Wrap models in prediction interface to use in application

We need to conveniently call AI models from software applications to bring them to users and get their full benefit. Even though AI is making headlines it’s impact in many fields of business, industry, and medicine have been low relative to the investment leading to low Return on Investment (ROI). Part of the reason is

Train and compare models

Setting up model training as a function taking a model parameter allows us to try and compare different model architectures. Here we train a linear based model: model = sensor_classification.LinearModel(input_dim=X_train.shape[1] * X_train.shape[2], output_dim=len(np.unique(y_train))) val_df = sensor_classification.train_gesture_classification(model, X_train, X_val, y_train, y_val) And a convolution based model: model = sensor_classification.ConvModel(input_dim=(X_train.shape[1], X_train.shape[2]), output_dim=len(np.unique(train_y))) val_df = sensor_classification.train_gesture_classification(model, X_train, X_val,

Splitting data

Data collected for AI model training and testing models gets randomly split into Training, Validation, and Testing datasets. Visualization confirms or spots problems in the datasets. See test. See plot method. Training is used to adjust AI models first. Validation is used to check the model during training. Testing data is used after the model

Check gesture measurements for normal motion and clean training data

Beginning and end measurements aren’t showing the standard gesture measurements because it takes time to start the recording then start moving the smartphone recording device. This again emphasizes the importance of visualizing training data as many data collection systems will have non-standard artifacts of the data collection. We want to remove these extreme low or