RuntimeError: 尺寸不匹配 m1: [a x b], m2: [c x d] [英] RuntimeError: size mismatch m1: [a x b], m2: [c x d]
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问题描述
任何人都可以帮助我吗?我得到以下错误.我使用谷歌 Colab.如何解决这个错误?
Can anyone help me in this.? I am getting below error. I use Google Colab. How to Solve this error.?
大小不匹配,m1:[64 x 100],m2:[784 x 128] 在/pytorch/aten/src/TH/generic/THTensorMath.cpp:2070
size mismatch, m1: [64 x 100], m2: [784 x 128] at /pytorch/aten/src/TH/generic/THTensorMath.cpp:2070
下面的代码我正在尝试运行.
Below Code I am trying to Run.
import torch
from torch import nn
import torch.nn.functional as F
from torchvision import datasets, transforms
# Define a transform to normalize the data
transform =
transforms.Compose([transforms.CenterCrop(10),transforms.ToTensor(),])
# Download the load the training data
trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True,
train=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64,
shuffle=True)
# Build a feed-forward network
model = nn.Sequential(nn.Linear(784, 128),nn.ReLU(),nn.Linear(128,
64),nn.ReLU(),nn.Linear(64, 10))
# Define the loss
criterion = nn.CrossEntropyLoss()
# Get our data
images, labels = next(iter(trainloader))
# Faltten images
images = images.view(images.shape[0], -1)
# Forward pass, get our logits
logits = model(images)
# Calculate the loss with the logits and the labels
loss = criterion(logits, labels)
print(loss)
推荐答案
你只需要关心 b=c
就完成了:
All you have to care is b=c
and you are done:
m1: [a x b], m2: [c x d]
m1
是 [a x b]
即 [batch size x in features]
m2
是 [c x d]
即 [in features x out features]
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