import tensorflow as tf def _int64_feature(value): return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) num_shards = 2 instances_per_shard = 2 for i in range(num_shards): filename = (‘E:\\temp\\data.tfrecords-%.5d-of-%.5d‘ % (i, num_shards)) # 将Example结构写入TFRecord文件。 writer = tf.python_io.TFRecordWriter(filename) for j in range(instances_per_shard): # Example结构仅包含当前样例属于第几个文件以及是当前文件的第几个样本。 example = tf.train.Example(features=tf.train.Features(feature={ ‘i‘: _int64_feature(i), ‘j‘: _int64_feature(j)})) writer.write(example.SerializeToString()) writer.close() files = tf.train.match_filenames_once("E:\\temp\\data.tfrecords-*") filename_queue = tf.train.string_input_producer(files, shuffle=False) reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) features = tf.parse_single_example( serialized_example, features={ ‘i‘: tf.FixedLenFeature([], tf.int64), ‘j‘: tf.FixedLenFeature([], tf.int64), }) with tf.Session() as sess: sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()]) print(sess.run(files)) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) for i in range(6): print(sess.run([features[‘i‘], features[‘j‘]])) coord.request_stop() coord.join(threads)
example, label = features[‘i‘], features[‘j‘] batch_size = 2 capacity = 1000 + 3 * batch_size example_batch, label_batch = tf.train.batch([example, label], batch_size=batch_size, capacity=capacity) with tf.Session() as sess: tf.global_variables_initializer().run() tf.local_variables_initializer().run() coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) for i in range(3): cur_example_batch, cur_label_batch = sess.run([example_batch, label_batch]) print(cur_example_batch, cur_label_batch) coord.request_stop() coord.join(threads)
原文地址:https://www.cnblogs.com/tszr/p/10885390.html
时间: 2024-10-04 12:56:17