Chapter 12: Training a Magic Wand Model

Model Architectures

Both the LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) models are designed for classifying gestures based on accelerometer data. Here's a quick comparison:

Architecture

  • CNN: This model uses two convolutional layers followed by max-pooling and dropout. Finally, it has a dense layer with softmax activation. It uses 2D convolutions, considering the sequence length and 3 accelerometer axes. Dropout layers help in reducing overfitting.
  • LSTM: The model uses a bidirectional LSTM followed by a dense layer with a sigmoid activation function. It takes sequence data over time and 3 accelerometer axes into account.

Strengths and Weaknesses

CNN

  • Strengths: CNNs are generally faster to train and can capture spatial features very effectively. They are good at recognizing local patterns.
  • Weaknesses: May not be as effective in capturing temporal dependencies or sequence-to-sequence patterns.

LSTM

  • Strengths: LSTMs are good at handling time-series data. They can capture long-term dependencies, which could be useful if the gestures are complex and involve a series of movements.
  • Weaknesses: Generally slower to train and more prone to overfitting compared to CNNs. They may also require more data for effective training.

Model Complexity

  • CNN: Here, you use a total of 2 convolution layers and 2 max-pooling layers followed by a dense layer, resulting in moderate complexity.
  • LSTM: You use a bidirectional LSTM layer, which makes this model computationally heavier than a standard LSTM.

Activation Functions

  • CNN: Uses ReLU (Rectified Linear Units) for hidden layers and softmax for the output layer.
  • LSTM: Uses sigmoid for the output layer.

Interpretability

  • CNN: Typically harder to interpret due to the convolution operations.
  • LSTM: May offer a bit more interpretability, especially because it models the data in sequences which can be easier to understand.

Summary

  • Use CNN if you're more concerned with training speed and the gestures are simple enough to be captured by spatial features.
  • Use LSTM if the gestures are complex, involve time dependencies, and you have enough computational resources for a potentially slower training process.