def forward(self, track):
for note in track:
gate_input = torch.cat(note, self.state)
forget = F.sigmoid(self.forgetgate(gate_input))
self.state *= forget
inp = F.sigmoid(self.inputgate(gate_input))
candidates = F.tanh(self.cadidate_gen(note))
self.state += inp * candidates
output = F.relu(self.outputlayer1(self.state))
output = F.relu(self.outputlayer2(output))
return F.log_softmax(output)
def forward(self, sentence):
concatenated_output_state = []
word_indices = []
for word in sentence:
self.char_hidden_state = self.init_hidden(
) # Refresh hidden state, detaching it from the earlier sequence
character_indices = word[
1] # This has already been wrapped as a Torch.LongTensor
character_level_embeddings = self.character_embeddings(
character_indices
) # Use the tensor to index into the lookup table
output_lstm, self.char_hidden_state = self.character_lstm(
character_level_embeddings.view(len(sentence), 1,
EMBEDDING_DIM),
self.char_hidden_state)
concatenated_output_state.append(
output_lstm) # Append LSTM state to the list
word_indices.append(word[0])
concatenated_output_state = torch.unsqueeze(
concatenated_output_state,
0) # Convert to a tensor, add an extra first dimension
word_embeddings = self.word_embeddings(
torch.tensor(word_indices,
dtype=torch.long).view(len(sentence), 1,
EMBEDDING_DIM))
concatenated_characters_and_words = torch.cat(
(word_embeddings, concatenated_output_state),
len(list(word_embeddings.size())) -
1) # Concatenate the tensors along their last axis
lstm_output_state, self.hidden = self.word_lstm(
concatenated_characters_and_words, self.hidden)
tag_space = self.hidden2tag(lstm_output_state.view(
len(sentence),
-1)) # A Linear layer mapping from tag space to scores
tag_scores = F.log_softmax(
tag_space, dim=None
) # Softmax along all dimensions. Log_softmax is required for NLLLoss
return tag_scores
def forward(self, x): """Performs generator step on input""" return F.log_softmax(self.proj(x), dim=-1)