How Backpropagation Makes Neural Networks Accurate

Backpropagation sits at the heart of neural networks, where it helps deep learning models produce more accurate results. 

It plays a key role in training the model, which helps artificial neural networks deliver accurate predictions in applications like image recognition, natural language processing, or autonomous driving. 

The weights and biases are neural network parameters that feed data forward. Weights manage the connection between two basic units in a neural network, while biases are additional critical units that are key in sending the data forward. This process of propelling data flow forward is known as forward propagation. 

Backpropagation is found to be used in both supervised and unsupervised learning. However, it’s primarily associated with supervised learning as it requires a desired output value to compare against the model’s output. The comparison helps calculate the loss function gradient, determining how far the outcome is from the expected prediction. 

Let’s take an example to explain how backpropagation works. 

Suppose the autocorrect feature on your smartphone uses deep learning to learn that you misspelled “desert” while typing. Initially, the programmer might have included common misspellings such as “Desertt” or “Desirt”. However, if you accidentally typed “Sesert” due to a slip of the finger, the model may not catch it unless it has been specifically programmed to recognize it. 

After a series of backpropagations and forward propagation, the model will eventually be able to catch that you misspelled “Desert” as “Sesert.”

How does backpropagation work?

Backpropagation requires a desired output to calculate the loss function gradient and adjust weights and biases. These calculations happen at each layer of the neural network. 

Backpropagation allows multilayer neural networks to understand complex nonlinear relationships between a layer's inputs and outputs. With adjustments in weights, the network becomes better at recognizing patterns in input data to produce the desired outcome accurately.  

Source: DataCamp

Here, the input layer receives “X,” the input data. This data is modeled with weights “W” and passed into the hidden layers for processing, finally coming out of the output layer. The model then calculates the difference between the calculated output and the desired output. 

Based on the error, it goes back to hidden layers, where it adjusts weights to reduce future errors. The same process continues until the model delivers the desired output. 

Let's look at the process in detail. A backpropagation algorithm comprises different steps.

1. Forward pass

X1 and X2 are fed into the input layer, from where they move into the hidden layer's N1X and N2X neurons. 

In the hidden layer, an activation function is applied to the inputs. This function estimates the weighted sum, adds a direction, and decides if it should activate a particular neuron. A neuron is only activated if the weighted sum exceeds a certain threshold value. 

2. Error calculation

The output received from the network is compared with the desired output supplied by an engineer. Unless it matches, the process continues in a loop to generate a final output matching the desired result. 

3. Backward pass

This is where backpropagation actually begins. The algorithm calculates the gradient of the loss function based on error values. This gradient propagates back through the network, starting from the output layer and moving to hidden layers. 

During this propagation, the weights get corrected based on their contribution to the error. If the learning rate of the model is small, then the weights would be corrected by a small amount. The opposite is also true. 

4. Weight update

The weights are updated in the direction that is opposite to that of the gradient. This is known as gradient descent. The error is reduced based on corrected weights in the next forward pass. 

This process continues until you get satisfactory performance from the network or it stops improving. 

Types of backpropagation networks

There are two types of backpropagation networks: 

• Static backpropagation
• Recurrent backpropagation 

Let’s dive deeper into the details of each in chronological order. 

Static backpropagation is used to resolve static classification problems like optical character recognition (OCR). The output generated here is static as it comes through the mapping of static inputs. An example would be predicting the class of an image, where the input image and the output class won’t change.

In recurrent backpropagation, the flow is directed forward until it reaches a threshold value. Error evaluation and backpropagation begin once this threshold has been met. It usually considers non-static problems and applies to time-series models like recurrent neural networks (RNNs).

Benefits of backpropagation

Backpropagation reduces the difference between actual and desired output while training the model to produce more accurate predictions. This is particularly beneficial for deep neural networks working on tasks like image recognition or speech recognition use cases, which are generally prone to errors. 

Below are some of its notable benefits. 

• It’s memory-efficient. Backpropagation uses less memory while calculating derivatives compared to other optimization algorithms. It considerably helps large networks where extensive calculations are required. 

• The algorithm is fast. As long as small and medium-sized networks are involved, backpropagation trains them effectively and quickly. However, as networks grow larger, the speed of calculating derivatives can slow down.

• It’s flexible. The algorithm is generic and can easily work with different network architectures, such as convolutional neural networks (CNNs), fully connected networks, generative adversarial networks, and various others. 

• There is less overhead. Backpropagation algorithms are free of any parameters that tune them. The gradient descent algorithm uses a few, such as the learning rate parameter. 

Challenges of backpropagation

There are some downsides of backpropagation algorithms, for example: 

• It takes longer to train large networks. Backpropagation can be computationally expensive and time-taking when training large networks. 

• It’s more sensitive to initial weights. The initial weights need to be carefully assessed. If not fine-tuned, the network might take longer to train, especially if the learning rate is small and random weights get assigned. 

• It’s less suitable for non-linear problems. Backpropagation relies on gradient descent, which is not very effective in these problems. In these problems, the output cannot be reproduced from a linear combination of inputs.
• It considers all neurons equally. The backpropagation algorithm estimates derivatives for every backward pass, even for dropout layers. The algorithm calculates derivatives for doped neurons and then drops them. 

Furthermore, a backpropagation algorithm won’t work if the activation and error functions are non-differentiable. 

Best practices for optimizing backpropagation

Apply these best practices to ensure the backpropagation algorithm operates at its peak. 

• Understand how learning rate affects performance. The learning rate depends on the size of the dataset, the problem type, and various other factors. While you’ll get faster results with backpropagation in a model with a higher learning rate, it may sacrifice performance accuracy. Conversely, a lower learning rate would produce slower results but better outcomes. 
• Choose the training method wisely. You need to choose between the training speed you want and the level of fine-tuning needed. If you go with stochastic gradient descent, you’ll have more speed, but fine-tuning will be tricky. Similarly, it’s easier to perform batch gradient descent, but the overall training process takes a long time. Overall, it makes stochastic gradient a better choice.
• Ensure high-quality training data. Make sure the data you feed into the system is clean and normalized. This will help the backpropagation algorithm reach the desired output faster and deliver more accurate results using easier-to-analyze data. 
• Feed plenty of data. It's best to use large data sets to help the model gain better experience. The training data is crucial in making the model accustomed to different types of input data and producing better results. Decide the learning rate based on your situation and what’s best for you. 

Let the networks learn

Backpropagation trains neural networks to produce outputs that users desire. The algorithm minimizes errors consistently with every forward and backward pass, allowing users to train the model to make predictions and recognize patterns.

Learn more about recurrent neural networks and understand how they’re trained to deliver better outputs. 

Edited by Monishka Agrawal