I am trying to figure out if I can use fastai for my problem.
I am trying to classify sequences of floats. Each sequence is a vector of 24 floats. In principle, item 0 in the vector effects item 1, which effects item 2, etc., so an LSTM is of interest. I am open treating the data as a non-sequence and modeling with some kind of 1d CNN as well. I would like to be able to be able to predict (binary classification) the label for each vector I pass in to the model.
Can fastai support this kind of model? I have a LSTM trained in keras on this data that performs well, but I need to use torch or fastai for a variety of reasons. The architecture looks like this:
model = Sequential()
model.add(Bidirectional(LSTM(32, input_shape=(n_timesteps,n_features))))
model.add(Dropout(0.5))
model.add(Dense(12, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_outputs, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
I’ve used fastai a bunch for image and text classification, but I can’t figure out how to formulate this problem in fastai. I would appreciate any ideas. Thank you!