How to Install Package Keras in R

Keras is a high-level neural networks API, written in Python, and capable of running on top of TensorFlow. The keras package in R provides an interface to the Keras library, allowing R users to build and train deep learning models in a user-friendly way. Below is a comprehensive guide on how to install the Keras package in R.

System Requirements

Before installing Keras, make sure your system meets the following requirements:

• R Version: R 3.4.0 or later.
• Python: Python 3.x installed. You can use either a system-wide Python installation or a Python environment managed by Anaconda.
• TensorFlow: Keras requires TensorFlow as the backend. The keras package in R can automatically install TensorFlow.

Now we will discuss step-by-step implementation of Installing Keras in R Programming Language.

Step 1: Install the keras Package

You can install the keras package from CRAN:

Step 2: Load the Keras Library

After installation, load the Keras library:

Step 3: Install TensorFlow and Keras Backend

Once the keras package is loaded, you need to install TensorFlow, which Keras uses as the backend. The install_keras() function will install both Keras and TensorFlow:

You can specify the TensorFlow version by using the version argument (e.g., install_keras(version = "2.3.0")).

Step 4: Verifying the Installation

After installation, it's important to verify that Keras and TensorFlow are properly installed and configured:

Output:

List of 2
 $ train:List of 2
 ..$ x: num [1:60000, 1:28, 1:28] ...
 ..$ y: int [1:60000] ...
 $ test :List of 2
 ..$ x: num [1:10000, 1:28, 1:28] ...
 ..$ y: int [1:10000] ...

• mnist$train:
  • x: A 3-dimensional array of training images. Each image is 28x28 pixels, and there are 60,000 images in total.
  • y: A vector of labels corresponding to the training images. Each label is an integer from 0 to 9.
• mnist$test:
  • x: A 3-dimensional array of test images. Each image is 28x28 pixels, and there are 10,000 images in total.
  • y: A vector of labels corresponding to the test images. Each label is an integer from 0 to 9.

The str(mnist) function provides a summary of these structures, showing the dimensions and types of data contained in the dataset.

Conclusion

The keras package in R simplifies the process of building deep learning models by providing an easy-to-use interface to Keras and TensorFlow. With the installation steps outlined above, you should be able to get Keras up and running in your R environment.