tensorflow 读取cifar_TensorFlow TFRecords 写入和读取
一.图片和图片标签写入TFRecords
1.创建文件存储器
writer = tf.io.TFRecordWriter('./data/tfrecords/cifar.tfrecords')
2.for循环将读取的数据存入导example存入TFRecords
#每个batch存十个图片数据
for i in range(10)
image=image_batch[i].eval().tostring()
lable=label_batch[i].eval()[0]
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]))
}))
writer.write(example.SerializeToString())
writer.close()
二.读取TFRecords
1.构建文件队列
queue_list=tf.train.string_input_producer(['./data/tfrecords/cifar.tfrecords'])
2.构造阅读器
reader=tf.TFRecordReader()
key,value=reader.read(queue_list)
3.解析读取的example
features=tf.parse_single_example(value,features={
'image':tf.FixedLengthFeature([],tf.string(0)
'label':tf.FixedLengthFeature([],tf.int64)
})
4.解码内容
image=tf.decode_raw(features['image',tf.uint8])
label=tf.cast(features['label'],tf.float32)
5.固定图片形状
image_reshape=tf.reshape(image,[self.height, self.width, self.channel])
6.进行批处理
batch_image,label_batch=tf.train.batch([image_reshape,label],batch_size=10,num_threads=1,capacity=10)
完整的读取代码
import tensorflow as tf
import os
FLAGS = tf.app.flags.FLAGS
cifar_tfrecords = tf.app.flags.DEFINE_string('cifar_tfrecords', './data/tfrecords/cifar.tfrecords', 'tfrecords目录')
class CirarReader():
def __init__(self, filelsit):
self.file_list = filelsit
self.height = 32
self.width = 32
self.channel = 3
self.label_bytes = 1
self.image_bytes = self.width * self.height * self.channel
self.bytes = self.label_bytes + self.image_bytes
def read_decode_cifar(self):
queue_list = tf.train.string_input_producer(self.file_list)
reader = tf.FixedLengthRecordReader(self.bytes)
key, value = reader.read(queue_list)
# 解析
label_image = tf.decode_raw(value, tf.uint8)
# 将数据分割成标签数据和图片数据,特征值和目标值
label = tf.slice(label_image, [0], [self.label_bytes])
image = tf.slice(label_image, [self.label_bytes], [self.image_bytes])
# 特征数据形状的改变
image_reshape = tf.reshape(image, [self.height, self.width, self.channel])
# 批处理
image_batch, label_batch = tf.train.batch([image_reshape, label], batch_size=10, num_threads=3, capacity=20)
return image_batch, label_batch
def write_to_tfrecords(self, image_batch, label_batch):
# 简历文件存储器
writer = tf.io.TFRecordWriter('./data/tfrecords/cifar.tfrecords')
# 循环写入每一个样本每张图片都要构造example协议
for i in range(10):
# 获取图片的值
image = image_batch[1].eval().tostring()
# 获取标签的值
label = label_batch[i].eval()[0]
# 创建example从存储图片和标签
example = 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]))
}))
writer.write(example.SerializeToString())
writer.close()
def read_from_tfrecords(self):
# 构建队列
queue_list = tf.train.string_input_producer(['./data/tfrecords/cifar.tfrecords'])
# 构建阅读器读取数据
reader = tf.TFRecordReader()
key, value = reader.read(queue_list)
# 解析数据
features = tf.parse_single_example(value, features={
"image": tf.FixedLenFeature([], tf.string),
"label": tf.FixedLenFeature([], tf.int64)
})
# 解码内容,
image = tf.decode_raw(features['image'], tf.uint8)
label = tf.cast(features['label'], tf.float32)
# 固定图片形状
image_shape = tf.reshape(image, [self.height, self.width, self.channel])
# 进行批处理
batch_image, batch_label = tf.train.batch([image_shape, label], batch_size=10, num_threads=1, capacity=10)
return batch_image, batch_label
if __name__ == '__main__':
path = './data/cifar/'
file_names = os.listdir(path)
file_list = [os.path.join(path, file_name) for file_name in file_names if file_name.endswith('.bin')]
reader = CirarReader(file_list)
# image_batch, label_batch = reader.read_decode_cifar()
batch_image, batch_label = reader.read_from_tfrecords()
with tf.Session() as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess, coord=coord)
# 写入tfRecords文件
# print("-------start----------")
# reader.write_to_tfrecords(image_batch, label_batch)
# print("-------end------------")
print(sess.run([batch_image, batch_label]))
coord.request_stop()
coord.join(threads)