8
# In[1]:
import os
import tensorflow as tf
from PIL import Image
#from nets import nets_factory
import numpy as np
import time
start=time.clock()
#批次?
BATCH_SIZE=16
#学习率
learn_rate=5e-5
#tfrecord文件存放路径
TFRECORD_FILE="D:/jupyter_pycode/buildings/33333/train.tfrecords"
##函数:从tfrecord读取数据
def read_and_decode(filename):
#根据文件名生成一个队列
filename_queue=tf.train.string_input_producer([filename])
reader=tf.TFRecordReader()
#返回文件名和文件
_,serialized_example=reader.read(filename_queue)
features=tf.parse_single_example(serialized_example,
features={
'order':tf.FixedLenFeature([],tf.int64),
'name':tf.FixedLenFeature([],tf.string),
'image':tf.FixedLenFeature([],tf.string),
'label':tf.FixedLenFeature([],tf.string),
})
#tf.train.shuffle_batch必须确定shape
image_order=features['order']
image_name=features['name']
#获取图片数据
image=tf.decode_raw(features['image'],tf.uint8)
#未处理图
image_raw=tf.reshape(image,[64,64,3])
#图片预处理
image=tf.reshape(image,[64,64,3])
image=tf.cast(image,tf.float32)
#image=tf.subtract(image,0.5)
#image=tf.multiply(image,2.0)
#获取标签数据
label=tf.decode_raw(features['label'],tf.uint8)
label=tf.reshape(label,[16,16])
return image_order,image_name,image,image_raw,label
##获取图片数据和标签
image_order,image_name,image,image_raw,label=read_and_decode(TFRECORD_FILE) #tf文件
##【v2】将标签转为一维
label=tf.reshape(label,[256])
#给训练样本分批次
image_order_batch,image_name_batch,image_batch,image_raw_batch,label_batch=tf.train.shuffle_batch(
[image_order,image_name,image,image_raw,label],batch_size=BATCH_SIZE,capacity=5000,min_after_dequeue=1000,num_threads=1) #参数设多少
##定义网络结构
with tf.Session() as sess:
#初始化权值
def weight_variable(shape):
initial=tf.truncated_normal(shape,stddev=0.01) #生成一个截断的正态分布
return tf.Variable(initial)
#初始化偏置
def bias_variable(shape):
initial=tf.constant(0.1,shape=shape)
return tf.Variable(initial)
#卷积层
def conv2d_L1(x,W):
# x input tensor of shape: [batch_size,in_height,in_width,in_channels]
# W filter / kernel tensor of shape [filter_height,filter_width,in_channels,out_channels]
# strides[0]=strides[3]=1, strides[1]表示x方向步长,strides[2]表示y方向步长
#padding: A string from: "SAME","VALID"
return tf.nn.conv2d(x,W,strides=[1,2,2,1],padding='SAME')
#池化层
def max_pool_2x2(x):
#ksize [1,x,y,1]
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,1,1,1],padding='SAME')
def conv2d_L2(x,W):
return tf.nn.conv2d(x,W,strides=[1,2,2,1],padding='SAME')
def conv2d_L3(x,W):
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
def batch_normalize(x):
axes=[d for d in range(len(x.get_shape()))]
mean,var=tf.nn.moments(x,axes=axes)
scale = tf.Variable(tf.constant(1.0,shape=[]))
offset = tf.Variable(tf.constant(0.0,shape=[]))
variance_epsilon = 0.00001
x2 = tf.nn.batch_normalization(x, mean, var, offset, scale, variance_epsilon)
return x2
#定义两个placeholoder
x=tf.placeholder(tf.float32,[None,64,64,3])
y=tf.placeholder(tf.int32,[None,256])
#改变x的格式,转为4D向量[batch_size,in_height,in_width,in_channels]
x_image=tf.reshape(x,[-1,64,64,3])
#初始化第一个卷积层的权值和偏置
W_conv1=weight_variable([9,9,3,64]) #16*16采样窗口,64个卷积核,从3个平面抽取特征,得到64个特征平面
b_conv1=bias_variable([64]) #每一个卷积核有一个偏置值
#把x_image和权值向量进行卷积,再加上偏置,然后应用于relu激活函数
h_conv1=tf.nn.relu(conv2d_L1(x_image,W_conv1)+b_conv1)
h_pool1=max_pool_2x2(h_conv1) #最大值池化
#h_pool1=batch_normalize(h_pool1)
#第二个卷积层
W_conv2=weight_variable([7,7,64,128]) #112个卷积核从64个平面抽取特征
b_conv2=bias_variable([128])
h_conv2=tf.nn.relu(conv2d_L2(h_pool1,W_conv2)+b_conv2)
#h_pool2=max_pool_2x2(h_conv2)
h_pool2=h_conv2 #第二层不池化
#h_pool2=batch_normalize(h_pool2)
#第三个卷积层
W_conv3=weight_variable([5,5,128,64]) #64个卷积核从32个平面抽取特征
b_conv3=bias_variable([64])
h_conv3=tf.nn.relu(conv2d_L3(h_pool2,W_conv3)+b_conv3)
#h_pool2=max_pool_2x2(h_conv2)
h_pool3=h_conv3 #第三层不池化
#h_pool3=batch_normalize(h_pool3)
#64*64的图片第一次卷积后还是32*32,第一次池化后变为32*32,第二次卷积后16*16,第三次卷积后16*16,得到64张16*16的特征平面
#初始化第一个全连接层的权值
W_fc1=weight_variable([16*16*64,4096]) #上一层输出64*64*80个神经元,全连接层有4096个神经元
b_fc1=bias_variable([4096])
#池化层2的输出扁平化为1维
h_pool3_flat=tf.reshape(h_pool3,[-1,16*16*64])
#求第一个全连接层的输出
h_fc1=tf.nn.relu(tf.matmul(h_pool3_flat,W_fc1)+b_fc1)
#dropout层稍降维,keep_prob表示使用神经元的概率
keep_prob=tf.placeholder(tf.float32)
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)
#h_fc1_drop=batch_normalize(h_fc1_drop)
#初始化第二个全连接层
W_fc2=weight_variable([4096,256*2])
b_fc2=bias_variable([256*2])
###########要改
#计算输出
net_output=tf.matmul(h_fc1_drop,W_fc2)+b_fc2 #256*1 概率
#net_output=batch_normalize(net_output)
logits=tf.reshape(net_output,[-1,256,2])
with tf.name_scope('cross_entropy'):
#交叉熵代价函数
cross_entropy=tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,logits=logits),name='cross_entropy')
tf.summary.scalar('cross_entropy',cross_entropy)
#Momentum优化
train_step=tf.train.AdamOptimizer(learn_rate).minimize(cross_entropy)
prediction=tf.nn.softmax(logits)
prediction2=tf.argmax(prediction,2)
prediction2=tf.cast(prediction2,tf.int32)
#【v2】二值化
#prediction2=tf.round(prediction) #取距离最近的整数(0.5以下--0;0.5以上--1) Q:tensor格式中怎样自己设阈值
#判断预测正确性,结果存放在bool型列表中
correct_prediction=tf.equal(y,prediction2) #对比,返回True或False;
correct_prediction=tf.reshape(correct_prediction,[-1])
with tf.name_scope('accuracy'):
#求准确率
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) #cast将bool型转为浮点型(t&f转为1&0)
tf.summary.scalar('accuracy',accuracy)
merged=tf.summary.merge_all()
#保存模型
saver=tf.train.Saver()
#初始化
sess.run(tf.global_variables_initializer())
writer=tf.summary.FileWriter('logs_cnn/',sess.graph) #存储图的结构
#创建一个协调器,管理线程
coord=tf.train.Coordinator()
#启动QueueRunner,此时文件名队列已经进队
threads=tf.train.start_queue_runners(sess=sess,coord=coord)
for i in range(14031):
#获取一个批次的数据和标签
b_image,b_label=sess.run([image_batch,label_batch])
summary,loss,_=sess.run([merged,cross_entropy,train_step],feed_dict={x:b_image,y:b_label,keep_prob:0.7})
writer.add_summary(summary,i)
if i%1403==0:
acc=sess.run(accuracy,feed_dict={x:b_image,y:b_label,keep_prob:1.0})
#print("Iter "+ str(i) + "accuracy= " + str(acc))
print("Iter "+str(i)+" loss= "+str(loss)+" accuracy= " + str(acc))
if i%7015==0:
learn_rate=learn_rate*0.1
'''
if i%200==0:
show_predict=sess.run(prediction,feed_dict={x:b_image,keep_prob:0.7})
print(show_predict)
'''
#if acc>0.9:
saver.save(sess,"D:/jupyter_pycode/buildings/33333/cnn/trial_model.model")
#break
#通知其他线程关闭
coord.request_stop()
#其他所有线程关闭后,返回函数
coord.join(threads)
print('complete.')
end=time.clock()
print(end-start,' s')