Similarly, as discussed earlier, tools like ActiVis, RNNVis, LSTMVis are also used after training to visualize and even compare different models. Some tools, like Embedding Projector, specialize in visualizing 2D and 3D embeddings produced by trained neural networks. They’re usually performed in the computer vision domain after training the model. There are techniques like attribution visualization, to regenerate an image with important regions highlighted, and feature visualization, to generate an entirely new image that supposedly is representative of the same class. There are many tools out there precisely for this (neptune.ai, weights, and biases, etc), we’ll discuss them in a moment.įor example, Deep View visualizes models using its own metrics for monitoring such as discriminability and density metrics, which help detect overfitting by simply observing the neuron density early in the training phase.Īnother tool, Deep Eyes, identifies stable and unstable layers and neurons so users can prune their models to speed up training. Using visualization while training is a good way to monitor and track model performance. Not only do these metrics describe a single model’s performance, but they’re also crucial for comparing multiple models at once. Representing a model through a set of numbers may seem a bit abstract, however, they help keep track of the model’s progress while training. Summary statistics such as loss, accuracy, and other measures of error which are computed every epoch, can be represented as a time-series over the model’s training course.
Similarly, in Convolutional Neural Networks, we can take a look at the learned filters to see what kind of image features the model has learned. Visualizing weights and biases could be worthwhile to understand what the model has learned. Parameters that are tweaked under the backpropagation phase of training fall under this category. This part also includes the computational graph that defines how a model would train, test, save to the disk and checkpoint after epoch iterations.Īll of this can help developers get a better grasp of what’s going on inside their model. It tells us how many layers there are, in what order they’re positioned, and more. The first and main thing you can visualize is the model architecture.
This group can also be extended further to include inquisitive consumers who hesitate to use ML-powered applications due to fear of privacy invasion.Ī couple of web-based JavaScript frameworks like ConvNetJS & TensorFlow.js have enabled developers to create highly interactive explorable explanations for models.
In the “Why” section, I mentioned how visualization could help new students to learn what machine learning is-this point stands true here as well. Such visualization tools are really useful for those who only wish to use pretrained models to get predictions for their own tasks. Visualization might be good for other stakeholders, maybe with some technical background, but mainly dealing with consuming the services of the model via an API.Įxamples include Activis, a visual analytics system developed by Facebook for their own engineers to explore in-house deployed neural networks. All these tools give users extended control over things like hyperparameter tuning, pruning unnecessary layers, and more, thus allowing their models to achieve better performance. Some famous tools which many practitioners already use include TensorBoard, DeepEyes, or Blocks. People who mainly focus on developing, experimenting with, and deploying models are the ones who will benefit the most from visualization. Who should use visualization?ĭata Scientists / Machine Learning Engineers This could seriously help to build intuition about how models work. Interactive platforms can be designed where users play around with multiple datasets and toggle parameters to observe the effects on the model’s intermediate states and outputs. Perhaps teaching is where visualization is most useful, for educating novice users about fundamental concepts of machine learning.
#VISUALIZE DECISION TREE PYTHON HOW TO#
How to Compare Machine Learning Models and Algorithms