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Table of contents
1.
Introduction
2.
Feedforward Architecture
3.
Developing a simple Feedforward Neural Network Model
4.
Applications of Feedforward Neural Networks
5.
Frequently asked questions
5.1.
What is meant by Feedforward neural network?
5.2.
What are Feedforward neural networks good for? 
5.3.
What is Feedforward in Machine Learning? 
5.4.
What is Feedforward backpropagation? 
6.
Conclusion
Last Updated: Mar 27, 2024
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Introduction to Feedforward Neural Networks

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Prerita Agarwal
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23 Jul, 2024 @ 01:30 PM

Introduction

Feedforward neural networks are the inspiration from biological neural networks, which basically contain infinite neural networks connected in loops, that pass the messages. Generally, to solve regression problems or classification problems, the last layer of neural networks contains only one unit. This is the major problem the obtained output with one unit data is not efficient, so people invented Feedforward neural networks by utilizing human nerves architecture.

 

Source

 

To understand the above image let’s take a generalized example. If you are walking on the road and suddenly a snake has been encountered in front of you, this is the input the snake image is the input layer. Now your brain needs to process all the information and give you some possible cases like running, beating it with a stick, or just waiting until snakes move another path or scream. So this process is done under hidden layers. From all the possible outcomes you will select the good output that is best with your situation, this is called as output layer. Now let’s explore some architecture and try to develop a simple model.

Feedforward Architecture

A Feedforward Neural Network is an artificial neural network in which the connections between nodes do not form a cycle. The opposite of a Feedforward neural network is a recurrent neural network, in which certain pathways are cycled. The Feedforward model is the simplest form of a neural network as information is only processed in one direction. While the data may pass through multiple hidden nodes, it always moves in one direction and never backward.

 

 

A Feedforward Neural Network is commonly seen in its simplest form as a single layer perceptron. In this model, a series of inputs enter the layer and are multiplied by the weights. Each value is then added together to get a sum of the weighted input values. If the sum of the values is above a specific threshold, usually set at zero, the value produced is often 1, whereas if the sum falls below the threshold, the output value is -1. 

The single-layer perceptron is an important model of Feedforward neural networks and is often used in classification tasks. Furthermore, single-layer perceptrons can incorporate aspects of machine learning. Using a property known as the delta rule, the neural network can compare the outputs of its nodes with the intended values, thus allowing the network to adjust its weights through training in order to produce more accurate output values. This process of training and learning produces a form of gradient descent. 

In multi-layered perceptrons, the process of updating weights is nearly analogous, however, the process is defined more specifically as back-propagation. In such cases, each hidden layer within the network is adjusted according to the output values produced by the final layer.

 

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Developing a simple Feedforward Neural Network Model

Now let’s develop a simple Feedforward neural network model using Tensorflow.

  • Initially, we declare the variable and assign it to the type of architecture we'll be declaring, which is a “Sequential()” architecture in this case. 
  • Next, we directly add layers in a sequential manner using the model.add() method. 
  • The type of layer can be imported from tf. layers as shown in the code snippet below. 
  • We use adam function as an optimizer and crossentropy function as parameters for our architecture. 
  • Once the model is defined, the next step is to start the training process for which we will be using the model.fit() method. 
  • The evaluation will be done on the test dataset which can be called using model.evaluate() method.
     
import tensorflow as tf 
mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data() 
x_train, x_test=x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([ 
    tf.keras.layers.Flatten(input_shape=(28, 28)), 
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2), 
    tf.keras.layers.Dense(10, activation='softmax')
])

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

model.fit(x_train, y_train, epochs=5) 
model.evaluate(x_test, y_test)

 

Output

 

Applications of Feedforward Neural Networks

There are many applications of Feedforward neural networks, some of them are as:

  • Physiological feedforward system: during this, the feedforward management is epitomized by the conventional prevenient regulation of heartbeat prior to work out by the central involuntary
  • Gene regulation and feedforward: during this, a motif preponderantly seems altogether the illustrious networks and this motif has been shown to be a feedforward system for the detection of the non-temporary modification of the atmosphere.
  • Automation and machine management: feedforward control may be a discipline among the sphere of automation controls utilized in
  • Parallel feedforward compensation with derivative: This is a rather new technique that changes the part of AN open-loop transfer operations of a non-minimum part system into the minimum part.

Frequently asked questions

What is meant by Feedforward neural network?

A feed-forward neural network is a biologically inspired classification algorithm. It consists of a number of simple neuron-like processing units, organized in layers, and every unit in a layer is connected with all the units in the previous layer.
 

What are Feedforward neural networks good for? 

Feedforward neural networks are primarily used for supervised learning in cases where the data to be learned is neither sequential nor time-dependent.
 

What is Feedforward in Machine Learning? 

A Feedforward neural network is an Artificial Neural Network in which connections between the nodes do not form a cycle. The Feedforward neural network was the first and simplest type of artificial neural network.
 

What is Feedforward backpropagation? 

A Feedforward neural network is an Artificial Neural Network in which connections between the nodes do not form a cycle. The Feedforward neural network was the first and simplest type of artificial neural network.

 

Conclusion

In this blog, we discussed Feedforward neural network and its architecture. By using the above simple example try to build your own model and explore the applications of the Feedforward neural network. Let us know in the comments if you have tried any model!

Check out this article - Padding In Convolutional Neural Network

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