传统机器学习与深度学习的特征工程
特征向量
pytorch实现minist代码解析
归一化
损失函数
计算图
Mnist分类
- 获取Mnist数据集，预处理，输出一张图像
- 面向工具包编程
- 使用TensorDataset和DataLoader来简化数据预处理
- 计算验证集准确率
气温预测
- 回归
- 构建神经网络
- 调包
- 预测训练结果
- 画图对比

传统机器学习与深度学习的特征工程

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卷积层：原始输入中间提取有用的一个局部特征
激活函数：用于增加模型的一些非线性，可以让模型学习更加复杂模式
池化层：用于减少数据的维度

特征向量

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pytorch实现minist代码解析

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首先继承nn.Module类的一个子类ConvNet，super方法就是在调用nn.Module的一个__init__方法，确保__init__方法中定义的属性和方法都可以在ConvNet中使用

归一化

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损失函数

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计算图

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Mnist分类

获取Mnist数据集，预处理，输出一张图像

import torch
print(torch.__version__)

#win用户
DEVICE=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#mac用户
DEVICE=torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
print('当前设备',DEVICE)

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#将图像嵌入输出的单元格
%matplotlib inline

from pathlib import Path # 处理文件路径
import requests

DATA_PATH = Path("data") 
PATH = DATA_PATH / "mnist"
PATH.mkdir(parents=True, exist_ok=True)

URL = "http://deeplearning.net/data/mnist/"
FILENAME = "mnist.pkl.gz"

if not (PATH / FILENAME).exists():
    content = requests.get(URL + FILENAME).content
    (PATH / FILENAME).open("wb").write(content)

import pickle
import gzip

with gzip.open((PATH / FILENAME).as_posix(), "rb") as f:
    ((x_train, y_train), (x_valid, y_valid), (x_test, y_test)) = pickle.load(f, encoding="latin-1")

print("x_train: ", type(x_train), x_train.dtype, x_train.size, x_train.shape, "; y_train: ", y_train.shape)

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print("x_valid: ", type(x_valid), x_valid.dtype, x_valid.size, x_valid.shape, "; y_valid: ", y_valid.shape)

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from matplotlib import pyplot

pyplot.imshow(x_train[2].reshape((28, 28)), cmap="gray")

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y_train[:10]

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x_train, y_train, x_valid, y_valid = map(
    lambda x: torch.tensor(x, device=DEVICE),
    (x_train, y_train, x_valid, y_valid)
)
print("x_train: ", x_train, "; y_train: ", y_train)

x_train[0]

import torch.nn.functional as F

loss_func = F.cross_entropy # 损失函数，传入预测、真实值的标签

def model(xb):
    xb = xb.to(DEVICE)
    return xb.mm(weights) + bias  # x*w+b

bs = 64

xb = x_train[0:bs] # 64, 784

yb = y_train[0:bs] # 真实标签

weights = torch.randn([784, 10], dtype = torch.float, requires_grad = True)

bias = torch.zeros(10, requires_grad = True)

weights = weights.to(DEVICE)
bias = bias.to(DEVICE)

print(loss_func(model(xb), yb))

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补充：关于map函数的例子

def square(x):
    return x**2
numbers=[1,2,3,4,5]
squares=map(square,numbers)
print(list(squares))

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也就是map函数第一个参数是函数，第二个参数是数值，将函数作用于数值

面向工具包编程

from torch import nn # 提供神经网网络的类和函数 ,nn.Module

class Mnist_NN(nn.Module):
    def __init__(self): # 设计房屋图纸
        super(Mnist_NN, self).__init__()
        self.hidden1 = nn.Linear(784, 256) # 784-输入层，256-隐藏层1
        self.hidden2 = nn.Linear(256, 128)
        self.out = nn.Linear(128, 10)
    def forward(self, x): # 实际造房子
        x2 = F.relu(self.hidden1(x)) # x: [bs, 784], w1: [784, 256], b1: [256] -> x2:[bs,256]
        x3 = F.relu(self.hidden2(x2)) # x2: [bs, 256], w2:[256, 128], b2[128] -> x3[bs, 128]
        x_out = self.out(x3) # x3: [bs, 128], w3: [128, 10], b3[10] -> x_out: [bs, 10]
        
        return x_out

net = Mnist_NN().to(DEVICE)
print(net)

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print(net.hidden1.weight)

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for name, parameter in net.named_parameters():
    print(name, parameter)

使用TensorDataset和DataLoader来简化数据预处理

from torch.utils.data import TensorDataset

from torch.utils.data import DataLoader

 
train_ds = TensorDataset(x_train, y_train) #torch.utils.data.Dataset
train_dl = DataLoader(train_ds, batch_size=64, shuffle=True)

valid_ds = TensorDataset(x_valid, y_valid)
valid_dl = DataLoader(valid_ds, batch_size=bs)

data_iter = iter(train_dl)

batch_x, batch_y = next(data_iter)
print(batch_x.shape, batch_y.shape)
print(batch_y)

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batch_x, batch_y = next(data_iter)
print(batch_x.shape, batch_y.shape)
print(batch_y)

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def get_data(train_bs, valid_bs, bs): # 创建数据加载器
    return (
        DataLoader(train_ds, batch_size=bs, shuffle=True),
        DataLoader(valid_ds, batch_size=bs)
    )

from torch import optim
def get_model():
    model = Mnist_NN().to(DEVICE)
    optimizer = optim.SGD(model.parameters(), lr=0.01) # model.parameters()包含了所有的权重和偏执参数
    return model, optimizer

注：adam相比于SGD是引入了一个惯性，相当于一个平行四边形的一个合成法则
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def loss_batch(model, loss_func, xb, yb, opt=None):
    loss = loss_func(model(xb), yb)
    
    if opt is not None: # 此时是训练集
        
        opt.zero_grad()
        loss.backward()
        opt.step()
        
    return loss.item(), len(xb)

opt为True是训练集测试损失，opt为None是验证集测试损失

def loss_batch(model, loss_func, xb, yb, opt=None):
    loss = loss_func(model(xb), yb)
    
    if opt is not None: # 此时是训练集
        
        opt.zero_grad()
        loss.backward()
        opt.step()
        
    return loss.item(), len(xb)

import numpy as np

def fit(epoch, model, loss_func, opt, train_dl, valid_dl):
    for step in range(epoch):
        model.train()
        for xb, yb in train_dl:
            loss_batch(model, loss_func, xb, yb, opt)
        
        model.eval() # 考试
        with torch.no_grad():
            
            losses, nums = zip(
                *[loss_batch(model, loss_func, xb, yb) for xb, yb in valid_dl] # "*"——解包/解开
            )
            # print(f"losses: {losses}")
            # print(f"nums: {nums}")
            
        val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums) # 加权平均损失
        print('当前step: '+str(step), '验证集损失: '+str(val_loss))

train_dl, valid_dl = get_data(train_ds, valid_ds, bs=64)
model, optimizer = get_model()
fit(30, model, loss_func, optimizer, train_dl, valid_dl)

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计算验证集准确率

torch.set_printoptions(precision=4, sci_mode=False)
for xb, yb in valid_dl:
    output = model(xb)
    print(output)
    print(output.shape)
    break

for xb, yb in valid_dl:
    output = model(xb)
    probs = torch.softmax(output, dim=1)
    print(probs)
    print(probs.shape)
    break

for xb, yb in valid_dl:
    output = model(xb)
    probs = torch.softmax(output, dim=1)
    preds = torch.argmax(probs, dim=1)
    print(preds)
    print(preds.shape)
    break

correct_predict = 0 # 计数正确预测图片的数目
total_quantity = 0 # 计数验证集总数

for xb, yb in valid_dl:
    output = model(xb)
    probs = torch.softmax(output, dim=1)
    preds = torch.argmax(probs, dim=1)
    total_quantity += yb.size(0)
    # print(yb.size(0))
    # print((preds == yb).sum())
    # print((preds == yb).sum().item())
    correct_predict += (preds == yb).sum().item()

print(f"验证集的准确率是: {100 * correct_predict / total_quantity} % ")

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气温预测

回归

import numpy as np # 矩阵运算
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.optim as optim

import warnings
warnings.filterwarnings("ignore")

%matplotlib inline

features = pd.read_csv('temps.csv')

features.head()

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print("数据维度: ", features.shape)

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# 处理时间数据
import datetime

years = features['year']
months = features['month']
days = features['day']

dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]

dates[:5]

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dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]

dates[:5]

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plt.style.use('fivethirtyeight')

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10, 10))
fig.autofmt_xdate(rotation=45) #x轴翻转45度

# 标签值
ax1.plot(dates, features['actual'])
ax1.set_xlabel(''); ax1.set_ylabel('Temoerature'); ax1.set_title('Actual Max Temp')

# 昨天温度
ax2.plot(dates, features['temp_1'])
ax2.set_xlabel(''); ax2.set_ylabel('Temoerature'); ax2.set_title('Previous Max Temp')

# 前天温度
ax3.plot(dates, features['temp_2'])
ax3.set_xlabel('Date'); ax3.set_ylabel('Temoerature'); ax3.set_title('Two Days Prior Max Temp')

# 朋友预测温度
ax4.plot(dates, features['friend'])
ax4.set_xlabel('Date'); ax4.set_ylabel('Temoerature'); ax4.set_title('Friend Max Temp')

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features = pd.get_dummies(features)
features.head()

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labels = np.array(features['actual'])

# 在特征中去掉标签
features = features.drop('actual', axis=1)

feature_list = list(features.columns)

features = np.array(features)

features.shape

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from sklearn import preprocessing
input_features = preprocessing.StandardScaler().fit_transform(features)

input_features[:5]

构建神经网络

x = torch.tensor(input_features, dtype = float)
y = torch.tensor(labels, dtype=float)
print(x.shape, y.shape)

# 权重初始化
weights = torch.randn((14, 128), dtype = float, requires_grad = True) 
biases = torch.randn(128, dtype = float, requires_grad = True) 
weights2 = torch.randn((128, 1), dtype = float, requires_grad = True) 
biases2 = torch.randn(1, dtype = float, requires_grad = True) 


learning_rate = 0.001
losses = []

for i in range(1000):
    
    hidden = x.mm(weights) + biases
    
    hidden = torch.relu(hidden)
    
    predictions = hidden.mm(weights2) + biases2
    
    loss = torch.mean((predictions - y)**2)
    losses.append(loss.item())
    
    if i % 100 == 0:
        print(f"loss: {loss}")
    
    # 反向传播
    loss.backward()
    
    # 更新，相当于optim.step()
    weights.data.add_(- learning_rate * weights.grad.data)  
    biases.data.add_(- learning_rate * biases.grad.data)
    weights2.data.add_(- learning_rate * weights2.grad.data)
    biases2.data.add_(- learning_rate * biases2.grad.data)
        
    # 清空梯度，optim.zero_grad()
    weights.grad.data.zero_()
    biases.grad.data.zero_()
    weights2.grad.data.zero_()
    biases2.grad.data.zero_()

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调包

import torch.optim as optim

# 数据准备
# 将数据都转化为tensor张量
x = torch.tensor(input_features, dtype = torch.float)
y = torch.tensor(labels, dtype=torch.float).view(-1, 1) # 改成(n, 1)
print(x.shape, y.shape)

model = torch.nn.Sequential(
    torch.nn.Linear(14, 128),
    torch.nn.ReLU(),
    torch.nn.Linear(128, 1)
)

# 均方误差MSE
criterion = torch.nn.MSELoss(reduction='mean')

optimizer = optim.Adam(model.parameters(), lr=0.001)


losses = [] # 存储每一次迭代的损失

for i in range(3000):
    predictions = model(x) # [348, 1]
    
    loss = criterion(predictions, y)
    losses.append(loss.item())
    if i % 200 == 0:
        print(f"loss: {loss.item()}")
        
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

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预测训练结果

x = torch.tensor(input_features, dtype = torch.float)
predict = model(x).data.numpy()

dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]

# 创建一个表格来存日期和对应的真实标签
true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})

# 创建一个表格来存日期和对应的预测值
predictions_data = pd.DataFrame(data = {'date': dates, 'prediction': predict.reshape(-1)})

predict.shape, predict.reshape(-1).shape

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true_data[:5]

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predictions_data[:5]

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画图对比

# 真实值
plt.plot(true_data['date'], true_data['actual'], 'b-', label = "actual")

# 预测值
plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = "prediction")

plt.xticks(rotation = 60)

plt.legend()

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# 真实值
plt.plot(true_data['date'], true_data['actual'], 'b-', label = "actual")

# 预测值
plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = "prediction")

plt.xticks(rotation = 60)

plt.legend()

plt.xlabel('Date'); plt.ylabel('Maximum Tempurate(F)'); plt.title('Actual and Predicted Values')