pytorch常用函数手册 哈工大 pytorch例子

基础知识

import torch
torch.empty(5,3) # 返回未初始化张量（矩阵）
torch.rand(5,3) # 随机生成服从均匀分布的数据，返回值为张量。
torch.zeros(5,3,dtype=torch.long) # 返回零矩阵
x = torch.tensor([5.5,3]) # 转化成张量
x = x.new_ones(5,3,dtype=torch.double) # 返回5行3列的1矩阵
x = torch.randn_like(x,dtype=torch.float) # 返回形状和x一样的随机矩阵

相加

x+y
torch.add(x,y) # 两者相等

索引

x[:,1] # 索引操作

随机数据

x = torch.randn(4,4) # 随机生成服从正态分布的数据，返回值为张量。
y = x.view(16)       # 改变矩阵维度
z = x.view(-1,8)     # -1代表自动计算
print(x.size(),y.size(),z.size()) # 获取张量的形状

torch.Torch 转 numpy

a = torch.ones(5)
b = a.numpy()

numpy 转 torch.Torch

a = np.ones(5)
b = torch.from_numpy(a)

自动求导

# 自动求导
# 方法一
x = torch.randn(3,4,requires_grad=True)
# 方法二
x = torch.randn(3,4)
x.requires_grad=True

b = torch.randn(3,4,requires_grad=True)
t = x+b
y = t.sum() # tensor(-7.2647, grad_fn=<SumBackward0>)
y.backward(retain_graph=True)   # 梯度默认累加
b.grad
x.requires_grad,b.requires_grad,t.requires_grad # (True, True, True)
t.is_leaf,x.is_leaf  # 是不是叶子 (False, True)

处理数据分包

目的：将数据打乱分成多份。

def get_data(train_ds: TensorDataset, valid_ds: TensorDataset, bs: int):
    return (
        DataLoader(train_ds, batch_size=bs, shuffle=True),
        DataLoader(valid_ds, batch_size=bs * 2)
    )

# 把训练集和测试集的特征和标签打包
train_ds = TensorDataset(
    torch.from_numpy(X_train).float(), 
    torch.from_numpy(Y_train).float()
)  
valid_ds = TensorDataset(
    torch.from_numpy(X_test).float(),
    torch.from_numpy(Y_test).float()
)
# 把训练集和测试集分包
bs = 16
train_dl, valid_dl = get_data(train_ds, valid_ds, bs)

线性回归模型

import warnings

import sklearn
from sklearn import preprocessing
import torch.nn.functional as F
import torch
from sklearn import datasets
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
# 其实线性回归就是一个不加激活函数的全链接层
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm

warnings.filterwarnings("ignore")


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

        self.loss_func = nn.MSELoss()  # MSE损失函数  数据记得标准化,数值要求在[-1,1]之间
        self.opt = torch.optim.SGD(self.parameters(), lr=0.03)  # SGD优化器
        self.val_loss = None

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

    def loss_batch(self, xb, yb, opt=None):
        loss = self.loss_func(self(xb), yb)  # 计算损失
        if opt is not None:
            loss.backward()  # 反向传播
            opt.step()  # 更新权重参数
            opt.zero_grad()  # 每一次迭代 梯度要清零
        return loss.item(), len(xb)

    def fit(self, steps, train_dl: DataLoader, valid_dl: DataLoader):
        with tqdm(total=steps) as pbar:
            for step in range(steps):
                self.train()  # 开始训练的过程
                for xb, yb in train_dl:
                    self.loss_batch(xb, yb, self.opt)
                self.eval（)  # 开始测试的过程
                with torch.no_grad():
                    losses, nums = zip(
                        *[self.loss_batch(xb, yb) for xb, yb in valid_dl]
                    )
                self.val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)
                pbar.set_description("model")
                pbar.set_postfix(step=step, val_loss=self.val_loss)
                pbar.update(1)
            pbar.write('当前数据损失：{}'.format(self.val_loss))

训练线性回归模型

def get_data(train_ds: TensorDataset, valid_ds: TensorDataset, bs: int):
    return (
        DataLoader(train_ds, batch_size=bs, shuffle=True),
        DataLoader(valid_ds, batch_size=bs * 2)
    )
if __name__ == '__main__':
    # 生成一个线性回归问题
    # X, Y = make_regression(n_features=1, n_samples=506, n_informative=2, n_targets=1, noise=1.2)
    # n_features(default=100) 表示每一个样本有多少特征值
    # n_samples (default=100)表示样本的个数
    # n_informative(default=10)有信息的特征数量，也就是用来构造线性模型，生成输出的特征数量
    # n_targets(default=1)回归目标的数量，也就是对应于一个样本输出向量y的维度。默认输出是标量
    # noise(default=0.0)施加于输出的高斯噪声的标准差（越大越离散）

    # 获取波士顿房价数据
    loaded_data = datasets.load_boston()
    X = loaded_data.data
    Y = loaded_data.target

    # 处理数据
    X = sklearn.preprocessing.StandardScaler().fit_transform(X)  # 数据标准化
    X_train, X_test, Y_train, Y_test = train_test_split(
        X, Y.reshape(-1, 1), test_size=0.01
    )
    train_ds = TensorDataset(torch.from_numpy(X_train).float(), torch.from_numpy(Y_train).float())
    valid_ds = TensorDataset(torch.from_numpy(X_test).float(), torch.from_numpy(Y_test).float())
    bs = 16
    train_dl, valid_dl = get_data(train_ds, valid_ds, bs)

    # 训练模型
    model = LinearRegressionModel(13, 1)
    model.fit(1000, train_dl, valid_dl)
    pass

单分类模型

import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader
import torch
from torch import nn
import numpy as np
import torch.nn.functional as F
import warnings
from sklearn.datasets import make_blobs
from torch import optim
from tqdm import tqdm

warnings.filterwarnings("ignore")


# torch.nn.functional(一般用于没有可学习的参数) 和 nn.Module (一般用于有可学习的参数)
# 创建一个model来更简化代码
# 必须继承mm.Module 且在其构造函数中需要调用nn.Module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 中的可学习参数可以通过named_parameters()或者parameters()返回迭代器
class Mnist_NN(nn.Module):
    def __init__(self, input_dim, output_dim):
        super().__init__()

        self.hidden1 = nn.Linear(input_dim, 128)
        self.hidden2 = nn.Linear(128, 256)
        self.out = nn.Linear(256, output_dim)

        self.loss_func = F.cross_entropy  # 损失函数
        self.opt = optim.SGD(self.parameters(), lr=0.001)  # 随机梯度下降
        self.val_loss = None

    def forward(self, x):
        x = F.relu(self.hidden1(x))
        x = F.relu(self.hidden2(x))
        x = self.out(x)
        return x

    def loss_batch(self, xb, yb, opt=None):
        loss = self.loss_func(self(xb), yb)
        if opt is not None:
            loss.backward()
            opt.step()
            opt.zero_grad()
        return loss.item(), len(xb)

    def fit(self, steps, train_dl: DataLoader, valid_dl: DataLoader):
        with tqdm(total=steps) as pbar:
            for step in range(steps):
                self.train()  # 开始训练的过程
                for xb, yb in train_dl:
                    self.loss_batch(xb, yb, self.opt)
                self.eval（)  # 开始测试的过程
                with torch.no_grad():
                    losses, nums = zip(
                        *[self.loss_batch(xb, yb) for xb, yb in valid_dl]
                    )
                self.val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)
                pbar.set_description("model")
                pbar.set_postfix(step=step, val_loss=self.val_loss)
                pbar.update(1)
            pbar.write('当前数据损失：{}'.format(self.val_loss))

    def predict(self, X):
        return self(X)

训练单分类模型

def get_data(train_ds: TensorDataset, valid_ds: TensorDataset, bs: int):
    return (
        DataLoader(train_ds, batch_size=bs, shuffle=True),
        DataLoader(valid_ds, batch_size=bs * 2)
    )


if __name__ == '__main__':
    # 生成一个分类问题
    x, y = make_blobs(n_features=10, n_samples=100, centers=3, random_state=3)
    # n_features 表示每一个样本有多少特征值
    # n_samples 表示样本的个数
    # centers 是聚类中心点的个数，可以理解为label的种类数
    # random_state 是随机种子，可以固定生成的数据
    # cluster_std 设置每个类别的方差,若要指定需要传入长度为centers的列表

    # 处理数据
    x_train, x_test, y_train, y_test = train_test_split(x, y)  # 分类 - （训练集  ， 测试集）
    train_ds = TensorDataset(torch.from_numpy(x_train), torch.from_numpy(y_train).long())
    valid_ds = TensorDataset(torch.from_numpy(x_test), torch.from_numpy(y_test).long())
    bs = 16
    torch.set_default_tensor_type(torch.DoubleTensor)
    train_dl, valid_dl = get_data(train_ds, valid_ds, bs)

    # 训练模型
    model = Mnist_NN(10, 3)
    model.fit(100, train_dl, valid_dl)
    pass

Dataset

from torch.utils.data import Dataset

class OneData(Dataset):
    def __init__(self,):
        pass

	def __getitem__(self,):
        pass

	def __len__(self,):
        pass

模型的保存与读取

torch.save(model.state_dict(),'data/model.pkl')
model.load_state_dict(torch.load('data/model.pkl'))