Feedforward Neural Networks: How They Predict Outcomes

Neural networks are the heart of deep learning models. They’re loosely inspired by how a human brain processes inputs to arrive at a conclusion. 

Technically speaking, just as the human brain comprises billions of neurons connected via synapses, an artificial neural network (ANN) consists of layers of interconnected nodes. These connections allow the model to learn from data and produce output. 

Feedforward neural networks, a version of ANNs, were one of the first successful learning algorithms. These networks rely on training data and improve their accuracy over time. 

The network doesn’t have any cycles or loops. 

The input layer comprises neurons that receive inputs and pass them to the next layer. Dimensions of input data determine the number of neurons in this layer. Hidden layers are like the neural network’s computational engine. 

Each hidden layer neuron takes the weighted sum of the previous layer’s output, applies an activation function, and passes the result to the next layer. The activation function decides whether the neuron’s input is essential and activates a node accordingly. The hidden layers aren’t directly exposed to the input and output layers present. 

Source: Research Gate 

Finally, the output layer produces the output. The number of possible outputs governs the number of neurons present in this layer. 

Feedforward neural networks are comparatively simpler than their counterparts, such as recurrent neural networks (RNNs) or convolutional neural networks (CNNs).  

Feedforward neural network architecture

FNNs consist of layers, weights biases, and nodes.

We discussed layers in the previous section, so let's look at other elements of a feedforward neural network’s architecture.  

Weights and biases

Weights represent the strength of the connection between two neurons. Before an input signal passes through an activation function, weights scale it. Simply put, they determine the influence of input on a neuron’s output. 

On the other hand, biases control a neuron's baseline activation. Like weights, bases are represented by matrices, with one matrix for each layer. 

Both weights and biases are iteratively updated at the time of training to minimize the loss function. The loss function determines how well a neural network is performing its task by essentially quantifying how “wrong” the output of a network is compared to the desired output. Optimization algorithms like stochastic gradient descent (SGD) or its variants are used to update the weights and biases. 

This process of updating is known as backpropagation. It plays a critical role in training a neural network’s feedforward. 

Nodes

Nodes are tiny, interconnected processing units within a neural network. They receive data and perform mathematical operations to produce results that are transmitted to subsequent layers or the output layer. 

Although a single node performs simple tasks and calculations, the collective work of many nodes makes neural networks powerful. When nodes work together, they can recognize patterns and provide solutions to complex patterns.

How does a feedforward neural network work?  

A feedforward neural network works through the feedforward phase and the backpropagation phase. 

The feedforward phase feeds input data that propagates forward through the network. Its weighted sum of inputs is calculated and passed through an activation function, introducing non-linearity in the model. The exact process continues until the output stage is reached. 

At the output stage, the network calculates the difference between the predicted and actual outputs. This error propagates back through the network to adjust weights, minimizing future errors. This sums up the backpropagation phase. 

The network keeps adjusting the weights to minimize errors, helping it learn and improve over time. The learning rate decides the amount by which weights are adjusted. Many inputs and outputs are fed into a network until it reasonably learns the relationship between input and output data. 

This repetitious learning process involves comparing the network’s output to the desired output and updating the weights accordingly.

Did you know? Frank Rosenblatt introduced the term “back-propagating error correction” in 1962. However, David E. Rumelhart and others popularized the current stochastic gradient descent method.

Recurrent neural networks (RNNs) vs. feedforward neural networks

Recurrent neural networks, also known as feedback neural networks, are derived from FNNs. RNNs remember the input data, making them suitable for machine learning (ML) problems involving sequential data. Its state-of-the-art algorithm for sequential data is used by Apple’s Siri and Google voice search. 

These neural networks can easily recognize patterns in data sequences, which can be in the form of text, speech, or time series. A standout feature of RNNs is their algorithm’s memory. Unlike FNNs, which process each input independently, RNNs take information from previous steps to improve processing. 

You can think of RNNs as people reading a book, using the context from previous steps to process current data. 

Feedforward neural networks are good for applications where the input data is fixed in size and doesn’t have temporal dependencies. However, if the order of data points is crucial, FNNs won’t work for such use cases. They process each input independently without considering the context of the previous input. 

For instance, FNNs can classify an image into different categories based on pixel values but they struggle with tasks like predicting the next word in a sentence since there is no context retention from the previous words. 

In contrast, recurrent neural networks can do a better job here. They can model temporal dependencies and process sequences of varying lengths. For example, a recurrent neural network can easily predict the next word in the sentence. The prediction becomes more accurate as each word in the sequence is processed, capturing the existing temporal dependencies. 

To summarize, if there’s no feedback from the output toward neurons of the network, it’s a feedforward neural network. However, if there’s feedback from the output layer toward the input of neurons (own or different), it’s a recurrent neural network. 

Benefits of feedforward neural network

Feedforward neural networks offer several benefits to users, including: 

• Self-learning capabilities: These neural networks learn independently through backpropagation. They adjust weights and other parameters to produce the desired output, helping them quickly adapt to new datasets. 

• Speed: FNNs can be trained faster than other models due to their parallelizable nature, making them efficient in handling large datasets. 

• Non-linear classifiers: FNNs are non-linear classifiers, which means they can handle complex data better than other linear models. When datasets contain multiple variables that interact non-linearly, FNNs would be able to understand the information contained within the datasets more accurately. 

Challenges of feedforward neural networks

Feedforward neural networks present some challenges for its users, including: 

• Inability to retain information: FNNs rely solely on current inputs and don’t use any context from previous processing. They struggle with sequential data, making RNNs a suitable choice for users who require temporal dependencies. 

• Prone to overfitting: When FNNs deal with complex data, the network becomes specialized in training data. It fails to generalize well, making it challenging to work with new and unseen data. 

• Computational complexity: When dealing with large-scale datasets, the training process requires more computational resources, making it challenging for applications that need real-time processing in resource-constrained environments. 

• Hyperparameter tuning: FNNs have several hyperparameters (learning rate, batch size, activation functions, etc.) that need careful tuning to achieve optimal performance.

• Interpretability: The network's complex architecture, which is a black-box in nature and high-dimensional, makes it challenging to understand its decision-making process. 

• Dependency on labeled data: FNNs deliver satisfactory performance when they’re trained on a significant amount of training data. Getting large datasets and labeling them can be a time-consuming and expensive process. It limits applications of FNNs in places where labeled data isn’t accessible easily. 

Explore beyond FNNs

Feedforward neural networks find applications across many sectors. For example, they’re used in predictive maintenance, helping industries save on costs and prevent setbacks. As the usage of AI and deep learning models continues to rise, we’ll likely see more sophisticated applications of FNNs in the foreseeable future. 

However, with FNNs, we have simply scratched the surface of deep learning technology. There’s a lot more to learn and understand when it comes to deep-learning models. 

Learn more about deep learning and understand how machines learn and progress. 

Edited by Monishka Agrawal