pytorch+lstm实现的pos示例

 # Author: Robert Guthrie import torch import torch.autograd as autograd import torch.nn as nn import torch.nn.functional as F import torch.optim as optim torch.manual_seed(1) lstm = nn.LSTM(3, 3) # Input dim is 3, output dim is 3 inputs = [autograd.Variable(torch.randn((1, 3))) for _ in range(5)] # make a sequence of length 5 # initialize the hidden state. hidden = (autograd.Variable(torch.randn(1, 1, 3)), autograd.Variable(torch.randn((1, 1, 3)))) for i in inputs: # Step through the sequence one element at a time. # after each step, hidden contains the hidden state. out, hidden = lstm(i.view(1, 1, -1), hidden) # alternatively, we can do the entire sequence all at once. # the first value returned by LSTM is all of the hidden states throughout # the sequence. the second is just the most recent hidden state # (compare the last slice of "out" with "hidden" below, they are the same) # The reason for this is that: # "out" will give you access to all hidden states in the sequence # "hidden" will allow you to continue the sequence and backpropagate, # by passing it as an argument to the lstm at a later time # Add the extra 2nd dimension inputs = torch.cat(inputs).view(len(inputs), 1, -1) hidden = (autograd.Variable(torch.randn(1, 1, 3)), autograd.Variable( torch.randn((1, 1, 3)))) # clean out hidden state out, hidden = lstm(inputs, hidden) #print(out) #print(hidden) #准备数据 def prepare_sequence(seq, to_ix): idxs = [to_ix[w] for w in seq] tensor = torch.LongTensor(idxs) return autograd.Variable(tensor) training_data = [ ("The dog ate the apple".split(), ["DET", "NN", "V", "DET", "NN"]), ("Everybody read that book".split(), ["NN", "V", "DET", "NN"]) ] word_to_ix = {} for sent, tags in training_data: for word in sent: if word not in word_to_ix: word_to_ix[word] = len(word_to_ix) print(word_to_ix) tag_to_ix = {"DET": 0, "NN": 1, "V": 2} # These will usually be more like 32 or 64 dimensional. # We will keep them small, so we can see how the weights change as we train. EMBEDDING_DIM = 6 HIDDEN_DIM = 6 #继承自nn.module class LSTMTagger(nn.Module): def __init__(self, embedding_dim, hidden_dim, vocab_size, tagset_size): super(LSTMTagger, self).__init__() self.hidden_dim = hidden_dim #一个单词数量到embedding维数的矩阵 self.word_embeddings = nn.Embedding(vocab_size, embedding_dim) #传入两个维度参数 # The LSTM takes word embeddings as inputs, and outputs hidden states # with dimensionality hidden_dim. self.lstm = nn.LSTM(embedding_dim, hidden_dim) #线性layer从隐藏状态空间映射到tag便签 # The linear layer that maps from hidden state space to tag space self.hidden2tag = nn.Linear(hidden_dim, tagset_size) self.hidden = self.init_hidden() def init_hidden(self): # Before we've done anything, we dont have any hidden state. # Refer to the Pytorch documentation to see exactly # why they have this dimensionality. # The axes semantics are (num_layers, minibatch_size, hidden_dim) return (autograd.Variable(torch.zeros(1, 1, self.hidden_dim)), autograd.Variable(torch.zeros(1, 1, self.hidden_dim))) def forward(self, sentence): embeds = self.word_embeddings(sentence) lstm_out, self.hidden = self.lstm(embeds.view(len(sentence), 1, -1), self.hidden) tag_space = self.hidden2tag(lstm_out.view(len(sentence), -1)) tag_scores = F.log_softmax(tag_space) return tag_scores #embedding维度，hidden维度，词语数量，标签数量 model = LSTMTagger(EMBEDDING_DIM, HIDDEN_DIM, len(word_to_ix), len(tag_to_ix)) #optim中存了各种优化算法 loss_function = nn.NLLLoss() optimizer = optim.SGD(model.parameters(), lr=0.1) # See what the scores are before training # Note that element i,j of the output is the score for tag j for word i. inputs = prepare_sequence(training_data[0][0], word_to_ix) tag_scores = model(inputs) print(tag_scores) for epoch in range(300): # again, normally you would NOT do 300 epochs, it is toy data for sentence, tags in training_data: # Step 1. Remember that Pytorch accumulates gradients. # We need to clear them out before each instance model.zero_grad() # Also, we need to clear out the hidden state of the LSTM, # detaching it from its history on the last instance. model.hidden = model.init_hidden() # Step 2. Get our inputs ready for the network, that is, turn them into # Variables of word indices. sentence_in = prepare_sequence(sentence, word_to_ix) targets = prepare_sequence(tags, tag_to_ix) # Step 3. Run our forward pass. tag_scores = model(sentence_in) # Step 4. Compute the loss, gradients, and update the parameters by # calling optimizer.step() loss = loss_function(tag_scores, targets) loss.backward() optimizer.step() # See what the scores are after training inputs = prepare_sequence(training_data[0][0], word_to_ix) tag_scores = model(inputs) # The sentence is "the dog ate the apple". i,j corresponds to score for tag j # for word i. The predicted tag is the maximum scoring tag. # Here, we can see the predicted sequence below is 0 1 2 0 1 # since 0 is index of the maximum value of row 1, # 1 is the index of maximum value of row 2, etc. # Which is DET NOUN VERB DET NOUN, the correct sequence! print(tag_scores)