TensorFlow制作TFRecord文件方式的数据集的完整程序，最好标明怎么输入输出地址

# 将图片转换为TFrecord 格式并读取

import os

import tensorflow as tf

from PIL import Image

# 源数据地址

cwd = 'C:\\Users\\xiaodeng\\Desktop\\UCMerced_LandUse\\Images'

# 生成record路径及文件名

train_record_path = r"C:\\Users\\xiaodeng\\Desktop\\train.tfrecords"

test_record_path = r"C:\\Users\\xiaodeng\\Desktop\\test.tfrecords"

# 分类

classes = {'agricultural','airplane','baseballdiamond',

'beach','buildings','chaparral','denseresidential',

'forest','freeway','golfcourse','harbor',

'intersection','mediumresidential','mobilehomepark','overpass',

'parkinglot','river','runway','sparseresidential','storagetanks','tenniscourt'}

def _byteslist(value):

"""二进制属性"""

return tf.train.Feature(bytes_list = tf.train.BytesList(value = [value]))

def _int64list(value):

"""整数属性"""

return tf.train.Feature(int64_list = tf.train.Int64List(value = [value]))

#def create_train_record(cwd,classes):

"""创建训练集tfrecord"""

writer = tf.python_io.TFRecordWriter(train_record_path) # 创建一个writer

NUM = 1 # 显示创建过程（计数）

for index, name in enumerate(classes):

class_path = cwd + "/" + name + '/'

l = int(len(os.listdir(class_path)) * 0.7) # 取前70%创建训练集

for img_name in os.listdir(class_path)[:l]:

img_path = class_path + img_name

img = Image.open(img_path)

img = img.resize((256, 256))# resize图片大小

img_raw = img.tobytes() # 将图片转化为原生bytes

example = tf.train.Example( # 封装到Example中

features=tf.train.Features(feature={

"label":_int64list(index), # label必须为整数类型属性

'img_raw':_byteslist(img_raw) # 图片必须为二进制属性

}))

writer.write(example.SerializeToString())

print('Creating train record in ',NUM)

NUM += 1

writer.close() # 关闭writer

print("Create train_record successful!")

#def create_test_record(cwd,classes):

"""创建测试tfrecord"""

writer = tf.python_io.TFRecordWriter(test_record_path)

NUM = 1

for index, name in enumerate(classes):

class_path = cwd + '/' + name + '/'

l = int(len(os.listdir(class_path)) * 0.7)

for img_name in os.listdir(class_path)[l:]: # 剩余30%作为测试集

img_path = class_path + img_name

img = Image.open(img_path)

img = img.resize((256, 256))

img_raw = img.tobytes() # 将图片转化为原生bytes

# print(index,img_raw)

example = tf.train.Example(

features=tf.train.Features(feature={

"label":_int64list(index),

'img_raw':_byteslist(img_raw)

}))

writer.write(example.SerializeToString())

print('Creating test record in ',NUM)

NUM += 1

writer.close()

在发布逾一周年之际，TensorFlow 终于将迎来史上最重大更新：TensorFlow 1.0。

对于不熟悉开源框架的读者，TensorFlow 是谷歌 2015 年底推出的深度学习框架，在开发者社区享有盛誉。去年，它已成为 GitHub 最受欢迎的机器学习开源项目。因其高度普及率，尤其是在 Python 生态圈中，TensorFlow 的功能变化会对全世界的机器学习开发者造成重大影响。

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