Breast Cancer on PyTorch

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：

nn.Sequential

# 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


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 = nn.Sequential(
        nn.Linear(30, 60),
        nn.Sigmoid(),
        nn.Linear(60, 2),
        nn.ReLU(),
        nn.Softmax(dim=1)
    )

    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))

模型性能：

              precision    recall  f1-score   support

   malignant       0.91      0.91      0.91        54
      benign       0.94      0.94      0.94        89

    accuracy                           0.93       143
   macro avg       0.93      0.93      0.93       143
weighted avg       0.93      0.93      0.93       143