pytorch-07.处理多维特征的输入

import numpy as np
import torch

xy = np.loadtxt('diabetes.csv.gz',delimiter=',',dtype=np.float32)   # delimiter=',' : 分隔符为,
x_data = torch.from_numpy(xy[:,:-1])  #所有行 从第一列开始，最后一列不要
y_data = torch.from_numpy(xy[:,[-1]])  # 所有行 只要最后一列  []代表拿出来的是矩阵不是向量

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, 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()

    def forward(self,x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x

model = Model()

criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(),lr=0.1)

for epoch in range (10000):
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    print('epoch=', epoch, " loss=", loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()



#--------------------------------------------------------
# Test Model
x_test = torch.Tensor([[-0.29,0.48,0.18,-0.29,0,0,-0.53,-0.03]])
y_test = model(x_test)
print('y_pred=',y_test.data.item())

 测试一组数据：

把激活函数换成RELU：

import numpy as np
import torch

xy = np.loadtxt('diabetes.csv.gz',delimiter=',',dtype=np.float32)   # delimiter=',' : 分隔符为,
x_data = torch.from_numpy(xy[:,:-1])  #所有行 从第一列开始，最后一列不要
y_data = torch.from_numpy(xy[:,[-1]])  # 所有行 只要最后一列  []代表拿出来的是矩阵不是向量

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, 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()
        self.activate = torch.nn.ReLU()

    def forward(self,x):
        x = self.activate(self.linear1(x))
        x = self.activate(self.linear2(x))
        x = self.sigmoid(self.linear3(x))  #RELU，x小于0时的的y值都是0，算损失时有可能出现ln0
        return x

model = Model()

criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(),lr=0.1)

for epoch in range (10000):
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    print('epoch=', epoch, " loss=", loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()



#--------------------------------------------------------
# Test Model
x_test = torch.Tensor([[-0.29,0.48,0.18,-0.29,0,0,-0.53,-0.03]])
y_test = model(x_test)
print('y_pred=',y_test.data.item())