6、Multiple Dimension Input 处理多维特征的输入
B站视频教程传送门：PyTorch深度学习实践 - 处理多维特征的输入
6.1 Revision
我们先来回顾一下回归与分类：
差别： 主要在于输出值
回归（Regressiom）：y ∈ R
分类（Classification）：y ∈ { } 离散的集合
6.2 Diabetes Dataset 糖尿病数据集
如果我们安装过sklearn（python编程安装sklearn），其中就包含糖尿病数据集，可以进入该目录（D:\Software\Anaconda\Lib\site-packages\sklearn\datasets\data）下查看，如下图所示：
6.3 Logistic Regression Model 逻辑斯蒂回归模型
由于这里的 x不再是简简单单的一维，而是 8维，所以应该看成下方两个矩阵相乘：
6.4 Mini-Batch（N samples）
import torch class Liang(torch.nn.Module): def __init__(self): super(Liang, self).__init__() self.linear = nn.Linear(8, 1) self.sigmoid = torch.nn.Sigmoid() def forward(self, x): x = self.sigmoid(self.linear(x)) return x model = Liang()
6.5 Neural Network 神经网络
当输入8维，输出2维时：
self.linear = torch.nn.Linear(8, 2)
当输入8维，输出6维时：
self.linear = torch.nn.Linear(8, 6)
可以降维，可以升维，也可以一降（升）一升（降）：
6.6 Diabetes Prediction 糖尿病预测
X1~X8：病人相应的指标
Y：一年后病情是否加重（预测）
6.6.1 Prepare Dataset
import numpy as np xy = np.loadtxt('../data/diabetes.csv.gz', delimiter=',', dtype=np.float32) x_data = torch.from_numpy(xy[:, :-1]) # 所有行，除了最后一列 y_data = torch.from_numpy(xy[:, [-1]]) # 所有行，最后一列 转为矩阵而不是向量
6.6.2 Define Model
6.6.3 Construct Loss and Optimizer
criterion = torch.nn.BCELoss(reduction='mean') optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
6.6.4 Training Cycle
for epoch in range(100): # Forward y_pred = model(x_data) # This program has not use Mini-Batch for training. We shall talk about DataLoader later. loss = criterion(y_pred, y_data) print(epoch, loss.item()) # Backward optimizer.zero_grad() loss.backward() # Update optimizer.step()
6.6.5 Activate function
神经网络中激活函数的可视化：https://dashee87.github.io/deep%20learning/visualising-activation-functions-in-neural-networks/
PyTorch文档：https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity
6.6.5 完整代码
import torch import numpy as np import matplotlib.pyplot as plt xy = np.loadtxt('../data/diabetes.csv.gz', delimiter=',', dtype=np.float32) x_data = torch.from_numpy(xy[:, :-1]) # 所有行，除了最后一列 y_data = torch.from_numpy(xy[:, [-1]]) # 所有行，最后一列 转为矩阵而不是向量 class Liang(torch.nn.Module): def __init__(self): super(Liang, self).__init__() self.linear1 = torch.nn.Linear(8, 6) self.linear2 = torch.nn.Linear(6, 4) self.linear3 = torch.nn.Linear(4, 1) self.sigmoid = torch.nn.Sigmoid() # Sigmoid self.tanh = torch.nn.Tanh() def forward(self, x): x = self.tanh(self.linear1(x)) x = self.tanh(self.linear2(x)) x = self.sigmoid(self.linear3(x)) return x model = Liang() criterion = torch.nn.BCELoss(reduction='mean') optimizer = torch.optim.SGD(model.parameters(), lr=0.1) epoch_list = [] loss_list = [] for epoch in range(100): # Forward y_pred = model(x_data) loss = criterion(y_pred, y_data) print(epoch, loss.item()) epoch_list.append(epoch) loss_list.append(loss.item()) # Backward optimizer.zero_grad() loss.backward() # Update optimizer.step() plt.plot(epoch_list, loss_list) plt.xlabel('Epoch') plt.ylabel('Loss') plt.title('Tanh') plt.show()
