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)]))
最佳答案
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.
关于tensorflow - 为什么 dataset.output_shapes 在批处理后返回维度(无)，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/44299379/