"""
Created on Thu Jul 5 21:17:21 2018
@author: muli
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _make_example(pixels, label, image):
image_raw = image.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'pixels': _int64_feature(pixels),
'label': _int64_feature(np.argmax(label)),
'image_raw': _bytes_feature(image_raw)
}))
return example
mnist = input_data.read_data_sets("./datasets/MNIST_data",dtype=tf.uint8, one_hot=True)
images = mnist.train.images
labels = mnist.train.labels
pixels = images.shape[1]
num_examples = mnist.train.num_examples
with tf.python_io.TFRecordWriter("./TFRecord/output.tfrecords") as writer:
for index in range(num_examples):
example = _make_example(pixels, labels[index], images[index])
writer.write(example.SerializeToString())
print("TFRecord训练文件已保存。")
images_test = mnist.test.images
labels_test = mnist.test.labels
pixels_test = images_test.shape[1]
num_examples_test = mnist.test.num_examples
with tf.python_io.TFRecordWriter("./TFRecord/output_test.tfrecords") as writer:
for index in range(num_examples_test):
example = _make_example(
pixels_test, labels_test[index], images_test[index])
writer.write(example.SerializeToString())
print("TFRecord测试文件已保存。")
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 6 10:10:17 2018
@author: muli
"""
import tensorflow as tf
# 读取文件
reader = tf.TFRecordReader()
# 创建一个队列来维护输入文件列表,里面包含多个文件
filename_queue = tf.train.string_input_producer(
["./TFRecord/output.tfrecords"])
# 读取列表中的文件
_,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)
})
# 数据格式转换
images = tf.decode_raw(features['image_raw'],tf.uint8)
labels = tf.cast(features['label'],tf.int32)
pixels = tf.cast(features['pixels'],tf.int32)
# 创建会话
sess = tf.Session()
# 声明一个tf.train.Coordinator类来协同多个进程
coord = tf.train.Coordinator()
# 启动多线程处理输入数据
threads = tf.train.start_queue_runners(sess=sess,coord=coord)
for i in range(10):
image, label, pixel = sess.run([images, labels, pixels])
print("正在处理第"+str(i+1)+"个文件...")