It will soon be important to approach the customization of models, optimizers, loss functions, layers, and other essential algorithmic elements as a whole as we continue on our trip into the realm of machine learning and deep learning. Numerous pre-implemented and optimized loss functions that are simple to call up in the working environment are available in Tensorflow and Keras. To completely meet our desire for model characterization, it could be required to design individualized and original loss functions.
What are Loss Functions?
The Loss Function is a function that establishes the relationship between a set of values and a real number in the fields of mathematical optimization, statistics, machine learning, and deep learning. That sum conceptually denotes the expense related to an occasion or a collection of items. An optimization procedure's general objective is to minimize the loss function.
It is one of the fundamental components of a machine-learning model. You must have heard of it if you've worked in data science for a while. Loss functions often referred to as cost functions, are certain sorts of functions that assist us in reducing mistakes and producing results that are as close to the expected as feasible. When employing backpropagation in deep learning, the loss is computed to obtain the gradients for the model weights and update those weights appropriately.
The error may be defined as the discrepancy between the actual value and the expected value. This may be expressed as the following equation:
Therefore, our objective is to reduce the discrepancy between the projected value, h(x), and the actual value, y. In other words, you need to reduce the cost function's value.
One simple explanation is that a machine learning model's objective is to maximize the evaluation metric while minimizing the cost. This may be accomplished by employing an algorithm, such as Gradient Descent, to update the weights of a machine-learning model.
Here, you can examine the cost function and the weight of a machine learning model that is now being updated.
Custom Loss Function in Tensorflow
Let's begin with a little example to further understand why a custom loss function is important. Your dataset contains too much noise to allow you to train a regressor on it. The next step will likely be data preparation (cleaning the data, eliminating or repairing outliers, etc.), but let's say this step is insufficient. Your neural network topology has been established once your data has been loaded, normalized, and divided; however, you still need to build your model and set an appropriate optimizer and loss function.
The training might be overly lengthy and imprecise because the mean squared error (mse) penalizes outliers too harshly while the mean absolute error (MAE) does not punish them harshly enough. We may be able to come up with a fully unique loss function for this application that is not offered by the Keras API. The Huber loss could work well for this purpose. This loss function is already implemented (tf.keras.losses.Huber), but let's build a fully customized version of it.
We can see that all we need to do is define the parameters for our unique loss function: the actual and anticipated values of the outputs from our model. The Tensorflow syntax only needs these two parameters, though. With the suggested workaround—a double function with the exterior one yielding the inside method—we may create a parametric loss function. This enables us to add new characteristics to this range, such as establishing a Huber loss function threshold!
Depending on how well our algorithm works, we might: prefer to implement our function vectorized. To take advantage of TensorFlow's graph features, exclusively utilize TensorFlow operations. Instead of returning the mean loss, return a Tensor with one loss per instance. This makes it possible for Keras to use class/sample weights as needed.
A unique accuracy loss for a regression issue can be described as:
Here in this blog we reviewed the fundamental ideas behind loss functions, demonstrated how to design a fully customized loss function in Tensorflow for two separate issues, and provided an example application. We also saw several hints for maximizing the effectiveness of our solution.