Iris Classification 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

      setosa       1.00      1.00      1.00        14
  versicolor       1.00      1.00      1.00        16
   virginica       1.00      1.00      1.00        20

    accuracy                           1.00        50
   macro avg       1.00      1.00      1.00        50
weighted avg       1.00      1.00      1.00        50