python - PureFrameworkTensorFoundError,运行时错误-FedeartedLearning

我正在尝试使用Pytorch进行联合学习的线性回归算法,但遇到以下错误。我正在Colab上实现它。根据我的说法,该错误可能是由于train()函数中的某些代码行引起的。友善的帮助是您曾经与Pysyft合作,并且之前曾遇到过此类错误。

RuntimeError: invalid argument 8: lda should be at least max(1, 0), but have 0 at /pytorch/aten/src/TH/generic/THBlas.cpp:363


以下是代码:

#import the necessasry packages
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import syft as sy

#create target and data variables as tensors
x_data=Variable(torch.Tensor([[1.0],[0.0],[1.0],[0.0]]))
y_data=Variable(torch.Tensor([[0.0],[0.0],[1.0],[1.0]]))

#Create virtual Workers
hook = sy.TorchHook(torch)
bob = sy.VirtualWorker(hook, id="bob")
alice = sy.VirtualWorker(hook, id="alice")

data_bob = x_data[0:2]
target_bob = y_data[0:2]
data_alice = x_data[2:0]
target_alice = y_data[2:0]

#creating a class that does Linear Regression
class LinearRegression (nn.Module):

  def __init__(self):
    super(LinearRegression,self). __init__ ()
    self.linear = torch.nn.Linear(1,1)

  def forward(self, x):
    y_pred = self.linear(x)
    return y_pred

#assign the function to the variable name 'Model'
model=LinearRegression()

#send the data to the virtual worker pointers
data_bob = data_bob.send(bob)
data_alice = data_alice.send(alice)

target_bob = target_bob.send(bob)
target_alice = target_alice.send(alice)

# organize pointers into a list
datasets = [(data_bob,target_bob),(data_alice,target_alice)]

#create optimizer and calculate the loss
opt = torch.optim.SGD(params=model.parameters(),lr=0.1)
criterion = torch.nn.MSELoss(size_average=False)

def train():
  opt = torch.optim.SGD(params=model.parameters(),lr=0.1)
  for epoch in range (20):
    model.train()
    print("Training started..")

    for x_data,y_data in datasets:

      model.send(x_data.location) 

      opt.zero_grad()

       #forwardpass
       #the model here is the linear regression model
      y_pred = model(x_data)

      #ComputeLoss
      loss=criterion(y_pred,y_data)

      #BackwardPass
      loss.backward()

      opt.step()

      model.get() 

      print(loss.get())

train()

最佳答案

您在这里有错字:

data_alice = x_data[2:0]
target_alice = y_data[2:0]


应该是 [2:]

由于 data_alice失败,因此出现此错误。