e交易投标文件制作软件怎样制作jstf文件

e交易投标文件制作软饥银卖件制作jstf文件的方法如下：

1、翻搏山开江苏招标文件制作工具烂逗，双击江苏招标文件制作工具图标，翻开软件。

2、点击工具栏新建按钮，选择盐城，进入制作界面即可。

# 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\州谈\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"""册灶碰

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) # 图辩旅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()

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