如何使用Tensorflow数据集管道进行可变长度输入？

Question

我正在训练一个张量流的递归神经网络，在一个由不同长度的数字序列组成的数据集上，我一直在尝试使用tf.dataAPI来创建一个有效的管道。不过，我好像不能让这东西工作

我的方法

我的数据集是一个形状为[10000, ?, 32, 2]的NumPy数组，它以.npy格式保存在磁盘上。这里的?表示元素在第二维度中具有可变长度。10000表示数据集中的小批量数量，32表示小批量的大小。在

我正在使用np.load打开这个数据集，我试图使用from_tensor_slices方法创建一个tf.data.dataset对象，但似乎只有当所有输入张量都具有相同的形状时，这才有效！在

我试着读了docs，但他们只给出了一个非常简单的例子。在

我的代码

所以numpy文件生成如下-

dataset = []
for i in xrange(num_items):
  #add an element of shape [?, 32, 2] to the list where `?` takes
  # a random value between [1, 40]
  dataset.append(generate_random_rnn_input())

with open('data.npy', 'w') as f:
  np.save(f, dataset)

下面给出的代码是我创建tf.data.Dataset对象的尝试

^{pr2}$

我得到的错误是“TypeError:Expected binary or unicode string，get array（[[[0.0875，0。]，…“

继续，还需要帮助！

所以我尝试了@mry的答案，现在我可以创建一个数据集对象了。但是我不能像教程中所说的那样使用迭代器迭代这个数据集。这就是我现在的代码-

dataset_list = np.load('data.npy')

dataset = tf.data.Dataset.from_generator(lambda: dataset_list, 
                                         dataset_list[0].dtype,
                                         tf.TensorShape([None, 32, 2]))

dataset = dataset.map(lambda x : tf.cast(x, tf.float32))

iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()

with tf.Session() as sess:
  print sess.run(next_element) # The code fails on this line

我得到的错误是AttributeError: 'numpy.dtype' object has no attribute 'as_numpy_dtype'。我完全不知道这意味着什么。在

这是完整的堆栈跟踪-

2018-05-15 04:19:25.559922: W tensorflow/core/framework/op_kernel.cc:1261] Unknown: exceptions.AttributeError: 'numpy.dtype' object has no attribute 'as_numpy_dtype'
Traceback (most recent call last):

  File "/home/vastolorde95/virtualenvs/thesis/local/lib/python2.7/site-packages/tensorflow/python/ops/script_ops.py", line 147, in __call__
    ret = func(*args)

  File "/home/vastolorde95/virtualenvs/thesis/local/lib/python2.7/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 378, in generator_py_func
    nest.flatten_up_to(output_types, values), flattened_types)

AttributeError: 'numpy.dtype' object has no attribute 'as_numpy_dtype'


2018-05-15 04:19:25.559989: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at iterator_ops.cc:891 : Unknown: exceptions.AttributeError: 'numpy.dtype' object has no attribute 'as_numpy_dtype'
Traceback (most recent call last):

  File "/home/vastolorde95/virtualenvs/thesis/local/lib/python2.7/site-packages/tensorflow/python/ops/script_ops.py", line 147, in __call__
    ret = func(*args)

  File "/home/vastolorde95/virtualenvs/thesis/local/lib/python2.7/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 378, in generator_py_func
    nest.flatten_up_to(output_types, values), flattened_types)

AttributeError: 'numpy.dtype' object has no attribute 'as_numpy_dtype'


     [[Node: PyFunc = PyFunc[Tin=[DT_INT64], Tout=[DT_DOUBLE], token="pyfunc_1"](arg0)]]
Traceback (most recent call last):
  File "pipeline_test.py", line 320, in <module>
    tf.app.run()
  File "/home/vastolorde95/virtualenvs/thesis/local/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 126, in run
    _sys.exit(main(argv))
  File "pipeline_test.py", line 316, in main
    train(FLAGS.num_training_iterations, FLAGS.report_interval, FLAGS.report_interval_verbose)
  File "pipeline_test.py", line 120, in train
    print(sess.run(next_element))
  File "/home/vastolorde95/virtualenvs/thesis/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 905, in run
    run_metadata_ptr)
  File "/home/vastolorde95/virtualenvs/thesis/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1140, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/vastolorde95/virtualenvs/thesis/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
    run_metadata)
  File "/home/vastolorde95/virtualenvs/thesis/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.UnknownError: exceptions.AttributeError: 'numpy.dtype' object has no attribute 'as_numpy_dtype'
Traceback (most recent call last):

  File "/home/vastolorde95/virtualenvs/thesis/local/lib/python2.7/site-packages/tensorflow/python/ops/script_ops.py", line 147, in __call__
    ret = func(*args)

  File "/home/vastolorde95/virtualenvs/thesis/local/lib/python2.7/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 378, in generator_py_func
    nest.flatten_up_to(output_types, values), flattened_types)

AttributeError: 'numpy.dtype' object has no attribute 'as_numpy_dtype'


     [[Node: PyFunc = PyFunc[Tin=[DT_INT64], Tout=[DT_DOUBLE], token="pyfunc_1"](arg0)]]
     [[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[?,32,2]], output_types=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](OneShotIterator)]]

Answer 1

正如您所注意到的，^{}只适用于可以转换为（密集）tf.Tensor或tf.SparseTensor的对象。将可变长度NumPy数据放入Dataset的最简单方法是使用^{}，如下所示：

dataset = tf.data.Dataset.from_generator(lambda: dataset_list, 
                                         tf.as_dtype(dataset_list[0].dtype),
                                         tf.TensorShape([None, 32, 2]))