A data scientist uses SAS DLPy to build a text classification model using a recurrent neural network. Which sequence of steps correctly sets up the model?
Select an answer to reveal the explanation.
Short Explanation
You create a Model object in SAS DLPy, then add layers in order: InputLayer, Embedding, Recurrent, and OutputLayer. That's the standard DLPy setup for a recurrent text classifier.
Full Explanation
The correct setup is to create a Model object, then add InputLayer, Embedding, Recurrent, and OutputLayer. In SAS DLPy, the typical workflow is to instantiate a Model object (Model(conn=s)), then use add_layer() to add layers in sequence: InputLayer, Embedding, Recurrent (LSTM/GRU), and OutputLayer. The option using a Sequential model is wrong because 'Sequential model' is Keras terminology; SAS DLPy uses the Model class. The PROC HPNEURAL option is wrong because PROC HPNEURAL does not support ARCHITECTURE=RNN or TASK=CLASSIFY options. The CAS session option is wrong because cas.deepLearn.buildRNN() is not a standard CAS action name.