This week I migrated the existing 3D CNN model to TensorFlow. I translated the PyTorch code to TensorFlow, because I found that TensorFlow has better functionalities that are easier to work with. A part of this was getting the shape dimensions to work, because the Gent University dataset was 166 x 127 x 195 where the training data was much smaller. After this translation, I initially trained the model on the data that had been collected. The model reached a .99 validation accuracy. However, I wasn’t convinced by this model, because the vast majority it was training on (the data that we had collected) didn’t have humans. Therefore, Angie collected 1800 human and 1800 no human samples with a higher resolution. Then, I adjusted the shapes of the network to accommodate 128 x 32 x 8 and trained the network to a .99 validation accuracy and 1.00 F1 score. Additionally, this week the speaker the ambient temperature thermometer arrived. I have started getting those to work with the Raspberry Pi and plan on completing that process by tomorrow. Lastly, I worked on the final presentation slides.
My progress is on schedule.
Tomorrow, I will get the temperature sensor and speaker working with the Raspberry Pi. I will also be working with Ayesha to integrate our parts i.e. integrate the fronted end with my machine learning architecture. I will test the machine learning architecture on truly unseen data, which Angie has already collected.
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