#!/usr/bin/env python2
# -*- coding: utf-8 -*-"""Created on Wed Jan 18 08:42:55 2017@author: root"""#!/usr/bin/env python2# -*- coding: utf-8 -*-"""Created on Mon Jan 16 11:08:21 2017@author: root"""import tensorflow as tfimport frecordfortraintf.device(0)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() _, serialized_example = reader.read(filename_queue) #返回文件名和文件 features = tf.parse_single_example(serialized_example, features={ 'label': tf.FixedLenFeature([], tf.int64), 'img_raw' : tf.FixedLenFeature([], tf.string), }) img = tf.decode_raw(features['img_raw'], tf.uint8) img = tf.reshape(img, [227, 227, 3]) # img = tf.reshape(img, [39, 39, 3]) img = tf.cast(img, tf.float32) * (1. / 255) - 0.5 label = tf.cast(features['label'], tf.int32) print img,label return img, label def get_batch(image, label, batch_size,crop_size): #数据扩充变换 distorted_image = tf.random_crop(image, [crop_size, crop_size, 3])#随机裁剪 distorted_image = tf.image.random_flip_up_down(distorted_image)#上下随机翻转 #distorted_image = tf.image.random_brightness(distorted_image,max_delta=63)#亮度变化 #distorted_image = tf.image.random_contrast(distorted_image,lower=0.2, upper=1.8)#对比度变化 #生成batch #shuffle_batch的参数:capacity用于定义shuttle的范围,如果是对整个训练数据集,获取batch,那么capacity就应该够大 #保证数据打的足够乱 # images, label_batch = tf.train.shuffle_batch([distorted_image, label],batch_size=batch_size, # num_threads=16,capacity=50000,min_after_dequeue=10000) images, label_batch = tf.train.shuffle_batch([distorted_image, label],batch_size=batch_size, num_threads=2,capacity=2,min_after_dequeue=10) #images, label_batch=tf.train.batch([distorted_image, label],batch_size=batch_size) # 调试显示 #tf.image_summary('images', images) print "in get batch" print images,label_batch return images, tf.reshape(label_batch, [batch_size]) #from data_encoder_decoeder import encode_to_tfrecords,decode_from_tfrecords,get_batch,get_test_batch import cv2 import os class network(object): def inference(self,images): # 向量转为矩阵 # images = tf.reshape(images, shape=[-1, 39,39, 3]) images = tf.reshape(images, shape=[-1, 227,227, 3])# [batch, in_height, in_width, in_channels] images=(tf.cast(images,tf.float32)/255.-0.5)*2#归一化处理 #第一层 conv1=tf.nn.bias_add(tf.nn.conv2d(images, self.weights['conv1'], strides=[1, 4, 4, 1], padding='VALID'), self.biases['conv1']) relu1= tf.nn.relu(conv1) pool1=tf.nn.max_pool(relu1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID') #第二层 conv2=tf.nn.bias_add(tf.nn.conv2d(pool1, self.weights['conv2'], strides=[1, 1, 1, 1], padding='SAME'), self.biases['conv2']) relu2= tf.nn.relu(conv2) pool2=tf.nn.max_pool(relu2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID') # 第三层 conv3=tf.nn.bias_add(tf.nn.conv2d(pool2, self.weights['conv3'], strides=[1, 1, 1, 1], padding='SAME'), self.biases['conv3']) relu3= tf.nn.relu(conv3) # pool3=tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') conv4=tf.nn.bias_add(tf.nn.conv2d(relu3, self.weights['conv4'], strides=[1, 1, 1, 1], padding='SAME'), self.biases['conv4']) relu4= tf.nn.relu(conv4) conv5=tf.nn.bias_add(tf.nn.conv2d(relu4, self.weights['conv5'], strides=[1, 1, 1, 1], padding='SAME'), self.biases['conv5']) relu5= tf.nn.relu(conv5) pool5=tf.nn.max_pool(relu5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID') # 全连接层1,先把特征图转为向量 flatten = tf.reshape(pool5, [-1, self.weights['fc1'].get_shape().as_list()[0]]) drop1=tf.nn.dropout(flatten,0.5) fc1=tf.matmul(drop1, self.weights['fc1'])+self.biases['fc1'] fc_relu1=tf.nn.relu(fc1) fc2=tf.matmul(fc_relu1, self.weights['fc2'])+self.biases['fc2'] fc_relu2=tf.nn.relu(fc2) fc3=tf.matmul(fc_relu2, self.weights['fc3'])+self.biases['fc3'] return fc3 def __init__(self): with tf.variable_scope("weights"): self.weights={ #39*39*3->36*36*20->18*18*20 'conv1':tf.get_variable('conv1',[11,11,3,96],initializer=tf.contrib.layers.xavier_initializer_conv2d()), #18*18*20->16*16*40->8*8*40 'conv2':tf.get_variable('conv2',[5,5,96,256],initializer=tf.contrib.layers.xavier_initializer_conv2d()), #8*8*40->6*6*60->3*3*60 'conv3':tf.get_variable('conv3',[3,3,256,384],initializer=tf.contrib.layers.xavier_initializer_conv2d()), #3*3*60->120 'conv4':tf.get_variable('conv4',[3,3,384,384],initializer=tf.contrib.layers.xavier_initializer_conv2d()), 'conv5':tf.get_variable('conv5',[3,3,384,256],initializer=tf.contrib.layers.xavier_initializer_conv2d()), 'fc1':tf.get_variable('fc1',[6*6*256,4096],initializer=tf.contrib.layers.xavier_initializer()), 'fc2':tf.get_variable('fc2',[4096,4096],initializer=tf.contrib.layers.xavier_initializer()), #120->6 'fc3':tf.get_variable('fc3',[4096,2],initializer=tf.contrib.layers.xavier_initializer()), } with tf.variable_scope("biases"): self.biases={ 'conv1':tf.get_variable('conv1',[96,],initializer=tf.constant_initializer(value=0.0, dtype=tf.float32)), 'conv2':tf.get_variable('conv2',[256,],initializer=tf.constant_initializer(value=0.0, dtype=tf.float32)), 'conv3':tf.get_variable('conv3',[384,],initializer=tf.constant_initializer(value=0.0, dtype=tf.float32)), 'conv4':tf.get_variable('conv4',[384,],initializer=tf.constant_initializer(value=0.0, dtype=tf.float32)), 'conv5':tf.get_variable('conv5',[256,],initializer=tf.constant_initializer(value=0.0, dtype=tf.float32)), 'fc1':tf.get_variable('fc1',[4096,],initializer=tf.constant_initializer(value=0.0, dtype=tf.float32)), 'fc2':tf.get_variable('fc2',[4096,],initializer=tf.constant_initializer(value=0.0, dtype=tf.float32)), 'fc3':tf.get_variable('fc3',[2,],initializer=tf.constant_initializer(value=0.0, dtype=tf.float32)) } def inference_test(self,images): # 向量转为矩阵 images = tf.reshape(images, shape=[-1, 39,39, 3])# [batch, in_height, in_width, in_channels] images=(tf.cast(images,tf.float32)/255.-0.5)*2#归一化处理 #第一层 conv1=tf.nn.bias_add(tf.nn.conv2d(images, self.weights['conv1'], strides=[1, 1, 1, 1], padding='VALID'), self.biases['conv1']) relu1= tf.nn.relu(conv1) pool1=tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') #第二层 conv2=tf.nn.bias_add(tf.nn.conv2d(pool1, self.weights['conv2'], strides=[1, 1, 1, 1], padding='VALID'), self.biases['conv2']) relu2= tf.nn.relu(conv2) pool2=tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') # 第三层 conv3=tf.nn.bias_add(tf.nn.conv2d(pool2, self.weights['conv3'], strides=[1, 1, 1, 1], padding='VALID'), self.biases['conv3']) relu3= tf.nn.relu(conv3) pool3=tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') # 全连接层1,先把特征图转为向量 flatten = tf.reshape(pool3, [-1, self.weights['fc1'].get_shape().as_list()[0]]) fc1=tf.matmul(flatten, self.weights['fc1'])+self.biases['fc1'] fc_relu1=tf.nn.relu(fc1) fc2=tf.matmul(fc_relu1, self.weights['fc2'])+self.biases['fc2'] return fc2 #计算softmax交叉熵损失函数 def sorfmax_loss(self,predicts,labels): predicts=tf.nn.softmax(predicts) labels=tf.one_hot(labels,self.weights['fc3'].get_shape().as_list()[1]) loss = tf.nn.softmax_cross_entropy_with_logits(predicts, labels) # loss =-tf.reduce_mean(labels * tf.log(predicts))# tf.nn.softmax_cross_entropy_with_logits(predicts, labels) self.cost= loss return self.cost #梯度下降 def optimer(self,loss,lr=0.01): train_optimizer = tf.train.GradientDescentOptimizer(lr).minimize(loss) return train_optimizer def train(): # encode_to_tfrecords("data/train.txt","data",'train.tfrecords',(45,45)) # image,label=decode_from_tfrecords('data/train.tfrecords') # image,label=read_and_decode("/home/zenggq/data/imagedata/data.tfrecords") batch_image,batch_label=read_and_decode("/home/zenggq/data/imagedata/data.tfrecords") # batch_image = tf.random_crop(batch_image1, [39, 39, 3]) # batch_image,batch_label=get_batch(image,label,batch_size=5,crop_size=227)#batch 生成测试 #网络链接,训练所用 net=network() inf=net.inference(batch_image) loss=net.sorfmax_loss(inf,batch_label) opti=net.optimer(loss) #验证集所用 """ encode_to_tfrecords("data/val.txt","data",'val.tfrecords',(45,45)) test_image,test_label=decode_from_tfrecords('data/val.tfrecords',num_epoch=None) test_images,test_labels=get_test_batch(test_image,test_label,batch_size=120,crop_size=39)#batch 生成测试 test_inf=net.inference_test(test_images) correct_prediction = tf.equal(tf.cast(tf.argmax(test_inf,1),tf.int32), test_labels) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) """ init=tf.initialize_all_variables() with tf.Session() as session: with tf.device("/gpu:1"): session.run(init) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) max_iter=9000 iter=0 if os.path.exists(os.path.join("model",'model.ckpt')) is True: tf.train.Saver(max_to_keep=None).restore(session, os.path.join("model",'model.ckpt')) while iter<max_iter: loss_np,_,label_np,image_np,inf_np=session.run([loss,opti,batch_image,batch_label,inf]) #print image_np.shape #cv2.imshow(str(label_np[0]),image_np[0]) #print label_np[0] #cv2.waitKey() #print label_np if iter%50==0: print 'trainloss:',loss_np # if iter%500==0: # accuracy_np=session.run([accuracy]) # print '***************test accruacy:',accuracy_np,'*******************' # tf.train.Saver(max_to_keep=None).save(session, os.path.join('model','model.ckpt')) iter+=1 coord.request_stop()#queue需要关闭,否则报错 coord.join(threads) # session.close() if __name__ == '__main__': train()