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
Related
I run the following line at my terminal and I got an error, but it works fine in jupyter notebook.
df._repr_html_()
Traceback (most recent call last):
File "", line 1, in
File "/usr/lib/spark/python/pyspark/sql/dataframe.py", line 1020, in getattr
"'%s' object has no attribute '%s'" % (self.class.name, name))
AttributeError: 'DataFrame' object has no attribute '_repr_html_'
It doesn't work on my terminal because it's not a visual interpreter, so I had to go through the data frame collecting the values I needed and creating a string like html. Then, I could attached to an email I send from python at the terminal and works fine.
I want to convert trained python model (.pb) to tensorflowjs model. To accomplish this, first I saved the model with estimator.export_savedmodel function, then I run the tensorflowjs_converter command on Google Colab. However, no file is created for tensorflowjs. The conversion also gives a lot of warning and ends with an error.
Here is the full code, and please run to see the full output:
https://colab.research.google.com/drive/19k2s8eHpQY9Trps9dyaxPp0HqHWp5qpb
What is the reason of the problem and how can I fix it?
Part of the output:
Instructions for updating:
Use `tf.compat.v1.graph_util.extract_sub_graph`
Traceback (most recent call last):
File "/usr/local/bin/tensorflowjs_converter", line 8, in <module>
sys.exit(pip_main())
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/converter.py", line 638, in pip_main
main([' '.join(sys.argv[1:])])
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/converter.py", line 642, in main
convert(argv[0].split(' '))
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/converter.py", line 591, in convert
strip_debug_ops=args.strip_debug_ops)
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/tf_saved_model_conversion_v2.py", line 435, in convert_tf_saved_model
strip_debug_ops=strip_debug_ops)
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/tf_saved_model_conversion_v2.py", line 141, in optimize_graph
', '.join(unsupported))
ValueError: Unsupported Ops in the model before optimization
ParallelDynamicStitch, StringSplit, Unique, RegexReplace, DynamicPartition, StringToHashBucketFast, ParseExample, LookupTableFindV2, LookupTableSizeV2, SparseFillEmptyRows, StringJoin, AsString, SparseSegmentSqrtN, HashTableV2
Edit:
Seems like it isn't supported:
https://github.com/tensorflow/tfjs/issues/2322
This is because your model has ops that are not supported by tensorflow.js yet. And seems like you missed the missing op name in the output you pasted. Please feel free to update the output with missing op name or file a feature request in the tensorflow.js repo with more details.
I've already successfully fit a TFTransformOutput to some data (in this case, the Census dataset from UCI common amongst the TF and TFX examples.) I try to apply the transformer with the method transform_raw_features(raw_features) but keep getting the error:
ValueError: Node 'transform/transform/inputs/workclass_copy' has an
_output_shapes attribute inconsistent with the GraphDef for output #0: Shapes must be equal rank, but are 0 and 1
Digging into the source code, it seems the error originates in saved_transform_io in the method _partially_apply_saved_transform_impl while doing:
saver = tf_saver.import_meta_graph(meta_graph_def, import_scope=import_scope,
input_map=input_map)
I examined the meta_graph_def produced by TFX TFTransform and Beam and notice that the graph indeed has a series of copied variables with input/output rank differences. However, that is nothing I have control over.
The column in the error message is "workclass" which is a simple categorical column. What might I be doing incorrectly? What is the best way to debug this? At this point, I've already dug deep into the TF source code but the error seems to originate with how the TFTransform graph was written, not sure what levers I have to change/fix that.
This is using TF Transform v0.9 and the corresponding TF v1.9
Traceback (most recent call last): File
"/home/sahmed/workspace/ml_playground/TFX-TFT/trainers.py", line 449,
in parse_csv
transformed_stuff=xformer.transform_raw_features(raw_features) File
"/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow_transform/output_wrapper.py",
line 122, in transform_raw_features
self.transform_savedmodel_dir, raw_features)) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow_transform/saved/saved_transform_io.py",
line 360, in partially_apply_saved_transform_internal
saved_model_dir, logical_input_map, tensor_replacement_map) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow_transform/saved/saved_transform_io.py",
line 218, in _partially_apply_saved_transform_impl
input_map=input_map) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow/python/training/saver.py",
line 1960, in import_meta_graph
**kwargs) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow/python/framework/meta_graph.py",
line 744, in import_scoped_meta_graph
producer_op_list=producer_op_list) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py",
line 432, in new_func
return func(*args, **kwargs) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow/python/framework/importer.py",
line 422, in import_graph_def
raise ValueError(str(e)) ValueError: Node 'transform/transform/inputs/workclass_copy' has an _output_shapes
attribute inconsistent with the GraphDef for output #0: Shapes must be
equal rank, but are 0 and 1
The issue is likely that the shape of the workclass tensor is incompatible with what transform_raw_features expects.
TFTransformOutput.transform_raw_features() expects these features to have the same characteristics as described in the metadata given to tft.AnalyzeDataset() similarly to how it's done in this example:
https://github.com/tensorflow/transform/blob/master/examples/simple_example.py#L63
Could you take a look at the metadata used in your pipeline and see that it is compatible with the data fed into TFTransformOutput.transform_raw_features()?
When I run the example/image-classification/train_mnist.py, I was told that TypeError: fit() got an unexpected keyword argument 'monitor'. I have change nothing in this file. And I have just download the latest version from the github.
example\image-classification>python train_mnist.py
INFO:root:start with arguments Namespace(batch_size=64, disp_batches=100, gpus=N
one, kv_store='device', load_epoch=None, lr=0.05, lr_factor=0.1, lr_step_epochs=
'10', model_prefix=None, mom=0.9, monitor=0, network='mlp', num_classes=10, num_
epochs=20, num_examples=60000, num_layers=None, optimizer='sgd', test_io=0, top_
k=0, wd=0.0001)
Traceback (most recent call last):
File "train_mnist.py", line 76, in <module>
fit.fit(args, sym, get_mnist_iter)
File "D:\Zhenxingjian\MxNet\mxnet-0.9.3\mxnet-0.9.3\example\image-classificati
on\common\fit.py", line 183, in fit
monitor = monitor)
TypeError: fit() got an unexpected keyword argument 'monitor'
I was able to successfully run train_mnist.py example on v09.3, v0.9.5 and v0.10.0. If the problem still persists for you, maybe checkout the latest version and try again.
All parts of Python on my computer were recently installed from the Enthought academic package, but use Pyscripter for editing and running code. I'm very early in my learning curve, and so could very well be overlooking some obvious things here.
When I try to create a plot and save it like so:
import matplotlib.pylab as pl
pl.hist(myEst, bins=20, range=(.1,.60))
pl.ylabel("Freq")
pl.xlabel("Success Probability")
pl.title('Histogram of Binomial Estimator')
pl.axis([0, 1, 0, 500])
pl.vlines (.34,0,500)
pl.savefig('TestHist.png')
pl.show()
I get these errors:
Traceback (most recent call last):
File "<editor selection>", line 9, in <module>
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 1172, in savefig
self.canvas.print_figure(*args, **kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_wxagg.py", line 100, in print_figure
FigureCanvasAgg.print_figure(self, filename, *args, **kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backend_bases.py", line 2017, in print_figure
**kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 450, in print_png
filename_or_obj = file(filename_or_obj, 'wb')
IOError: [Errno 13] Permission denied: 'TestHist.png'
If I take out the pl.savefig('TestHist') line everything works fine, and I can see the plot I want, but when that line is in there I get the errors.
I've checked my backend version using pl.get_backend(), it returns 'WXAgg', which according to documentation should be able to use .png format.
I've also tried including an explicit format='png' and format=png within the savefig command, but still get errors.
Can anyone give me advice on how to proceed, or another approach for saving a plot?
There's nothing wrong with your code. I just tested it locally on my machine. The issue is this error:
IOError: [Errno 13] Permission denied: 'TestHist.png'
You are most likely trying to save the file somewhere that the Python process doesn't have permission to access. What OS are you on? Where are you trying to save the file?
If it helps others, I made the silly mistake of not actually designating a file name and as a result had returned the same error message that lead me to this question for review.
Here is the code that was generating the error:
plt.savefig('C:\\Users\\bwarn\\Canopy', format='png')
Here is my correction that resolved (you'll see I designated the actual file and name)
plt.savefig('C:\\Users\\bwarn\\Canopy\\myplot.png', format='png')
The following worked for me when I was running a neural network on my windows machine:
image_path = 'A:/DeepLearning/Padhai/MLFlow/images/%s.png' % (expt_id)
plt.savefig(image_path)
Or otherwise refer:
Using 'r' in front of the path