Visualizing your models with TensorBoard
To develop efficient and successful models, you will need to know what is happening during your experiments so that you can react as soon as possible by correcting possible anomalous or unwanted results, such as overfitting and slow learning. This is where the concept of a tactile callback comes into play.
A callback is an object (a class instance that implements specific methods) that is passed to the model on the call to fit and that is called by the model at various points during training. You have access to all available data on the status of the model and its performance and, based on this, take measures including the following:
- Interrupt training, because you have stopped learning or are overfitting
- Save a model; in this way, the training could be resumed from the saved point in the future
- Record metrics, such as precision or loss
- Alter its state, and modify its structure or hyperparameters, such as the learning...
