How To Install Keras?

Finding a way to deep dive into machine learning? Then you must have knocked on the doors of Keras. Using Keras, users can develop various neural network models in less time. In this blog, you will get an idea about Keras, its dependencies, and, last but not least, How to install it. Also, this article will walk you through the Python installation and the installation of various required libraries such as Pandas, NumPy, Spacy, Matplotlib, etc.

For most users, Keras is vital in making machine learning models, especially deep learning models. Keras is far more straightforward and flexible regarding integration with TensorFlow and other related frameworks. Because of the same reason, it has become an excellent choice for beginners as well as professional developers. Being open source, it evolves continuously and provides vivid features to users.

In this blog, you will learn how t install Keras and its dependencies in a detailed way. Also, you will learn about virtual environments in greater detail. Let’s dive deep into the blog!

How To Install Keras - Table of Contents

• What is Keras?
• Prerequisites To Install Keras
• Steps To Install Keras
• FAQs

What is Keras?

Now, let’s see what Keras is. Essentially, Keras is an open-source framework. It is an easy-to-learn, user-friendly, and widely used framework by developers. We use this tool to train deep-learning models. In a way, Keras is a deep-learning API for neural networks. This API is usually built using the programming language Python.

Keras can support convolutional networks and recurrent networks separately. Not only that, it can support them in combination too. It runs on GPU as well as on CPU and provides prototyping and deployment in a much faster way. Also, it is used by many well-known companies such as Netflix, Square, Uber, etc.

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Prerequisites To Install Keras

To install Keras on your Windows machine, you must have the following:

• Python version 3.5 or higher
• Python Libraries: Numpy, Matplotlib, Seaborn, sci-kit-learn, spacy, etc.
• Operating System
• Processor i3 or above (recommended).

How To Install Keras in Windows?

So, let's start with installing Python first, or if you already have Python installed, you can directly jump to Step 8.

Step 1: To install Python, visit  https://www.python.org/downloads/release/python-3114/ and download the appropriate version—preferably the latest one.

Step 2:  Open the ‘.exe’ file.

Step 3: Now, after opening, click on ‘Install Now’

Step 4: Going through the installation.

Step 5: Let's check if the installation is successful.

Step 6: To check if installation is done successfully, press 'Windows + R', type 'cmd', and hit 'enter'.: To check if installation is done successfully, press 'Windows + R', type 'cmd', and hit 'enter'.

Step 7: In the command prompt, type 'python --version', and if you see the below prompt with no error, then the installation is done.

Buckle up!! Now we are heading towards Keras. For the Keras installation, we will use a virtual environment.

Why Virtual Environment?

The Virtual environment helps manage versions of different packages to run smoothly with the other projects on the same system, which may use the same packages but of different versions. The tool used for creating a virtual environment is 'virtualenv'.

Step 8: Installing virtual environment. Type' pip install virtualenv' in the command prompt to install a virtual environment. After hitting enter, you will see the below prompt.

Step 9: Now, type ‘virtualenv—version’ if you want to check whether the installation is done successfully. Once it is successful, then you can install Keras with confidence.

Step 10: Let’s create the virtual environment at this stage. You can simply create the virtual environment by entering the ‘virtuallenv Keras command. This is because you can already you have installed Python. Note that you can choose your own environment name instead of Keras.

Step 11: You must activate the environment soon after you have created it. To achieve this, you can use the command 'keras\Scripts\activate'. The moment it is created, you can see the prompt shown in the image below where the environment name comes before the path.

Step 12: It is essential that we must install some dependencies of Keras before installing Keras. The dependencies are Matplotlib, Seaborn, Scikit-learn, Numpy, Pandas, and Scipy. So install them one by one.

The above images show the installation of the dependencies. Now it’s time to install Keras.

Step 13: You must enter the command ' pip install keras' to install Keras. Once the installation is done, you can view the below prompt in the display.

If you wish to confirm the installation, you must enter the command 'Python -m pip show keras'. As a result, you can see the Keras version and other associated details.

Congratulations! Now you are ready to work with Keras.

Step 14: If you want to close the virtual environment, you must type ‘deactivate’ and press enter button. Soon after, the ‘virtualenv’ will be closed.

Installing Keras FAQs

1. Can I train Keras models on multiple GPUs?

Yes. You can do that in two ways: data parallelism and device parallelism.

2. Can I train Keras models using TPU?

Yes. You can do that. You can use TPU through ML engines and deep-learning virtual machines.

3. How can I make hyperparameter tuning using Keras?

With Kerastuner, we can make hyperparameter tuning effectively.

4. Why is Keras so special in machine learning platforms?

Keras can run on both CPU and GPU. Also, it supports most of the Neural Network models.

5. What are the models of Keras?

• Sequential model
• Functional API.

6. How to use stateful RNNs?

• You must specify the batch size. It means you must pass batch input argument to the first layer of the Keras models.
• Setting stateful as true in RNN layers.

Conclusion

Keras is gaining much popularity among developers because of its wonderful features such as flexibility, multi-backend support, and modularity and abstraction process. In this blog, we have gone through installing Python, nuances of the virtual environment, dependencies of Keras, and finally, Keras installation. We hope this blog has given you a clear idea of Keras installation.

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