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PyTorch深度学习实践——加载数据集

说明：1、DataSet 是抽象类，不能实例化对象，主要是用于构造我们的数据集

2、DataLoader 需要获取DataSet提供的索引[i]和len;用来帮助我们加载数据，比如说做shuffle(提高数据集的随机性)，batch_size,能拿出Mini-Batch进行训练。它帮我们自动完成这些工作。DataLoader可实例化对象。DataLoader is a class to help us loading data in Pytorch.

3、__getitem__目的是为支持下标(索引)操作

代码说明：

1、需要mini_batch 就需要import DataSet和DataLoader

2、继承DataSet的类需要重写init，getitem,len魔法函数。分别是为了加载数据集，获取数据索引，获取数据总量。

3、DataLoader对数据集先打乱(shuffle)，然后划分成mini_batch。

4、len函数的返回值 除以 batch_size 的结果就是每一轮epoch中需要迭代的次数。

5、inputs, labels = data中的inputs的shape是[32,8],labels 的shape是[32,1]。也就是说mini_batch在这个地方体现的

6、diabetes.csv数据集老师给了下载地址，该数据集需和源代码放在同一个文件夹内。

import torch
import numpy as np
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
 
# prepare dataset
 
 
class DiabetesDataset(Dataset):
    def __init__(self, filepath):
        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
        self.len = xy.shape[0] # shape(多少行，多少列)
        self.x_data = torch.from_numpy(xy[:, :-1])
        self.y_data = torch.from_numpy(xy[:, [-1]])
 
    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]
 
    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
 
 
# design model using class
 
 
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.sigmoid = torch.nn.Sigmoid()
 
    def forward(self, x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x
 
 
model = Model()
 
# construct loss and optimizer
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
 
# training cycle forward, backward, update
if __name__ == '__main__':
    for epoch in range(100):
        for i, data in enumerate(train_loader, 0): # train_loader 是先shuffle后mini_batch
            inputs, labels = data
            y_pred = model(inputs)
            loss = criterion(y_pred, labels)
            print(epoch, i, loss.item())
 
            optimizer.zero_grad()
            loss.backward()
 
            optimizer.step()

自娱自乐部分

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
from torch.utils.data import DataLoader
from sklearn.model_selection import train_test_split
 
 
# 读取原始数据，并划分训练集和测试集
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)
 
# 将训练数据集进行批量处理
# prepare dataset
 
class DiabetesDataset(Dataset):
    def __init__(self, data,label):
 
        self.len = data.shape[0] # shape(多少行，多少列)
        self.x_data = torch.from_numpy(data)
        self.y_data = torch.from_numpy(label)
 
    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]
 
    def __len__(self):
        return self.len
 
 
train_dataset = DiabetesDataset(Xtrain,Ytrain)
train_loader = DataLoader(dataset=train_dataset, batch_size=32, shuffle=True, num_workers=0) #num_workers 多线程
 
# design model using class
 
 
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.sigmoid = torch.nn.Sigmoid()
 
    def forward(self, x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        x = self.sigmoid(self.linear4(x))
        return x
 
 
model = Model()
 
# construct loss and optimizer
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
 
 
# training cycle forward, backward, update
 
def train(epoch):
    train_loss = 0.0
    count = 0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        y_pred = model(inputs)
 
        loss = criterion(y_pred, labels)
 
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        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.size(0)
        print("test acc:", acc)
 
if __name__ == '__main__':
    for epoch in range(50000):
        train(epoch)
        if epoch%2000==1999:
            test()