class RNN(object):
def __init__(self, input_size, output_size):
super(RNN, self).__init__()
self.encoder = Encoder(input_size)
self.decoder = Decoder(output_size)
self.loss = nn.CrossEntropyLoss()
self.encoder_optimizer = optim.Adam(self.encoder.parameters())
self.decoder_optimizer = optim.Adam(self.decoder.parameters())
sos, eos = torch.LongTensor(1, 1).zero_(), torch.LongTensor(1,
1).zero_()
sos[0, 0], eos[0, 0] = 0, 1
self.sos, self.eos = sos, eos
def train(self, input, target):
self.encoder_optimizer.zero_grad()
self.decoder_optimizer.zero_grad()
hidden_state = self.encoder.first_hidden()
# Encoder
for ivec in input:
_, hidden_state = self.encoder.forward(Variable(ivec),
hidden_state)
# Decoder
target.insert(0, self.sos)
target.append(self.eos)
total_loss = 0
for i in range(len(target) - 1):
_, softmax, hidden_state = self.decoder.forward(
target[i], hidden_state)
total_loss += self.loss(softmax, Variable(target[i + 1][0]))
total_loss.backward()
self.decoder_optimizer.step()
self.encoder_optimizer.step()
return total_loss
def eval(self, input):
hidden_state = self.encoder.first_hidden()
# Encoder
for ivec in input:
_, hidden_state = self.encoder.forward(ivec, hidden_state)
outputs = []
output = self.sos
# Decoder
while output is not self.eos:
output, _, hidden_state = self.decoder.forward(
output, hidden_state)
outputs += output
return outputs
class RNN(object):
def __init__(self, input_size, output_size):
super(RNN, self).__init__()
self.encoder = Encoder(input_size)
self.decoder = Decoder(output_size)
self.loss = nn.CrossEntropyLoss()
self.encoder_optimizer = optim.Adam(self.encoder.parameters())
self.decoder_optimizer = optim.Adam(self.decoder.parameters())
sos, eos = torch.LongTensor(1, 1).zero_(), torch.LongTensor(1, 1).zero_()
sos[0, 0], eos[0, 0] = 0, 1
self.sos, self.eos = sos, eos
def train(self, input, target):
target.insert(0, self.sos)
target.append(self.eos)
self.encoder_optimizer.zero_grad()
self.decoder_optimizer.zero_grad()
# Encoder
hidden_state = self.encoder.first_hidden()
for ivec in input:
_, hidden_state = self.encoder.forward(Variable(ivec), hidden_state)
# Decoder
total_loss, outputs = 0, []
for i in range(len(target) - 1):
_, softmax, hidden_state = self.decoder.forward(Variable(target[i]), hidden_state)
outputs.append(np.argmax(softmax.data.numpy(), 1)[:, np.newaxis])
total_loss += self.loss(softmax, Variable(target[i+1][0]))
total_loss /= len(outputs)
total_loss.backward()
self.decoder_optimizer.step()
self.encoder_optimizer.step()
return total_loss.data[0], outputs # use total_loss.data[0] for version 0.3.0_4 and below, .item() for 0.4.0
def eval(self, input):
hidden_state = self.encoder.first_hidden()
# Encoder
for ivec in input:
_, hidden_state = self.encoder.forward(Variable(ivec), hidden_state)
sentence = []
input = self.sos
# Decoder
while input.data[0, 0] != 1:
output, _, hidden_state = self.decoder.forward(input, hidden_state)
word = np.argmax(output.data.numpy()).reshape((1, 1))
input = Variable(torch.LongTensor(word))
sentence.append(word)
return sentence
def save(self):
torch.save(self.encoder.state_dict(), "models/encoder.ckpt")
torch.save(self.decoder.state_dict(), "models/decoder.ckpt")
class RNN(object):
def __init__(self, input_size, output_size, resume=False):
super(RNN, self).__init__()
self.encoder = Encoder(input_size)
self.decoder = Decoder(output_size)
self.loss = nn.CrossEntropyLoss()
self.encoder_optimizer = optim.Adam(self.encoder.parameters())
self.decoder_optimizer = optim.Adam(self.decoder.parameters())
if resume:
self.encoder.load_state_dict(torch.load("models/encoder.ckpt"))
self.decoder.load_state_dict(torch.load("models/decoder.ckpt"))
def train(self, input, target):
self.encoder_optimizer.zero_grad()
self.decoder_optimizer.zero_grad()
# Encoder
hidden_state = self.encoder.first_hidden()
for ivec in input:
_, hidden_state = self.encoder.forward(ivec, hidden_state)
# Decoder
total_loss, outputs = 0, []
for i in range(len(target) - 1):
_, softmax, hidden_state = self.decoder.forward(
target[i], hidden_state)
outputs.append(np.argmax(softmax.data.numpy(), 1)[:, np.newaxis])
total_loss += self.loss(softmax, target[i + 1].squeeze(1))
total_loss /= len(outputs)
total_loss.backward()
self.decoder_optimizer.step()
self.encoder_optimizer.step()
return total_loss.data[0], outputs
def eval(self, input):
hidden_state = self.encoder.first_hidden()
# Encoder
for ivec in input:
_, hidden_state = self.encoder.forward(Variable(ivec),
hidden_state)
sentence = []
input = self.sos
# Decoder
while input.data[0, 0] != 1:
output, _, hidden_state = self.decoder.forward(input, hidden_state)
word = np.argmax(output.data.numpy()).reshape((1, 1))
input = Variable(torch.LongTensor(word))
sentence.append(word)
return sentence
def save(self):
torch.save(self.encoder.state_dict(), "models/encoder.ckpt")
torch.save(self.decoder.state_dict(), "models/decoder.ckpt")
