What is the difference between saving a machine learning model in .pkl format versus the .sav format?
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I've trained a classification model using pytorch and got the model in pkl format. I need to convert this file to ptl format to deploy in React Native platform. Is there any way to convert the pkl file to ptl or I need to train the classification model again in another way?
I have a repo that provided a model architecture, but not pretrained model. it actually provides a .pth file but it's dataset inside the file, is there any way to make the dataset to csv?
.pth files saved with toch.save() are supposed to be binaries. using torch.load() you can get the dataset, and then save it again as a .csv with pandas for example
For a multi class classification, I used label encoder to convert the categorical target variable to a numerical variable. got some good accuracy and saved the model to my local drive using joblib module.
So, in the future, if I load the model from my drive and try to predict using model.predict(['laptop not charging']) I will get numerical value as output because I have trained the model using numerical data only.
I know it won't work but mentioning here for reference that even this also won't work
le=LabelEncoder()
print(le.inverse_transform(model.predict(['laptop not charging'])))
I want the output as asset issue.
How to get asset issue as a output?
does onnx model support well defined output format? I have seen that many model eg PMML provides input and output fields and data types in the model itself. So the ONNX model input/output are also well defined and can it be deduced from metadata information.
Yes.
When representing models using the ONNX format, the neural network is stored according to a predefined protobuf format.
This contains fields like Graph with all nodes sorted in topological order but also Input and Output, which contains information about the inputs and outputs of the model.
Input and Output are both ValueInfoProto, which holds information about the name of the input/output, and also the datatype. They have an enum with all sorts of datatypes, hence you can easily determine the datatype of the inputs and outputs.
If you want more information (I'm not sure what you mean by format) it of course depends: shapes might differ when using dynamic dimensions (for batch sizes of timesteps for examples). However, if you export to onnx with fixed dimension sizes you can also find this in ValueInfoProto.
This information can all be found in: https://github.com/onnx/onnx/blob/main/onnx/onnx.proto3
I have a relatively large dataset (> 15 GB) stored in a single file as a Pandas dataframe. I would like to convert this data to TFRecords format and feed this into my computational graph later on. I was following this tutorial: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/how_tos/reading_data/convert_to_records.py.
However, this still involves loading the entirety of the dataset into memory. Is there a method that allows you to convert large datasets into TFrecords directly without loading everything into memory? Are TFRecords even needed in this context or can I just read the arrays from disk during training?
Alternatives are using np.memmap or breaking the dataframe apart into smaller parts, but I was wondering if it was possible to convert the entire dataset into TFrecord format.