I want to have an excel file of all the geometries with their dimensions present in Revit model (like doors, windows with their quantities and dimensions). How can I get that? Can I use Model derivative API for that? Or any other possible way?
Yes you can!
Check out Augusto's new sample viewer-javascript-extract.spreadsheet:
Test run
GitHub source repo
Related
I want to hand write a framework to perform inference of a given neural network. The network is so complicated, so to make sure my implementation is correct, I need to know how exactly the inference process is done on device.
I tried to use torchviz to visualize the network, but what I got seems to be the back propagation compute graph, which is really hard to understand.
Then I tried to convert the pytorch model to ONNX format, following the instruction enter link description here, but when I tried to visualize it, it seems that the original layers of the model had been seperated into very small operators.
I just want to get the result like this
How can I get this? Thanks!
Have you tried saving the model with torch.save (https://pytorch.org/tutorials/beginner/saving_loading_models.html) and opening it with Netron? The last view you showed is a view of the Netron app.
You can try also the package torchview, which provides several features (useful especially for large models). For instance you can set the display depth (depth in nested hierarchy of moduls).
It is also based on forward prop
github repo
Disclaimer: I am the author of the package
Note: The accepted format for tool is pytorch model
I have been using the form recognizer service and form labeller tool, using the version 2 of the api, to train my models to read a set of forms. But i have the need to use more than one layout of the forms, not knowing which form (pdf) layout is being uploaded.
Is it as simple as labelling the different layouts within the same model. Or is there another way to identify which model is to be used with which form.?
any help greatly appreciated
This is a common request. for now, if the 2 forms styles are not that different, you could try to train one model and see if that model could correctly extract key/value. Another option is to train two different forms, you could write a simple classification program to decide which model to use.
Form Recognizer team is working on a feature to allow user just submit the document and it would pick the most appropriate model to analyze the document. Please stay tuned for our update.
thanks
I am trying to compare two Vehicles using AWS-Recognition, I tried various models, Is there any in-build model to compare two vehicles. Actually, we want to implement, automated Vehicle claim.
Actually, while doing some research on this, Amazon does not provide any inbuild API for this like Face-Recognition or Number Reading. We have to build our custom model.
This is called build train and deploy, We have to follow certain steps.
Rekognition custom labels feature
Collect Images
Ground Truth for images.
Labeling of datasets Manual
Enable automated data labeling
Creating Datasets
Create projects
Train models
Then it has to be an amazon notebook, and Phyton, using.
Rekognition-SDK.
However, this process is time consuming.
I want to predict my input price based on a list of questions/answers using azure machine learning.
I built one using the "bayesian linear regression" but it seems that it is predicting the price based on the prices i have in my dataset and not based on the Q/A.
Am i in the wrong path or am i missing something?
Any suggestion would be helpful.
Check the Q/A s that you using is not having missing values. If there's any missing values follow data preprocessing techniques to fill those.
What kind of answers do you have as inputs? (yes/no, numeric values, different textual answers, etc...) In my opinion numerical values and yes/no inputs makes your model more accurate.
Try different regression algorithms (https://azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-cheat-sheet/) and check their accuracy.
you need to set features and label properly. if you publish your experiment in Gallery using unlisted mode and paste the link here, we can take a look.
I am new to create Experiments in Azure ML. I want to done a sample and small POC on Azure ML.
I have a data for the students consisting of StudentID, Student Name and Marks for Monthly Tests 1,2 and 3. I just to want to Predict data for the Final Monthly Test (i.e., Monthly Test 4).
I don't know how to create and what kind of Transformations to be used in Predicting the Data.
Anyone Please...
Thanks in Advance
Pradeep
You can simply start with basic tutorials.
https://azure.microsoft.com/en-in/documentation/articles/machine-learning-create-experiment/
It is real helpful. I also referred this.
You can draw simple flow chart for your experiment and simply apply when you need to drag the dataset.
HTH
This is a unsupervised machine learning problem. Do refer the algorithms that you can use for solving the problem (Most probably linear regression will suit for this case) Do the data pre-processing first. Then follow the steps in the above link mentioned by #kunal to build up the model.