How to use callbacks to monitor and modify the training process in keras?

A callback is an object. It can perform processes at various stages of training at the start or end of an epoch. It can also be performed before or after a single batch. We use callbacks to write logs after every training batch for metrics monitoring. Meanwhile, it saves your model to disk. Here, we are using the ModelCheckpoint callback to monitor our validation. This callback is only called after a certain epoch.  

A callback writes a log for TensorBoard, which is TensorFlow's excellent visualization tool. We may need to do many functions to achieve these basic tasks. That's why the TensorFlow callback comes here. The model training process proceeds of epochs, the number of times the training sample is. It will be fitted into the model, and errors will be backward propagated throughout it.  

Callback gives us an upper hand while training any Deep Learning model. It helps leverage by controlling the epoch. Along with the above functions, there are other callbacks. You might encounter or want to use it in your Deep Learning project. Performance optimization: determines if batch hooks need to be called. Error out if batch-level callbacks are passed with PSStrategy. Handles batch-level saving logic and supports steps_per_execution.  

So, using the EarlyStopping callback, we can write a Neural Network. The training will stop when the model's performance is not improving. The class EarlyStopping(Callback) Stop training when a monitored metric has stopped improving. Then run the tensorboard command to view the visualizations. With the number of epochs, the model will wait to be seen. It will see if the monitored metric is improving before stopping the training.  

Each layer present in the Sequential model has exactly one input and output. In Keras Dense layer, all the neurons are connected within themselves. Every neuron in the dense layer takes input from the neurons of the other layer. We also can append many dense layers as per our needs.  

ModelCheckpoint callback class helps define where the checkpoint of the model weighs. It is also where to name the file and in what situation to make a checkpoint. The args and kwargs are the keyword arguments in Python. Arguments usually contain numerical values. *args and **kwargs are passed into a function definition when writing functions.  

An epoch is used to indicate the number of passes of the whole training dataset that has been completed. When the amount of data is very large, the datasets are grouped into batches. The random.rand() is a library function numpy. It returns an array of samples in a uniform distribution. It will return the float value if we don't provide any argument. This callback is not compatible with eager execution disabled. If save_weights_only is true, then the model's weights will be saved(), else the full model is saved.  

Here is an example of how to use callbacks to monitor and modify the training process in keras: