Classification (Keras)
Last updated
Last updated
If you have selected the Classification learning block in the Create impulse page, a NN Classifier page will show up in the menu on the left. This page becomes available after you've extracted your features from your DSP block.
Tutorials
Want to see the Classification block in action? Check out our tutorials:
The basic idea is that a neural network classifier will take some input data, and output a probability score that indicates how likely it is that the input data belongs to a particular class.
So how does a neural network know what to predict? The neural network consists of a number of layers, each of which is made up of a number of neurons. The neurons in the first layer are connected to the neurons in the second layer, and so on. The weight of a connection between two neurons in a layer is randomly determined at the beginning of the training process. The neural network is then given a set of training data, which is a set of examples that it is supposed to predict. The network's output is compared to the correct answer and, based on the results, the weights of the connections between the neurons in the layer are adjusted. This process is repeated a number of times, until the network has learned to predict the correct answer for the training data.
A particular arrangement of layers is referred to as an architecture, and different architectures are useful for different tasks. This way, after a lot of iterations, the neural network learns; and will eventually become much better at predicting new data.
On this page, you can configure the model and the training process and, have an overview of your model performances.
Number of training cycles: Each time the training algorithm makes one complete pass through all of the training data with back-propagation and updates the model's parameters as it goes, it is known as an epoch or training cycle.
Learning rate: The learning rate controls how much the models internal parameters are updated during each step of the training process. Or you can also see it as how fast the neural network will learn. If the network overfits quickly, you can reduce the learning rate
Validation set size: The percentage of your training set held apart for validation, a good default is 20%
Auto-balance dataset Mix in more copies of data from classes that are uncommon. Might help make the model more robust against overfitting if you have little data for some classes.
Depending on your project type, we may offer to choose between different architecture presets to help you get started.
The neural network architecture takes as inputs your extracted features, and pass the features to each layer of your architecture. In the classification case, the last used layer is a softmax layer. It is this last layer that gives the probability of belonging to one of the classes.
From the visual (simple) mode, you can add the following layers:
If have advanced knowledge in machine learning and Keras, you can switch to the Expert Mode and access the full Keras API to use custom architectures:
This panel displays the output logs during the training. The previous training logs can also be retrieved from the Jobs tab in the Dashboard page (enterprise feature).
This section gives an overview of your model performances and helps you evaluate your model. It can help you determine if the model is capable of meeting your needs or if you need to test other hyper parameters and architectures.
From the Last training performances you can retrieve your validation accuracy and loss.
The Confusion matrix is one of most useful tool to evaluate a model. it tabulates all of the correct and incorrect responses a model produces given a set of data. The labels on the side correspond to the actual labels in each sample, and the labels on the top correspond to the predicted labels from the model.
The features explorer, like in the processing block views, indicated the spatial distribution of your input features. In this page, you can visualize which ones have been correctly classified and which ones have not.
On-device performance: Based on the target you chose in the Dashboard page, we will output estimations for the inferencing time, peak RAM usage and flash usage. This will help you validate that your model will be able to run on your device based on its constraints.
