第一步、导入需要的包
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import osimport scipy.io as sioimport numpy as npimport torchimport torch.nn as nnimport torch.backends.cudnn as cudnnimport torch.optim as optimfrom torch.utils.data import Dataset, DataLoaderfrom torchvision import transforms, utilsfrom torch.autograd import Variable |
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batchSize = 128 # batchsize的大小niter = 10 # epoch的最大值 |
第二步、构建神经网络
设神经网络为如上图所示，输入层4个神经元，两层隐含层各4个神经元，输出层一个神经。每一层网络所做的都是线性变换，即y=W×X+b；代码实现如下：
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class Neuralnetwork(nn.Module): def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim): super(Neuralnetwork, self).__init__() self.layer1 = nn.Linear(in_dim, n_hidden_1) self.layer2 = nn.Linear(n_hidden_1, n_hidden_2) self.layer3 = nn.Linear(n_hidden_2, out_dim) def forward(self, x): x = x.view(x.size(0), -1) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) return x model = Neuralnetwork(1*3, 4, 4, 1) print(model) # net architecture |
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Neuralnetwork( (layer1): Linear(in_features=3, out_features=4, bias=True) (layer2): Linear(in_features=4, out_features=4, bias=True) (layer3): Linear(in_features=4, out_features=1, bias=True)) |
第三步、读取数据
自定义的数据为demo_SBPFea.mat，是MATLAB保存的数据格式，其存储的内容如下：包括fea(1000*3)和sbp(1000*1)两个数组；fea为特征向量，行为样本数，列为特征宽度；sbp为标签
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class SBPEstimateDataset(Dataset): def __init__(self, ext='demo'): data = sio.loadmat(ext+'_SBPFea.mat') self.fea = data['fea'] self.sbp = data['sbp'] def __len__(self): return len(self.sbp) def __getitem__(self, idx): fea = self.fea[idx] sbp = self.sbp[idx] """Convert ndarrays to Tensors.""" return {'fea': torch.from_numpy(fea).float(), 'sbp': torch.from_numpy(sbp).float() } train_dataset = SBPEstimateDataset(ext='demo')train_loader = DataLoader(train_dataset, batch_size=batchSize, # 分批次训练 shuffle=True, num_workers=int(8)) |
整个数据样本为1000，以batchSize = 128划分，分为8份，前7份为104个样本，第8份则为104个样本。在网络训练过程中，是一份数据一份数据进行训练的
第四步、模型训练
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# 优化器，Adam optimizer = optim.Adam(list(model.parameters()), lr=0.0001, betas=(0.9, 0.999),weight_decay=0.004) scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.997) criterion = nn.MSELoss() # loss function if torch.cuda.is_available(): # 有GPU，则用GPU计算 model.cuda() criterion.cuda() for epoch in range(niter): losses = [] ERROR_Train = [] model.train() for i, data in enumerate(train_loader, 0): model.zero_grad()# 首先提取清零 real_cpu, label_cpu = data['fea'], data['sbp'] if torch.cuda.is_available():# CUDA可用情况下，将Tensor 在GPU上运行 real_cpu = real_cpu.cuda() label_cpu = label_cpu.cuda() input=real_cpu label=label_cpu inputv = Variable(input) labelv = Variable(label) output = model(inputv) err = criterion(output, labelv) err.backward() optimizer.step() losses.append(err.data[0]) error = output.data-label+ 1e-12 ERROR_Train.extend(error) MAE = np.average(np.abs(np.array(ERROR_Train))) ME = np.average(np.array(ERROR_Train)) STD = np.std(np.array(ERROR_Train)) print('[%d/%d] Loss: %.4f MAE: %.4f Mean Error: %.4f STD: %.4f' % ( epoch, niter, np.average(losses), MAE, ME, STD)) |
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[0/10] Loss: 18384.6699 MAE: 135.3871 Mean Error: -135.3871 STD: 7.5580[1/10] Loss: 17063.0215 MAE: 130.4145 Mean Error: -130.4145 STD: 7.8918[2/10] Loss: 13689.1934 MAE: 116.6625 Mean Error: -116.6625 STD: 9.7946[3/10] Loss: 8192.9053 MAE: 89.6611 Mean Error: -89.6611 STD: 12.9911[4/10] Loss: 2979.1340 MAE: 52.5410 Mean Error: -52.5279 STD: 15.0930[5/10] Loss: 599.7094 MAE: 22.2735 Mean Error: -19.9979 STD: 14.2069[6/10] Loss: 207.2831 MAE: 11.2394 Mean Error: -4.8821 STD: 13.5528[7/10] Loss: 189.8173 MAE: 9.8020 Mean Error: -1.2357 STD: 13.7095[8/10] Loss: 188.3376 MAE: 9.6512 Mean Error: -0.6498 STD: 13.7075[9/10] Loss: 186.8393 MAE: 9.6946 Mean Error: -1.0850 STD: 13.6332 |
以上这篇Pytorch 神经网络—自定义数据集上实现教程就是小编分享给大家的全部内容了，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/qq_21905401/article/details/82627402
