78 lines
3.1 KiB
Python
78 lines
3.1 KiB
Python
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import torch
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from torch import nn
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device = 'cuda'
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import torch.nn.functional as F
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import torch.nn.init as init
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from lang import SOS_token, EOS_token
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class EncoderRNN(nn.Module):
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def __init__(self, input_size, hidden_size):
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super(EncoderRNN, self).__init__()
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self.hidden_size = hidden_size
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self.embedding = nn.Embedding(input_size, hidden_size)
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self.lstm = nn.LSTM(hidden_size, hidden_size)
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def forward(self, input, hidden):
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embedded = self.embedding(input).view(1, 1, -1)
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output = embedded
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output, hidden = self.lstm(output, hidden)
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return output, hidden
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def initHidden(self):
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return (torch.zeros(1, 1, self.hidden_size, device=device), torch.zeros(1, 1, self.hidden_size, device=device))
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class DecoderRNN(nn.Module):
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def __init__(self, hidden_size, output_size):
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super(DecoderRNN, self).__init__()
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self.hidden_size = hidden_size
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self.embedding = nn.Embedding(output_size, hidden_size)
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self.lstm = nn.LSTM(hidden_size, hidden_size)
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self.out = nn.Linear(hidden_size, output_size)
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self.softmax = nn.LogSoftmax(dim=1)
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def forward(self, input, hidden):
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output = self.embedding(input).view(1, 1, -1)
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output = F.relu(output)
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output, hidden = self.lstm(output, hidden)
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output = self.softmax(self.out(output[0]))
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return output, hidden
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def initHidden(self):
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return (torch.zeros(1, 1, self.hidden_size, device=device), torch.zeros(1, 1, self.hidden_size, device=device))
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class AttnDecoderRNN(nn.Module):
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def __init__(self, hidden_size, output_size, dropout_p=0.1, max_length=300):
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super(AttnDecoderRNN, self).__init__()
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self.hidden_size = hidden_size
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self.output_size = output_size
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self.dropout_p = dropout_p
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self.max_length = max_length
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self.embedding = nn.Embedding(self.output_size, self.hidden_size)
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self.attn = nn.Linear(self.hidden_size, self.max_length)
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self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)
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self.dropout = nn.Dropout(self.dropout_p)
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self.lstm = nn.LSTM(self.hidden_size, self.hidden_size)
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self.out = nn.Linear(self.hidden_size, self.output_size)
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def forward(self, input, hidden, encoder_outputs):
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embedded = self.embedding(input).view(1, 1, -1)
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embedded = self.dropout(embedded)
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attn_weights = F.softmax(
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self.attn(torch.cat((embedded, hidden[0]), 1)), dim=1)
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attn_applied = torch.bmm(attn_weights.unsqueeze(0),
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encoder_outputs.unsqueeze(0))
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output = torch.cat((embedded[0], attn_applied[0]), 1)
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output = self.attn_combine(output).unsqueeze(0)
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output = F.relu(output)
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output, hidden = self.lstm(output, hidden)
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output = F.log_softmax(self.out(output[0]), dim=1)
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print(output.shape, hidden.shape)
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return output, hidden, attn_weights
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def initHidden(self):
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return (torch.zeros(1, 1, self.hidden_size, device=device), torch.zeros(1, 1, self.hidden_size, device=device))
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