问题
I am training a Recurrent Neural Network in Tensorflow over a dataset of sequence of numbers of varying lengths and have been trying to use the tf.data API to create an efficient pipeline. However I can't seem to get this thing to work
My approach
My data set is a NumPy array of shape [10000, ?, 32, 2] which is saved on my disk as a file in the .npy format. Here the ? denotes that elements have variable length in the second dimension. 10000 denotes the number of minibatches in the dataset and 32 denotes the size of a mini-batch.
I am using np.load to open this data set and I am trying to create a tf.data.Dataset object using the from_tensor_slices method but it seems that this only works if all input Tensors have the same shape!
I tried reading the docs but they have only given a very simple example.
My code
So the numpy files have been generated as follows -
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)
The code given below is my attempt to create a tf.data.Dataset object
# dataset_list is a list containing `num_items` number of itesm
# and each item has shape [?, 32, 2]
dataset_list = np.load('data.npy')
# error, this doesn't work!
dataset = tf.data.Dataset.from_tensor_slices(dataset_list)
The error I get is "TypeError: Expected binary or unicode string, got array([[[0.0875, 0. ], ..."
Continued, still need help!
So I tried @mrry's answer and I am now able to created a Dataset object. However, I am not able to iterate through this dataset using iterators as said in the tutorial. This is what my code looks like now -
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
The error I get is AttributeError: 'numpy.dtype' object has no attribute 'as_numpy_dtype'. I have no absolutely no clue what this means.
This is the complete stack trace -
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)]]
回答1:
As you have noticed, tf.data.Dataset.from_tensor_slices() only works on objects that can be converted to a (dense) tf.Tensor or a tf.SparseTensor. The easiest way to get variable-length NumPy data into a Dataset is to use tf.data.Dataset.from_generator(), as follows:
dataset = tf.data.Dataset.from_generator(lambda: dataset_list,
tf.as_dtype(dataset_list[0].dtype),
tf.TensorShape([None, 32, 2]))
来源：https://stackoverflow.com/questions/50329855/how-to-use-the-tensorflow-dataset-pipeline-for-variable-length-inputs