TensorFlow的tfrecords文件与queue队列组合读取数据方法

╰半橙微兮° 2022-05-26 12:17 122阅读 0赞

# TensorFlow的tfrecords文件与queue队列组合读取数据方法 #

自己制作了数据集，然而读取老是莫名其妙的读不出数据，参考了以下的文章：  
[https://blog.csdn.net/julialove102123/article/details/80085871][https_blog.csdn.net_julialove102123_article_details_80085871]

我的测试文件：

import os
    import tensorflow as tf
    from PIL import Image
    
    
    def read_tfRecord(file_tfRecord):  # 输入是.tfrecords文件地址
        queue = tf.train.string_input_producer([file_tfRecord])
        reader = tf.TFRecordReader()
        _, serialized_example = reader.read(queue)
        features = tf.parse_single_example(
            serialized_example,
            features={
                'img_raw': tf.FixedLenFeature([], tf.string),
                'label': tf.FixedLenFeature([], tf.int64)
            }
        )
        image = tf.decode_raw(features['img_raw'], tf.uint8)
        image = tf.reshape(image, [50, 50, 1])
        image = tf.cast(image, tf.float32)
        image = tf.image.per_image_standardization(image)
        label = tf.cast(features['label'], tf.int64)  # 这里设置了读取信息的格式
        return image, label
    
    
    traindata, trainlabel = read_tfRecord("./tmp_train.tfrecords")
    image_batch, label_batch = tf.train.shuffle_batch([traindata, trainlabel],
                            batch_size=100, capacity=2000, min_after_dequeue=1000)
    
    with tf.Session() as sess:
        sess.run(tf.local_variables_initializer())
        sess.run(tf.global_variables_initializer())
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)
        train_steps = 10
    
        try:
            while not coord.should_stop():  # 如果线程应该停止则返回True
                example, label = sess.run([image_batch, label_batch])
                print(example.shape, label)
    
                train_steps -= 1
                print(train_steps)
                if train_steps <= 0:
                    coord.request_stop()  # 请求该线程停止
    
        except tf.errors.OutOfRangeError:
            print('Done training -- epoch limit reached')
        finally:
            # When done, ask the threads to stop. 请求该线程停止
            coord.request_stop()
            # And wait for them to actually do it. 等待被指定的线程终止
            coord.join(threads)

[https_blog.csdn.net_julialove102123_article_details_80085871]: https://blog.csdn.net/julialove102123/article/details/80085871