When training a neural network, a model “learns” to recognize patterns, make predictions, or perform other tasks using large amounts of data. Before understanding the details of this training, we should talk about the basic concepts.
Table of contents
Basic terms
Neural network
A neural network is a computer model that imitates the functioning of the human brain. It is made up of many small building blocks called neurons. We have already discussed how it works, but it might be worth mentioning again that a neural network is a computer program that uses mathematical operations to process information. Within the neural network there are layers of neurons that process input data and produce output data.
Weights
Weights are numbers that connect neurons to each other. Each neuron has its own inputs and outputs. When data arrives at the inputs of a neuron, it does some calculations and passes the result to the output. Weights determine how much the input data influences the neuron's computational output. If the weight is large, then the input data greatly influences the output of the neuron. If the weight is small, the effect on the yield is weak.
The neural network's weights are adjusted during training and determine what data will be taken into account and how much it will influence the network's conclusions.
Neural network gradient
Gradient is a vector that shows the direction of the greatest increase in the function. In the context of neural network training, the gradient is used to optimize an error function that represents how well the model predicts correct responses from the training data.
During training, the gradient is calculated by finding the derivative of the error function with respect to each model parameter (weights and biases). It is then used to update the model parameters in such a way as to reduce the value of the error function. If the gradient is large, the change in model parameters will be large, which can lead to accelerated convergence, that is, a fast but approximate result.
When using gradient descent methods, the gradient indicates the direction in which the model parameters should be changed to minimize the error function.
Bias
In neural network training, the term "bias" refers to an additional parameter that helps the neural network process data better and make more accurate predictions.
To understand the concept of bias, let us look at how a neural network works. It consists of many neurons that process input data and transmit their output values to the next neurons. Each neuron has its own weights, which take into account the importance of various inputs. The weights are determined during the neural network training process.
Bias is an additional parameter for each neuron. It allows the neural network to shift the output value of a neuron in a certain direction, regardless of the input data. We can say that the bias determines the base level of activation of the neuron.
The interaction between weights and biases allows the neural network to find the optimal values to achieve the desired results.
Convergence
The convergence of a neural network in simple terms means that it has reached an optimal learning state. During the training process, it tries to improve its performance based on the data provided and adjusts its weights and parameters to minimize the error between predictions and expected results.
Convergence occurs when the neural network reaches a stable state in which it predicts the desired results reasonably well. This means that further training does not lead to significant improvements in performance. In practice, this usually manifests itself in the fact that the neural network’s training error begins to slowly decrease or ceases to change significantly.
Main steps of training neural networks
The neural network training process usually includes the following steps:
- Data collection: Collecting a representative and diverse dataset that includes labeled examples of the problem you want to solve using a neural network.
- Pre-processing of data: Cleaning and pre-processing of data provides a format suitable for training. This may include tasks such as normalization, feature scaling, and handling missing values.
- Model architecture design: Selecting the appropriate neural network architecture for your problem, including determining the number and type of layers, activation functions, and connection patterns.
- Initialization: Initialization of weights and biases of the neural network. This step is important because the initial values can affect convergence and network performance.
- Forward propagation: Feeding inputs through the network in a forward pass, where the activations and outputs of each layer are calculated based on the current weights and biases.
- Loss calculation: Comparing the network's predicted output with the ground truth labels in the training data and calculating a loss or error metric that quantifies the discrepancy between them.
- Backpropagation: Propagates the calculated loss back through the network to update the weights and biases. This step involves computing loss gradients with respect to network parameters using the chain rule.
- Optimization: Applying an optimization algorithm such as gradient descent to iteratively update network parameters. The goal is to minimize losses and improve network performance on training data.
- Training iterations: Repeating forward propagation, loss calculation, backpropagation, and optimization steps for multiple iterations or epochs. This allows the network to gradually adjust its weights and improve its ability to make accurate predictions.
- Validation and Monitoring: Periodically evaluating the network's performance on a separate validation dataset to monitor its generalization ability and detect overfitting. This helps in tuning hyperparameters or stopping the training process early if necessary.
- Testing: Evaluating the final trained model on an independent testing dataset to evaluate its performance and confirm its effectiveness in solving the given problem.
- Fine-tuning and deployment. After initial training, further fine-tuning and optimization of the model can be done based on specific requirements or feedback. Finally, the trained model can be used to make predictions on new, unseen data.
Neural network training methods
There are three main methods for training neural networks: supervised, unsupervised and reinforcement. It is worth adding transfer learning to the above listed. Let us take a closer look at these methods.
Tutored training
Supervised learning is the most common and widely used method for training neural networks. In supervised learning, a labeled dataset is provided for training a neural network. It consists of input samples and their corresponding target outputs. The network learns to map inputs to desired outputs by iteratively adjusting its weights and biases.
The backpropagation algorithm plays a crucial role in supervised learning. It calculates the error gradient of a network with respect to its weights, allowing parameters to be tuned using gradient descent optimization techniques. This iterative process continues until the network converges to a state in which its predictions exactly match the desired output.
Supervised learning is particularly effective for tasks such as image classification, speech recognition, and sentiment analysis, where labeled data is readily available.
Unsupervised learning
Unsupervised learning, unlike supervised learning, deals with unlabeled data. In this method, the network learns to discover internal structures, patterns, and relationships in data without explicit target outputs.
Clustering and dimensionality reduction are two common applications of unsupervised learning. Clustering algorithms group similar data points together based on their intrinsic properties. Dimensionality reduction techniques such as principal component analysis, autoencoders, reduce the complexity of high-dimensional data while preserving its important features.
By learning the underlying structure of data, unsupervised learning allows neural networks to learn representations that capture important information and facilitate downstream tasks such as anomaly detection, data compression, and recommendation systems.
Reinforcement learning
Reinforcement neural network training takes a different approach. Instead of using labeled data, such learning relies on the agent interacting with the environment and receiving feedback in the form of rewards or punishments. The agent learns to take actions that maximize total reward over time.
In reinforcement learning, a neural network learns through trial and error. It explores different actions, observes the consequences, and adjusts its actions depending on the rewards received. Reinforcement learning has demonstrated significant success in areas such as gaming, robotics, and autonomous vehicles. The ability to learn from interactions and adapt to dynamic environments makes it a powerful teaching method.
Transfer learning
Transfer learning is a learning method that uses knowledge gained from performing one task to improve performance on another related task. It involves pre-training a neural network on a large dataset and then fine-tuning it on a smaller dataset specific to the target task.
Using transfer learning, the network can benefit from the representations learned from the original task, saving significant amount of time and resources. This is particularly useful in scenarios where labeled data for a target task is limited or expensive to obtain.
Neural network training algorithms
In the methods section, we mentioned some neural network training algorithms. As of today, there are quite a lot of them, so we will focus on the most common ones.
Backpropagation algorithm
The backpropagation method involves repeating forward and backward steps multiple times. The forward propagation step involves passing the input sample through the neural network and computing the predicted output. The output of each neuron is calculated by applying an activation function to the weighted sum of its inputs
Starting from the input layer, the input values propagate forward layer by layer until the output layer is reached. The output layer provides the predicted output of the neural network for a given input.
In the backpropagation stage, the error between the predicted output and the true output is calculated using a predefined loss function (e.g., root mean square error or cross entropy loss). The error is then propagated back through the network to compute the gradients of the loss function with respect to the weights of each neuron.
Gradients are calculated using a chain calculus rule that allows the error to be attributed to each weight in the network. Gradients indicate the direction and magnitude of weight updates that reduce the loss function.
The weights are updated by taking a step in the opposite direction of the gradients, scaled by the learning rate, which determines the size of the update.
Backpropagation is critical for training deep neural networks with multiple layers because it allows gradients to be propagated efficiently throughout the network. Without backpropagation, it would be difficult to optimize neural network weights, especially in deep architectures where the number of parameters can be very large.
It's worth noting that while backpropagation is a widely used and effective learning method, there are variants and extensions of the algorithm that address problems such as vanishing gradients, exploding gradients, and overfitting. These options include techniques such as gradient pruning, skipping connections (for example, in residual networks), and more advanced optimization algorithms such as Adam or RMSprop that adaptively adjust the learning rate.
Resilient Backpropagation
Rprop (Resilient Backpropagation) is an optimization algorithm used in neural networks to learn from backpropagation. It belongs to a group of weight updating methods that are used in the process of training neural networks.
Simply put, during training, neural networks try to minimize the difference between their predicted output and their desired output. This involves adjusting the weight of connections between neurons. The Rprop method focuses on how these weight adjustments are made.
Rprop calculates weight updates based on the sign of the gradient, which indicates the direction in which the weights should be adjusted. Unlike other methods that use a fixed learning rate, Rprop adapts the learning rate for each weight separately. If the sign of the gradient remains the same, Rprop increases the learning rate to make larger weight adjustments. If the sign changes, it reduces the learning rate to make smaller adjustments. In this way, Rprop “flexibly” adapts to the behavior of each weight, providing a faster and more effective workout.
By adjusting the learning rate individually, Rprop can efficiently navigate complex and high-dimensional weight spaces, helping neural networks converge to optimal solutions faster and more reliably.
Genetic learning algorithm
Genetic neural network training algorithm is a machine learning method that uses ideas from genetics to tune the parameters of a neural network for maximum performance on a task.
The process begins by creating a random population of neural networks, where each network has randomly assigned parameters such as weights and biases. This population is evaluated on a given task, such as image classification.
The most successful networks are then selected from the population to be used to create the next generation. This happens by using genetic operators such as crossing and mutation to create new neural networks based on the most successful ones.
A new generation of networks is evaluated on the task and the process is repeated until a certain stopping criterion (such as achieving a certain accuracy) is reached.
From all the above, it is clear that training neural networks is a complex process that requires special knowledge and time. Moreover, modern learning is at the initial stage of development. In the future, many new algorithms and methods will appear that will make it possible to effectively use neural networks — a wonderful invention of mankind.