学习打卡系列Day5——李宏毅机器学习（10月）

目录

一、Pytorch Tutorial

 1. 输出整个tensor的最大值

 2. 按指定维度索引最大值

3. 比较并输出两个tensor中的最大值 

二、常见错误 

1. 数据和模型存储设备不同

2. 变量维度不同

3. cuda内存过载

4. tensor类型不匹配

三、Dataset和Dataloader

一、Pytorch Tutorial

torch函数有很多，举例说明torch.max的用法

# 导入库函数
import torch

# 生成示例数据
x = torch.randn(4,5)
y = torch.randn(4,5)
z = torch.randn(4,5)
print(x)
print(y)
print(z)

 1. 输出整个tensor的最大值

m = torch.max(x)
print(m)

 

 2. 按指定维度索引最大值

# torch.max(input, dim, keepdim=False, *, out=None)
'''
    input (Tensor) - 输入Tensor
    dim (int) - 指定维度(0, 按列计算；1, 按行计算)
    keepdim (bool) - 输出张量是否保持与输入张量有相同数量的维度
    out (tuple,optional) - 结果张量
'''

# 1. 根据位置参数调用有参函数
m, idx = torch.max(x,0)
m, idx = torch.max(x,0,False)

# 2. 关键字参数调用有参函数
m, idx = torch.max(input=x,dim=0)
m,idx = torch.max(x,1,keepdim=True)
p = (m,idx)
torch.max(x,0,False,out=p)

print(m)
print(idx)
print(p[0])
print(p[1])

3. 比较并输出两个tensor中的最大值 

t = torch.max(x,y)
print(t)

 

二、常见错误 

1. 数据和模型存储设备不同

import torch

# 错误代码
model = torch.nn.Linear(5,1).to("cuda:0")
x = torch.Tensor([1,2,3,4,5]).to("cpu")
y = model(x)

# 修改后
x = torch.Tensor([1,2,3,4,5]).to("cuda:0")
y = model(x)
print(y.shape)

2. 变量维度不同

# 错误代码
x = torch.randn(4,5)
y= torch.randn(5,4)
z = x + y

# 修改后
y= y.transpose(0,1)
z = x + y
print(z.shape)

3. cuda内存过载

import torch
import torchvision.models as models

# 错误代码
resnet18 = models.resnet18().to("cuda:0") # Neural Networks for Image Recognition
data = torch.randn(2048,3,244,244) # Create fake data (512 images)
out = resnet18(data.to("cuda:0")) # Use Data as Input and Feed to Model
print(out.shape)

# 修改后
for d in data:
  out = resnet18(d.to("cuda:0").unsqueeze(0))
print(out.shape)

4. tensor类型不匹配

import torch.nn as nn

# 错误代码
L = nn.CrossEntropyLoss()
outs = torch.randn(5,5)
labels = torch.Tensor([1,2,3,4,0])
lossval = L(outs,labels) # Calculate CrossEntropyLoss between outs and labels

# 修改后
labels = labels.long()
lossval = L(outs,labels)
print(lossval)

三、Dataset和Dataloader

示例简单的数据集和数据加载器，如下：

# Dataset
dataset = "abcdefghijklmnopqrstuvwxyz"

# Dataloader
for datapoint in dataset:
  print(datapoint)

利用torch的库函数可以自定义数据集和加载器，方便修改数据集和调用数据训练网络

import torch
import torch.utils.data 

class ExampleDataset(torch.utils.data.Dataset):
  def __init__(self):
    self.data = "abcdefghijklmnopqrstuvwxyz"
  
  def __getitem__(self,idx): # if the index is idx, what will be the data?
    return self.data[idx]
  
  def __len__(self): # What is the length of the dataset
    return len(self.data)

dataset1 = ExampleDataset() # create the dataset
dataloader = torch.utils.data.DataLoader(dataset = dataset1,shuffle = True,batch_size = 1)
for datapoint in dataloader:
  print(datapoint)

想增强数据时，仅用修改自定义类中的代码块即可，下面示例将"abcdefghijklmnopqrstuvwxyz"字符串扩增至2倍，并将扩增数据由小写变换成大写字符

import torch.utils.data 

class ExampleDataset(torch.utils.data.Dataset):
  def __init__(self):
    self.data = "abcdefghijklmnopqrstuvwxyz"
  
  def __getitem__(self,idx): # if the index is idx, what will be the data?
    if idx >= len(self.data): # if the index >= 26, return upper case letter
      return self.data[idx%26].upper()
    else: # if the index < 26, return lower case, return lower case letter
      return self.data[idx]
  
  def __len__(self): # What is the length of the dataset
    return 2 * len(self.data) # The length is now twice as large

dataset1 = ExampleDataset() # create the dataset
dataloader = torch.utils.data.DataLoader(dataset = dataset1,shuffle = False,batch_size = 1)
for datapoint in dataloader:
  print(datapoint)

参考链接：https://colab.research.google.com/github/ga642381/ML2021-Spring/blob/main/Pytorch/Pytorch_Tutorial.ipynb