Analysis and storage example of tensorflow------tfrecords

TensorFlow------Tfrecords Analysis and storage examples:

ImportOSImportTensorFlow as TF#defining command-line parameters such as CIFAR dataFLAGS =tf.app.flags.FLAGStf.app.flags.DEFINE_string ('Cifar_dir','./data/cifar10/cifar-10-batches-bin','Directory of Files') tf.app.flags.DEFINE_string ('Cifar_tfrecords','./tmp/cifar.tfrecords','files that store tfrecords')classCifarread (object):" "finish reading the binary file, write into Tfrecords, read Tfrecords:p Aram object:: Return:" "    def __init__(self,filelist):#file ListSelf.file_list =filelist#defines some properties of a picture that is readSelf.height = 32Self.width= 32Self.channel= 3#bytes of binary files per pictureSelf.label_bytes = 1self.image_bytes= Self.height * Self.width *Self.channel self.bytes= Self.label_bytes +self.image_bytesdefRead_and_decode (self):#1. Building the file queueFile_queue =tf.train.string_input_producer (self.file_list)#2. Build binary file Reader, read content, number of bytes per sampleReader =TF. Fixedlengthrecordreader (self.bytes) Key,value=Reader.read (file_queue)#3. Decoding content, decoding the contents of binary files Label_image contains target values and eigenvaluesLabel_image =Tf.decode_raw (value,tf.uint8)Print(label_image)#4. Segmentation of image and tag data, eigenvalues and target valuesLabel =Tf.slice (label_image,[0],[self.label_bytes]) image=Tf.slice (label_image,[self.label_bytes],[self.image_bytes])Print('---->')        Print(image)#5. You can change the shape of the image's characteristic data [3072]-->[32,32,3]Image_reshape =Tf.reshape (Image,[self.height,self.width,self.channel])Print('======>')        Print(label)Print('======>')        #6. Batch processing DataImage_batch,label_batch = Tf.train.batch ([image_reshape,label],batch_size=10,num_threads=1,capacity=10)        Print(Image_batch,label_batch)returnImage_batch,label_batchdefwrite_ro_tfrecords (self,image_batch,label_batch):" "Save the image's eigenvalues and target values into the Tfrecords:p Aram the eigenvalues of the image_batch:10 picture:p Aram Label_batch:10 The target value of the picture: Retur N:none" "        #1. Build Tfrecord Memorywriter =Tf.python_io. Tfrecordwriter (flags.cifar_tfrecords)#2. Loop writes all the samples to the file, each picture sample constructs the example protocol         forIinchRange (10):            #Remove the eigenvalues and target values for the I-picture dataImage =image_batch[i].eval (). ToString () label=Int (label_batch[i].eval () [0])#construct a example of a sampleExample = Tf.train.Example (Features=tf.train.features (feature={                'Image': Tf.train.Feature (Bytes_list=tf.train.byteslist (value=[Image]),'label': Tf.train.Feature (Int64_list=tf.train.int64list (value=[Label]),}))#Write a separate sampleWriter.write (example. Serializetostring ())#Closewriter.close ()returnNoneif __name__=='__main__':    #find file, build list path + Name listfile_name =Os.listdir (Flags.cifar_dir)#stitching paths re-forming listsFileList = [Os.path.join (flags.cifar_dir,file) forFileinchfile_nameifFile[-3:] = ='bin']    #calling a function to pass a parameterCF =Cifarread (filelist) Image_batch,label_batch=Cf.read_and_decode ()#Open SessionWith TF. Session () as Sess:#define a thread coordinatorCoord =Tf.train.Coordinator ()#to open a thread that reads a fileThreads = Tf.train.start_queue_runners (sess,coord=coord)#Save in Tfrecords file        Print('Start Storage') cf.write_ro_tfrecords (image_batch,label_batch)Print('End Storage')        #Print the Read content        #Print (Sess.run ([Image_batch,label_batch]))        #Reclaim Child Threadscoord.request_stop () coord.join (threads)

Analysis and storage example of tensorflow------tfrecords