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How to migrate from keras.engine.[...] to tf.keras.engine?

As discussed in keras vs. tensorflow.python.keras - which one to use? I generally do not want to include keras in my machine learning project. Yet in the following code example using keras works and tf.keras does not:
>>>import keras
>>>print(keras.engine.keras_tensor.KerasTensor)
<class 'keras.engine.keras_tensor.KerasTensor'>
and
>>>import tensorflow as tf
>>>print(tf.keras.engine.keras_tensor.KerasTensor)
Traceback (most recent call last):
File "c:\Users\reifv\root\Heidelberg Master\Netflix_AI_codes\working env - Copy\Netflix_AI\main copy.py", line 4, in <module>
print(tf.keras.engine.keras_tensor.KerasTensor)
File "C:\Users\reifv\root\Heidelberg Master\Netflix_AI_codes\working env - Copy\Netflix_AI\venvnf\lib\site-packages\tensorflow\python\util\lazy_loader.py", line 59, in __getattr__
return getattr(module, item)
AttributeError: module 'keras.api._v2.keras' has no attribute 'engine'
This is one example of tf.keras not working, but I encountered this issue several times along my project. Therefore, a general solution would be much appreciated, too.

Why does the Dequantize node fail to prepare?

Background
I'm playing around with MediaPipe for hand tracking and found this useful wrapper for loading MediaPipe's hand_landmark.tflite model. It works without any problems for me on Ubuntu 18.04 with Tensorflow 1.14.0.
However, when I try use a newer recently released model, I run into the following error:
INFO: Initialized TensorFlow Lite runtime.
Traceback (most recent call last):
File "/home/user/code/.../repo/models/test_model.py", line 12, in <module>
use_mediapipe_model()
File "/home/user/code/.../repo/models/test_model.py", line 8, in use_mediapipe_model
interp_joint.allocate_tensors()
File "/home/user/code/env/lib/python3.6/site-packages/tensorflow/lite/python/interpreter.py", line 95, in allocate_tensors
return self._interpreter.AllocateTensors()
File "/home/user/code/env/lib/python3.6/site-packages/tensorflow/lite/python/interpreter_wrapper/tensorflow_wrap_interpreter_wrapper.py", line 106, in AllocateTensors
return _tensorflow_wrap_interpreter_wrapper.InterpreterWrapper_AllocateTensors(self)
RuntimeError: tensorflow/lite/kernels/dequantize.cc:62 op_context.input->type == kTfLiteUInt8 || op_context.input->type == kTfLiteInt8 was not true.Node number 0 (DEQUANTIZE) failed to prepare.
When looking at the two models in Netron, I can see that the newer model uses nodes of the type Dequantize which seem to cause the problem. As I'm a beginner when it comes to Tensorflow I don't really know where to go from here.
Code to reproduce the error
from pathlib import Path
import tensorflow as tf
def use_mediapipe_model():
interp_joint = tf.lite.Interpreter(
f"{Path(__file__).parent}/hand_landmark.tflite") # path to model
interp_joint.allocate_tensors()
if __name__ == "__main__":
use_mediapipe_model()
Question
Is the problem related to the version of Tensorflow that I'm using or am I doing something wrong when it comes to loading the .tflite models?

Doesn't work in TF 1.14.0. You need at least 1.15.2

Pipeline error: "AttributeError: 'ColumnTransformer' object has no attribute '_feature_names_in'"

I am trying to use scikitlearn to predict over new data using a pipeline object I had trained back in February. Since Friday, February 28th, the predict function no longer works for my pipeline object, citing the error:
>>> df = pd.read_csv('test_df_for_example.csv')
>>> mdl = joblib.load('split_0_model.pkl')
>>> mdl.predict(df)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/utils/metaestimators.py", line 116, in <lambda>
out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/pipeline.py", line 419, in predict
Xt = transform.transform(Xt)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/compose/_column_transformer.py", line 587, in transform
self._validate_features(X.shape[1], X_feature_names)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/compose/_column_transformer.py", line 411, in _validate_features
if ((self._feature_names_in is None or feature_names is None)
AttributeError: 'ColumnTransformer' object has no attribute '_feature_names_in'
I am using Microsoft Azure's virtual machines to do this predicting (although the above code I ran on my local computer), so working with the versioning of the modules is difficult, and most of the time I am forced to use the latest versions of packages. I believe this error comes from scikitlearn's new version 0.22.2.post1, which I am using.
I have an example CSV with testing data here
The model file pickled with joblib here
And code to reproduce the error here
And yaml environment file here
Is there any way I can upgrade my model so that this error does not occur?
Thanks!
Kristine

I recommend pinning down versions in your YAML, especially with the speed of releases in the azureml space.
So downgrading sklearn to the last stable build for your use case may be the solution, or upgrading the rest of your code base to accommodate the new sklearn version.
Ex.
- pip:
- sklearn==0.20.0
- azureml-sdk==1.0.85
- etc...

Thanks to Nema, using the following specifications I'm able to use the previous scikit-learn version to load my model:
- scikit-learn<=0.21.3
- azureml-sdk<=1.0.83

Error in loading model in PyTorch

I Have the following code snippet
from train import predict
import random
import torch
ann=torch.load('ann.pt') #importing trained model
while True:
k=raw_input("User:")
intent,top_value,top_index = predict(str(k),ann)
print(intent)
when I run the script it is throwing the error as below:
Traceback (most recent call last):
File "test.py", line 6, in <module>
ann=torch.load('ann.pt') #importing trained model
File "/home/local/ZOHOCORP/raghav-5305/miniconda2/lib/python2.7/site-packages/torch/serialization.py", line 261, in load
return _load(f, map_location, pickle_module)
File "/home/local/ZOHOCORP/raghav-5305/miniconda2/lib/python2.7/site-packages/torch/serialization.py", line 409, in _load
result = unpickler.load()
AttributeError: 'module' object has no attribute 'ANN'
I have ann.pt file in the same folder as my script is.
Kindly help me identify fix the error and load the model.
Thanks in advance.

When trying to save both parameters and model, pytorch pickles the parameters but only store path the model Class. For instance, changing tree structure or refactoring can break loading.
Therefore as the documentation points out, it is not recommended, prefer only save/load parameters:
...the serialized data is bound to the specific classes and the exact directory structure used, so it can break in various ways when used in other projects, or after some serious refactors.
For more help, it'll be useful to show your saving code.

Code for gensim Word2vec as an HTTP service 'KeyedVectors' Attribute error

I am using the w2v_server_googlenews code from the word2vec HTTP server running at https://rare-technologies.com/word2vec-tutorial/#bonus_app. I changed the loaded file to a file of vectors trained with the original C version of word2vec. I load the file with
gensim.models.KeyedVectors.load_word2vec_format(fname, binary=True)
and it seems to load without problems. But when I test the HTTP service with, let's say
curl 'http://127.0.0.1/most_similar?positive%5B%5D=woman&positive%5B%5D=king&negative%5B%5D=man'
I got an empty result with only the execution time.
{"taken": 0.0003361701965332031, "similars": [], "success": 1}
I put a traceback.print_exc() on the except part of the related method, which is in this case def most_similar(self, *args, **kwargs): and I got:
Traceback (most recent call last):
File "./w2v_server.py", line 114, in most_similar
topn=5)
File "/usr/local/lib/python2.7/dist-packages/gensim/models/keyedvectors.py", line 304, in most_similar
self.init_sims()
File "/usr/local/lib/python2.7/dist-packages/gensim/models/keyedvectors.py", line 817, in init_sims
self.syn0norm = (self.syn0 / sqrt((self.syn0 ** 2).sum(-1))[..., newaxis]).astype(REAL)
AttributeError: 'KeyedVectors' object has no attribute 'syn0'
Any idea on why this might happens?
Note: I use python 2.7 and I installed gensim using pip, which gave me gensim 2.1.0.

FYI that demo code was baed on gensim 0.12.3 (from 2015, as listed in its requirements.txt), and would need updating to work with the latest gensim.
It might be sufficient to add a line to w2v_server.py at line 70 (just after the load_word2vec_format()), to force the creation of the needed syn0norm property (which in older gensims was auto-created on load), before deleting the raw syn0 values. Specifically:
self.model.init_sims(replace=True)
(You would leave out the replace=True if you were going to be doing operations other than most_similar(), that might require raw vectors.)
If this works to fix the problem for you, a pull-request to the w2v_server_googlenews repo would be favorably received!