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:  

Fig : Preview of the output that you will get on running this code from your IDE.

Code

In this solution we used keras and numpy libraries of Python.

Instructions

Follow the steps carefully to get the output easily.

1. Download and Install the PyCharm Community Edition on your computer.
2. Open the terminal and install the required libraries with the following commands.
3. Install Numpy - pip install Numpy
4. Install Keras - pip install keras
5. Create a new Python file on your IDE.
6. Copy the snippet using the 'copy' button and paste it into your Python file.
7. Run the current file to generate the output.

I hope you found this useful.

I found this code snippet by searching for ' Callback active after certain epoch Keras' in Kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. PyCharm Community Edition 2022.3.1
2. The solution is created in Python 3.11.1 Version
3. Numpy 1.24.2 Version
4. keras - 2.12.0 Version

Using this solution, we can able to use callbacks to monitor and modify the training process in keras with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use callbacks to monitor and modify the training process in keras.

Dependent Libraries

If you do not have keras, and Numpy libraries that are required to run this code, you can install them by clicking on the above link.

You can search for any dependent library on kandi like keras, and Numpy.

FAQ:  

1. What is the relationship between TensorFlow and keras callbacks?  

TensorFlow is an open-sourced end-to-end platform. It is a library for many machine-learning tasks. But Keras is a high-level neural network library that runs on top of TensorFlow. Both provide high-level APIs used for building and training models. But Keras is more user-friendly because it's built-in Python.  

2. How do I create keras model checkpoints to save a finished model training?  

Step 1 - Importing Libraries- Pandas, Numpy.  

Step 2 - Load the dataset  

(X_train, y_train), (X_test, y_test) = mnist.load_data()  

Step 3 - Define the model.  

model = Sequential()  

model.add(layers.Dense(64, kernel_initializer='uniform', input_shape=(10,)))  

Step 4 - Defining the activation function.  

model.add(layers.Activation('relu'))  

Step 5 - Adding layers  

model.add(Dense(512)) model.add(Dropout(0.2)) model.add(Dense(256, activation='relu')) model.add(Dropout(0.1))  

Step 6 - Creating Checkpoints  

filepath="mnist_data_checkpoint"  

checkpoint = ModelCheckpoint(filepath,  

monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')  

callbacks_list = [checkpoint]  

print(model)  

print(callbacks_list)  

3. How does Deep Learning interact with callback logs events?  

A callback is a function that is called during a process. It generally validates or corrects certain behaviors. In machine learning, we can use callbacks to define what happens before, during, or at the end of a training epoch.  

4. What Learning rate reduction mode can be implemented with callbacks in Keras?  

A typical way is to drop the learning rate by half every ten epochs. To implement this in Keras, we can define a step decay function. We should use the LearningRateScheduler callback. It takes the step decay function as an argument. It returns the updated learning rates for use in the SGD optimizer.  

5. Are any features available with the class Callback that make it more suitable?  

Callbacks give a view of internal states and statistics during training. You can pass a list of callbacks to the following model methods: keras.Model 

6. What examples are of how callback functions are used within keras?  

• History and BaseLogger: callbacks that are applied to your model by default.  
• TensorBoard: This is hands down my favorite Keras callback. This callback writes a log for TensorBoard. It is TensorFlow's excellent visualization tool. Suppose you have installed TensorFlow with pip. Then, you should be able to launch TensorBoard from the command line:  

            tensorboard — logdir=/full_path_to_your_logs  

• CSVLogger: This callback streams epoch results to a csv file  
• LambdaCallback: This callback allows you to build a custom callback 

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