Pytorch 入门

Pytorch 入门

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参考

• 文章1
• 视频教程
• 卷积理解
• torch.nn.Conv2d详解

一、常见的Tensor形式

1. scaler
   通常为一个数值

x = tensor(42.)

2. vector
   常见的向量

v = tensor([1,2,3])

3. matrix
   一般是矩阵，通常是多维的

M = tensor([[1,2],[4,5]])

4. n-dimensional tensor
   通常指更高纬度的表示

实现简单的线性回归Demo

主要通过该案例去理解Pytorch中的autograd机制

训练模型并保存模型

import torch
import torch.nn as nn
import numpy as np
# 数据准备
x_values = [i for i in range(11)]
x_train = np.array(x_values, dtype=np.float32)
x_train = x_train.reshape(-1, 1)

y_values = [i*2 + 2 for i in range(11)]
y_train = np.array(y_values, dtype=np.float32)
y_train = y_train.reshape(-1,1)

class LinearRegressionModel(nn.Module):
  def __init__(self, input_dim, output_dim):
    super(LinearRegressionModel, self).__init__()
    self.linear = nn.Linear(input_dim, output_dim)

  def forward(self, x):
    out = self.linear(x)
    return out

input_dim = 1
output_dim = 1

model = LinearRegressionModel(input_dim, output_dim)
epochs = 1000
learning_rate = 0.01
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
criterion = nn.MSELoss()

# 模型训练
for epoch in range(epochs):
    epoch += 1
    # 注意转行成tensor
    inputs = torch.from_numpy(x_train)
    labels = torch.from_numpy(y_train)

    # 梯度要清零每一次迭代
    optimizer.zero_grad() 

    # 前向传播
    outputs = model(inputs)

    # 计算损失
    loss = criterion(outputs, labels)

    # 返向传播
    loss.backward()

    # 更新权重参数
    optimizer.step()
    if epoch % 50 == 0:
        print('epoch {}, loss {}'.format(epoch, loss.item()))
torch.save(model.state_dict(), 'model.pkl')

加载模型并预测

input_dim = 1
output_dim = 1

model = LinearRegressionModel(input_dim, output_dim)
model.load_state_dict(torch.load('model.pkl'))

import matplotlib.pyplot as plt

pred = model(torch.from_numpy(x_train).requires_grad_()).data.numpy()
plt.plot(x_values, pred, 'ro')

预测结果

用神经网络进行气温预测

数据处理

导入数据

import numpy as np
import torch
import pandas as pd
import torch.optim as optim
import matplotlib.pyplot as plt
import warnings

warnings.filterwarnings('ignore')

features = pd.read_csv("./temps.csv")
print(features.head())

对时间数据进行转化并可视化

import datetime

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

# datetime格式
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]

plt.style.use('fivethirtyeight')

# 设置布局
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10,10))
fig.autofmt_xdate(rotation = 45)

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

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

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


ax4.plot(dates, features['friend'])
ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('Friend Estimate')

plt.tight_layout(pad=2)

获得训练集

# 对部分数据进行独热编码
features = pd.get_dummies(features)

#标签
labels = np.array(features['actual'])
# 在特征中去掉标签
features = features.drop('actual', axis = 1)
# 名字单独保存
features_list = list(features.columns)

# 转换成合适的格式
features = np.array(features)

# 标准化处理
from sklearn.preprocessing import StandardScaler
input_features = StandardScaler().fit_transform(features)

# 获得训练集和测试集
x = torch.tensor(input_features, dtype = float)
y = torch.tensor(labels, dtype = float)

在处理好数据后我们可以通过下面两种方式搭建模型

1. 较为复杂的构建形式（有助于理解）

下面我们手动去进行梯度下降，而不是使用torch 中内置的方法。

## 权重参数初始化
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(10000):
  # 计算隐层
  hidden = x.mm(weights) + biases
  # 加入激活函数
  hidden = torch.relu(hidden)
  # 计算输出层
  output = hidden.mm(weights2) + biases2
  # 计算损失
  loss = torch.mean((output - y) ** 2)
  losses.append(loss.data.numpy())
  # 计算梯度
  loss.backward()

  #更新参数
  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
  weights.grad.zero_()
  biases.grad.zero_()
  weights2.grad.zero_()
  biases2.grad.zero_()
  # 打印损失值
  if i % 100 == 0:
      print('loss:', loss)

2.较为简单的构建方式

可以将这种写法跟上面的方法一进行对比

input_size = input_features.shape[1]
hidden_size = 128
output_size = 1
batch_size = 16
my_nn = torch.nn.Sequential(
  torch.nn.Linear(input_size, hidden_size),
  torch.nn.Sigmoid(),
  torch.nn.Linear(hidden_size, output_size)
)
# 定义损失函数
cost = torch.nn.MSELoss(reduction = 'mean')
# 可以通过这个进行链式的梯度下降
optimizer = torch.optim.Adam(my_nn.parameters(), lr = 0.001)

# 训练网络
losses = []

for i in range(1000):
  batch_loss = []
  # MINI-BATCH 方法来进行训练
  # 每次仅用一部分数据投入训练
  for start in range(0, len(input_features), batch_size):
    end = start + batch_size if start + batch_size < len(input_features) else len(input_features)
    x_batch = torch.tensor(input_features[start:end], dtype = torch.float, requires_grad = True)
    y_batch = torch.tensor(labels[start:end], dtype = torch.float, requires_grad = True)
    # 前向传播
    y_pred = my_nn(x_batch)
    # 计算损失
    loss = cost(y_pred, y_batch)
    batch_loss.append(loss.item())
    # 反向传播
    loss.backward()
    # 更新参数
    optimizer.step()
    # 清空梯度
    optimizer.zero_grad()
    batch_loss.append(loss.data.numpy())
  
  # 打印损失
  if i % 100 == 0:
    losses.append(np.mean(batch_loss))
    print('loss:', np.mean(batch_loss))

使用模型预测

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

# 转换日期格式
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其对应的标签数值
true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})
pre_data = pd.DataFrame(data = {'date': dates, 'actual': predict.reshape(-1)})

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

# 预测值
plt.plot(pre_data['date'], pre_data['actual'], 'r-', label = 'Prediction')
plt.xticks(rotation = "60") 

plt.legend()
plt.xlabel('Date') 
plt.ylabel('Maximum Temperature (F)') 
plt.title('Actual and Predicted Values')

手写体识别（线性模型）

加载数据并查看

from pathlib import Path
import requests
import matplotlib.pyplot as plt

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), _) = pickle.load(f, encoding="latin-1")
       
plt.imshow(x_train[1].reshape(28, 28), cmap="gray")

数据格式化处理

# 将数据格式进行转化
x_train, y_train, x_valid, y_valid = map(
    torch.tensor, (x_train, y_train, x_valid, y_valid)
)
n,c = x_train.shape
x_train, x_train.shape, y_train.min(), y_train.max()

bs = 64
xb = x_train[0:bs]
yb = y_train[0:bs]
weights = torch.randn([784, 10], dtype=torch.float ,requires_grad=True)
bs = 64
bias = torch.zeros(10, requires_grad=True)

from torch.utils.data import DataLoader, TensorDataset


train_ds = TensorDataset(x_train, y_train)
train_dl = DataLoader(train_ds, batch_size=bs, shuffle=True)

valid_ds = TensorDataset(x_valid, y_valid)
valid_dl = DataLoader(valid_ds, batch_size=bs *2)

网络构建

创建一个model 来简化代码

• 必须继承nn.Module 且在其构造函数中需调用nn.Module 中的构造函数
• 无需写反向传播函数， nn.Module 能够利用autograd自动实现反向传播
• Module 中可学习参数可以通过named_parameters()或者parameters()返回迭代器

from torch import nn
import torch.nn.functional as F

class Mnist_NN(nn.Module):
  """
  	## 网络结构搭建
  	
  """
  def __init__(self):
    super().__init__()
    self.hidden1 = nn.Linear(784, 128)
    self.hidden2 = nn.Linear(128, 256)
    self.out = nn.Linear(256, 10)

  def forward(self, xb):
    """
      ## 尽心前向传播
    """
    xb = F.relu(self.hidden1(xb))
    xb = F.relu(self.hidden2(xb))
    xb = self.out(xb)
    return xb
    
def loss_batch(mode, loss_func, xb, yb, opt = None):
  """
    ## 计算损失值的时候，同时进行反向传播
  """
  loss = loss_func(mode(xb), yb)
  if opt is not None:
    loss.backward()
    opt.step()
    opt.zero_grad()
  return loss.item(), len(xb)

def getModel():
  """
    ## 获取模, 并获得优化函数
  """
  model = Mnist_NN()
  return model, torch.optim.SGD(model.parameters(), lr=0.1)

def fit(steps, model, loss_func,opt, train_dl, valid_dl):
  """
    ## 模型训练的入口
  """
  for epoch in range(steps):
    model.train()
    for xb, yb in train_dl:
      loss_batch(model, loss_func, xb, yb, opt)
    model.eval()
    with torch.no_grad():
      losses, nums = zip(
        *[loss_batch(model, loss_func, xb, yb) for xb, yb in valid_dl]
        )
      val_loss = np.sum(losses) / np.sum(nums)
    val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)
    print(epoch, val_loss)

## 模型训练入口
loss_func = F.cross_entropy
train_dl, valid_dl = get_data(train_ds, valid_ds, bs)
model, opt = getModel()
fit(25, model, loss_func, opt, train_dl, valid_dl)

目前可以将这个当作模板写法，主要包含：
1… 模型获取函数
2. 网络模型结构
3. 损失计算梯度下降
4. 模型训练

手写体识别（CNN网络模型）

数据导入部分

• 分别构建训练集和测试集
• DataLoader 来迭代获取数据

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np

# 定义超参数
input_size = 28   # 输入图片的大小
num_classes = 10  # 标签的类数
num_epochs = 5    # 训练次数
batch_size = 64   # 一批次训练64个样本

# 训练集
train_dataset = datasets.MNIST(root='./data',
                                train=True,
                                transform=transforms.ToTensor(),
                                download=True)

# 测试集
test_dataset = datasets.MNIST(root='./data',
                                train=False,
                                transform=transforms.ToTensor(),)

train_loader = torch.utils.data.DataLoader(train_dataset, 
                                          batch_size=batch_size, 
                                          shuffle=True)

test_loader = torch.utils.data.DataLoader(test_dataset,
                                          batch_size=batch_size,
                                          shuffle=True)

卷积网络构建模块

• 一般卷积层， relu层，池化层可以写在一起
• 注意卷积后结果还是一个特诊图，需要把图转化成向量才能做分类或者回归任务

这里我们采用两层卷积

class CNN(nn.Module):
  """
    ## 搭建两层卷积神经网络
  """
  def __init__(self):
    super(CNN, self).__init__()
    self.conv1 =  nn.Sequential(
      nn.Conv2d(
        in_channels=1,                    # 灰度图像，即1个通道
        out_channels=16,                  # 要得到的特征图数
        kernel_size=5,                    # 卷积核的大小
        stride=1,                         # 步长大小
        padding=2,                        # 填充大小，往外补充的大小
      ),
      nn.ReLU(),
      nn.MaxPool2d(kernel_size=2),
    )
    self.conv2 = nn.Sequential(
      nn.Conv2d(16, 32, 5, 1, 2),
      nn.ReLU(),
      nn.MaxPool2d(2),
    )
    self.out = nn.Linear(32 * 7 * 7, 10)     # 全连接层 得到结果
  
  def forward(self, x):
    x = self.conv1(x)
    x = self.conv2(x)
    x = x.view(x.size(0), -1)                # flatten的操作，转化为向量
    x = self.out(x)
    return x

准确率计算

def accuracy(output, target):
  """
    ## 计算准确率
  """
  pred = torch.max(output.data, 1)[1]
  rights = pred.eq(target.data.view_as(pred)).sum()
  return rights, len(target)

获得模型和优化器

def get_model():
  """
    获得模型以及优化器
  """
  net = CNN()
  optimizer = optim.SGD(net.parameters(), lr=0.001)
  return net, optimizer

计算损失值，并进行反向传播

def loss_process(loss_func,output, target, opt=None):
  """
    计算损失
  """
  loss = loss_func(output, target)
  if opt is not None:
    loss.backward()
    opt.step()
    opt.zero_grad()
  return loss

模型训练

def fit(epochs, model, optimizer, train_loader, test_loader, loss_func):
  """
    训练模型
    
  """
  for epoch in range(epochs):
    #当前epoch的结果保存下来
    train_rights = [] 
    for i, (images, labels) in enumerate(train_loader):
      model.train()
      output = model(images)
      right = accuracy(output, labels) 
      loss = loss_process(loss_func, output, labels, optimizer)
      train_rights.append(right) 
      
      if i % 100 == 0: 
        # 每训练一百轮数据
            model.eval() 
            val_rights = [] 
            for (data, target) in test_loader:
                output = model(data) 
                right = accuracy(output, target) 
                val_rights.append(right)
            #准确率计算
            train_r = (sum([tup[0] for tup in train_rights]), sum([tup[1] for tup in train_rights]))
            val_r = (sum([tup[0] for tup in val_rights]), sum([tup[1] for tup in val_rights]))

            print('当前epoch: {} [{}/{} ({:.0f}%)]\t损失: {:.6f}\t训练集准确率: {:.2f}%\t测试集正确率: {:.2f}%'.format(
                epoch, i * batch_size, len(train_loader.dataset),
                100. * i / len(train_loader), 
                loss.data, 
                100. * train_r[0].numpy() / train_r[1], 
                100. * val_r[0].numpy() / val_r[1]))

训练入口

# 实例化
model, optimizer = get_model()
# 损失函数
criterion = nn.CrossEntropyLoss()
# 优化器
fit(num_epochs, model, optimizer, train_loader, test_loader, criterion)

训练过程输出

–

总结

后续继续添加其它的一些任务，这里篇博客主要是理解Pytorch框架的作用，不过多关注模型原理