import torch import torchvision import torch.nn as nn import torchvision.transforms as transforms #超参数 num_classes = 10 #类别个数 num_epochs = 10 #迭代次数/训练次数 learning_rate = 0.01 #学习率，方法更新的速率 batch_size = 64 #批次，批量大小，每次传输图片的个数 #训练集 train_dataset = torchvision.datasets.MNIST(root = './data', train = True, download = True, transform = transforms.Compose([ transforms.Resize((28,28)),transforms.ToTensor()]) ) #测试集 test_dataset = torchvision.datasets.MNIST(root = './data', train = False, download = True, transform = transforms.Compose([ transforms.Resize((28,28)),transforms.ToTensor()]) )

你好！这段代码是用于加载MNIST数据集的。MNIST是一个手写数字识别数据集，包含了60000张训练图片和10000张测试图片，每张图片是28x28像素的灰度图像。

代码中使用了torchvision库来加载MNIST数据集。首先定义了一些超参数，包括类别个数、迭代次数、学习率和批次大小。

接下来，创建了训练集和测试集的数据集对象。train_dataset对象表示训练集，通过指定`train=True`来加载训练集。test_dataset对象表示测试集，通过指定`train=False`来加载测试集。

在加载数据集时，还进行了一些预处理操作。使用了transforms.Compose()函数来将多个预处理操作组合起来。这里使用了两个预处理操作：Resize将图像大小调整为28x28像素，ToTensor将图像转换为Tensor类型。

这样，训练集和测试集的数据集对象就创建好了。接下来可以使用DataLoader来实现数据的批量加载和随机打乱。

相关问题

基于以下代码，加入图像高斯模糊处理代码：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 import pylab %matplotlib inline # 定义超参数 input_size = 28 #图像的总尺寸28*28 num_classes = 10 #标签的种类数 num_epochs = 10 #训练的总循环周期 batch_size = 64 #一个撮（批次）的大小，64张图片 # 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()) # 构建batch数据 train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)

可以将以下代码加入到定义超参数的代码块后面，实现对训练集和测试集图像的高斯模糊处理：

# 定义高斯模糊的卷积核
blur_kernel = np.array([[1, 2, 1],
                        [2, 4, 2],
                        [1, 2, 1]]) / 16.

# 定义高斯模糊的函数
def gaussian_blur(x):
    x = np.array(x)
    _, c, h, w = x.shape
    blur_kernel_tensor = torch.from_numpy(blur_kernel).unsqueeze(0).repeat(c, 1, 1, 1).float().to(x.device)
    x_blur = F.conv2d(x, blur_kernel_tensor, padding=(1, 1))
    return x_blur

# 对训练集图像进行高斯模糊处理
for i, (images, labels) in enumerate(train_loader):
    train_loader.dataset.data[i*batch_size:(i+1)*batch_size] = gaussian_blur(images).squeeze().detach().numpy()*255

# 对测试集图像进行高斯模糊处理
for i, (images, labels) in enumerate(test_loader):
    test_loader.dataset.data[i*batch_size:(i+1)*batch_size] = gaussian_blur(images).squeeze().detach().numpy()*255

注意，由于 MNIST 数据集中的像素值范围为 0~255，高斯模糊处理后的像素值可能会出现小数，需要将其乘以 255 后转换为整数的像素值。

修改一下这段代码在pycharm中的实现，import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim #from torchvision import datasets,transforms import torch.utils.data as data #from torch .nn:utils import weight_norm import matplotlib.pyplot as plt from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import cohen_kappa_score data_ = pd.read_csv(open(r"C:\Users\zhangjinyue\Desktop\rice.csv"),header=None) data_ = np.array(data_).astype('float64') train_data =data_[:,:520] train_Data =np.array(train_data).astype('float64') train_labels=data_[:,520] train_labels=np.array(train_data).astype('float64') train_data,train_data,train_labels,train_labels=train_test_split(train_data,train_labels,test_size=0.33333) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) start_epoch=1 num_epoch=1 BATCH_SIZE=70 Ir=0.001 classes=('0','1','2','3','4','5') device=torch.device("cuda"if torch.cuda.is_available()else"cpu") torch.backends.cudnn.benchmark=True best_acc=0.0 train_dataset=data.TensorDataset(train_data,train_labels) test_dataset=data.TensorDataset(train_data,train_labels) train_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True) test_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)

修改后的代码如下所示：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, cohen_kappa_score

data_ = pd.read_csv(r"C:\Users\zhangjinyue\Desktop\rice.csv", header=None)
data_ = np.array(data_).astype('float64')

train_data = data_[:, :520]
train_labels = data_[:, 520]
train_data, _, train_labels, _ = train_test_split(train_data, train_labels, test_size=0.33333)

train_data = torch.Tensor(train_data)
train_labels = torch.LongTensor(train_labels)
train_data = train_data.reshape(-1, 1, 20, 26)

start_epoch = 1
num_epoch = 1
BATCH_SIZE = 70
Ir = 0.001
classes = ('0', '1', '2', '3', '4', '5')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cudnn.benchmark = True
best_acc = 0.0

train_dataset = data.TensorDataset(train_data, train_labels)
test_dataset = data.TensorDataset(train_data, train_labels)

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

请注意，我做了以下修改：

1. 移除了不需要的导入语句。
2. 修复了变量名拼写错误。
3. 移除了重复的代码行。
4. 修正了 `torch.utils.data.DataLoader` 的拼写错误。
5. 修正了数据集分割时的变量名错误。

请根据你的实际需求进一步调整代码。