Breast Cancer on PyTorch
Code
# encoding:utf8
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import torch
import torch.nn as nn
import torch.optim as optim
from matplotlib import pyplot as plt
import numpy as np
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.l1 = nn.Linear(30, 60)
self.a1 = nn.Sigmoid()
self.l2 = nn.Linear(60, 2)
self.a2 = nn.ReLU()
self.l3 = nn.Softmax(dim=1)
def forward(self, x):
x = self.l1(x)
x = self.a1(x)
x = self.l2(x)
x = self.a2(x)
x = self.l3(x)
return x
if __name__ == '__main__':
breast_cancer = load_breast_cancer()
x_train, x_test, y_train, y_test = train_test_split(breast_cancer.data, breast_cancer.target, test_size=0.25)
x_train, x_test = torch.tensor(x_train, dtype=torch.float), torch.tensor(x_test, dtype=torch.float)
y_train, y_test = torch.tensor(y_train, dtype=torch.long), torch.tensor(y_test, dtype=torch.long)
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.005) # PyTorch suit to tiny learning rate
error = list()
for epoch in range(250):
optimizer.zero_grad()
y_pred = net(x_train)
loss = criterion(y_pred, y_train)
loss.backward()
optimizer.step()
error.append(loss.item())
y_pred = net(x_test)
y_pred = torch.argmax(y_pred, dim=1)
# it is necessary that drawing the loss plot when we fine tuning the model
plt.plot(np.arange(1, len(error)+1), error)
plt.show()
print(classification_report(y_test, y_pred, target_names=breast_cancer.target_names))
损失函数图像: