Feedforward Neural Networks
Deep learning technology has become indispensable in the domain of modern machine interaction, search engines, and mobile applications. It has revolutionized modern technology by mimicking the human brain and enabling machines to possess independent reasoning. Although the concept of deep learning extends to a wide range of industries, the onus falls on software engineers and ML engineers to create actionable real-world implementations around those concepts. This is where the Feedforward Neural Network pitches in.
The simplified architecture of Feedforward Neural Networks presents useful advantages when employing neural networks individually to achieve moderation or cohesively to process larger, synthesized outputs.
Today, we’ll dive deep into the architecture of feedforward neural network and find out how it functions. So, let’s dive right in!
Feedforward Neural Networks are artificial neural networks where the node connections do not form a cycle. They are biologically inspired algorithms that have several neurons like units arranged in layers. The units in neural networks are connected and are called nodes. Data enters the network at the point of input, seeps through every layer before reaching the output. However, the connections differ in strength or weight. The weight of the connections provides vital information about a network.
Feedforward Neural Networks are also known as multi-layered networks of neurons (MLN). The neuron network is called feedforward as the information flows only in the forward direction in the network through the input nodes. There is no feedback connection so that the network output is fed back into the network without flowing out.
These networks are depicted through a combination of simple models, known as sigmoid neurons. The sigmoid neuron is the foundation for a feedforward neural network.
Here’s why feedforward networks have the edge over conventional models:
- Conventional models such as Perceptron take factual inputs and render Boolean output only if the data can be linearly separated. This means the positive and negative points should be positioned at the two sides of the boundary.
- The selection of the best decision to segregate the positive and the negative points is also relatively easier.
- The output from the sigmoid neuron model is smoother than that of the perceptron.
- Feedforward neural networks overcome the limitations of conventional models like perceptron to process non-linear data efficiently using sigmoid neurons.
- With convolutional neural networks and recurrent neural networks delivering cutting-edge performance in computer science, they are finding extensive use in a wide range of fields to solve complex decision-making problems.
The feedforward neural networks comprise the following components:
- Input layer
- Output layer
- Hidden layer
- Neuron weights
- Activation function
Input layer: This layer comprises neurons that receive the input and transfer them to the different layers in the network. The number of neurons in the input layer must be the same as the number of the features or attributes in the dataset.
Output layer: This layer is the forecasted feature that depends on the type of model being built.
Hidden layer: The hidden layers are positioned between the input and the output layer. The number of hidden layers depends on the type of model. Hidden layers have several neurons that impose transformations on the input before transferring. The weights in the network are constantly updated to make it easily predictable.
Neuron weights: The strength or the magnitude of connection between two neurons is called weights. The input weights can be compared just as coefficients in linear regression. The value of the weights is usually small and falls within the range of 0 to 1.
Neurons: The feedforward network has artificial neurons, which are an adaptation of biological neurons. Artificial neurons are the building blocks of the neural network. The neurons work in two ways: first, they determine the sum of the weighted inputs, and, second, they initiate an activation process to normalize the sum.
The activation function can be either linear or nonlinear. Weights are related to each input of the neuron. The network studies these weights during the learning phase.
Activation Function: This is the decision-making center at the neuron output. The neurons finalize linear or non-linear decisions based on the activation function. It prevents the enlargement of neuron outputs due to cascading effect because of passing through many layers. The three most important activation functions are sigmoid, Tanh, and Rectified Linear Unit ( ReLu).
- Sigmoid: It maps the input values within the range of 0 to 1.
- Tanh: It maps the input values between -1 and 1.
- Rectified linear Unit: This function allows only the positive values to flow through. The negative values are mapped at 0.
Data travels through the neural network’s mesh. Each layer of the network acts as a filter and filters outliers and other known components, following which it generates the final output.
- Step 1: A set of inputs enter the network through the input layer and are multiplied by their weights.
- Step 2: Each value is added to receive a summation of the weighted inputs. If the sum value exceeds the specified limit ( usually 0), the output usually settles at 1. If the value falls short of the threshold ( specified limit), the result will be -1.
- Step 3: A single-layer perceptron uses the concepts of machine learning for classification. It is a crucial model of a feedforward neural network.
- Step 4: The outputs of the neural network can then be compared with their predicted values with the help of the delta rule, thereby facilitating the network to optimize its weights through training to obtain output values with better accuracy. This process of training and learning generates a gradient descent.
- Step 5: In multi-layered networks, updating weights are analogous and more specifically defined as backpropagation. Here, each hidden layer is modified to stay in tune with the output value generated by the final layer.
The operation on this network can be divided into two phases:
First: Learning Phase
This is the first phase of the network operation, during which the weights in the network are adjusted. The weights are modified to make sure the output unit has the largest value.
The feedforward network uses a supervised learning algorithm that enhances the network to know not just the input pattern but also the category to which the pattern belongs. The pattern gets modified as it passes through other layers until the output layer. The units present in the output layer will be of different categories.
The output values will be compared with the ideal values of the pattern under the correct category. The output unit with the right category will have the largest value than the other units. The connection weights are modified according to this to make sure the unit with the correct category re-enters the network as the input. This is known as back-propagation.
The length of the learning phase depends on the size of the neural network, the number of patterns under observation, the number of epochs, tolerance level of the minimizer, and the computing time (that depends on the computer speed).
Second: Classification Phase
The weights of the network remain the same (fixed) during the classification phase. The input pattern will be modified in every layer till it lands on the output layer. The classification is done based on a selection of categories related to the output unit that has the largest value. The feedforward network must be selected along with a list of patterns to perform the classification process. The classification phase is much faster than the learning phase.
- The simplified architecture of Feed Forward Neural Network offers leverage in machine learning.
- A series of Feedforward networks can run independently with a slight intermediary to ensure moderation.
- The network requires several neurons to carry out complicated tasks.
- The handling and processing of non-linear data can be done easily with a neural network that is otherwise complex in perceptron and sigmoid neurons.
- The excruciating decision boundary problem is alleviated in neural networks.
- The architecture of the neural network can be of different types based on the data. For instance, a convolutional neural network (CNNs) has registered exceptional performance in image processing, whereas recurrent neural networks (RNNs) are highly optimized for text and voice processing.
- Neural networks require massive computational and hardware performance for handling large datasets, and hence, they require graphics processing units (GPUs). Kaggle Notebooks and Google Collab Notebooks are two popular GPUs used extensively in the market.
Given that we’ve only scratched the surface of deep learning technology, it holds huge potential for innovation in the years to come. Naturally, the future scope of deep learning is very promising.
In fact, neural networks have gained prominence in recent years following the emerging role of Artificial Intelligence in various fields. Since deep learning models are capable of mimicking human reasoning abilities to overcome faults through exposure to real-life examples, they present a huge advantage in problem-solving and are witnessing growing demand.
From image and language processing applications to forecasting, speech and face recognition, language translation, and route detection, artificial neural networks are being used in various industries to solve complex problems.