def translate(enc_input='thisissungkim.iloveyou.', predict_len=100, temperature=0.9):
input_var = str2tensor(enc_input)
encoder_hidden = encoder.init_hidden()
encoder_outputs, encoder_hidden = encoder(input_var, encoder_hidden)
hidden = encoder_hidden
predicted = ''
dec_input = str2tensor(SOS_token)
for c in range(predict_len):
output, hidden = decoder(dec_input, hidden)
# Sample from the network as a multi nominal distribution
output_dist = output.data.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
# Stop at the EOS
if top_i is EOS_token:
break
predicted_char = chr(top_i)
predicted += predicted_char
dec_input = str2tensor(predicted_char)
return enc_input, predicted
def translate(enc_input='thisissungkim.iloveyou.',
predict_len=100,
temperature=0.9):
input_var = str2tensor(enc_input)
encoder_hidden = encoder.init_hidden()
encoder_outputs, encoder_hidden = encoder(input_var, encoder_hidden)
hidden = encoder_hidden
predicted = ''
dec_input = str2tensor(SOS_token)
for c in range(predict_len):
output, hidden = decoder(dec_input, hidden)
# Sample from the network as a multi nominal distribution
output_dist = output.data.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
# Stop at the EOS
if top_i is EOS_token:
break
predicted_char = chr(top_i)
predicted += predicted_char
dec_input = str2tensor(predicted_char)
return enc_input, predicted
def train(src, target):
loss = 0
src_var = str2tensor(src)
target_var = str2tensor(target, eos=True) # Add the EOS token
encoder_hidden = encoder.init_hidden()
encoder_outputs, encoder_hidden = encoder(src_var, encoder_hidden)
hidden = encoder_hidden
context = cuda_variable(torch.zeros(1, decoder.hidden_size))
for c in range(len(target_var)):
# First, we feed SOS. Others, we use teacher forcing.
token = target_var[c - 1] if c else str2tensor(SOS_token)
output, context, hidden, attention = decoder(
token, context, hidden, encoder_outputs)
loss += criterion(output, target_var[c])
encoder.zero_grad()
decoder.zero_grad()
loss.backward()
optimizer.step()
return loss.data[0] / len(target_var)
def test():
encoder_hidden = encoder.init_hidden()
word_input = str2tensor('hello')
encoder_outputs, encoder_hidden = encoder(word_input, encoder_hidden)
print(encoder_outputs)
decoder_hidden = encoder_hidden
word_target = str2tensor('pytorch')
for c in range(len(word_target)):
decoder_output, decoder_hidden = decoder(word_target[c],
decoder_hidden)
print(decoder_output.size(), decoder_hidden.size())
def test():
encoder_hidden = encoder.init_hidden()
word_input = str2tensor('hello')
encoder_outputs, encoder_hidden = encoder(word_input, encoder_hidden)
print(encoder_outputs)
decoder_hidden = encoder_hidden
word_target = str2tensor('pytorch')
for c in range(len(word_target)):
decoder_output, decoder_hidden = decoder(
word_target[c], decoder_hidden)
print(decoder_output.size(), decoder_hidden.size())
def train(src, target):
src_var = str2tensor(src)
target_var = str2tensor(target, eos=True) # Add the EOS token
encoder_hidden = encoder.init_hidden()
encoder_outputs, encoder_hidden = encoder(src_var, encoder_hidden)
hidden = encoder_hidden
loss = 0
for c in range(len(target_var)):
# First, we feed SOS
# Others, we use teacher forcing
token = target_var[c - 1] if c else str2tensor(SOS_token)
output, hidden = decoder(token, hidden)
loss += criterion(output, target_var[c])
encoder.zero_grad()
decoder.zero_grad()
loss.backward()
optimizer.step()
return loss.data[0] / len(target_var)
def train(src, target):
src_var = str2tensor(src)
target_var = str2tensor(target, eos=True) # Add the EOS token
encoder_hidden = encoder.init_hidden()
encoder_outputs, encoder_hidden = encoder(src_var, encoder_hidden)
hidden = encoder_hidden
loss = 0
for c in range(len(target_var)):
# First, we feed SOS
# Others, we use teacher forcing
token = target_var[c - 1] if c else str2tensor(SOS_token)
output, hidden = decoder(token, hidden)
loss += criterion(output, target_var[c])
encoder.zero_grad()
decoder.zero_grad()
loss.backward()
optimizer.step()
return loss.data[0] / len(target_var)
