I have a deep learning model trained in matlab and it is available in .mat format, How can i use this file for prediction in python environment?
I tried scipy.io.loadmat(filename.mat) but getting some errors
One option for you might be to export the model from MATLAB to ONYX. From there, you should be able to use the result in any deep learning framework that supports ONYX, or move from from the ONYX representation to something supported in your framework of choice.
https://www.mathworks.com/help/deeplearning/ref/exportonnxnetwork.html
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I trained a model using matterport maskrcnn. I already had .h5 model file but i am not able to convert it to .mlmodel. As there are many custom layers involved. I already tried whatever I am able to find on google regarding the same. I also tried https://github.com/edouardlp/Mask-RCNN-CoreML for conversion. So far no success.
Does anybody able to did the conversion so far successfully, if yes can you share the codebase or tutorial for the same.
I am able to convert using the same github repo mentioned in the question. But you can't debug the code in Xcode as maskrcnn is to memory heavy. Its better to use another architecture like deeplab.
Here's a github project https://github.com/edouardlp/Mask-RCNN-CoreML/releases/tag/0.2 with a MaskRCNN.ml model.
Note: You have to copy the models into the project to get it to compile.
I am very new to machine learning. I have a python file with a very simple TensorFlow model that I need to deploy on Android using Google's ML Kit (which would be creating tflite file). I absolutely don't understand what should be the structure of my python file and Google's documentation doesn't make it any easier. Maybe someone has a good example of converting CUSTOM MODEL WRITTEN FROM SCRATCH and then using it in Java. I need to pass a string from Android's text field and get a predicted answer.
You need to first train your model on whatever the dataset you have. The layers in the model must comply with the supported layers by the TFLite library. Here is a list of layers that are supported and unsupported.
Once you have trained it, based on how you saved it (Let's say using kerasmodel.save). Convert it to TFLite following this tutorial or other tutorials on this page.
Now you can use this .tflite model in Android studio. For this you can follow this good tutorial.
Given I trained a simple neural network using Tensorflow and Python on my laptop and I want to use this model on my phone in C++ app.
Is there any compatibility format I can use? What is the minimal framework to run neural networks (not to train)?
UDP. I'm also interested in Tensorflow to NOT-Tensorflow compatibility. Do I need to build it up from scratch or there're any best practices?
Yes if you are using iOS or Android. Depending on your specific needs, you have a choice between TensorFlow for Mobile and TensorFlow Lite
https://www.tensorflow.org/mobile/
In particular, to load pre-trained models
https://www.tensorflow.org/mobile/prepare_models
Technically you don't need a framework at all. A conventional fully connected neural network is simple enough that you can implement it in straight C++. It's about 100 lines of code for the matrix multiplication and a dozen or so for the non-linear activation function.
The biggest part is figuring out how to parse a serialized Tensorflow model, especially given that there are quite a few ways to do so. You probably will want to freeze your TensorFlow model; this inserts the weights from the latest training into the model.
I'm having trouble figuring out how to use a support vector machine trained on Weka for real time processing with python.
For example when you train a back propagation algorithm on Matlab, you can extract the weights and biases and use them to replicate the network on other programs (e.g python) in feed-forward.
Thanks for your suggestions.
Assuming you want to continue using Python and Weka, the easiest way is to just call the Weka command line using subprocess (see https://docs.python.org/2/library/subprocess.html). You can then train and save your models and use them as needed. See this reference: https://weka.wikispaces.com/Saving+and+loading+models
I trained a convolutional neural net with Lasagne and Theano frameworks on Python.
I am satisfied with the architecture and the performance of the net on test data and I want to use it on an iPad application.
I was wondering if there is any simple way to take that net and use it on iOS without rewriting the code in another framework and/or train it again?
As far a I know, there is no way to convert the Python code directly to c/c++ (which might be a method for exporting the net/training code).