Fully connected neural network

What is a Fully Connected Neural Network? Fully Connected Neural Network Explained

A fully connected neural network, also known as a dense or feedforward neural network, is a type of artificial neural network where each neuron in one layer is connected to every neuron in the subsequent layer. In a fully connected network, information flows in one direction, from the input layer through the hidden layers to the output layer, without any loops or feedback connections.

Here are the key characteristics and components of a fully connected neural network:

Input Layer: The input layer consists of neurons that receive the input data. Each neuron in the input layer corresponds to a feature or input variable, and the number of neurons in this layer depends on the dimensionality of the input data.

Hidden Layers: The hidden layers are intermediate layers between the input and output layers. They perform computations on the input data and extract relevant features or representations. In a fully connected network, each neuron in a hidden layer is connected to every neuron in the previous layer and every neuron in the subsequent layer.

Neurons and Activation Functions: Each neuron in a fully connected network performs a weighted sum of the inputs it receives, applies an activation function to the sum, and produces an output. The activation function introduces non-linearity and enables the network to model complex relationships and patterns in the data. Common activation functions include sigmoid, tanh, ReLU (Rectified Linear Unit), and softmax for classification problems.

Weights and Biases: The connections between neurons in different layers are associated with weights. Each weight represents the strength or importance of the connection. Additionally, each neuron (except for the input layer) typically has an associated bias term, which allows the network to adjust the output based on certain thresholds. The weights and biases are learned during the training process to optimize the network’s performance.

Output Layer: The output layer produces the final output or prediction of the network. The number of neurons in the output layer depends on the nature of the problem. For example, in a binary classification task, there would be one neuron in the output layer with a sigmoid activation function, while in a multi-class classification task, there would be multiple neurons with a softmax activation function.

Forward Propagation: In a fully connected network, forward propagation refers to the process of passing input data through the network’s layers, applying weights and biases, and computing the output. During forward propagation, each neuron calculates a weighted sum of the inputs it receives, applies the activation function, and passes the output to the neurons in the subsequent layer.

Backpropagation and Training: To train a fully connected neural network, backpropagation is used. Backpropagation involves calculating the error between the network’s predictions and the true labels, and then propagating this error backward through the network to adjust the weights and biases. The goal is to minimize the error and optimize the network’s performance.

Fully connected neural networks are powerful models capable of learning complex patterns and relationships in data. However, they can be prone to overfitting when dealing with high-dimensional or noisy data. Regularization techniques, such as dropout or weight decay, and optimization algorithms, such as stochastic gradient descent (SGD) or Adam, are often employed to mitigate overfitting and improve generalization.

In practice, fully connected neural networks are used for a variety of tasks, including image classification, natural language processing, regression, and more. They can be designed with different architectures, including varying numbers of hidden layers, different numbers of neurons per layer, and different activation functions, depending on the specific problem and dataset.