import tensorflow as tf
def _int64_feature(value):# value必须是可迭代对象# 非int的数据使用bytes取代int64即可return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
num_shards = 2
instance_perPshard = 2
for i in range(num_shards):filename = ('FTR/data.tfrecords-%.5d-of-%.5d' % (i, num_shards))writer = tf.python_io.TFRecordWriter(filename)                      #<---------书写器打开for j in range(instance_perPshard):example = tf.train.Example(features=tf.train.Features(feature={ #<---------书写入缓冲区'i':_int64_feature(i),'j':_int64_feature(j)}))writer.write(example.SerializeToString())                       #<---------书写入实际文件writer.close()                                                      #<---------书写器关闭

'''读取TFR'''files = ["FTR/data.tfrecords-00000-of-00002","FTR/data.tfrecords-00001-of-00002"]
# files = tf.train.match_filenames_once("FTR/data.tfrecords-*")# 输入文件名列表
# 返回QueueRunner & FIFOQueue
# 打乱顺序&加入队列 和 输出队列获取文件 属于单独的线程
filename_queue = tf.train.string_input_producer(files, shuffle=False) #<---------输入文件队列
reader = tf.TFRecordReader()                        #<---------读取器打开
_,serialized_example = reader.read(filename_queue)  #<---------读取原始文件
features = tf.parse_single_example(                 #<---------读取解析后文件serialized_example,features={'i':tf.FixedLenFeature([],tf.int64),'j':tf.FixedLenFeature([],tf.int64)})with tf.Session() as sess:tf.global_variables_initializer().run()coord = tf.train.Coordinator() #<---------多线程threads = tf.train.start_queue_runners(sess=sess,coord=coord) #<---------文件名队列填充线程启动for i in range(6):print(sess.run([features['i'],features['j']]))            #<---------实际会话中启动读取过程coord.request_stop()           #<---------多线程coord.join(threads)            #<---------多线程

example_batch, label_batch = tf.train.batch([example, label],     #<---------多线程batch生成batch_size=batch_size,num_threads=3,capacity=capacity)example_batch, label_batch = tf.train.shuffle_batch([example, label],     #<---------多线程随机batch生成batch_size=batch_size,num_threads=3,capacity=capacity,min_after_dequeue=30)     由于元素太少随机意义就不大了，所以多了个参数

files = ["FTR/data.tfrecords-00000-of-00002","FTR/data.tfrecords-00001-of-00002"]
# files = tf.train.match_filenames_once("FTR/data.tfrecords-*")# 输入文件名列表
# 返回QueueRunner & FIFOQueue
# 打乱顺序&加入队列 和 输出队列获取文件 属于单独的线程
filename_queue = tf.train.string_input_producer(files, shuffle=False) #<---------输入文件队列
reader = tf.TFRecordReader()                        #<---------读取
_,serialized_example = reader.read(filename_queue)  #<---------读取
features = tf.parse_single_example(                 #<---------读取serialized_example,features={'i':tf.FixedLenFeature([],tf.int64),'j':tf.FixedLenFeature([],tf.int64)})example, label = features['i'], features['j']
batch_size = 2
capacity = 1000 + 3 * batch_size# 入队单个样例，出队batch
# 可以指定多个线程同时执行入队操作
example_batch, label_batch = tf.train.batch([example, label],     #<---------多线程batch生成batch_size=batch_size,num_threads=3,capacity=capacity)
with tf.Session() as sess:tf.global_variables_initializer().run()coord = tf.train.Coordinator()                                #<---------多线程管理器threads = tf.train.start_queue_runners(sess=sess,coord=coord) #<---------文件名队列填充线程启动for i in range(3):cur_example_batch, cur_label_batch = sess.run([example_batch, label_batch])print(cur_example_batch, cur_label_batch)coord.request_stop()                                          #<---------多线程关闭coord.join(threads)

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import tensorflow as tf
import matplotlib.pyplot as pltdef _bytes_feature(value):return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(value):return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
img_raw = tf.gfile.FastGFile('123123.jpeg','rb').read()filename = ('FTR/image.tfrecords')
writer = tf.python_io.TFRecordWriter(filename) #<---------书写
example = tf.train.Example(features=tf.train.Features(feature={ #<---------书写'image':_bytes_feature(img_raw),'label':_int64_feature(1)}))
writer.write(example.SerializeToString()) #<---------书写
writer.close()

filename_queue = tf.train.string_input_producer(['FTR/image.tfrecords'], shuffle=False) #<---------输入文件队列
reader = tf.TFRecordReader()                        #<---------读取
_,serialized_example = reader.read(filename_queue)  #<---------读取
features = tf.parse_single_example(                 #<---------读取serialized_example,features={'image':tf.FixedLenFeature([],tf.string),'label':tf.FixedLenFeature([],tf.int64)})
img = tf.image.decode_jpeg(features['image'])
with tf.Session() as sess:tf.global_variables_initializer().run()coord = tf.train.Coordinator()  # <---------多线程threads = tf.train.start_queue_runners(sess=sess, coord=coord)  # <---------文件名队列填充线程启动# img_raw, label = sess.run([features['image'], features['label']])image = sess.run(img)plt.imshow(image)plt.show()coord.request_stop()  # <---------多线程coord.join(threads)  # <---------多线程

# 导入tensorflow
import tensorflow as tf# 新建一个Session
with tf.Session() as sess:# 我们要读三幅图片A.jpg, B.jpg, C.jpgfilename = ['123.png', '123123.jpeg']# string_input_producer会产生一个文件名队列filename_queue = tf.train.string_input_producer(filename, shuffle=False, num_epochs=5)# reader从文件名队列中读数据。对应的方法是reader.readreader = tf.WholeFileReader()                             #<---------注意读取器不一样了key, value = reader.read(filename_queue)# tf.train.string_input_producer定义了一个epoch变量，要对它进行初始化tf.local_variables_initializer().run()# 使用start_queue_runners之后，才会开始填充队列threads = tf.train.start_queue_runners(sess=sess)i = 0while True:i += 1# 获取图片数据并保存image_data = sess.run(value)with open('test_%d.jpg' % i, 'wb') as f:f.write(image_data)

import tensorflow as tf'''创建文件列表'''files = tf.train.match_filenames_once("Records/output.tfrecords")
filename_queue = tf.train.string_input_producer(files, shuffle=False)'''解析TFRecord文件里的数据'''# 读取文件。reader = tf.TFRecordReader()
_,serialized_example = reader.read(filename_queue)# 解析读取的样例。
features = tf.parse_single_example(serialized_example,features={'image_raw':tf.FixedLenFeature([],tf.string),'pixels':tf.FixedLenFeature([],tf.int64),'label':tf.FixedLenFeature([],tf.int64)})decoded_images = tf.decode_raw(features['image_raw'],tf.uint8)
retyped_images = tf.cast(decoded_images, tf.float32)
labels = tf.cast(features['label'],tf.int32)
#pixels = tf.cast(features['pixels'],tf.int32)
images = tf.reshape(retyped_images, [784])'''将文件以100个为一组打包'''min_after_dequeue = 10000
batch_size = 100
capacity = min_after_dequeue + 3 * batch_sizeimage_batch, label_batch = tf.train.shuffle_batch([images, labels],batch_size=batch_size,capacity=capacity,min_after_dequeue=min_after_dequeue)'''训练模型'''def inference(input_tensor, weights1, biases1, weights2, biases2):layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)return tf.matmul(layer1, weights2) + biases2# 模型相关的参数
INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500
REGULARAZTION_RATE = 0.0001
TRAINING_STEPS = 5000weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE, LAYER1_NODE], stddev=0.1))
biases1 = tf.Variable(tf.constant(0.1, shape=[LAYER1_NODE]))weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE], stddev=0.1))
biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))y = inference(image_batch, weights1, biases1, weights2, biases2)# 计算交叉熵及其平均值
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=label_batch)
cross_entropy_mean = tf.reduce_mean(cross_entropy)# 损失函数的计算
regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)
regularaztion = regularizer(weights1) + regularizer(weights2)
loss = cross_entropy_mean + regularaztion# 优化损失函数
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)# 初始化回话并开始训练过程。
with tf.Session() as sess:tf.global_variables_initializer().run()coord = tf.train.Coordinator()threads = tf.train.start_queue_runners(sess=sess, coord=coord)# 循环的训练神经网络。for i in range(TRAINING_STEPS):if i % 1000 == 0:print("After %d training step(s), loss is %g " % (i, sess.run(loss)))sess.run(train_step)coord.request_stop()coord.join(threads)