Develop Reference

FieldOutputRequestState in Abaqus Scripting, can anyone get it to work?

I am try to extract the status (e.g. 'CREATED' or 'MODIFIED') from a specific step of a CAE model using Abaqus scripting using the line below:
value = mdb.models['my_model_name'].steps['Step-1'].fieldOutputRequestState['F-Output-1'].status
However I am getting the following error:
exceptions.AttributeError: 'StaticStep' object has no attribute 'fieldOutputRequestState'
What am I doing wrong?? I am able to confirm that 'F-Output-1' is the correct name from using the following line which does work:
field_outputs = mdb.models['my_model_name'].fieldOutputRequests.keys()
Also using the field output requests manager in the GUI shows as expected so it is definitely a scripting issue. Any help appreciated!

DeepPavlov FAQ Bot is returning a 'collections.OrderedDict' object is not callable error

I'm trying to use collab to build a bot for FAQ with DeepPavlov and I modified a tutorial notebook that DeepPavlov has on their site, the only thing I change is using my sample dataset yet I get the 'collections.OrderedDict' object is not callable error when calling on
answer=model_config(["help"])
answer
The full code for this (seperated in cells) is
!pip install -q deeppavlov
from deeppavlov import configs
from deeppavlov.core.common.file import read_json
from deeppavlov.core.commands.infer import build_model
from deeppavlov import configs, train_model
model_config = read_json(configs.faq.tfidf_logreg_en_faq)
model_config["dataset_reader"]["data_path"] = None
model_config["dataset_reader"]["data_url"] = "https://docs.google.com/spreadsheets/d/e/2PACX-1vSUFqHL9u_KkSCfw03bYCIuzfCzfOwXlvsQeTb8tMVaSDYohcHbfL8jNtV24AZiSLNnJJQs58dIsO8A/pub?gid=788315638&single=true&output=csv"
model_config
answer=model_config(["help"])
answer
Anyone know the fix to help my bot run with the sample dataset url I provided in my code? I'm new to bots, deeppavlov, and collab so I'm having a steep learning curve here.

Your code is missing the model training part - you are trying to call the config object instead of actually training and using a model for prediction on your data.
However, this is not the only problem here. Firstly, you might want to change the data_path variable to a string object, otherwise you will face problems here (you may try it yourself to check). Secondly, while trying to run your code with my corrections I have faced a csv-parsing error - please check your csv file again and make sure to get rid of empty rows in it. After you do that, this code should work correctly.
model_config = read_json(configs.faq.tfidf_logreg_en_faq)
model_config["dataset_reader"]["data_path"] = ''
model_config["dataset_reader"]["data_url"] = "your-dataset-link"
faq = train_model(model_config)
answer = faq(["help"])
answer

'tensorflow' has no attribute 'to_int32'

I am trying to implement CTC loss to audio files but I get the following error:
TensorFlow has no attribute 'to_int32'
I'm running tf.version 2.0.0.
I think it's with the version, I'm currently using, as we see the error is thrown in the package itself ' tensorflow_backend.py' code.
I have imported packages as "tensorflow.keras.class_name" with backend as K. Below is the screenshot.

You can cast the tensor in TensorFlow 2 as follows:
tf.cast(my_tensor, tf.int32)
You can read the documentation of the method in https://www.tensorflow.org/api_docs/python/tf/cast
You can also see that the to_int32 is deprecated and was used in TensorFlow 1
https://www.tensorflow.org/api_docs/python/tf/compat/v1/to_int32

After you make the import just write
tf.to_int=lambda x: tf.cast(x, tf.int32)
This is similar to writing the behavior of tf.to_int in everywhere in the code, so you don't have to manually edit a TF1.0 code

Gensim Attribute Error when trying to use pre_scan on a doc2vec object

I am following the tutorial here:
https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/doc2vec-wikipedia.ipynb
But when I get to this part:
pre = Doc2Vec(min_count=0)
pre.scan_vocab(documents)
I get the following error on scan_vocab:
AttributeError: 'Doc2Vec' object has no attribute 'scan_vocab'
Does anyone know how to fix this? Thanks.

That's a known problem after a 2018 refactoring of the Doc2Vec code:
https://github.com/RaRe-Technologies/gensim/issues/2085
You can just skip that cell to proceed with the rest of that demo notebook. (If you really needed to adjust the min_count using the info from a full-scan, you might be able to call some internal classes/methods mentioned in the above issue.)

Model object has no attribute save while reducing size of CoreML?

i am reducing the size of CoreML. I make this CoreML with Python Turicate but i am getting a error Model object has no attribute save. I have Python 2.7 and pip install coremltools==2.0b1 before execute python file. Here's my code -
import coremltools
from coremltools.models.neural_network.quantization_utils import *
model = coremltools.models.MLModel('/Users/Desktop/MLClassifier/animals.mlmodel')
lin_quant_model = quantize_weights(model, 16, "linear")
lin_quant_model.save('/Users/Desktop/animals2.mlmodel')

My guess is you're not on macOS 10.14 or later (Mojave), in which case you don't get an MLModel but the model specification when you call quantize_weights() (according to the docs). No idea why, but that's what it says.

I have also faced this problem, running model quantization on Ubuntu (Python 3.8, coremltools==4.1).
I don't know why exactly this error occurs (maybe it should be run on macOS), but as you are getting specs, you can get and save model as follows:
model_fp16_specs = quantization_utils.quantize_weights(model_fp32, nbits=16)
model_fp16 = ct.models.MLModel(model_fp16_specs)
model_fp16.save("model_quantized.mlmodel")