Keras Tutorial: Building Deep Learning Models Using Keras in Python

Are you interested in learning about Keras, a popular and widely-used deep learning framework in Python? Look no further! In this tutorial, we will explore Keras and dive into creating neural network models using this framework. Whether you are a beginner or looking for some insight into Keras, this tutorial is for you.

What is Keras?

Keras is a python-based deep learning framework that serves as the high-level API for TensorFlow. With the ability to run on top of Theano, TensorFlow, or CNTK, Keras is incredibly easy to work with. Building models is as simple as stacking layers and connecting them like a graph.

Contributors and Backers

Keras has gained a lot of attention because it is open source and actively developed by a community of contributors worldwide. With over 4,800 contributors during its launch, Keras now boasts 250,000 active developers. Notable contributors include big players like Microsoft, Google, Nvidia, and Amazon.

Models Offered by Keras

Keras offers two major models: Sequential and Functional. The Sequential model is a linear stack of layers, making it perfect for building simple classification networks or encoder/decoder models. On the other hand, the Functional model allows for more flexibility, supporting multi-input and multi-output networks with an arbitrary static graph topology.

Steps to Implement a Neural Network using Keras

To implement your own neural network using Keras, follow these five steps:

1. Prepare the inputs for the model by analyzing your requirements and specifying the input dimensions.
2. Define the artificial neural network model, including its architecture and connectivity.
3. Specify the optimizer, which simplifies the process of making the machine learn.
4. Define the loss function, which measures the difference between predicted and actual values during training.
5. Train the network using the training data and evaluate the model using the testing data.

Use Case: Wine Classifier

Let’s explore a use case to predict the price of a bottle of wine based on its description and variety. We will build a wide and deep network using Keras to make accurate predictions. By combining the strengths of wide and deep models, we can extract patterns from the description and predict the price.

Data Preprocessing and Model Construction

To start, we’ll install the necessary libraries and download the wine dataset. We will preprocess the data, splitting it into training and testing datasets. Next, we’ll analyze the description column and limit our bag of words vocabulary to the top 12,000 words.

Building the Wide and Deep Models

We’ll build a wide model using the Sequential API and define the input layer as a 12,000 element vector. We’ll then compile the model. Next, we’ll create an embedding layer for the deep model using the Functional API and connect it to a dense output layer. After compiling the model, we’ll flatten the embedding layer and connect it to the deep model.

Training and Evaluation

We’ll train the combined model using the training dataset and evaluate its performance using the testing dataset. The training process involves reducing the loss and improving the accuracy with each epoch. Finally, we’ll compare the predicted prices with the actual prices to measure the performance of the model.

Conclusion

Keras is a powerful deep learning framework that simplifies the process of building neural network models. With its user-friendly interface and support from the community, Keras is an excellent choice for both beginners and experienced machine learning practitioners. In this tutorial, we explored Keras and implemented a wine classifier example. We hope this tutorial has inspired you to dive deeper into the world of Keras. Stay tuned for more informative and exciting content from Techal! To learn more about Keras, visit the official Techal website.

FAQs

1. What is Keras?
   Keras is a python-based deep learning framework that serves as the high-level API for TensorFlow. It provides a simple and intuitive interface for building neural network models.

2. How does Keras perform compared to other frameworks?
   Keras is known for its simplicity and ease of use. Its high-level API allows for easy model creation and training. Keras is actively developed and supported by a large community of contributors, including major companies like Microsoft, Google, Nvidia, and Amazon.

3. What models are offered by Keras?
   Keras offers two major models: Sequential and Functional. The Sequential model is a linear stack of layers, while the Functional model allows for more flexibility and supports multi-input and multi-output networks.

4. Can Keras be used for text data analysis?
   Yes, Keras can be used for text data analysis. The tutorial demonstrated a wine classifier example where Keras was used to predict the price of a bottle of wine based on its description.

5. How can I get started with Keras?
   To get started with Keras, you will need to install the necessary libraries and dependencies. Then, you can explore the official Keras documentation and tutorials to learn more about the framework and its functionalities.