Pytorch 深度学习实践 第7讲

import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader

class DiabetesDataset(Dataset):
    def __init__(self, filepath):
        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
        self.len = xy.shape[0]
        self.x_data = torch.from_numpy(xy[:, :-1])
        self.y_data = torch.from_numpy(xy[:, [-1]])

    def __getitem__(self, item):
        return self.x_data[item], self.y_data[item]

    def __len__(self):
        return self.len


dataset = DiabetesDataset('diabetes.csv')
train_loader = DataLoader(dataset=dataset, batch_size=32, shuffle=True, num_workers=0)  # num_workers 多线程 影响运行速度


class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)
        self.linear2 = torch.nn.Linear(6, 4)
        self.linear3 = torch.nn.Linear(4, 1)
        self.relu = torch.nn.ReLU()
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x):
        x = self.relu(self.linear1(x))
        x = self.relu(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x


model = Model()

criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.Adam(model.parameters(), lr=0.1)

if __name__ == '__main__':
    for epoch in range(100):
        for i, data in enumerate(train_loader, 0):
            inputs, labels = data
            y_pred = model(inputs)
            loss = criterion(y_pred, labels)
            print(epoch, i, loss.item())

            loss.backward()

            optimizer.step()
            optimizer.zero_grad()

增加内容

1、将原始数据集分为训练集和测试集

2、对训练集进行批量梯度下降

3、评估测试集的准确率

import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split


raw_data = np.loadtxt('diabetes.csv', delimiter=',', dtype=np.float32)
X = raw_data[:, :-1]
y = raw_data[:, [-1]]
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, y, test_size=0.3)
Xtest = torch.from_numpy(Xtest)
Ytest = torch.from_numpy(Ytest)


class DiabetesDataset(Dataset):
    def __init__(self, data, label):
        self.len = data.shape[0]
        self.x_data = torch.from_numpy(data)
        self.y_data = torch.from_numpy(label)

    def __getitem__(self, item):
        return self.x_data[item], self.y_data[item]

    def __len__(self):
        return self.len


train_dataset = DiabetesDataset(Xtrain, Ytrain)
train_loader = DataLoader(dataset=train_dataset, batch_size=32, shuffle=True, num_workers=2)  # num_workers 多线程


class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)
        self.linear2 = torch.nn.Linear(6, 4)
        self.linear3 = torch.nn.Linear(4, 2)
        self.linear4 = torch.nn.Linear(2, 1)
        self.relu = torch.nn.ReLU()
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x):
        x = self.relu(self.linear1(x))
        x = self.relu(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        x = self.sigmoid(self.linear4(x))
        return x


model = Model()

criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)


def train(epoch):
    train_loss = 0
    count = 0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        y_pred = model(inputs)
        loss = criterion(y_pred, labels)
        train_loss += loss.item()
        loss.backward()

        optimizer.step()
        optimizer.zero_grad()
        count = i

    if epoch%2000 == 1999:
        print('train loss:', train_loss/count, end=',')


def test():
    with torch.no_grad():
        y_pred = model(Xtest)
        y_pred_label = torch.where(y_pred>=0.5, torch.tensor([1.0]), torch.tensor([0.0]))
        acc = torch.eq(y_pred_label, Ytest).sum().item() / Ytest.shape[0]
        print("test acc:", acc)


if __name__ == '__main__':
    for epoch in range(50000):
        print(epoch)
        train(epoch)
        if epoch%2000 == 1999:
            test()