Loss Surface refers to a graphical representation of how well a neural network’s predictions match the true values, as a function of its parameters. It visualizes the performance of a model at different parameter settings. High-accuracy model regions are valleys, while error-prone regions are hills.
Imagine you’re playing a game where you’re blindfolded and have to find a bowl of candy hidden somewhere on a field. The game organizer gives you an idea if you’re getting closer to or farther from the candy based on your steps. That’s the loss - the distance from the candy. Now, imagine a bird’s view of your game - this bird can see the ups and downs of your search, the near-misses, and the wandering. This bird’s eye view of your journey to find the candy is what we call the “Loss Surface” in machine learning.
In machine learning, a loss function, or cost function, quantitatively measures how off a model’s predictions are from the actual result. The values that this function takes on, across different modelParameter settings, give rise to what is called the loss surface or the error surface.
Hypothetically, if a model only had two parameters, you could easily visualize this loss surface on a 3D graph, where x and y axes represent the parameters, and z represents the loss. Each point on this graph tells you how much error you’d have for a specific set of parameters. However, modern neural networks can have millions of parameters making it impossible to construct such a comprehensive 3D representation. Nevertheless, the conceptual idea of a loss surface is useful for understanding and debugging our models.
The process of training a neural network is then conceptualized as navigating this high-dimensional loss surface. The goal is to find the lowest point (global minima), which represents the model parameters that give you the least amount of error on your training data. However, this surface can be filled with ridges, plateaus, and multiple local minima, making the navigation process non-trivial.
The commonly used optimization algorithms, like gradient descent, incrementally adjust each parameter in the direction that has the steepest decrease in the loss function. This is somewhat like blindly navigating a mountainous landscape by always walking downhill. Over time, the model parameters converge to a set of values that minimize the loss, but only to a local minimum, not necessarily the global one.
The complexity and topology of the loss surface depend highly on the specific form of the loss function, the number of parameters, the data, and the model itself. Studies on exploring these loss surfaces have revealed insights into the operating of deep learning models and given birth to advanced optimization techniques like momentum and learning rate schedules, which can assist in faster and more robust convergence.
Loss Function, Gradient Descent, Backpropagation, Stochastic Gradient Descent (SGD), Optimization Algorithm, Overfitting, Underfitting, Global Minimum, Local Minimum.