Numpy to TFrecords:有没有更简单的方法来处理来自tfrecords的批量输入？

Question

我的问题是如何从多个(或分片)tfrecords获得批量输入.我已经阅读了示例https://github.com/tensorflow/models/blob/master/inception/inception/image_processing.py#L410.基本的管道,把培训作为集为例,(1)首先产生一系列tfrecords(例如,train-000-of-005,train-001-of-005,...),(2)从这些文件名,生成一个列表并将其塞进了tf.train.string_input_producer获得队列,(3)同时生成一个tf.RandomShuffleQueue做其他的东西,(4)tf.train.batch_join用来生成批量输入.

我认为这很复杂,我不确定这个程序的逻辑.在我的情况下,我有一个.npy文件列表,我想生成分片的tfrecords(多个分离的tfrecords,而不只是一个单个大文件).这些.npy文件中的每一个都包含不同数量的正样本和负样本(2个类).一种基本方法是生成一个单个大型tfrecord文件.但文件太大(~20Gb).所以我采用分片的tfrecords.有没有更简单的方法来做到这一点？谢谢.

Answer 1

整个过程简化了使用Dataset API.以下是两个部分:(1): Convert numpy array to tfrecords和(2,3,4): read the tfrecords to generate batches.

1. 从numpy数组创建tfrecords:

    def npy_to_tfrecords(...):
       # write records to a tfrecords file
       writer = tf.python_io.TFRecordWriter(output_file)

       # Loop through all the features you want to write
       for ... :
          let say X is of np.array([[...][...]])
          let say y is of np.array[[0/1]]

         # Feature contains a map of string to feature proto objects
         feature = {}
         feature['X'] = tf.train.Feature(float_list=tf.train.FloatList(value=X.flatten()))
         feature['y'] = tf.train.Feature(int64_list=tf.train.Int64List(value=y))

         # Construct the Example proto object
         example = tf.train.Example(features=tf.train.Features(feature=feature))

         # Serialize the example to a string
         serialized = example.SerializeToString()

         # write the serialized objec to the disk
         writer.write(serialized)
      writer.close()

2. 使用数据集API(tensorflow> = 1.2)读取tfrecords:

    # Creates a dataset that reads all of the examples from filenames.
    filenames = ["file1.tfrecord", "file2.tfrecord", ..."fileN.tfrecord"]
    dataset = tf.contrib.data.TFRecordDataset(filenames)
    # for version 1.5 and above use tf.data.TFRecordDataset

    # example proto decode
    def _parse_function(example_proto):
      keys_to_features = {'X':tf.FixedLenFeature((shape_of_npy_array), tf.float32),
                          'y': tf.FixedLenFeature((), tf.int64, default_value=0)}
      parsed_features = tf.parse_single_example(example_proto, keys_to_features)
     return parsed_features['X'], parsed_features['y']

    # Parse the record into tensors.
    dataset = dataset.map(_parse_function)  

    # Shuffle the dataset
    dataset = dataset.shuffle(buffer_size=10000)

    # Repeat the input indefinitly
    dataset = dataset.repeat()  

    # Generate batches
    dataset = dataset.batch(batch_size)

    # Create a one-shot iterator
    iterator = dataset.make_one_shot_iterator()

    # Get batch X and y
    X, y = iterator.get_next()