124 lines
3.6 KiB
Python
Executable File
124 lines
3.6 KiB
Python
Executable File
#!/usr/bin/python3
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# https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html
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import sys
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import torch
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from torch import nn, optim
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nb_of_char_codes = 128 + 2
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SOS_token_id = 128 # start of sentence
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EOS_token_id = 129 # end of sentence
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MAX_LENGTH = 20
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hidden_size = 32
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step = 200
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device = torch.device('cpu')
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f = open('eng-fra.txt')
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def char_source():
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for line in f:
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s, t = line.rstrip('\n').split('\t')
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s_list = []
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t_list = []
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for c in s:
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c_code = ord(c)
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if c_code < nb_of_char_codes:
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s_list.append(ord(c))
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for c in t:
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c_code = ord(c)
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if c_code < nb_of_char_codes:
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t_list.append(ord(c))
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yield s_list, t_list
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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.gru = nn.GRU(hidden_size, hidden_size)
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def forward(self, input, hidden):
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embedded = self.embedding(input)
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output = embedded
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output, hidden = self.gru(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)
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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.gru = nn.GRU(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)
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output = torch.nn.functional.relu(output)
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output, hidden = self.gru(output, hidden)
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output = self.softmax(self.out(output[0]))
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return output, hidden
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encoder = EncoderRNN(nb_of_char_codes, hidden_size).to(device)
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decoder = DecoderRNN(hidden_size, nb_of_char_codes).to(device)
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criterion = nn.NLLLoss().to(device)
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optimizer = optim.Adam((list(encoder.parameters()) + list(decoder.parameters())))
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counter = 0
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losses = []
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for s,t in char_source():
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counter += 1
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encoder.zero_grad()
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decoder.zero_grad()
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x = torch.tensor(s, dtype=torch.long, device=device)
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encoder_hidden = encoder.initHidden()
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encoder_output = torch.zeros(MAX_LENGTH, hidden_size, device=device)
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for i in range(x.shape[0]):
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output, encoder_hidden = encoder(x[i].unsqueeze(0).unsqueeze(0), encoder_hidden)
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encoder_output[i] = output[0,0]
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decoder_hidden = encoder_hidden
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decoder_input = torch.tensor([[SOS_token_id]], device=device)
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t.append(EOS_token_id)
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y = torch.tensor(t, dtype=torch.long, device=device)
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loss = 0
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output_string = ''
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for di in range(y.shape[0]):
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decoder_output, decoder_hidden = decoder(
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decoder_input, decoder_hidden)
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topv, topi = decoder_output.topk(1)
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decoder_input = topi.detach() # detach from history as input
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output_string += chr(topi)
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loss += criterion(decoder_output, y[di].unsqueeze(0))
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if chr(topi) == EOS_token_id:
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break
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losses.append(loss.item())
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if counter % step == 0:
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# print(counter, end='\t')
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avg_loss = sum(losses)/len(losses)
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print(f"{counter}: {avg_loss}")
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losses = []
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print('IN :\t', ''.join([chr(a) for a in s]))
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print('EXP:\t', ''.join([chr(a) for a in t]))
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print('OUT:\t', output_string)
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loss.backward()
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optimizer.step()
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