[英]Pytorch getting RuntimeError: Found dtype Double but expected Float
I am trying to implement a neural net in PyTorch but it doesn't seem to work.我正在尝试在 PyTorch 中实现神经网络,但它似乎不起作用。 The problem seems to be in the training loop.
问题似乎出在训练循环中。 I've spend several hours into this but can't get it right.
我已经花了几个小时来解决这个问题,但无法做到这一点。 Please help, thanks.
请帮忙,谢谢。
I haven't added the data preprocessing parts.我还没有添加数据预处理部分。
# importing libraries
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import torch.nn.functional as F
# get x function (dataset related stuff)
def Getx(idx):
sample = samples[idx]
vector = Calculating_bottom(sample)
vector = torch.as_tensor(vector, dtype = torch.float64)
return vector
# get y function (dataset related stuff)
def Gety(idx):
y = np.array(train.iloc[idx, 4], dtype = np.float64)
y = torch.as_tensor(y, dtype = torch.float64)
return y
# dataset
class mydataset(Dataset):
def __init__(self):
super().__init__()
def __getitem__(self, index):
x = Getx(index)
y = Gety(index)
return x, y
def __len__(self):
return len(train)
dataset = mydataset()
# sample dataset value
print(dataset.__getitem__(0))
(tensor([ 5., 5., 8., 14.], dtype=torch.float64), tensor(-0.3403, dtype=torch.float64)) (张量([ 5., 5., 8., 14.], dtype=torch.float64), tensor(-0.3403, dtype=torch.float64))
# data-loader
dataloader = DataLoader(dataset, batch_size = 1, shuffle = True)
# nn architecture
class Net(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 4)
self.fc2 = nn.Linear(4, 2)
self.fc3 = nn.Linear(2, 1)
def forward(self, x):
x = x.float()
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
model = Net()
# device
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
# hyper-parameters
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
# training loop
for epoch in range(5):
for batch in dataloader:
# unpacking
x, y = batch
x.to(device)
y.to(device)
# reset gradients
optimizer.zero_grad()
# forward propagation through the network
out = model(x)
# calculate the loss
loss = criterion(out, y)
# backpropagation
loss.backward()
# update the parameters
optimizer.step()
Error:错误:
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/loss.py:446: UserWarning: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([1, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
return F.mse_loss(input, target, reduction=self.reduction)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-18-3f68fcee9ff3> in <module>
20
21 # backpropagation
---> 22 loss.backward()
23
24 # update the parameters
/opt/conda/lib/python3.7/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph)
219 retain_graph=retain_graph,
220 create_graph=create_graph)
--> 221 torch.autograd.backward(self, gradient, retain_graph, create_graph)
222
223 def register_hook(self, hook):
/opt/conda/lib/python3.7/site-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
130 Variable._execution_engine.run_backward(
131 tensors, grad_tensors_, retain_graph, create_graph,
--> 132 allow_unreachable=True) # allow_unreachable flag
133
134
RuntimeError: Found dtype Double but expected Float
You need the data type of the data to match the data type of the model.您需要数据的数据类型与 model 的数据类型相匹配。
Either convert the model to double (recommended for simple nets with no serious performance problems such as yours)将 model 转换为双倍(推荐用于没有严重性能问题的简单网络,例如您的)
# nn architecture
class Net(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 4)
self.fc2 = nn.Linear(4, 2)
self.fc3 = nn.Linear(2, 1)
self.double()
or convert the data to float.或将数据转换为浮点数。
class mydataset(Dataset):
def __init__(self):
super().__init__()
def __getitem__(self, index):
x = Getx(index)
y = Gety(index)
return x.float(), y.float()
Check data type of "out" and "y"检查“out”和“y”的数据类型
print(out.dtype)
print(y.dtype)
you may find a difference like你可能会发现不同之处
"torch.float32"
"torch.float64"
Set them in the same type.将它们设置为相同的类型。
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