pytorch 交叉熵损失教程(3)-torch.nn.BCEL

1.2 torch.nn.NLLLoss
Binary Cross Entropy between the target and the input probabilities
(1)计算公式
计算N个标签的损失

将N个标签的损失求和或平均(默认是平均)

(2)输入
1)模型经过softmax后预测输出 ,shape(n_samples), dtype(torch.float)
2)真实标签,shape=(n_samples), dtype(torch.float)

(3)例子1

    >>> m = nn.Sigmoid()
    >>> loss = nn.BCELoss()
    >>> input = torch.randn(3, requires_grad=True)
    >>> target = torch.empty(3).random_(2)
    >>> output = loss(m(input), target)
    >>> output.backward()

In [66]: input
Out[66]: tensor([-1.2269,  1.3667,  0.0922], requires_grad=True)

In [67]: target
Out[67]: tensor([1., 1., 0.])

(4)例子2
data

from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
x = data['data']
y = data['target']


#数据缩放，平均0, 正态1
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x = sc.fit_transform(x)

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)

#
from torch.utils.data import TensorDataset, DataLoader
x_train = torch.Tensor(x_train) # transform to torch tensor
x_test = torch.Tensor(x_test)
y_train = torch.Tensor(y_train)
y_test = torch.Tensor(y_test)


train_dataset = TensorDataset(x_train,y_train) # create your datset
#test_dataset = TensorDataset(x_test,y_test)

train_loader = DataLoader(train_dataset,batch_size=32,shuffle=True) # create your dataloader
#test_loader = DataLoader(train_dataset) # create your dataloader

model

from torch import nn
from torch.nn import functional as F
class Net(nn.Module):
  def __init__(self,input_shape):
    super(Net,self).__init__()
    self.fc1 = nn.Linear(input_shape,128)
    self.fc2 = nn.Linear(128,128)
    self.fc3 = nn.Linear(128,1) 
  def forward(self,x):
    x = torch.relu(self.fc1(x))
    x = torch.relu(self.fc2(x))
    x = torch.sigmoid(self.fc3(x))
    return x

model = Net(x_train.shape[1])
optimizer = torch.optim.SGD(model.parameters(),lr=0.001)
#optimizer = torch.optim.Adam(model.parameters(),lr=0.001)
loss_fn = nn.BCELoss()

trainning

train_acc_list = []
val_acc_list = []
train_loss_list = []
val_loss_list = []

epochs=1000
for i in range(epochs):
  for j,(input,target) in enumerate(train_loader):
    #calculate output
    output = model(input) #shape(n_sample,1)
 
    #calculate loss
    loss = loss_fn(output,target.reshape(-1,1))

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

  with torch.no_grad():
    y_pred = model(x_test).reshape(-1)
    val_acc = (torch.round(y_pred) == np.round(y_test)).type(torch.float).mean()
    val_loss = loss_fn(y_pred.reshape(-1,1), y_test.reshape(-1,1))
   
    y_pred = model(x_train).reshape(-1)
    train_acc = (torch.round(y_pred) == np.round(y_train)).type(torch.float).mean()
    train_loss = loss_fn(y_pred.reshape(-1,1), y_train.reshape(-1,1))

    print(f'epoch:{i} train_acc:{train_acc:.3f}   train_loss: {train_loss:.3f} val_acc:{val_acc:.3f}  val_loss: {val_loss:.3f}')
    train_acc_list.append(train_acc)
    train_loss_list.append(train_loss)
    val_acc_list.append(val_acc)
    val_loss_list.append(val_loss)

analysis of the model

plt.title('Loss vs Epochs')
plt.xlabel('Epochs')
plt.ylabel('acc and loss')
plt.plot(train_acc_list,label='train_acc')
plt.plot(val_acc_list,label='test_acc')
plt.plot(train_loss_list,label='train_loss')
plt.plot(val_loss_list,label='test_loss')
plt.legend()
plt.show()

Figure_1.png