tensorflow - 为什么 dataset.output_shapes 在批处理后返回维度(无)

Question

我在 TensorFlow(版本:r1.2)中将数据集 API 用于输入管道。我构建了我的数据集，并以 128 的批量大小对其进行了批处理。该数据集输入到 RNN 中。

不幸的是，dataset.output_shape返回第一个维度中的维度(无)，因此 RNN 引发错误:

Traceback (most recent call last):
  File "untitled1.py", line 188, in <module>
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
  File "/home/harold/anaconda2/envs/tensorflow_py2.7/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "untitled1.py", line 121, in main
    run_training()
  File "untitled1.py", line 57, in run_training
    is_training=True)
  File "/home/harold/huawei/ConvLSTM/ConvLSTM.py", line 216, in inference
    initial_state=initial_state)
  File "/home/harold/anaconda2/envs/tensorflow_py2.7/lib/python2.7/site-packages/tensorflow/python/ops/rnn.py", line 566, in dynamic_rnn
    dtype=dtype)
  File "/home/harold/anaconda2/envs/tensorflow_py2.7/lib/python2.7/site-packages/tensorflow/python/ops/rnn.py", line 636, in _dynamic_rnn_loop
    "Input size (depth of inputs) must be accessible via shape inference,"
ValueError: Input size (depth of inputs) must be accessible via shape inference, but saw value None.

我认为这个错误是由输入的形状引起的，第一个维度应该是批量大小但不是没有。

这是代码:

origin_dataset = Dataset.BetweenS_Dataset(FLAGS.data_path)
train_dataset = origin_dataset.train_dataset
test_dataset = origin_dataset.test_dataset
shuffle_train_dataset = train_dataset.shuffle(buffer_size=10000)
shuffle_batch_train_dataset = shuffle_train_dataset.batch(128)
batch_test_dataset = test_dataset.batch(FLAGS.batch_size)

iterator = tf.contrib.data.Iterator.from_structure(
                           shuffle_batch_train_dataset.output_types,
                            shuffle_batch_train_dataset.output_shapes)
(images, labels) = iterator.get_next()

training_init_op = iterator.make_initializer(shuffle_batch_train_dataset)
test_init_op = iterator.make_initializer(batch_test_dataset)

print(shuffle_batch_train_dataset.output_shapes)

我打印 output_shapes它给出:

(TensorShape([Dimension(None), Dimension(36), Dimension(100)]), TensorShape([Dimension(None)]))

我想它应该是 128，因为我有批处理数据集:

(TensorShape([Dimension(128), Dimension(36), Dimension(100)]), TensorShape([Dimension(128)]))

Answer 1

此功能已添加到 drop_remainder 使用的参数如下:

batch_test_dataset = test_dataset.batch(FLAGS.batch_size, drop_remainder=True)

从文档:

drop_remainder: (Optional.) A tf.bool scalar tf.Tensor, representing whether the last batch should be dropped in the case its has fewer than batch_size elements; the default behavior is not to drop the smaller batch.