What is Forward Propagation? Forward Propagation Explained
Forward propagation, also known as forward pass, is a fundamental process in neural networks where the input data is passed through the network’s layers to generate predictions or outputs. It involves the flow of information from the input layer, through the hidden layers, and finally to the output layer. Forward propagation is a key step in training a neural network and making predictions.
Here’s how forward propagation works in a neural network:
Input Layer: The process begins with the input layer, which consists of nodes (also called neurons) representing the input features or variables. Each node in the input layer corresponds to a feature of the input data. The values of the input features are fed into the network.
Weights and Biases: Each connection between nodes in different layers is associated with a weight. These weights represent the strength or importance of the connection. Additionally, each node (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.
Activation Function: At each node in the hidden layers and output layer, the weighted sum of inputs is computed. The weighted sum is then passed through an activation function. The activation function introduces non-linearity to the network and determines the output of the node. Common activation functions include sigmoid, tanh, ReLU (Rectified Linear Unit), and softmax.
Hidden Layers: The weighted sum of inputs at each node in the hidden layers is computed by multiplying the input values by their corresponding weights and adding the bias term. This computation is repeated for all nodes in each hidden layer.
Output Layer: The computed values from the final hidden layer are then passed to the output layer. The output layer applies its own set of weights and biases to compute the final output or prediction of the network. The specific activation function used in the output layer depends on the type of problem being solved. For example, sigmoid activation is commonly used for binary classification, softmax activation for multi-class classification, and linear activation for regression tasks.
Prediction: The output values from the output layer represent the predictions or outputs of the neural network for the given input data. These predictions can be used for tasks such as classification, regression, or other types of pattern recognition.
During training, forward propagation is used to compute the predictions of the network for a given set of input data. The computed outputs are then compared to the actual values in order to calculate the error or loss. This error is used to update the network’s weights and biases through the process of backpropagation, which adjusts the parameters to minimize the error and improve the network’s performance.
In summary, forward propagation is the process of passing input data through a neural network, applying weights and biases, and computing the outputs or predictions of the network. It forms the foundation for training and inference in neural networks.
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