Written by Coursera Staff • Updated on

Learn more about what the Softmax activation function is, how it operates within deep learning neural networks, and how to determine if this function is the right choice for your data type.

The Softmax activation function is a function in deep learning algorithms that transforms—using multiclass classification rather than binary—the output of your algorithm into a format that is easier for you to understand. Softmax is typically used for classification problems where the algorithm utilizes probability values to tell you the likelihood of your input (image, audio, etc.) falling into different classes. To better understand this algorithm, let’s explore what deep learning is, the role activation functions play, and how the Softmax activation function works within this framework.

Deep learning refers to a specialized branch of machine learning that uses algorithms and other data to mimic the neural pathways in a human brain. These deep neural networks can identify complex patterns in large amounts of data by analyzing information through multiple layers of neurons, each performing different transformations on the data. Every transformation is essentially trying to figure out a new clue, and together, the neurons (also known as nodes) produce a final output. These layers include input layers, multiple hidden layers (each taking one step toward the solution), and an output layer that produces the final product in a given format.

The layering of deep learning neural networks is much denser than traditional machine learning models. It's more akin to your brain and contains hundreds or even thousands of layers to transform the data. A deep learning model's learning ability allows it to perform tasks such as image and speech recognition, natural language processing, social media algorithm refinement, health care innovation tasks, and more accurately than traditional machine learning models.

In its simplest form, activation functions in neural networks are a procedure for determining whether a neuron should activate. Each neuron is responsible for a specific data processing operation, so the activation function within the neuron becomes active if the input data is deemed pertinent. If it is, the activation function converts the data into an output, passing it on to the next layer.

Activation functions perform this task by introducing nonlinearity, a property of neural networks that is important to increase the accuracy of the predictions and allow for more complex analysis. You can think of activation functions as an assist between layers of neurons (nodes). The activation function calculates the combined value of different inputs from previous nodes and calculates a final output value, which acts as the input for the next layer. A mathematical transformation process accomplishes the aforementioned step that produces the output. Depending on your algorithm needs, you can choose between several activation functions, such as Tanh, Sigmoid, and Softmax.

The Softmax activation function processes and formats your output in a way that is easier to understand. In deep learning, you typically use the Softmax function when dealing with multiclass classification tasks. Multiclass classification is the process of assigning the likelihood that each of your data points will fall into a particular category or class. The goal of this type of algorithm is to correctly categorize the data. This means that if your model’s task is to classify input data into one of several categories, Softmax is a great choice.

Once data is processed through the activated neurons, the Softmax activation function translates the output data into a series of probabilities rather than a binary sequence. Each probability represents the likelihood that the input object is a part of each class. This involves several mathematical steps, including transforming and normalizing different input values throughout the neural network path. The output is generally an output vector of probabilities matching the number of classes in which you classify your data. Each value in the vector adds up to 1.0 and will represent how likely your data is to fall into each class or category.

When using Softmax for multiclass classification, your classes are mutually exclusive, which means the probability of each class, when combined, should add up to one.

Let’s say you are using a Softmax activation function to classify images into one of four categories: 1) fruits, 2) vegetables, 3) flowers, or 4) birds. You then show the algorithm a picture of a bird. If the algorithm works well, the output vector might look like: Softmax(output vector) = [0.005, 0.005, 0.02, 0.97]. This is saying that the algorithm thinks there’s a 97 percent chance your image is a bird, a 2 percent chance your image is a flower, a 0.05 percent chance that your image is a vegetable and a 0.05 percent chance your image is a fruit. In this example, birds are a very different category type than the others, so the algorithm will likely be confident in the classification.

If you showed a more ambiguous image for the categories, such as an image of a rose, your algorithm might output something like: Softmax(output vector) = [0.1, 0.1, 0.78, 0.02]. This is saying that the algorithm thinks there’s a 2 percent chance your image is a bird, a 78 percent chance your image is a flower, a 10 percent chance that your image is a vegetable and a 10 percent chance your image is a fruit.

You can choose between two variants of the Softmax activation function: full Softmax and candidate sampling.

For full Softmax, the Softmax algorithm produces a vector with probabilities for each class, as described above. For candidate sampling, the algorithm produces a probability for the full complement of samples with a positive label while only doing so for a random sample of the negative labels. This can reduce the computational load on your algorithm as it focuses on the more important classification aspects.

For example, if you were classifying animal images into different categories, an image of a hyena would only output probabilities within similar categories (wolf, coyote, hyena, etc.) and not produce probabilities for all options (such as frog, butterfly, etc.).

You should choose a Softmax activation approach if you have a clear photo with one object you want to classify into a singular category. Softmax is a great algorithm that bypasses many of the performance issues found with other algorithm types. If you have images with several objects or want to classify objects into more than one category, alternate approaches (such as multiple logistic regression approaches) may be more appropriate for your data.

Suppose you’re interested in pursuing a career involving artificial neural networks (ANNs). In that case, you can find work in several industries, as organizations throughout the economy are utilizing ANNs along with deep learning to advance their businesses and upgrade their customer service. You can choose from a variety of career paths within this field. Some examples, along with their average annual base salary in the US, include:

**Applied Scientist:**$137,894**Business Intelligence Developer:**$115,526**Computational Linguist:**$96,177**Data Scientist:**$121,055**Machine Learning Engineer:**$128,305**Research Scientist:**$126,940**Software Engineer:**$115,643**Test Engineer:**$98,486

**All annual base salary data is sourced from Glassdoor as of April 2024 and does not include additional pay, such as commission and benefits.*

On the Coursera learning platform, you can find courses related to deep learning, machine learning, and artificial intelligence. Designed by industry experts, the Deep Learning Specialization, provided by DeepLearning.AI, covers an exciting range of topics, expanding from neural networks to designing machine learning projects, which can help you build important professional and technical skills to begin a career in this area. Best of all, you can expand your knowledge of deep learning topics at your own pace and in the comfort of your own home.

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