Introduction to Keras: A Beginner’s Guide to Keras Sequential Model, Training, and More by Intellipaat

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation and easy prototyping while being user-friendly and modular. In this Keras tutorial for beginners, we will explore the basics of Keras, including what it is, how to use the Keras Sequential Model, how to train a model using Keras, and more.

What is Keras?

Keras is a deep learning library that allows for easy and fast prototyping of neural networks. It is designed to be user-friendly, modular, and extensible, making it ideal for beginners as well as experienced researchers and developers. Keras provides a high-level API that allows for easy construction of neural networks with just a few lines of code.

Keras Sequential Model

One of the key features of Keras is the Sequential model, which allows for the easy construction of neural networks in a sequential manner. The Sequential model is a linear stack of layers, meaning that each layer feeds its output into the next layer. This allows for the easy creation of deep neural networks with multiple layers.

To create a Sequential model in Keras, you first need to import the necessary libraries:

from keras.models import Sequential

Next, you can create a Sequential model by instantiating an object of the Sequential class:

model = Sequential()

Once you have created a Sequential model, you can add layers to it using the add() method. For example, to add a dense (fully connected) layer with 64 units to the model, you can use the following code:

from keras.layers import Dense

model.add(Dense(64, activation='relu'))

You can add as many layers as needed to the Sequential model in a similar manner. Finally, to compile the model and configure the training process, you need to specify the loss function, optimizer, and metrics to use:

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

Keras Training

Once you have created and compiled your Sequential model, you can train it on your data using the fit() method. To train the model, you need to provide input data and the corresponding target labels. For example, to train the model on input data X and target labels y for a specified number of epochs, you can use the following code:

model.fit(X, y, epochs=10, batch_size=32)

During training, Keras will update the weights of the model based on the specified loss function, optimizer, and metrics, with the goal of minimizing the loss and improving accuracy on the training data. After training is complete, you can evaluate the model on a separate validation set using the evaluate() method:

loss, accuracy = model.evaluate(X_val, y_val)

This will give you the loss and accuracy of the model on the validation set, allowing you to assess the performance of the model on unseen data.

Conclusion

In this Keras tutorial for beginners, we have explored the basics of Keras, including what it is, how to use the Keras Sequential Model, how to train a model using Keras, and more. Keras is a powerful and user-friendly deep learning library that makes it easy to create and train neural networks. By following this tutorial, you should have a good understanding of how to get started with Keras and begin building your own deep learning models.