The code is in Python 3

• Initialize parameters

• Run the optimization loop

  • Forward propagation to compute the loss function
  • Backward propagation to compute the gradients with respect to the loss function
  • Clip the gradients to avoid exploding gradients
  • Using the gradients, update your parameters with the gradient descent update rule.

• Return the learned parameters

• At each time-step, the RNN tries to predict what is the next character given the previous characters.

• The dataset X=(x⟨1⟩,x⟨2⟩,...,x⟨Tx⟩) is a list of characters in the training set.

• Y=(y⟨1⟩,y⟨2⟩,...,y⟨Tx⟩) is the same list of characters but shifted one character forward.

• At every time-step t, y⟨t⟩=x⟨t+1⟩. The prediction at time t is the same as the input at time t+1.

I used Sampling to generate new names, as shown in the figure .

We assume the model is trained and paas a dummy vector as input and generate a new name based on the parameters learned by the RNN.

The model is already implemented in model.py, just run it on command line

• Output The code outputs a set of names on every 2000th optimization loop along with the loss.
  

  • sample output

     Iteration: 34000, Loss: 22.447230
    
      Onyxipaledisons
      Kiabaeropa
      Lussiamang
      Pacaeptabalsaurus
      Xosalong
      Eiacoteg
      Troia