I am trying to use Mlflow to manage my Bayesian Optimization Model, which has several methods other than the predict (run_optimization() for example). My doubt is that when I log my model to the tracking server the model and retrieve it, it only contains the predict() as it is wrapped as a PyFunctModel; that's a problem because I need the model also to run prescriptions (suggestion of a possible new optimum), does anyone ever tried it? Thanks
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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
This is a question regarding training models on SPACY3.x.
I couldn't find a good answer/solution on StackOverflow hence the query.
If I am using the existing model in spacy like the en model and want to add my own entities in the model and train it, let's say since I work in the biomedical domain, things like virus name, shape, length, temperature, temperature value, etc. I don't want to lose the entities tagged by Spacy like organization names, country, etc.
All suggestions are appreciated.
Thanks
There are a few ways to do that.
The best way is to train your own model separately and then combine both models in one pipeline, with one before the other. See the double NER example project for an overview of that.
It's also possible to update the pretrained NER model, see this example project. However this isn't usually a good idea, and definitely not if you're adding completely different entities. You'll run into what's called "catastrophic forgetting", where even though you're technically updating the model, it ends up forgetting everything not represented in your current training data.
I worked on a Transfer Learning project in which I created a training dataset (labeled) and I used a pre-trained BERT model and fine-tuned it. The project was an NLP project in which I performed customized named entities recognition.
I'm working now on documenting the work so I have to specify which strategy of transfer learning I did.
i found this blog https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a
but even after reading it I still confused about the strategy and I want to make sure of my choice.
Do not try to come up with an abstract entity such as "strategy" - just describe what you have done.
Is it possible to add a new face features into trained face recognition model, without retraining it with previous faces?
Currently am using facenet architecture,
Take a look in Siamese Neural Network.
Actually if you use such approach you don't need to retrain the model.
Basically you train a model to generate an embedding (a vector) that maps similar images near and different ones far.
After you have this model trainned, when you add a new face it will be far from the others but near of the samples of the same person.
basically, by the mathematics theory behind the machine learning models, you basically need to do another train iteration with only this new data...
but, in practice, those models, especially the sophisticated ones, rely on multiple iterations for training and a various technics of suffering and nose reductions
a good approach can be train of the model from previous state with a subset of the data that include the new data, for a couple of iterations
I'm currently learning implementing layer-wise training model with Keras. My solution is complicated and time-costing, could someone give me some suggestions to do it in a easy way? Also could someone explain the topology of Keras especially the relations among nodes.outbound_layer, nodes.inbound_layer and how did they associated with tensors: input_tensors and output_tensors? From the topology source codes on github, I'm quite confused about:
input_tensors[i] == inbound_layers[i].inbound_nodes[node_indices[i]].output_tensors[tensor_indices[i]]
Why the inbound_nodes contain output_tensors, I'm not clear about the relations among them....If I wanna remove layers in certain positions of the API model, what should I firstly remove? Also, when adding layers to some certain places, what shall I do first?
Here is my solution to a layerwise training model. I can do it on Sequential model and now trying to implement in on the API model:
To do it, I'm simply add a new layer after finish previous training and re-compile (model.compile()) and re-fit (model.fit()).
Since Keras model requires output layer, I would always add an output layer. As a result, each time when I wanna add a new layer, I have to remove the output layer then add it back. This can be done using model.pop(), in this case model has to be a keras.Sequential() model.
The Sequential() model supports many useful functions including model.add(layer). But for customised model using model API: model=Model(input=...., output=....), those pop() or add() functions are not supported and implement them takes some time and maybe not convenient.