!pip install -q jovian matplotlib numpy https://download.pytorch.org/whl/cpu/torch-1.1.0-cp37-cp37m-linux_x86_64.whl

import torch
import torch.nn.functional as F
import torch.optim as optim
import torch.nn as nn
from torch.nn import Parameter
import numpy as np

import matplotlib
%matplotlib inline
from matplotlib import pyplot as plt

import jovian
jovian.commit()

class RNN(nn.Module):
  
  def __init__(self,input_size, hidden_size, output_size):
    
    super(RNN,self).__init__()
    self.tanh = nn.Tanh()
    self.linear_x = nn.Linear(input_size,hidden_size)
    self.linear_h = nn.Linear(hidden_size,hidden_size)
    self.linear_y = nn.Linear(hidden_size,output_size)
    self.LogSoftmax = nn.LogSoftmax(dim=1)
    self.hidden_size = hidden_size
    
  def forward(self,x,hidden):
    hidden = self.tanh(self.linear_x(x)+self.linear_h(hidden))
    output = self.LogSoftmax(self.linear_y(hidden))
    return output,hidden
  
  def initHidden(self):
        return torch.zeros(1, self.hidden_size)