utils.py 文件源码

def read_and_decode_batch(filename_queue, batch_size, capacity, min_after_dequeue):
    """Dequeue a batch of data from the TFRecord.
    Args:
    filename_queue: Filename Queue of the TFRecord.
    batch_size: How many records dequeued each time.
    capacity: The capacity of the queue.
    min_after_dequeue: Ensures a minimum amount of shuffling of examples.
    Returns:
     List of the dequeued (batch_label, batch_ids, batch_values).
    """
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    batch_serialized_example = tf.train.shuffle_batch([serialized_example], 
        batch_size=batch_size, capacity=capacity, min_after_dequeue=min_after_dequeue)
    # The feature definition here should BE consistent with LibSVM TO TFRecord process.
    features = tf.parse_example(batch_serialized_example,
                                       features={
                                           "label": tf.FixedLenFeature([], tf.float32),
                                           "ids": tf.VarLenFeature(tf.int64),
                                           "values": tf.VarLenFeature(tf.float32)
                                       })
    batch_label = features["label"]
    batch_ids = features["ids"]
    batch_values = features["values"]
    return batch_label, batch_ids, batch_values