预期输入 batch_size (500) 以匹配目标 batch_size (1000)
Expected input batch_size (500) to match target batch_size (1000)
我正在尝试在 PyTorch 中使用 MNIST 数据训练 CNN。但是，我收到 ValueError: Expected input batch_size (500) to match target batch_size (1000). 这发生在我 运行 测试时() 命令在下面的代码中。我已经查找了此问题的解决方案，但其中 none 有助于解决此问题。
我的代码如下：
n_epochs = 20
batch_size_train = 64
batch_size_test = 1000
learning_rate = 1e-4
log_interval = 50
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=5)
self.conv2 = nn.Conv2d(64, 128, kernel_size=1)
self.fc1 = nn.Linear(9216, 100)
self.fc2 = nn.Linear(100, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 9216)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
def loss_function(self, out, target):
return F.cross_entropy(out, target)
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
torch.nn.init.xavier_uniform_(m.weight)
m.bias.data.fill_(0.01)
network = Net()
network.apply(init_weights)
network.cuda()
optimizer = optim.Adam(network.parameters(), lr=1e-4)
def train(epoch):
network.train()
for batch_idx, (data, target) in enumerate(train_loader):
data = data.cuda()
target = target.cuda()
optimizer.zero_grad()
output = network(data)
loss = network.loss_function(output, target)
loss.backward()
optimizer.step()
if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def test():
network.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data = data.cuda()
target = target.cuda()
target = target.view(batch_size_test)
output = network(data)
test_loss += network.loss_function(output, target).item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).sum()
test_loss /= len(test_loader.dataset)
print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
test()
for epoch in range(1, n_epochs + 1):
train(epoch)
test()
完整错误日志：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-ef6e122ea50c> in <module>()
----> 1 test()
2 for epoch in range(1, n_epochs + 1):
3 train(epoch)
4 test()
3 frames
<ipython-input-9-23a4b65d1ae9> in test()
9 target = target.view(batch_size_test)
10 output = network(data)
---> 11 test_loss += network.loss_function(output, target).item()
12 pred = output.data.max(1, keepdim=True)[1]
13 correct += pred.eq(target.data.view_as(pred)).sum()
<ipython-input-5-d97bf44ef6f0> in loss_function(self, out, target)
91
92 def loss_function(self, out, target):
---> 93 return F.cross_entropy(out, target)
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2466 if size_average is not None or reduce is not None:
2467 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2468 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
2469
2470
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
2260 if input.size(0) != target.size(0):
2261 raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).'
-> 2262 .format(input.size(0), target.size(0)))
2263 if dim == 2:
2264 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
ValueError: Expected input batch_size (500) to match target batch_size (1000).
请告诉我如何解决这个问题。
谢谢，
文尼
您的数据具有以下形状 [batch_size, c=1, h=28, w=28]。 batch_size 火车等于 64，测试集等于 1000，但这没有任何区别，我们不应该处理第一个 dim。
要使用F.cross_entropy，你必须提供大小为[batch_size, nb_classes]的张量，这里nb_classes是10。所以你的模型的最后一层应该有总共10个神经元。
附带说明一下，在使用此标准时，您不应在模型输出中使用 F.log_softmax（请参阅 here ).
This criterion combines log_softmax and nll_loss in a single function.
但这不是问题所在。问题是您的模型没有输出 [batch_size, 10] 张量。问题是您使用 view：张量从 torch.Size([64, 128, 6, 6]) 变为 torch.Size([32, 9216])。您基本上是说“在 dim=1 上将所有内容压缩到总共 9216 (128*6*6*64/2)，让其余的 (32) 保持在 dim=0”。这是不希望的，因为您弄乱了批次。
在 CNN 层之后的这个特定实例中使用 Flatten 层会更容易。这将使每个通道的所有值变平。确保使用 start_dim=1.
保留第一个维度
这是一个示例，旨在说明图层是 随机的 但代码可以运行。您应该根据自己的喜好调整内核大小、通道数等！
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=4)
self.conv2 = nn.Conv2d(32, 32, kernel_size=8)
self.fc1 = nn.Linear(128, 100)
self.fc2 = nn.Linear(100, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = torch.flatten(x, start_dim=1)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
我正在尝试在 PyTorch 中使用 MNIST 数据训练 CNN。但是，我收到 ValueError: Expected input batch_size (500) to match target batch_size (1000). 这发生在我 运行 测试时() 命令在下面的代码中。我已经查找了此问题的解决方案，但其中 none 有助于解决此问题。
我的代码如下：
n_epochs = 20
batch_size_train = 64
batch_size_test = 1000
learning_rate = 1e-4
log_interval = 50
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=5)
self.conv2 = nn.Conv2d(64, 128, kernel_size=1)
self.fc1 = nn.Linear(9216, 100)
self.fc2 = nn.Linear(100, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 9216)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
def loss_function(self, out, target):
return F.cross_entropy(out, target)
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
torch.nn.init.xavier_uniform_(m.weight)
m.bias.data.fill_(0.01)
network = Net()
network.apply(init_weights)
network.cuda()
optimizer = optim.Adam(network.parameters(), lr=1e-4)
def train(epoch):
network.train()
for batch_idx, (data, target) in enumerate(train_loader):
data = data.cuda()
target = target.cuda()
optimizer.zero_grad()
output = network(data)
loss = network.loss_function(output, target)
loss.backward()
optimizer.step()
if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def test():
network.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data = data.cuda()
target = target.cuda()
target = target.view(batch_size_test)
output = network(data)
test_loss += network.loss_function(output, target).item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).sum()
test_loss /= len(test_loader.dataset)
print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
test()
for epoch in range(1, n_epochs + 1):
train(epoch)
test()
完整错误日志：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-ef6e122ea50c> in <module>()
----> 1 test()
2 for epoch in range(1, n_epochs + 1):
3 train(epoch)
4 test()
3 frames
<ipython-input-9-23a4b65d1ae9> in test()
9 target = target.view(batch_size_test)
10 output = network(data)
---> 11 test_loss += network.loss_function(output, target).item()
12 pred = output.data.max(1, keepdim=True)[1]
13 correct += pred.eq(target.data.view_as(pred)).sum()
<ipython-input-5-d97bf44ef6f0> in loss_function(self, out, target)
91
92 def loss_function(self, out, target):
---> 93 return F.cross_entropy(out, target)
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2466 if size_average is not None or reduce is not None:
2467 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2468 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
2469
2470
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
2260 if input.size(0) != target.size(0):
2261 raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).'
-> 2262 .format(input.size(0), target.size(0)))
2263 if dim == 2:
2264 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
ValueError: Expected input batch_size (500) to match target batch_size (1000).
请告诉我如何解决这个问题。 谢谢， 文尼
您的数据具有以下形状 [batch_size, c=1, h=28, w=28]。 batch_size 火车等于 64，测试集等于 1000，但这没有任何区别，我们不应该处理第一个 dim。
要使用F.cross_entropy，你必须提供大小为[batch_size, nb_classes]的张量，这里nb_classes是10。所以你的模型的最后一层应该有总共10个神经元。
附带说明一下，在使用此标准时，您不应在模型输出中使用 F.log_softmax（请参阅 here ).
This criterion combines log_softmax and nll_loss in a single function.
但这不是问题所在。问题是您的模型没有输出 [batch_size, 10] 张量。问题是您使用 view：张量从 torch.Size([64, 128, 6, 6]) 变为 torch.Size([32, 9216])。您基本上是说“在 dim=1 上将所有内容压缩到总共 9216 (128*6*6*64/2)，让其余的 (32) 保持在 dim=0”。这是不希望的，因为您弄乱了批次。
在 CNN 层之后的这个特定实例中使用 Flatten 层会更容易。这将使每个通道的所有值变平。确保使用 start_dim=1.
这是一个示例，旨在说明图层是 随机的 但代码可以运行。您应该根据自己的喜好调整内核大小、通道数等！
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=4)
self.conv2 = nn.Conv2d(32, 32, kernel_size=8)
self.fc1 = nn.Linear(128, 100)
self.fc2 = nn.Linear(100, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = torch.flatten(x, start_dim=1)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x