Is there any possible way to save data in ".t7" format with python?
".t7" is the serialization format, which can be opened by lua torch. However, when I save my data in pickle with python with ".t7" at the end of the naming, it does not work.
I have been searching through internet, but I could not find out any working answer.
there is currently no such converter but there is a workaround. What you can do is convert pyTorch model to Caffe model and from Caffe to Torch (lua) model. Here is the table of converters by framework.
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I have been working with neural networks in Deeplearning4j and needed to switch it to Python. To use the same model (MultiLayerNetwork in DL4J) I saved it as a .h5 file. Like this:
File newFile = new File("newModel.h5");
ModelSerializer.writeModel(network, newFile, true);
Now, when i try to load it in Python I get the following error:
OSError: SavedModel file does not exist at: newModel.h5/{saved_model.pbtxt|saved_model.pb}
I have tried to use different extensions like .pb and used relative and absolute paths in python. Nothing helped. Can anyone explain to me why this happens? There seems to be not enough information on this issue on the internet and it seems to be the only way to implement the same code in python is to train a new model, etc.
a dl4j model is a zip file. Could you clarify what you're trying to do? If you imported it from keras and need to resave it, the best you can do is export the weights as a numpy array and recreate the architecutre. You can do that with model.params() which gives you the weights.
I'm looking for a way to save my sklearn Pipeline for data pre-processing so that I can re-load it to make predictions.
So far I've only seen options like pickle or joblib, that will serialize arbitrary python objects, but the resulting file
is opaque if I wanted to store the pipeline in version control,
will serialize any python object and therefore might not be safe to unserialize, and
may run into issues with different Python version or library versions
It seems like ONNX is a great way to save models in a safe & interoperable way -- Is there any alternative for data pre-processing pipelines?
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.
I was using sklearn2pmml to export my model to .pmml file.
How to read the PMML file back to python PMMLpipeline ?
I was checking the repo but could not find the solution in the documentation.
If you want to save a fitted Scikit-Learn pipeline so that it could be loaded back into Scikit-Learn/Python, then you need to use "native" Python serialization libraries and dataformats such as Pickle.
The conversion from Scikit-Learn to PMML should be regarded one-way operation. There is no easy way of getting PMML back into Scikit-Learn.
You can always save the fitted Scikit-Learn pipeline in multiple data formats. Save one copy in Pickle data format so that it could be loaded back into Scikit-Learn/Python, and save another copy in PMML data format so that it would be loaded in non-Python environments.