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訓(xùn)練模型時(shí)，我們并不是直接將圖像送入模型，而是先將圖像轉(zhuǎn)換為tfrecord文件，再將tfrecord文件送入模型。為進(jìn)一步理解tfrecord文件，本例先將6幅圖像及其標(biāo)簽轉(zhuǎn)換為tfrecord文件，然后讀取tfrecord文件，重現(xiàn)6幅圖像及其標(biāo)簽。
1、生成tfrecord文件

				?

									import os

									import numpy as np

									import tensorflow as tf

									from PIL import Image

									filenames = [

									'images/cat/1.jpg',

									'images/cat/2.jpg',

									'images/dog/1.jpg',

									'images/dog/2.jpg',

									'images/pig/1.jpg',

									'images/pig/2.jpg',]

									labels = {'cat':0, 'dog':1, 'pig':2}

									def int64_feature(values):

									    if not isinstance(values, (tuple, list)):

									        values = [values]

									    return tf.train.Feature(int64_list=tf.train.Int64List(value=values))

									def bytes_feature(values):

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

									with tf.Session() as sess:

									    output_filename = os.path.join('images/train.tfrecords')

									    with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:

									        for filename in filenames:

									            #讀取圖像

									            image_data = Image.open(filename)

									            #圖像灰度化

									            image_data = np.array(image_data.convert('L'))

									            #將圖像轉(zhuǎn)化為bytes

									            image_data = image_data.tobytes()

									            #讀取label

									            label = labels[filename.split('/')[-2]]

									            #生成protocol數(shù)據(jù)類型

									            example = tf.train.Example(features=tf.train.Features(feature={'image': bytes_feature(image_data),

									                                                                            'label': int64_feature(label)}))

									            tfrecord_writer.write(example.SerializeToString())

2、讀取tfrecord文件

				?

									import tensorflow as tf

									import matplotlib.pyplot as plt

									from PIL import Image

									# 根據(jù)文件名生成一個(gè)隊(duì)列

									filename_queue = tf.train.string_input_producer(['images/train.tfrecords'])

									reader = tf.TFRecordReader()

									# 返回文件名和文件

									_, serialized_example = reader.read(filename_queue)

									features = tf.parse_single_example(serialized_example, 

									                                    features={'image': tf.FixedLenFeature([], tf.string), 

									                                                'label': tf.FixedLenFeature([], tf.int64)})

									# 獲取圖像數(shù)據(jù)

									image = tf.decode_raw(features['image'], tf.uint8)

									# 恢復(fù)圖像原始尺寸[高，寬]

									image = tf.reshape(image, [60, 160])

									# 獲取label

									label = tf.cast(features['label'], tf.int32)

									with tf.Session() as sess:

									    # 創(chuàng)建一個(gè)協(xié)調(diào)器，管理線程

									    coord = tf.train.Coordinator()

									    # 啟動(dòng)QueueRunner, 此時(shí)文件名隊(duì)列已經(jīng)進(jìn)隊(duì)

									    threads = tf.train.start_queue_runners(sess=sess, coord=coord)

									    for i in range(6):

									        image_b, label_b = sess.run([image, label])

									        img = Image.fromarray(image_b, 'L')

									        plt.imshow(img)

									        plt.axis('off')

									        plt.show()

									        print(label_b)

									    # 通知其他線程關(guān)閉

									    coord.request_stop()

									    # 其他所有線程關(guān)閉之后，這一函數(shù)才能返回

									    coord.join(threads)

到此這篇關(guān)于tensorflow學(xué)習(xí)筆記之tfrecord文件的生成與讀取的文章就介紹到這了,更多相關(guān)tfrecord文件的生成與讀取內(nèi)容請(qǐng)搜索服務(wù)器之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持服務(wù)器之家！

原文鏈接：https://blog.csdn.net/wxsy024680/article/details/115291692