I am trying to use serialized data using the proto interface as suggested here
https://www.tensorflow.org/versions/master/how_tos/reading_data/index.html#reading-from-files
I try to use the example :
https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
But it fails because :
In [99]: tf.FixedLenFeature
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-99-e5629528302a> in <module>()
----> 1 tf.FixedLenFeature
AttributeError: 'module' object has no attribute 'FixedLenFeature'
I guess I am missing something here...
The version of fully_connected_reader.py includes some (breaking) changes to the tf.parse_example() API that were made after the TensorFlow 0.6.0 release. These changes included adding the tf.FixedLenFeature class as a helper for defining the schema to be used in tf.parse_example(). You should build TensorFlow from source or wait for the upcoming 0.7.0 release to use this version of the API.
Alternatively, the old version of the example code is available here, and the documentation for tf.parse_example() is available here.
Related
I'm trying to reload another model to another jupyter notebook using this code:
import tensorflow as tf
reloaded = tf.saved_model.load('m_translator')
result = reloaded.tf_translate(input_text)
and I recently got this error:
KeyError Traceback (most recent call last)
File ~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py:4177, in Graph._get_op_def(self, type)
4176 try:
-> 4177 return self._op_def_cache[type]
4178 except KeyError:
KeyError: 'NormalizeUTF8'
FileNotFoundError: Op type not registered 'NormalizeUTF8' in binary running on LAPTOP-D3PPA576. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.
You may be trying to load on a different device from the computational device. Consider setting the `experimental_io_device` option in `tf.saved_model.LoadOptions` to the io_device such as '/job:localhost'.
I had this issue before. To solve it, you need to install tensorflow_text. You should try to :
>>> tf.__version__
2.8.2
>>>!pip install tensorflow-text==2.8.2
In addition to installing tensorflow_text library, what helped me with a similar problem was importing the library at the top of the notebook:
import tensorflow_text
In my code I want to check whether returned object type is EagerTensor:
import tensorflow as tf
import inspect
if __name__ == '__main__':
tf.enable_eager_execution()
iterator = tf.data.Dataset.from_tensor_slices([[1, 2], [3, 4]]).__iter__()
elem = iterator.next()
print(type(elem))
print(inspect.getmodule(elem))
assert type(elem) == tf.python.framework.ops.EagerTensor
But the result is:
<class 'EagerTensor'>
<module 'tensorflow.python.framework.ops' from '/home/antek/anaconda3/envs/mnist_identification/lib/python3.6/site-packages/tensorflow/python/framework/ops.py'>
Traceback (most recent call last):
File "/home/antek/.PyCharm2018.1/config/scratches/scratch_4.py", line 11, in <module>
assert type(elem) == tf.python.framework.ops.EagerTensor
AttributeError: module 'tensorflow' has no attribute 'python'
Here: AttributeError: module 'tensorflow' has no attribute 'python' I found out that tensorflow purposely deletes its reference to the python module. So how can I check that my object is an EagerTensor instance?
I am not sure if you can, but I think you probably don't need to. You already have the following tools:
tf.is_tensor (previously tf.contrib.framework.is_tensor) that will return True for an EagerTensor
tf.executing_eagerly that returns True if you are, well, executing eagerly.
I believe they should cover 99% of your needs -- and I would be curious to hear about your problem if it falls in that percentage left out.
In the modern version of TensorFlow (2.2), you can use the is_tensor function documented here.
assert(tf.is_tensor(elem))
I try to follow this nice MXNet Tutorial. I create an extremely simple neural network (two input unit, no hidden units and one output unit) doing this:
from mxnet import gluon
net = gluon.nn.Dense(1, in_units=2)
After that I try to take a look at the shape of the weight matrix (the same way as it is described in the tutorial):
print(net.weight)
As a result I expect to see this:
Parameter dense4_weight (shape=(1, 2), dtype=None)
However, I see the following error message:
Traceback (most recent call last):
File "tmp.py", line 5, in <module>
print(net.weight)
File "/usr/local/lib/python3.6/site-packages/mxnet/gluon/parameter.py", line 120, in __repr__
return s.format(**self.__dict__)
KeyError: 'shape'
Am I doing something wrong?
This is a regression that happened here and has since been fixed on master branch here. Expect it to be fixed in the next MXNet release.
I am creating new ops (https://www.tensorflow.org/extend/adding_an_op) for TensorFlow (r1.0) running both on x86 and ARMv7.
Minor code modifications are necessary to run TensorFlow on ARMv7, but this guide helps a lot:
https://github.com/samjabrahams/tensorflow-on-raspberry-pi/blob/master/GUIDE.md.
But I noticed that the custom operations do not work on my ARMv7 installation of TensorFlow.
For example, when I test my custom operation in a Python script on ARMv7:
import tensorflow as tf
_custom_op_module = tf.load_op_library('custom_op.so')
custom_op = _custom_op_module.add_stub
I get the following error (that does not show up on x86):
$ python test_custom_op.py
Traceback (most recent call last):
File "custom_op.py", line 3, in <module>
add_stub = _custom_op_module.add_stub
AttributeError: 'module' object has no attribute 'custom_op'
I further investigated the issue, and apparently there is not my custom operation in the .so library file.
$ python
>>> import tensorflow as tf
>>> _custom_op_module = tf.load_op_library('custom_op.so')
>>> dir(_custom_op_module)
>>> ['LIB_HANDLE', 'OP_LIST', '_InitOpDefLibrary', '__builtins__', '__doc__', '__name__', '__package__', '_collections', '_common_shapes', '_op_def_lib', '_op_def_library', '_op_def_pb2', '_op_def_registry', '_ops', '_text_format']
>>> _custom_op_module.OP_LIST
>>>
The same commands on x86 have the following output:
>>> import tensorflow as tf
>>> _custom_op_module = tf.load_op_library('custom_op.so')
>>> dir(_custom_op_module)
>>> ['LIB_HANDLE', 'OP_LIST', '_InitOpDefLibrary', '__builtins__', '__doc__', '__name__', '__package__', '_add_stub_outputs', '_collections', '_common_shapes', '_op_def_lib', '_op_def_library', '_op_def_pb2', '_op_def_registry', '_ops', '_text_format', 'custom_op']
>>> _custom_op_module.OP_LIST
op {
name: "CustomOp"
...
}
>>>
Does anybody have similar issue? Can we consider this a bug?
I hit a similar issue with a similar error message when I tried to load my new op, however, my problem was I tried to register a customized op that had the same op name as tensorflow, and that led to a name collision. Changing the name fixed it without recompiling TF.
The error message I encountered:
AttributeError: module '6e237d88703da016805889179d3f5baa' has no attribute 'custom_op'
Apparently, recompiling and re-installing the TF made it works.
When running a Tensorflow implementation, I got the following error/warning messages, which does not include the line of python code that causes this issue. At the same time, the result is still generated. I am not sure what do these messages indicate?
Exception ignored in: <bound method Session.__del__ of <tensorflow.python.client.session.Session object at 0x2b48ec89f748>>
Traceback (most recent call last):
File "/data/tfw/lib/python3.4/site- packages/tensorflow/python/client/session.py", line 140, in __del__
File "/data/tfw/lib/python3.4/site-packages/tensorflow/python/client/session.py", line 137, in close
UnboundLocalError: local variable 'status' referenced before assignment
Today I also encountered this exception while running some Multi layer perceptron model on Windows 10 64 Bit with Python 3.5 and TensorFlow 0.12
I have seen this answer for this exception
it induced by different gc sequence, if python collect session first , the program will exit successfully, if python collect swig memory(tf_session) first, the program exit with failure.
Here