batch的lstm
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# 导入相应的包import torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimimport torch.utils.data as Data torch.manual_seed(1) # 准备数据的阶段def prepare_sequence(seq, to_ix): idxs = [to_ix[w] for w in seq] return torch.tensor(idxs, dtype=torch.long) with open("/home/lstm_train.txt", encoding='utf8') as f: train_data = [] word = [] label = [] data = f.readline().strip() while data: data = data.strip() SP = data.split(' ') if len(SP) == 2: word.append(SP[0]) label.append(SP[1]) else: if len(word) == 100 and 'I-PRO' in label: train_data.append((word, label)) word = [] label = [] data = f.readline() word_to_ix = {}for sent, _ in train_data: for word in sent: if word not in word_to_ix: word_to_ix[word] = len(word_to_ix) tag_to_ix = {"O": 0, "I-PRO": 1}for i in range(len(train_data)): train_data[i] = ([word_to_ix[t] for t in train_data[i][0]], [tag_to_ix[t] for t in train_data[i][1]]) # 词向量的维度EMBEDDING_DIM = 128 # 隐藏层的单元数HIDDEN_DIM = 128 # 批大小batch_size = 10class LSTMTagger(nn.Module): def __init__(self, embedding_dim, hidden_dim, vocab_size, tagset_size, batch_size): super(LSTMTagger, self).__init__() self.hidden_dim = hidden_dim self.batch_size = batch_size self.word_embeddings = nn.Embedding(vocab_size, embedding_dim) # The LSTM takes word embeddings as inputs, and outputs hidden states # with dimensionality hidden_dim. self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True) # The linear layer that maps from hidden state space to tag space self.hidden2tag = nn.Linear(hidden_dim, tagset_size) def forward(self, sentence): embeds = self.word_embeddings(sentence) # input_tensor = embeds.view(self.batch_size, len(sentence) // self.batch_size, -1) lstm_out, _ = self.lstm(embeds) tag_space = self.hidden2tag(lstm_out) scores = F.log_softmax(tag_space, dim=2) return scores def predict(self, sentence): embeds = self.word_embeddings(sentence) lstm_out, _ = self.lstm(embeds) tag_space = self.hidden2tag(lstm_out) scores = F.log_softmax(tag_space, dim=2) return scores loss_function = nn.NLLLoss()model = LSTMTagger(EMBEDDING_DIM, HIDDEN_DIM, len(word_to_ix), len(tag_to_ix), batch_size)optimizer = optim.SGD(model.parameters(), lr=0.1) data_set_word = []data_set_label = []for data_tuple in train_data: data_set_word.append(data_tuple[0]) data_set_label.append(data_tuple[1])torch_dataset = Data.TensorDataset(torch.tensor(data_set_word, dtype=torch.long), torch.tensor(data_set_label, dtype=torch.long))# 把 dataset 放入 DataLoaderloader = Data.DataLoader( dataset=torch_dataset, # torch TensorDataset format batch_size=batch_size, # mini batch size shuffle=True, # num_workers=2, # 多线程来读数据) # 训练过程for epoch in range(200): for step, (batch_x, batch_y) in enumerate(loader): # 梯度清零 model.zero_grad() tag_scores = model(batch_x) # 计算损失 tag_scores = tag_scores.view(-1, tag_scores.shape[2]) batch_y = batch_y.view(batch_y.shape[0]*batch_y.shape[1]) loss = loss_function(tag_scores, batch_y) print(loss) # 后向传播 loss.backward() # 更新参数 optimizer.step() # 测试过程with torch.no_grad(): inputs = torch.tensor([data_set_word[0]], dtype=torch.long) print(inputs) tag_scores = model.predict(inputs) print(tag_scores.shape) print(torch.argmax(tag_scores, dim=2)) |
补充：PyTorch基础-使用LSTM神经网络实现手写数据集识别
看代码吧~
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import numpy as npimport torchfrom torch import nn,optimfrom torch.autograd import Variablefrom torchvision import datasets,transformsfrom torch.utils.data import DataLoader |
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# 训练集train_data = datasets.MNIST(root="./", # 存放位置 train = True, # 载入训练集 transform=transforms.ToTensor(), # 把数据变成tensor类型 download = True # 下载 )# 测试集test_data = datasets.MNIST(root="./", train = False, transform=transforms.ToTensor(), download = True ) |
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# 批次大小batch_size = 64# 装载训练集train_loader = DataLoader(dataset=train_data,batch_size=batch_size,shuffle=True)# 装载测试集test_loader = DataLoader(dataset=test_data,batch_size=batch_size,shuffle=True) |
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for i,data in enumerate(train_loader): inputs,labels = data print(inputs.shape) print(labels.shape) break |
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# 定义网络结构class LSTM(nn.Module): def __init__(self): super(LSTM,self).__init__()# 初始化 self.lstm = torch.nn.LSTM( input_size = 28, # 表示输入特征的大小 hidden_size = 64, # 表示lstm模块的数量 num_layers = 1, # 表示lstm隐藏层的层数 batch_first = True # lstm默认格式input（seq_len,batch,feature）等于True表示input和output变成（batch，seq_len，feature） ) self.out = torch.nn.Linear(in_features=64,out_features=10) self.softmax = torch.nn.Softmax(dim=1) def forward(self,x): # (batch,seq_len,feature) x = x.view(-1,28,28) # output:(batch,seq_len,hidden_size)包含每个序列的输出结果 # 虽然lstm的batch_first为True，但是h_n,c_n的第0个维度还是num_layers # h_n :[num_layers,batch,hidden_size]只包含最后一个序列的输出结果 # c_n:[num_layers,batch,hidden_size]只包含最后一个序列的输出结果 output,(h_n,c_n) = self.lstm(x) output_in_last_timestep = h_n[-1,:,:] x = self.out(output_in_last_timestep) x = self.softmax(x) return x |
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# 定义模型model = LSTM()# 定义代价函数mse_loss = nn.CrossEntropyLoss()# 交叉熵# 定义优化器optimizer = optim.Adam(model.parameters(),lr=0.001)# 随机梯度下降 |
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# 定义模型训练和测试的方法def train(): # 模型的训练状态 model.train() for i,data in enumerate(train_loader): # 获得一个批次的数据和标签 inputs,labels = data # 获得模型预测结果（64，10) out = model(inputs) # 交叉熵代价函数out（batch，C：类别的数量），labels（batch） loss = mse_loss(out,labels) # 梯度清零 optimizer.zero_grad() # 计算梯度 loss.backward() # 修改权值 optimizer.step() def test(): # 模型的测试状态 model.eval() correct = 0 # 测试集准确率 for i,data in enumerate(test_loader): # 获得一个批次的数据和标签 inputs,labels = data # 获得模型预测结果（64，10) out = model(inputs) # 获得最大值，以及最大值所在的位置 _,predicted = torch.max(out,1) # 预测正确的数量 correct += (predicted==labels).sum() print("Test acc:{0}".format(correct.item()/len(test_data))) correct = 0 for i,data in enumerate(train_loader): # 训练集准确率 # 获得一个批次的数据和标签 inputs,labels = data # 获得模型预测结果（64，10) out = model(inputs) # 获得最大值，以及最大值所在的位置 _,predicted = torch.max(out,1) # 预测正确的数量 correct += (predicted==labels).sum() print("Train acc:{0}".format(correct.item()/len(train_data))) |
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# 训练for epoch in range(10): print("epoch:",epoch) train() test() |
以上为个人经验，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/weixin_40939578/article/details/104462188
