pytorch+lstm实现的pos示例

Python  /  管理员 发布于 5年前   238

学了几天终于大概明白pytorch怎么用了

这个是直接搬运的官方文档的代码

之后会自己试着实现其他nlp的任务

# Author: Robert Guthrieimport torchimport torch.autograd as autogradimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimtorch.manual_seed(1)lstm = nn.LSTM(3, 3) # Input dim is 3, output dim is 3inputs = [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 dimensioninputs = 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 stateout, 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 = 6HIDDEN_DIM = 6#继承自nn.moduleclass 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 traininginputs = 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)

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