def forward(self, sentence):
"""
This function has two parts
1. Look up the embeddings for the words in the sentence.
These will be the inputs to the LSTM sequence model.
NOTE: At this step, rather than be a list of embeddings,
it should be a tensor of shape (len(sentence_idxs), 1, embedding_dim)
The 1 is for the mini-batch size. Don't get confused by it,
just make it that shape.
2. Now that you have your tensor of embeddings of shape (len(sentence_idxs, 1, embedding_dim)),
You can pass it through your LSTM.
Refer to the Pytorch documentation to see what the outputs are
3. Convert the outputs into the correct return type, which is a list of
embeddings of shape (1, embedding_dim)
NOTE: Make sure you are reassigning self.hidden_state to the new hidden state!!!
:param sentence A list of strs, the words of the sentence
"""
assert self.word_to_ix is not None, "ERROR: Make sure to set word_to_ix on \
the embedding lookup components"
inp = utils.sequence_to_variable(sentence, self.word_to_ix,
self.use_cuda)
# STUDENT
embeds = self.word_embeddings(inp)
embeds = embeds.view(len(sentence), 1, self.word_embedding_dim)
lstm_out, self.hidden = self.lstm(embeds, self.hidden)
num = lstm_out.size(0)
lstm_out = lstm_out.view(1, -1)
out_l = list(torch.chunk(lstm_out, num, dim=1))
return out_l
def forward(self, sentence):
"""
This function has two parts
1. Look up the embeddings for the words in the sentence.
These will be the inputs to the LSTM sequence model.
NOTE: At this step, rather than be a list of embeddings,
it should be a tensor of shape (len(sentence_idxs), 1, embedding_dim)
The 1 is for the mini-batch size. Don't get confused by it,
just make it that shape.
2. Now that you have your tensor of embeddings of shape (len(sentence_idxs, 1, embedding_dim)),
You can pass it through your LSTM.
Refer to the Pytorch documentation to see what the outputs are
3. Convert the outputs into the correct return type, which is a list of
embeddings of shape (1, embedding_dim)
NOTE: Make sure you are reassigning self.hidden_state to the new hidden state!!!
:param sentence A list of strs, the words of the sentence
"""
assert self.word_to_ix is not None, "ERROR: Make sure to set word_to_ix on \
the embedding lookup components"
inp = utils.sequence_to_variable(sentence, self.word_to_ix,
self.use_cuda)
# STUDENT
wordEmbedding = self.word_embeddings(inp)
wordEmbedding = wordEmbedding.expand(1,
wordEmbedding.size()[0],
wordEmbedding.size()[1])
output, hidden = self.lstm(wordEmbedding, self.hidden)
self.hidden = hidden
embeddings = []
for idx in range(len(sentence)):
embeddings.append(output[0, idx].expand(1, output.size()[2]))
return embeddings
def forward(self, sentence):
inp = utils.sequence_to_variable(sentence, self.word_to_ix,
self.use_cuda)
idxs = map(lambda w: self.word_to_ix[w], sentence)
tensor = torch.LongTensor(idxs)
inputs = ag.Variable(tensor)
embeds = self.word_embeddings(inputs)
lstm_out, self.hidden = self.lstm(embeds.view(len(sentence), 1, -1),
self.hidden)
return [i for i in lstm_out]
def forward(self, sentence):
"""
:param sentence A list of strings, the text of the sentence
:return A list of autograd.Variables, where list[i] is the
embedding of word i in the sentence.
NOTE: the Variables returned should be row vectors, that
is, of shape (1, embedding_dim)
"""
inp = utils.sequence_to_variable(sentence, self.word_to_ix, self.use_cuda)
embeds = [] # store each Variable in here
# STUDENT
# END STUDENT
return embeds
def forward(self, sentence):
"""
:param sentence A list of strings, the text of the sentence
:return A list of autograd.Variables, where list[i] is the
embedding of word i in the sentence.
NOTE: the Variables returned should be row vectors, that
is, of shape (1, embedding_dim)
"""
inp = utils.sequence_to_variable(sentence, self.word_to_ix, self.use_cuda)
embeds = [] # store each Variable in here
for i in inp:
embeds.append(self.word_embeddings(i))
return embeds
def forward(self, sentence):
"""
:param sentence A list of strings, the text of the sentence
:return A list of autograd.Variables, where list[i] is the
embedding of word i in the sentence.
NOTE: the Variables returned should be row vectors, that
is, of shape (1, embedding_dim)
"""
inp = utils.sequence_to_variable(sentence, self.word_to_ix,
self.use_cuda)
# STUDENT
# END STUDENT
embeds = self.word_embeddings(inp)
embeds = list(torch.chunk(embeds, embeds.size(0)))
return embeds
def forward(self, sentence):
"""
This function has two parts
1. Look up the embeddings for the words in the sentence.
These will be the inputs to the LSTM sequence model.
NOTE: At this step, rather than be a list of embeddings,
it should be a tensor of shape (len(sentence_idxs), 1, embedding_dim)
The 1 is for the mini-batch size. Don't get confused by it,
just make it that shape.
2. Now that you have your tensor of embeddings of shape (len(sentence_idxs, 1, embedding_dim)),
You can pass it through your LSTM.
Refer to the Pytorch documentation to see what the outputs are
3. Convert the outputs into the correct return type, which is a list of
embeddings of shape (1, embedding_dim)
NOTE: Make sure you are reassigning self.hidden_state to the new hidden state!!!
:param sentence A list of strs, the words of the sentence
"""
assert self.word_to_ix is not None, "ERROR: Make sure to set word_to_ix on \
the embedding lookup components"
inp = utils.sequence_to_variable(sentence, self.word_to_ix, self.use_cuda)
def forward(self, sentence):
"""
This function has two parts
1. Look up the embeddings for the words in the sentence.
These will be the inputs to the LSTM sequence model.
NOTE: At this step, rather than be a list of embeddings,
it should be a tensor of shape (len(sentence_idxs), 1, embedding_dim)
The 1 is for the mini-batch size. Don't get confused by it,
just make it that shape.
2. Now that you have your tensor of embeddings of shape (len(sentence_idxs, 1, embedding_dim)),
You can pass it through your LSTM.
Refer to the Pytorch documentation to see what the outputs are
3. Convert the outputs into the correct return type, which is a list of
embeddings of shape (1, embedding_dim)
NOTE: Make sure you are reassigning self.hidden_state to the new hidden state!!!
:param sentence A list of strs, the words of the sentence
"""
assert self.word_to_ix is not None, "ERROR: Make sure to set word_to_ix on \
the embedding lookup components"
inp = utils.sequence_to_variable(sentence, self.word_to_ix,
self.use_cuda)
embeds = ag.Variable(
torch.FloatTensor(len(inp), 1, self.word_embedding_dim))
for i in range(len(inp)):
embed = self.word_embeddings(inp[i])
embeds[i, 0, :] = embed
out, self.hidden = self.lstm(embeds, self.hidden)
#out=out.view(len(inp),1,self.hidden_dim)
#hout =self.hiddenlayer(out)
#self.hidden=out
return out
def forward(self, sentence):
inp = utils.sequence_to_variable(sentence, self.word_to_ix)
embeds = []
for v in inp:
embeds.append(self.word_embeddings(v))
return embeds
