我试图通过PyTorch在CIFAR10数据集上创建逻辑模型。但是我遇到一个错误：
ValueError：预期输入的batch_size（900）匹配目标batch_size（300）。
我认为正在发生的事是3 * 100是300。所以RGB图像的3轴可能正在这样做，但是我不知道该怎么解决。
这些是我的超参数。
batch_size = 100
learning_rate = 0.001
# Other constants
input_size = 32*32
num_classes = 10
在这里，我将数据分为训练，验证和测试数据。
transform_train = transforms.Compose([transforms.Resize((32,32)),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
transforms.RandomAffine(0, shear=10, scale=(0.8,1.2)),
transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform = transforms.Compose([transforms.Resize((32,32)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
training_dataset = CIFAR10(root='D:\PyTorch\cifar-10-python', train=True, download=True, transform=transform_train)
train_ds, val_ds = random_split(training_dataset, [40000, 10000])
test_ds = CIFAR10(root='D:\PyTorch\cifar-10-python', train=False, download=True, transform=transform)
train_loader = DataLoader(train_ds, batch_size=100, shuffle=True)
val_loader = DataLoader(val_ds, batch_size = 100, shuffle = False)
test_loader = DataLoader(test_ds, batch_size = 100, shuffle=False)
这是模型。
class CifarModel(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(input_size, num_classes)
def forward(self, xb):
xb = xb.view(-1, 32*32)
#xb = xb.reshape(-1, 784)
print(xb.shape)
out = self.linear(xb)
return out
def training_step(self, batch):
images, labels = batch
out = self(images) # Generate predictions
loss = F.cross_entropy(out, labels) # Calculate loss
return loss
def validation_step(self, batch):
images, labels = batch
out = self(images) # Generate predictions
loss = F.cross_entropy(out, labels) # Calculate loss
acc = accuracy(out, labels) # Calculate accuracy
return {'val_loss': loss.detach(), 'val_acc': acc.detach()}
def validation_epoch_end(self, outputs):
batch_losses = [x['val_loss'] for x in outputs]
epoch_loss = torch.stack(batch_losses).mean() # Combine losses
batch_accs = [x['val_acc'] for x in outputs]
epoch_acc = torch.stack(batch_accs).mean() # Combine accuracies
return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}
def epoch_end(self, epoch, result):
print("Epoch [{}], val_loss: {:.4f}, val_acc: {:.4f}".format(epoch, result['val_loss'], result['val_acc']))
model = CifarModel()
def accuracy(outputs, labels):
_, preds = torch.max(outputs, dim=1)
return torch.tensor(torch.sum(preds == labels).item() / len(preds))
def evaluate(model, val_loader):
outputs = [model.validation_step(batch) for batch in val_loader]
return model.validation_epoch_end(outputs)
def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
history = []
optimizer = opt_func(model.parameters(), lr)
for epoch in range(epochs):
# Training Phase
for batch in train_loader:
loss = model.training_step(batch)
loss.backward()
optimizer.step()
optimizer.zero_grad()
# Validation phase
result = evaluate(model, val_loader)
model.epoch_end(epoch, result)
history.append(result)
return history
evaluate(model, val_loader)
这是我运行评估函数时遇到的错误：
torch.Size([900, 1024])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-23-3621eab8de1a> in <module>
21 history.append(result)
22 return history
---> 23 evaluate(model, val_loader)
<ipython-input-23-3621eab8de1a> in evaluate(model, val_loader)
3 return torch.tensor(torch.sum(preds == labels).item() / len(preds))
4 def evaluate(model, val_loader):
----> 5 outputs = [model.validation_step(batch) for batch in val_loader]
6 return model.validation_epoch_end(outputs)
7
<ipython-input-23-3621eab8de1a> in <listcomp>(.0)
3 return torch.tensor(torch.sum(preds == labels).item() / len(preds))
4 def evaluate(model, val_loader):
----> 5 outputs = [model.validation_step(batch) for batch in val_loader]
6 return model.validation_epoch_end(outputs)
7
<ipython-input-22-c9e17d21eaff> in validation_step(self, batch)
19 images, labels = batch
20 out = self(images) # Generate predictions
---> 21 loss = F.cross_entropy(out, labels) # Calculate loss
22 acc = accuracy(out, labels) # Calculate accuracy
23 return {'val_loss': loss.detach(), 'val_acc': acc.detach()}
~\Anaconda3\lib\site-packages\torch\nn\functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2019 if size_average is not None or reduce is not None:
2020 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2021 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
2022
2023
~\Anaconda3\lib\site-packages\torch\nn\functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1834 if input.size(0) != target.size(0):
1835 raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).'
-> 1836 .format(input.size(0), target.size(0)))
1837 if dim == 2:
1838 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
ValueError: Expected input batch_size (900) to match target batch_size (300).
答案 0 :(得分：1)
我看到的一个问题是以下行：
xb = xb.view(-1, 32*32)
在这里您说的是输入图像只有一个通道。换句话说，灰度。对其进行更改以反映通道数（RGB）：
xb = xb.view(-1, 32*32*3)