Tensorflow 实现分批量读取数据

import tensorflow as tf

def read_data(fileNameQue):

 reader = tf.TFRecordReader()
 key, value = reader.read(fileNameQue)
 features = tf.parse_single_example(value, features={'label': tf.FixedLenFeature([], tf.int64),
              'img': tf.FixedLenFeature([], tf.string),})
 img = tf.decode_raw(features["img"], tf.uint8)
 img = tf.reshape(img, [92,112]) # 恢复图像原始大小
 label = tf.cast(features["label"], tf.int32)

 return img, label

def batch_input(filename, batchSize):

 fileNameQue = tf.train.string_input_producer([filename], shuffle=True)
 img, label = read_data(fileNameQue) # fetch图像和label
 min_after_dequeue = 1000
 capacity = min_after_dequeue+3*batchSize
 # 预取图像和label并随机打乱，组成batch，此时tensor rank发生了变化，多了一个batch大小的维度
 exampleBatch,labelBatch = tf.train.shuffle_batch([img, label],batch_size=batchSize, capacity=capacity,
              min_after_dequeue=min_after_dequeue)
 return exampleBatch,labelBatch

if __name__ == "__main__":

 init = tf.initialize_all_variables()
 exampleBatch, labelBatch = batch_input("./data/faceTF.tfrecords", batchSize=10)

 with tf.Session() as sess:

  sess.run(init)
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(coord=coord)

  for i in range(100):
   example, label = sess.run([exampleBatch, labelBatch])
   print(example.shape)

  coord.request_stop()
  coord.join(threads)