Introduction to Feedforward Neural Networks

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)

You can also try this code with Online Python Compiler

Run Code

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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