I'm trying to import a metagraph in tensorflow and when I want to initialize my weights variable:
W = tf.get_collection('W')[0]
session.run([W.initializer])
I get the error:
'Tensor' object has no attribute 'initializer'"
I found here (TensorFlow:'Tensor' object has no attribute 'initializer', occurs after several turns when reading dataset) that this problem is related to the fact that "local variables on target collection is not Variable but Tensor because local variable do not have it's proto fn registered". They propose to add this code to the model core:
from tensorflow.core.framework import variable_pb2
from tensorflow.python.framework import ops
from tensorflow.python.ops import variables
from tensorflow.python.framework.ops import register_proto_function
register_proto_function(
ops.GraphKeys.LOCAL_VARIABLES,
proto_type=variable_pb2.VariableDef,
to_proto=variables.Variable.to_proto,
from_proto=variables.Variable.from_proto)
So I added the above code in my add_final_training_ops function but now get the following error:
KeyError: "Registering two proto functions with name 'local_variables' !(Previous registration was in _find_and_load_unlocked <frozen importlib._bootstrap>:950)"
Can someone help?
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
I just coded a quadratic programming and it has worked very well but after the someday it works not at all.
Does anyone have any idea what the problem is?
My code is:
import time
import numpy as np
from numpy import array, dot
from qpsolvers import solve_qp
Matrix10 = np.load(r'C:\Users\skqkr\Desktop\Semesterarbeit/Chiwan_Q7.npz')
start = time.time()
P = Matrix10['Q'] # quick way to build a symmetric matrix
q = Matrix10['p']
G = Matrix10['G']
h = Matrix10['h']
x = solve_qp(P, q, G, h )>print("QP solution: x = {}".format(x))
print("time :", time.time() - start)
And the result is:
ImportError: cannot import name 'solve_qp' from 'qpsolvers' (C:\Users\skqkr\qpsolvers.py)
I don't understand why it isn't suddenly going well.
I do not think the code you shared is the one you are really using hence it is not easy to understand what is going on. However there are few reason for your problem to happen
The python ImportError: cannot import name error occurs when the import class is inaccessible or the imported class in circular dependence. The import keyword is used to load class and function. The keyword from is used to load the module. For any reason, if the import class is not available in the python class path, The “ImportError: cannot import name” python error is thrown.
The following are the reasons for the ImportError: cannot import name
The import class is not available or not created.
The import class name is mis-named or mis-spelled
The import class name and module name is mis-placed.
The import class is not available in python class path
The import class is not available in python library
The import class is in circular dependency
The python module is just a python file with the .py extension. The keyword from will be used to load the python module. A class in a python module is imported using the keyword import. If the imported class is not in the referred python file, the python interpreter will throw the error ImportError: Cannot import name.
If two python files refer to each other and attempt to load the other file, it will create the circular load dependence. That will cause error in heap memory. If the python interpreter detects the circular dependence, it throws the error ImportError: Can’t Import Name.
I am using tensorflow-gpu==1.10.0 and keras from tensorflow as tf.keras.
I am trying to use source code written by someone else to implement it on my network.
I saved my network using save_model and load it using load_model. when I use model.get_config(), I expect a dictionary, but i"m getting a list. Keras source documentation also says that get_config returns a dictionary (https://keras.io/models/about-keras-models/).
I tried to check if it has to do with saving type : save_model or model.save that makes the difference in how it is saved, but both give me this error:
TypeError: list indices must be integers or slices, not str
my code block :
model_config = self.keras_model.get_config()
for layer in model_config['layers']:
name = layer['name']
if name in update_layers:
layer['config']['filters'] = update_layers[name]['filters']
my pip freeze :
absl-py==0.6.1
astor==0.7.1
bitstring==3.1.5
coverage==4.5.1
cycler==0.10.0
decorator==4.3.0
Django==2.1.3
easydict==1.7
enum34==1.1.6
futures==3.1.1
gast==0.2.0
geopy==1.11.0
grpcio==1.16.1
h5py==2.7.1
image==1.5.15
ImageHash==3.7
imageio==2.5.0
imgaug==0.2.5
Keras==2.1.3
kiwisolver==1.1.0
lxml==4.1.1
Markdown==3.0.1
matplotlib==2.1.0
networkx==2.2
nose==1.3.7
numpy==1.14.1
olefile==0.46
opencv-python==3.3.0.10
pandas==0.20.3
Pillow==4.2.1
prometheus-client==0.4.2
protobuf==3.6.1
pyparsing==2.3.0
pyquaternion==0.9.2
python-dateutil==2.7.5
pytz==2018.7
PyWavelets==1.0.1
PyYAML==3.12
Rtree==0.8.3
scikit-image==0.13.1
scikit-learn==0.19.1
scipy==0.19.1
Shapely==1.6.4.post1
six==1.11.0
sk-video==1.1.8
sklearn-porter==0.6.2
tensorboard==1.10.0
tensorflow-gpu==1.10.0
termcolor==1.1.0
tqdm==4.19.4
utm==0.4.2
vtk==8.1.0
Werkzeug==0.14.1
xlrd==1.1.0
xmltodict==0.11.0
I am trying to use Google colab for my project for which I have to upload a few python files because I need those class files.But while executing the main function.It is constantly throwing me an error 'module object has no attribute' . Is there some memory issue with colab or what! Help would be much appreciated.
import numpy as np
import time
import tensorflow as tf
import NN
import Option
import Log
import getData
import Quantize
AttributeError: 'module' object has no attribute 'NN'
I uploaded all files using following code :
from google.colab import files
src = list(files.upload().values())[0]
open('Option.py','wb').write(src)
import Option
But its always giving me error on some or the other files which I am importing.
The updated version (for a few weeks) can save the files without you having to call open(fname, 'wb').write(src)
So, you only have to upload your 5 files: NN.py, Option.py, Log.py, getData.py, and Quantize.py (and probably other dependency + data) then try importing each one e.g. import NN to see if there's any error.
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.