mat1 and mat2 must have the same dtype, but got Double and Float

[Jimmy3214]

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.../
import torch
from torch import nn
import numpy as np
print(x.dtype)

x=torch.from_numpy(np.array(x))
y=torch.from_numpy(np.array(y))

device="cuda" if torch.cuda.is_available() else "cpu"

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)
class CircleModelV0(nn.Module):
def init(self):
super().init()
self.layer_1=nn.Linear(in_features=2,out_features=5)
self.layer_2=nn.Linear(in_features=5,out_features=1)

def forward(self,x):
return self.layer_2(self.layer_1(x))
model_0=CircleModelV0().to(device)

with torch.inference_mode():
untrained_preds=model_0(x_test.to(device))

ERROR GENERATED IS
----> 2 untrained_preds=model_0(x_test.to(device))

5 frames
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py in forward(self, input)
114
115 def forward(self, input: Tensor) -> Tensor:
--> 116 return F.linear(input, self.weight, self.bias)
117
118 def extra_repr(self) -> str:

RuntimeError: mat1 and mat2 must have the same dtype, but got Double and Float

[LuluW8071]

Trying using uppercase X for datasets
Also your error was from here

X = torch.from_numpy(X).type(torch.float)   
y = torch.from_numpy(y).type(torch.float)

You didnt set type for X and y.

Full code

import torch
from torch import nn
import numpy as np
from sklearn.datasets import make_circles

n_samples = 1000

X, y = make_circles(n_samples,
                    noise = 0.03,
                    random_state = 42)

X = torch.from_numpy(X).type(torch.float)   
y = torch.from_numpy(y).type(torch.float)     # works for int dtype as well
# print(X.dtype, y.dtype)

device="cuda" if torch.cuda.is_available() else "cpu"

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)

class CircleModelV0(nn.Module):
  def __init__(self):
    super().__init__()
    self.layer_1=nn.Linear(in_features=2,out_features=5)
    self.layer_2=nn.Linear(in_features=5,out_features=1)

  def forward(self,x):
    return self.layer_2(self.layer_1(x))


model_0=CircleModelV0().to(device)

with torch.inference_mode():
  untrained_preds=model_0(x_test.to(device))