Building and Visualising a Model Building the Model with Keras There are more visualizers out there, but in this article we will use the netron neural network visualizer. ![]() It tracks the tensors, the parameters and metrics and how they change throughout the implementation of the model, and helps the programmer inspect the overall structure of the code. Tensorflow came up with tensorboard, a built-in visualizer. There are a number of visualizers available. For this purpose, model visualizers are needed to promote more efficient model tuning and training inspection. This also helps when debugging, tuning parameters and fixing problematic code/models. Specifically for machine learning engineers, there is hence a need to visualize the overall flow of our model, to know which tensors are affecting and are affected by what and to see how our model makes a prediction, etc. Programmers, and coders alike, benefit from well structured code that clearly shows the flow of the project. They can be very bulky programs with 500+ lines of code when you are building models from scratch, or they can be 10-20 lines of code if you employ tensorflow, pandas, scikit-learn, kera and other libraries, which have details embeded in them that you might have missed causing the problems in the model. ![]() Unlike traditional programming, machine learning models can be unpredictable and hence tedious to manage. In this article, we have explored the approach to visualize Neural Network Models in TensorFlow.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |