《PyTorch深度学习实战》第八讲

Dataset and DataLoader

传送门：https://www.bilibili.com/video/BV1Y7411d7Ys?p=8

数据集

常见定义：

Epoch：全部训练样本通过一次前向传播和反向传播
Batch-Size：通过一次前向传播和反向传播的训练样本数量
Iteration：Batch-Size的个数

公式：$Epoch=Batch-Size\times Iteration$

多Batch训练

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：

1、需要mini_batch 就需要import DataSet和DataLoader数据集方案

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数据集第七讲已给下载地址，该数据集需和源代码放在同一个文件夹内。

import numpy as np
import torch
from torch.utils.data import Dataset #Dataset 抽象类
from torch.utils.data import DataLoader #dataloader 可实例化

#1.准备数据集
class DiabetesDataset(Dataset):
    def __init__(self, filepath): # 初始化数据
        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32) # 载入数据
        self.len = xy.shape[0]
        self.x_data = torch.from_numpy(xy[:,:-1])
        self.y_data = torch.from_numpy(xy[:,[-1]])

    def __getitem__(self, index):  # 重载数据切片功能
        return self.x_data[index], self.y_data[index]

    def __len__(self): # 数据长度
        return self.len

dataset = DiabetesDataset('diabetes.csv.gz') # 加载数据，保证diabets和代码放到同一个文件，否则更改文件地址

#模型参数
train_loader = DataLoader(dataset=dataset, # 数据集
                          batch_size=32, # 训练batch
                          shuffle=True, # 随机打乱
                          num_workers=2)# 并行化，进程数
#2.设计模型
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.activate = torch.nn.ReLU()  # ReLu激活函数
        self.sigmoid = torch.nn.Sigmoid() # Sigmoid激活函数

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

model = Model()

#3.构造损失函数和优化器
criterion = torch.nn.BCELoss(size_average=True)

optimizer = torch.optim.Adam(model.parameters(), lr=0.1)

#4.训练周期

if __name__ == "__main__":
    for epoch in range(100):
        for i, data in enumerate(train_loader, 0):
            #1.prepare data
            inputs, labels = data # tenor类别
            #2.forward
            y_pred = model(inputs)
            loss = criterion(y_pred, labels)
            print(epoch, i, loss.item())
            #3.backward
            optimizer.zero_grad()
            loss.backward()

            #4.updata
            optimizer.step()

Tantic作业

数据集下载 链接：https://pan.baidu.com/s/1_cTzeBt6BLUo9xxkQ9Ce0w

提取码：n9ph

#Titanic Dataset training
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from torch.utils.data import Dataset
from torch.utils.data import DataLoader

#prepare dataset
train_data = pd.read_csv("train.csv")

del train_data['PassengerId']
del train_data['Cabin']
del train_data['Name']
del train_data['Ticket']

train_data['Age'] = train_data['Age'].fillna(train_data["Age"].mean())

ans = train_data['Embarked'].value_counts()
fillstr = ans.idxmax()

train_data['Embarked'] = train_data['Embarked'].fillna(fillstr)

train_data.replace(["female", "male"], [0, 1], inplace=True)
train_data.replace(["Q", "C", "S"], [0, 1, 2], inplace=True)

train_data = train_data.astype(np.float32)
# 查看数据
# train_data.info()
# print(train_data.head())
# print(train_data.describe())

train_data = np.array(train_data)
x_data = torch.from_numpy(train_data[:, 1:8])
y_data = torch.from_numpy(train_data[:, [0]])

#Design Model
class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(7, 4)
        self.linear2 = torch.nn.Linear(4, 2)
        self.linear3 = torch.nn.Linear(2, 1)

        self.activate = torch.nn.ReLU()
        self.sigmoid = torch.nn.Sigmoid()

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

model = Model()

#loss and optim

criterion = torch.nn.BCELoss(size_average=True, reduction='mean')

optim = torch.optim.Adam(model.parameters(), lr=0.01)

epoch_list = []
loss_list = []

#------------training cycle-----------------

for epoch in range(15000):
    y_pred = model(x_data)
    res = y_pred.detach().numpy()

    loss = criterion(y_pred, y_data)

    optim.zero_grad()
    loss.backward()
    optim.step()

    if (epoch % 5000) == 0:
        print("Epoch = " + str(epoch) + '\t' + 'loss = ' + str(format(loss.item(), '.4f')))
    epoch_list.append(epoch)
    loss_list.append(loss.item())


#绘制结果
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
plt.plot(epoch_list, loss_list)
plt.xlabel('epoch')
plt.ylabel('loss')
plt.title('result of predicting of Titanic Dataset')
plt.show()

res[res >= 0.5] = 1
res[res < 0.5] = 0

train_size = x_data.shape[0]
print(train_size)
cnt = 0
for i in range(train_size):
    cnt += (res[i].item() == y_data[i])
accuracy = cnt / train_size
print("精确率为：", accuracy)

#--------------test---------------
test_data = pd.read_csv('test.csv')
del test_data['Cabin']
del test_data['Name']
del test_data['Ticket']

test_data['Age'] = test_data['Age'].fillna(test_data['Age'].median())

ans = test_data['Embarked'].value_counts()

fillstr = ans.idxmax()

test_data['Embarked'] = test_data['Embarked'].fillna(fillstr)

test_data.replace(['female', 'male'], [0, 1], inplace=True)
test_data.replace(["Q", "C", "S"], [0, 1, 2], inplace=True)

test_data = test_data.astype(np.float32)
print(test_data.head())

test = np.array(test_data)
x_test = torch.from_numpy(test[:, 1:8])
y_test = model(x_test)
y = y_test.data.numpy()
y[y>=0.5] = 1
y[y<0.5] = 0

# 导出结果
pIds = pd.DataFrame(data=test_data['PassengerId'].astype(int), columns=['PassengerId'],dtype=int)
preds = pd.DataFrame(data=y, columns=['Survived'], dtype=int)
result = pd.concat([pIds, preds], axis=1)
result.to_csv('tantic_prediction.csv', index=False)