How to create FeedForward Neural Networks in Keras?
A feedforward neural network is a type of artificial intelligence that is modeled after the way the human brain works. These networks are made up of layers of interconnected “neurons,” which process and transmit information. In order to create a feedforward neural network in the programming library Keras, there are a few steps that need to be followed.
First, you will need to import the necessary libraries and modules from Keras. This includes the “Sequential” module, which is used to build the structure of the neural network, and the “Dense” module, which is used to add layers of neurons to the network.
Next, you will need to create an instance of the “Sequential” module and assign it to a variable. This will be the container for the layers of neurons in your network.
After that, you can begin adding layers to the network using the “Dense” module. The first layer added will be the input layer, and it is important to specify the number of neurons in this layer, as well as the shape of the input data. The following layers will be the hidden layers, and you can add as many as you want. You will need to specify the number of neurons in each hidden layer, as well as the activation function to use. The activation function is a mathematical equation that determines how the neurons in a layer will process the information they receive.
The last layer will be the output layer, and it is important to specify the number of neurons in this layer and the activation function to use.
Once all the layers have been added, you will need to compile the network. This step involves specifying the optimizer to use (which is the algorithm that will adjust the weights and biases of the network to improve its performance), the loss function (which is the metric that will be used to measure the accuracy of the network), and any additional metrics you want to track.
Finally, you can train the network using a dataset. This step is where the network will adjust its weights and biases based on the input data.
It is important to note that creating a feedforward neural network in Keras involves many parameters and settings that can be adjusted to improve the performance of the network. This is a general description of how to create a feedforward neural network in Keras, and is not an exhaustive guide.
In this Applied Machine Learning & Data Science Recipe, the reader will find the practical use of applied machine learning and data science in Python & R programming: Learn By Example | How to create FeedForward Neural Networks in Keras?
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R: