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| import torch | |
| import torch.nn as nn | |
| class RNN(nn.Module): | |
| def __init__(self, input_size, hidden_size, output_size): | |
| super(RNN, self).__init__() | |
| self.hidden_size = hidden_size | |
| self.i2h = nn.Linear(input_size + hidden_size, hidden_size) | |
| self.i2o = nn.Linear(input_size + hidden_size, output_size) | |
| self.softmax = nn.LogSoftmax(dim=1) | |
| def forward(self, input_tensor, hidden_tensor): | |
| combined = torch.cat((input_tensor, hidden_tensor), 1) | |
| hidden = self.i2h(combined) | |
| output = self.i2o(combined) | |
| output = self.softmax(output) | |
| return output, hidden | |
| def init_hidden(self): | |
| return torch.zeros(1, self.hidden_size) |