如何正确修改train_data = DermaMNIST(split="train", download=True) train_images=train_data.data.numpy() train_labels = train_data.targets.numpy()
时间: 2024-06-03 21:10:30 浏览: 25
您可以通过以下步骤来正确修改train_data = DermaMNIST(split="train", download=True) train_images=train_data.data.numpy() train_labels = train_data.targets.numpy():
1. 确保您已经正确导入DermaMNIST数据集并且已经安装了相应的库和模块。
2. 修改train_data = DermaMNIST(split="train", download=True)中的split参数来指定要使用的数据集分割方式。例如,split="test"将会使用测试集的数据进行训练。
3. 修改train_images=train_data.data.numpy()来获取训练集数据集中的图像数据,您可以使用其他方法来获取数据,例如PIL库,opencv等。
4. 修改train_labels = train_data.targets.numpy()来获取训练集数据集中的标签数据。
请注意,这里的修改仅是示例,根据您的具体情况需要自行修改相应的代码。
相关问题
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:
```python
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。
train_data = np.load('train_data.npy') 是什么意思
这行代码的意思是从名为 "train_data.npy" 的 numpy 数组文件中加载数据,并将其存储在名为 "train_data" 的变量中。 numpy.load() 函数是用于从文件中加载数组的函数。这里,我们将加载的数组存储在名为 "train_data" 的变量中,以便在训练模型时使用。