Source: dzone.com
Machine Learning Model Analysis Using TensorBoardCategory: Data, machine-learning
These models are trained for a specific dataset and are proven for accuracy and processing speed. Developers need to evaluate ML models and ensure that they meet specific threshold values and functions as expected before deployment.
However, the Model Summary and Model Plot are not that effective to understand each and every detail about any large, complex model in the form of Protocol Buffer.
It is quite powerful, considering the various visualization options that it provides like Model (of course), Scalars and Metrics (training and validation data), Images (from the dataset), Hyperparameter tuning, etc.
This option helps, especially when a custom model is received in the form of a protocol buffer, and it is required to understand it before making any modification or training it.
However, the Model Summary and Model Plot are not that effective to understand each and every detail about any large, complex model in the form of Protocol Buffer.
It is quite powerful, considering the various visualization options that it provides like Model (of course), Scalars and Metrics (training and validation data), Images (from the dataset), Hyperparameter tuning, etc.
This option helps, especially when a custom model is received in the form of a protocol buffer, and it is required to understand it before making any modification or training it.
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