Please refer to the document classification. It talks about implementation of a Feed Forward Neural Network with a set of specifications.
For the same document classification problem, implement the following algorithms:
i. Vanilla RNN (Consider two hidden layers, and also use the other specifications as mentioned in Assignment-4 )
ii. Re-implement the Feed Forward Neural Network (FFN) of Assignment-4 by initializing the weights to the (near) optimal weights of the FFN of Assignment-4 (only tanh as the activation layer, and take the weights after the model converges).
iii. You have three models now: FFN of Assignment-4 with tanh as the activation function, FFN of (ii) above and the Vanilla RNN of (i) above. Form an ensemble by combining the decisions of all these models by majority voting. Create another version of the ensemble model by weighted voting, where weights can be the accuracy value of these models on the validation data (i.e. 10% of the data).
