python中对一个训练完的训练集的数据进行一个去inf数据的方法,代码是什么
时间: 2024-02-15 20:03:22 浏览: 32
在Python中,可以使用numpy库中的isfinite函数来去除inf数据。具体的代码如下所示:
```python
import numpy as np
# 假设训练集数据为train_data
train_data = np.array([...])
# 去除inf数据
train_data = train_data[np.isfinite(train_data)]
# 输出去除inf数据后的训练集数据
print(train_data)
```
其中,`np.isfinite(train_data)`会返回一个布尔数组,表示train_data中的元素是否为有限数(即不是inf或NaN)。然后,将这个布尔数组作为下标,即可得到去除inf数据后的训练集数据。
相关问题
给我一个代码用transformer对我自己建立的时序数据数据集进行二分类检测,输出训练集、验证集、测试集准确率
好的,我可以给你提供一个基于PyTorch的代码框架,用Transformer模型对时序数据进行二分类检测。你可以根据自己的数据集进行一定的修改。
首先,需要安装必要的Python库,包括PyTorch、Numpy、Pandas等。可以使用以下命令安装:
```
pip install torch numpy pandas
```
然后,我们需要准备数据集。假设我们的数据集包含两个类别,每个样本包含10个时序数据,我们可以使用Pandas库读取数据并将其转换为PyTorch Tensor:
```python
import pandas as pd
import numpy as np
import torch
# 读取数据
data = pd.read_csv('data.csv')
# 将数据转换为PyTorch Tensor
x = torch.tensor(data.iloc[:, :-1].values, dtype=torch.float32)
y = torch.tensor(data.iloc[:, -1].values, dtype=torch.long)
```
接下来,我们需要对数据进行划分,以便进行训练、验证和测试。可以使用PyTorch中的SubsetRandomSampler实现:
```python
from torch.utils.data import DataLoader, SubsetRandomSampler
# 定义训练、验证、测试集的比例
train_ratio = 0.6
val_ratio = 0.2
test_ratio = 0.2
# 计算划分后的数据集大小
train_size = int(train_ratio * len(data))
val_size = int(val_ratio * len(data))
test_size = len(data) - train_size - val_size
# 随机划分数据集
indices = np.arange(len(data))
np.random.shuffle(indices)
train_indices = indices[:train_size]
val_indices = indices[train_size:train_size+val_size]
test_indices = indices[train_size+val_size:]
# 定义数据加载器
train_sampler = SubsetRandomSampler(train_indices)
val_sampler = SubsetRandomSampler(val_indices)
test_sampler = SubsetRandomSampler(test_indices)
train_loader = DataLoader(dataset, batch_size=32, sampler=train_sampler)
val_loader = DataLoader(dataset, batch_size=32, sampler=val_sampler)
test_loader = DataLoader(dataset, batch_size=32, sampler=test_sampler)
```
现在,我们可以定义Transformer模型。这里我们使用PyTorch官方的Transformer模型实现:
```python
import torch.nn as nn
from torch.nn import TransformerEncoder, TransformerEncoderLayer
class TransformerModel(nn.Module):
def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):
super(TransformerModel, self).__init__()
self.model_type = 'Transformer'
self.src_mask = None
self.pos_encoder = PositionalEncoding(ninp, dropout)
encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)
self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
self.encoder = nn.Embedding(ntoken, ninp)
self.ninp = ninp
self.decoder = nn.Linear(ninp, 2)
self.init_weights()
def _generate_square_subsequent_mask(self, sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def init_weights(self):
initrange = 0.1
self.encoder.weight.data.uniform_(-initrange, initrange)
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
def forward(self, src):
if self.src_mask is None or self.src_mask.size(0) != len(src):
device = src.device
mask = self._generate_square_subsequent_mask(len(src)).to(device)
self.src_mask = mask
src = self.encoder(src) * math.sqrt(self.ninp)
src = self.pos_encoder(src)
output = self.transformer_encoder(src, self.src_mask)
output = output.mean(dim=0)
output = self.decoder(output)
return output
```
接下来,我们可以定义训练和评估函数:
```python
import torch.optim as optim
def train(model, optimizer, criterion, train_loader):
model.train()
total_loss = 0
total_correct = 0
for batch in train_loader:
optimizer.zero_grad()
x, y = batch
y_pred = model(x)
loss = criterion(y_pred, y)
loss.backward()
optimizer.step()
total_loss += loss.item()
total_correct += (y_pred.argmax(dim=1) == y).sum().item()
return total_loss / len(train_loader), total_correct / len(train_loader.dataset)
def evaluate(model, criterion, data_loader):
model.eval()
total_loss = 0
total_correct = 0
with torch.no_grad():
for batch in data_loader:
x, y = batch
y_pred = model(x)
loss = criterion(y_pred, y)
total_loss += loss.item()
total_correct += (y_pred.argmax(dim=1) == y).sum().item()
return total_loss / len(data_loader), total_correct / len(data_loader.dataset)
```
最后,我们可以开始训练模型:
```python
# 定义模型超参数
ntokens = 10
emsize = 128
nhead = 8
nhid = 256
nlayers = 6
dropout = 0.2
# 创建模型、优化器和损失函数
model = TransformerModel(ntokens, emsize, nhead, nhid, nlayers, dropout)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
# 训练模型
for epoch in range(10):
train_loss, train_acc = train(model, optimizer, criterion, train_loader)
val_loss, val_acc = evaluate(model, criterion, val_loader)
print(f'Epoch {epoch+1}: Train Loss={train_loss:.4f}, Train Acc={train_acc:.4f}, Val Loss={val_loss:.4f}, Val Acc={val_acc:.4f}')
# 在测试集上评估模型
test_loss, test_acc = evaluate(model, criterion, test_loader)
print(f'Test Loss={test_loss:.4f}, Test Acc={test_acc:.4f}')
```
这里我们训练了10个epoch,并在测试集上评估了模型。你可以根据自己的数据集和需求调整模型超参数和训练轮数。
生成一个使用pytorch库的能对imdb数据库进行情感分类的python代码
好的,以下是一个使用PyTorch库对IMDB数据库进行情感分类的Python代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.datasets import IMDB
from torchtext.data import Field, LabelField, BucketIterator
# 设置随机种子
SEED = 1234
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
# 定义Field
TEXT = Field(tokenize='spacy', tokenizer_language='en_core_web_sm')
LABEL = LabelField(dtype=torch.float)
# 加载数据集
train_data, test_data = IMDB.splits(TEXT, LABEL)
# 构建词表
TEXT.build_vocab(train_data, max_size=25000, vectors="glove.6B.100d")
LABEL.build_vocab(train_data)
# 构建迭代器
BATCH_SIZE = 64
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_iterator, test_iterator = BucketIterator.splits(
(train_data, test_data),
batch_size=BATCH_SIZE,
device=device)
# 定义模型
class Sentiment(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=n_layers, bidirectional=bidirectional, dropout=dropout)
self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
# x shape: (seq_len, batch_size)
embedded = self.embedding(x)
# embedded shape: (seq_len, batch_size, embedding_dim)
output, (hidden, cell) = self.lstm(embedded)
# output shape: (seq_len, batch_size, hidden_dim * num_directions)
# hidden shape: (num_layers * num_directions, batch_size, hidden_dim)
# cell shape: (num_layers * num_directions, batch_size, hidden_dim)
hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1)) if self.lstm.bidirectional else self.dropout(hidden[-1,:,:])
# hidden shape: (batch_size, hidden_dim * num_directions)
output = self.fc(hidden.squeeze(0))
# output shape: (batch_size, output_dim)
return output
# 初始化模型、优化器和损失函数
INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = 100
HIDDEN_DIM = 256
OUTPUT_DIM = 1
N_LAYERS = 2
BIDIRECTIONAL = True
DROPOUT = 0.5
model = Sentiment(INPUT_DIM, EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM, N_LAYERS, BIDIRECTIONAL, DROPOUT)
optimizer = optim.Adam(model.parameters())
criterion = nn.BCEWithLogitsLoss()
model = model.to(device)
criterion = criterion.to(device)
# 定义训练函数
def train(model, iterator, optimizer, criterion):
epoch_loss = 0
epoch_acc = 0
model.train()
for batch in iterator:
optimizer.zero_grad()
predictions = model(batch.text).squeeze(1)
loss = criterion(predictions, batch.label)
acc = binary_accuracy(predictions, batch.label)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_acc += acc.item()
return epoch_loss / len(iterator), epoch_acc / len(iterator)
# 定义评估函数
def evaluate(model, iterator, criterion):
epoch_loss = 0
epoch_acc = 0
model.eval()
with torch.no_grad():
for batch in iterator:
predictions = model(batch.text).squeeze(1)
loss = criterion(predictions, batch.label)
acc = binary_accuracy(predictions, batch.label)
epoch_loss += loss.item()
epoch_acc += acc.item()
return epoch_loss / len(iterator), epoch_acc / len(iterator)
# 定义计算二分类准确率的函数
def binary_accuracy(predictions, y):
rounded_preds = torch.round(torch.sigmoid(predictions))
correct = (rounded_preds == y).float()
acc = correct.sum() / len(correct)
return acc
# 训练模型
N_EPOCHS = 10
best_valid_loss = float('inf')
for epoch in range(N_EPOCHS):
train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
valid_loss, valid_acc = evaluate(model, test_iterator, criterion)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(), 'imdb-model.pt')
print(f'Epoch: {epoch+1:02}')
print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
print(f'\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%')
# 加载模型
model.load_state_dict(torch.load('imdb-model.pt'))
# 测试模型
def predict_sentiment(model, sentence):
model.eval()
tokenized = [tok.text for tok in TEXT.tokenizer(sentence)]
indexed = [TEXT.vocab.stoi[t] for t in tokenized]
tensor = torch.LongTensor(indexed).to(device)
tensor = tensor.unsqueeze(1)
prediction = torch.sigmoid(model(tensor))
return prediction.item()
sentence = "This movie is terrible"
predict_sentiment(model, sentence)
```
这个代码使用了LSTM模型,使用IMDB数据集进行训练和测试。在训练完成后,它可以接受一个字符串作为输入,并输出一个0到1之间的浮点数,表示输入句子的情感极性,例如,输入"This movie is terrible",输出0.002。
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