Designing and training a CNN
In this recipe, we will be leveraging the following CNN architecture:
Figure 4.28: CNN architecture
The model presented in Figure 4.28 is a modified version of what Edge Impulse will propose when designing the neural network (NN). Our network has two 2D convolution layers with 8 and 16 output feature maps (OFMs), one dropout layer, and one fully connected layer, followed by a softmax activation.
The network’s input is the MFE feature extracted from the 1-s audio sample.
Getting ready
To get ready for this recipe, we need to understand how to design and train an ML model in Edge Impulse. Edge Impulse uses different ML frameworks for training depending on the chosen learning block. For a classification learning block, the framework employs TensorFlow with Keras. The model can be designed in two ways:
- Visual mode (simple mode): This is the quickest method performed through the user interface (UI). Edge Impulse...
