Model Architecture & Training - Deepstash
Model Architecture & Training

Model Architecture & Training

The basic idea is that two RNN captures different aspects of input sequences from both identifiers and code context.

Next, the output of two RNNs is concatenated into a single vector, which is passed through a fully-connected linear layer.

The final linear layer maps the learned type annotation into a high-dimensional feature space, called Type Clusters. In order to create Type Clusters, we need to formulate the type prediction task as a similarity learning problem, rather than a classification problem.

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decebaldobrica

#engineering, #machinelearning and #crypto

The idea is part of this collection:

Machine Learning With Google

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Understanding machine learning models

Improving data analysis and decision-making

How Google uses logic in machine learning

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