吴裕雄--天生自然 pythonTensorFlow图形数据处理：输入文件队列

import tensorflow as tf
 
# 1. 生成文件存储样例数据。
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:\\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()  

# 2. 读取文件。
files = tf.train.match_filenames_once("E:\\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)

# 3. 组合训练数据（Batching）
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)