This sample code contains implementations of the models given in the YouTube-8M technical report.
LogisticModel: Linear projection of the output features into the label space, followed by a sigmoid function to convert logit values to probabilities.MoeModel: A per-class softmax distribution over a configurable number of logistic classifiers. One of the classifiers in the mixture is not trained, and always predicts 0.
LstmModel: Processes the features for each frame using a multi-layered LSTM neural net. The final internal state of the LSTM is input to a video-level model for classification. Note that you will need to change the learning rate to 0.001 when using this model.DbofModel: Projects the features for each frame into a higher dimensional 'clustering' space, pools across frames in that space, and then uses a video-level model to classify the now aggregated features.FrameLevelLogisticModel: Equivalent to 'LogisticModel', but performs average-pooling on the fly over frame-level features rather than using pre-aggregated features.