train_dataset = Dataset('C:\\Users\\865878275\\Desktop\\1\\mode\\Code\\Code\\Test\\gan.npz', True)
时间: 2024-05-07 13:14:42 浏览: 7
根据你提供的信息,train_dataset是一个数据集对象,它是由一个npz文件初始化而来,这个npz文件的路径是'C:\\Users\\865878275\\Desktop\\1\\mode\\Code\\Code\\Test\\gan.npz',第二个参数True表示这个数据集需要进行shuffle(随机打乱)操作。npz文件通常是numpy保存的多个数组的压缩文件,这意味着train_dataset可能包含多个数组,你可以通过调用train_dataset中的方法获取这些数组。例如,你可以通过train_dataset.get_images()获取图像数据。你也可以通过train_dataset.get_labels()获取标签数据(如果有的话)。
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_dataset = datasets.MNIST(root='D:\\dataset\\MNIST', train=True, download=True, transform=transform) train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size) test_dataset = datasets.MNIST(root='D:\\dataset\\MNIST', train=False, download=True, transform=transform) test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size) 怎么改变为.mat形式的输入
若要将MNIST数据集转换为.mat形式,则可以使用SciPy库中的`scipy.io.savemat()`函数。具体代码如下:
```python
import scipy.io as sio
import numpy as np
from torchvision import datasets, transforms
# 数据预处理
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
# 加载MNIST训练集和测试集
train_dataset = datasets.MNIST(root='D:\\dataset\\MNIST', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='D:\\dataset\\MNIST', train=False, download=True, transform=transform)
# 将MNIST训练集转换为.mat格式
train_data = train_dataset.data.numpy()
train_labels = train_dataset.targets.numpy()
sio.savemat('train_data.mat', {'train_data': train_data, 'train_labels': train_labels})
# 将MNIST测试集转换为.mat格式
test_data = test_dataset.data.numpy()
test_labels = test_dataset.targets.numpy()
sio.savemat('test_data.mat', {'test_data': test_data, 'test_labels': test_labels})
```
运行以上代码后,会生成`train_data.mat`和`test_data.mat`两个文件,分别包含MNIST训练集和测试集的图像数据和标签。