- Increase number of units in the hidden layer
- Increase number of hidden layers
- Increase data set size
Change activation function to tanh
- Try all of the above
The answer is d. When I use a linear activation function, the deep neural network is realizing a linear combination of linear functions which leads to modeling only a linear function. Increasing the depth or number of hidden units does not help. The network is not complex enough to learn a sine function and is underfitting the data. Change the linear function to tanh to realize a non-linear decision boundary helps.