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 and Infographic
Here's the deal — b is correct because in SAS DLPy, the typical workflow is: instantiate a Model object (Model(conn=s)), then use add_layer() to add layers in sequence — InputLayer, Embedding, Recurrent (LSTM/GRU), and OutputLayer. A uses 'Sequential model' which is Keras terminology; SAS DLPy uses the Model class.
Full explanation below image
Full Explanation
B is correct because in SAS DLPy, the typical workflow is: instantiate a Model object (Model(conn=s)), then use add_layer() to add layers in sequence — InputLayer, Embedding, Recurrent (LSTM/GRU), and OutputLayer. A uses 'Sequential model' which is Keras terminology; SAS DLPy uses the Model class. C is wrong because PROC HPNEURAL does not support ARCHITECTURE=RNN or TASK=CLASSIFY options. D is wrong because cas.deepLearn.buildRNN() is not a standard CAS action name.