数据展示
时间序列数据也就是自变量是时间的一维数据，平时接触到的y= x, y = sinx等都是可以认为是时间序列数据。本次实验使用的是波形数据，可以认为不同形态的反射波形代表不同的类别。以下分别是两种类别的数据集，和四种类别的数据集，同一种颜色代表同一种类别。
模型搭建
采用PyTorch搭建网络模型，两层卷积层，卷积核大小为64，32。模型结构如下
由于pytorch的网络模型画网络结构不像tensorflow那么方便，需要转化为onnx模型，在用netron画出来，参考后面的源码。
源码
网络模型
import torch import torch.nn as nn from torch.utils.data import Dataset class CNNnet(nn.Module): def __init__(self, *, inputLength = 80, kernelSize = 3, kindsOutput = 4): super().__init__() filterNum1 = 64 filterNum2 = 32 self.layer1 = nn.Sequential( nn.Conv1d(1, filterNum1, kernelSize), # inputLength - kernelSize + 1 = 80 - 3 + 1 = 78 nn.BatchNorm1d(filterNum1), nn.ReLU(inplace=True), nn.MaxPool1d(kernelSize, stride = 1) # 78 - 3 + 1 = 76 ) self.layer2 = nn.Sequential( nn.Conv1d(filterNum1, filterNum2, kernelSize), # 76 - 3 + 1 = 74 nn.BatchNorm1d(filterNum2), nn.ReLU(inplace=True), nn.MaxPool1d(kernelSize, stride = 1) # 74 - 3 + 1 = 72 ) self.dropout = nn.Dropout(0.2) self.fc = nn.Linear(filterNum2 * (inputLength - 8), kindsOutput) def forward(self,x): x = x.to(torch.float32) x = self.layer1(x) x = self.layer2(x) x = x.view(x.size(0), -1) x = self.fc(x) x = self.dropout(x) return x class DatasetOfDiv(Dataset): def __init__(self, data_features, data_target): self.len = len(data_features) self.features = torch.from_numpy(data_features) self.target = torch.from_numpy(data_target) def __getitem__(self, index): return self.features[index], self.target[index] def __len__(self): return self.len
请注意，这里的DatasetOfDiv是需要为自己的数据集，继承Dataset这个类来实现。
模型训练
def train(trainData, trainLabel, *, savePath='..\models\pytorch', modelName = 'model.pt', epochs = 100, batchSize = 4, classNum = 4): trainFeatures, trainTarget, testFeatures, testTarget = datasetSplit(trainData, trainLabel) print('trainFeatures shape:', trainFeatures.shape, '\ttestFeatures shape:', testFeatures.shape) trainSet = DatasetOfDiv(trainFeatures, trainTarget) trainLoader = DataLoader(dataset=trainSet, batch_size=batchSize, shuffle=True, drop_last=True) model = CNNnet(inputLength=trainFeatures.shape[1], kindsOutput = classNum) # criterion = nn.MSELoss() criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) model.train() start_time = time.time() for epoch in range(epochs): for seq, y_train in trainLoader: # 每次更新参数前都梯度归零和初始化 # sampleSize = seq.shape[0] optimizer.zero_grad() # 注意这里要对样本进行reshape，转换成conv1d的(batch size, channel, series length) # y_pred = model(seq.reshape(sampleSize, 1, -1)) y_pred = model(seq.reshape(batchSize, 1, -1)) y_train = y_train.long() loss = criterion(y_pred, y_train) loss.backward() optimizer.step() # compute test accuracy _y_pred = model(torch.from_numpy(testFeatures).reshape(testFeatures.shape[0], 1, -1)) y_pred = torch.max(_y_pred, 1)[1] numCorrect = (y_pred.data.numpy() == testTarget).astype(int).sum() numOfTestSample = testTarget.size accuracy = float(numCorrect)/numOfTestSample print(f'Epoch: \t{epoch+1} \t Accuracy: {accuracy:.2f} \t Loss: {loss.item():.5f} \ \t NumOfTestSample:{numOfTestSample} \t numOfPredictCorrect:{numCorrect}'.replace(" ","")) print(f'\nDuration: {time.time() - start_time:.0f} seconds') # torch.save(model.state_dict(), savePath + '\\' + modelName) # torch.save(model, savePath + '\\' + modelName) torch.onnx.export( model, torch.randn(5, 1, trainFeatures.shape[1]), savePath + '\\' + 'model.onnx', export_params=True, # opset_version=8, ) return model
模型测试
def testModelEval(self, modelPath, trainData, trainLabel, *, classNum = 4): model = CNNnet(inputLength = trainData.shape[1], kindsOutput = classNum) model.load_state_dict(torch.load(modelPath)) model.eval() testData = trainData _eval_result = model(torch.from_numpy(testData).reshape(testData.shape[0], 1, -1)) eval_result = torch.max(_eval_result, 1)[1] result = eval_result.data.numpy() predErrNum = result.size - result[trainLabel==result].size print('sum:', result.size, '\tpredErrNum:', predErrNum)
使用演示
def main(): filePath = '\your\data\path' trainData, trainLabel = getYourData(filePath) #getYourData是你自己的数据解析函数 train(trainData,trainLabel) ... if __name__ == '__main__': main()
enjoy~
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