Open KatJHuang opened 5 years ago
Currently following tutorial at https://cloud.google.com/ml-engine/docs/tensorflow/getting-started-training-prediction for an introduction to ML engine.
So far have done:
ece496emotionrecogntion
Observations
Next Steps
Further readings: https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/census/
Google ML Engine provides an ensemble of utilities that handles tasks from training to prediction. It also talks with other Google services: Cloud Function, Cloud Storage we employ for manipulating and storing our data.
Since we already have a trained model, the most pressing need is to deploy the model and relay the result to the Android app. Automated training using existing frameworks is then a desired feature but not immediately necessary.Acceptance Criteria GIVEN an architecture of a model WHEN I supply a training set THEN ML Engine trains the model and saves the model parameters
GIVEN an audio file and a trained model stored on Cloud Storage WHEN I signal for a prediction for the audio THEN ML engine returns me the prediction result
Update Prof Rose hinted at the peril of training and testing with different datasets in his comment to the project proposal, and suggested we obtain our own labelled datasets. Using additional datasets makes it necessary to automate the training process, as manually uploading from local training is prone to error and hard to version control. Therefore we should use Google ML Engine for training as well.