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())