如何解决PySyft AttributeError: 'DataFrame' 对象在从 csv 读取数据时没有属性 'federate'
我正在尝试为我的 csv 数据实现联合学习的 pysyft 代码。我正在关注的教程是 https://github.com/bt-s/Split-Learning-and-Federated-Learning/blob/master/src/federated_learning.py 他们使用了火炬库 FMNIST 数据,即 iamge 。我在为我的 csv 数据自定义此代码时遇到困难。
这是我收到的错误
文件“C:/user/python/PCA/federated_learning.py”,第 175 行,在 train_loader = sy.FederatedDataLoader(train_set,transform=data_transformer.federate(workers),train=True,batch_size=args.batch_size,shuffle=True,**kwargs) AttributeError: 'Compose' 对象没有属性 'federate
# Pysyft needs to be hooked to PyTorch to enable its features
hook = sy.TorchHook(torch)
# Define the workers
alfa = sy.VirtualWorker(hook,id="alfa")
bravo = sy.VirtualWorker(hook,id="bravo")
workers = (alfa,bravo)
device = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(device)
kwargs = {'num_workers': 1,'pin_memory': True} if device=="cuda" else {}
# Specify required data transformation
data_transformer = transforms.Compose([
transforms.ToTensor(),transforms.normalize((0.5,),(0.5,))
])
import pandas as pd
print("Loading CSV...")
test_set = pd.read_csv("C:/user/python/PCA/data/test.csv",encoding = "UTF-8")
train_set = pd.read_csv("C:/user/python/PCA/data/train.csv",encoding = "UTF-8")
train_loader = sy.FederatedDataLoader(train_set,**kwargs)
test_loader = torch.utils.data.DataLoader(test_set,transform=data_transformer,train=False,**kwargs)
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