爱站程序员基地1 导入实验需要的包
import numpy as npimport torchfrom torch import nnfrom sklearn.model_selection import train_test_splitimport matplotlib.pyplot as pltfrom torch.utils.data import TensorDataset,DataLoader
2 创建数据
num_example,num_input = 10000,200x1 = torch.normal(-2,1,size = (num_example,num_input))y1 = torch.ones((num_example,1))x2 = torch.normal(2,1,size = (num_example,num_input))y2 = torch.zeros((num_example,1))x = torch.cat((x1,x2),dim = 0)y = torch.cat((y1,y2),dim = 0)train_x,test_x,train_y,test_y =train_test_split(x,y,shuffle = True,stratify = y,random_state= 1,test_size=0.3)print(train_x.shape)print(test_x.shape)
3 加载数据
batch_size = 50train_dataset = TensorDataset(train_x,train_y)train_iter = DataLoader(dataset = train_dataset,shuffle = True,batch_size = batch_size,num_workers = 0)test_dataset = TensorDataset(test_x,test_y)test_iter = DataLoader(dataset = test_dataset,shuffle = True,batch_size = batch_size,num_workers = 0)
# print(np.unique(y))# np.sum([ele.data==1 for ele in train_y.flatten() ])# np.sum([ele.data==0 for ele in train_y.flatten() ])
4 初始化参数
num_hidden,num_output = 256,1torch.set_default_tensor_type = torch.float32# w1 = torch.tensor(torch.normal(0,0.001,size = (num_hidden,num_input))).type(torch.float32)w1 = torch.tensor(torch.normal(0,0.001,size = (num_hidden,num_input))).type(torch.FloatTensor)b1 = torch.ones(1)# b1 = torch.ones(1,dtype = torch.float32)print(w1.dtype)print(b1.dtype)w2 = torch.tensor(torch.normal(0,0.001,size = (num_output,num_hidden)),dtype = torch.float32)b2 = torch.ones(1)params = [w1,w2,b1,b2]for param in params:param.requires_grad_(requires_grad=True)
5 定义激活函数、模型、优化器、损失函数
def ReLU(x):return torch.max(x,other = torch.tensor(0.0))loss = nn.BCEWithLogitsLoss()# loss = nn.BCELoss()def net(x):H1 = ReLU(torch.matmul(x,w1.t())+b1)H2 = torch.matmul(H1,w2.t())+b2return H2def SGD(params,lr):param.data -= lr*param.grad/batch_size
6 定义训练模型
def train(net ,train_iter,test_iter,lr,num_epochs,params):train_l,test_l = [],[]for epoch in range(num_epochs):train_l_sum,n = 0,0for x,y in train_iter:n += y.shape[0]y_pred = net(x)l = loss(y_pred,y)train_l_sum += l.item()if params is not None and params[0].grad is not None:for param in params:param.grad.data.zero_()l.backward()SGD(params,lr)train_l.append(train_l_sum/n)test_l_sum,n = 0,0for x,y in test_iter:n += y.shape[0]y_pred = net(x)l = loss(y_pred,y)test_l_sum += l.item()test_l.append(test_l_sum/n)print(\'epoch %d, train_loss %.6f,test_loss %.6f\'%(epoch+1, train_l[epoch],test_l[epoch]))return train_l,test_l
7 训练
lr,num_epochs = 0.01,100train_loss,test_loss =train(net ,train_iter,test_iter,lr,num_epochs,params)
8 可视化
x = np.linspace(0,len(train_loss),len(train_loss))plt.plot(x,train_loss,label=\"train_loss\",linewidth=1.5)plt.plot(x,test_loss,label=\"test_loss\",linewidth=1.5)plt.xlabel(\"epoch\")plt.ylabel(\"loss\")plt.legend()plt.show()
