Multi class classification - RuntimeError: 1D target tensor expected, multi-target not supported
Multi class classification - RuntimeError: 1D target tensor expected, multi-target not supported
我的目标是使用 Pytorch 并基于 EMNIST 数据集（字母的黑白图片）构建多 class 图像 classifier。
我的训练数据 X_train 的形状是 (124800, 28, 28)。
原始目标变量的形状 y_train 是 (124800, 1)，但是我创建了一个单热编码，所以现在的形状是 (124800, 26)。
我正在构建的模型应该有 26 个输出变量，每个变量代表一个字母的概率。
我读入我的数据如下：
import scipy .io
emnist = scipy.io.loadmat(DATA_DIR + '/emnist-letters.mat')
data = emnist ['dataset']
X_train = data ['train'][0, 0]['images'][0, 0]
X_train = X_train.reshape((-1,28,28), order='F')
y_train = data ['train'][0, 0]['labels'][0, 0]
然后，我创建了一个one-hot-encoding如下：
y_train_one_hot = np.zeros([len(y_train), 27])
for i in range (0, len(y_train)):
y_train_one_hot[i, y_train[i][0]] = 1
y_train_one_hot = np.delete(y_train_one_hot, 0, 1)
我用以下方法创建数据集：
train_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_train), torch.from_numpy(y_train_one_hot))
batch_size = 128
n_iters = 3000
num_epochs = n_iters / (len(train_dataset) / batch_size)
num_epochs = int(num_epochs)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
然后我按如下方式构建我的模型：
class CNNModel(nn.Module):
def __init__(self):
super(CNNModel, self).__init__()
# Convolution 1
self.cnn1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=0)
self.relu1 = nn.ReLU()
# Max pool 1
self.maxpool1 = nn.MaxPool2d(2,2)
# Convolution 2
self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=0)
self.relu2 = nn.ReLU()
# Max pool 2
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
# Fully connected 1 (readout)
self.fc1 = nn.Linear(32 * 4 * 4, 26)
def forward(self, x):
# Convolution 1
out = self.cnn1(x.float())
out = self.relu1(out)
# Max pool 1
out = self.maxpool1(out)
# Convolution 2
out = self.cnn2(out)
out = self.relu2(out)
# Max pool 2
out = self.maxpool2(out)
# Resize
# Original size: (100, 32, 7, 7)
# out.size(0): 100
# New out size: (100, 32*7*7)
out = out.view(out.size(0), -1)
# Linear function (readout)
out = self.fc1(out)
return out
model = CNNModel()
criterion = nn.CrossEntropyLoss()
learning_rate = 0.01
optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
然后我按如下方式训练模型：
iter = 0
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# Add a single channel dimension
# From: [batch_size, height, width]
# To: [batch_size, 1, height, width]
images = images.unsqueeze(1)
# Forward pass to get output/logits
outputs = model(images)
# Clear gradients w.r.t. parameters
optimizer.zero_grad()
# Forward pass to get output/logits
outputs = model(images)
# Calculate Loss: softmax --> cross entropy loss
loss = criterion(outputs, labels)
# Getting gradients w.r.t. parameters
loss.backward()
# Updating parameters
optimizer.step()
iter += 1
if iter % 500 == 0:
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
for images, labels in test_loader:
images = images.unsqueeze(1)
# Forward pass only to get logits/output
outputs = model(images)
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
# Print Loss
print('Iteration: {}. Loss: {}. Accuracy: {}'.format(iter, loss.data[0], accuracy))
然而，当我运行这个时，我得到以下错误：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-11-c26c43bbc32e> in <module>()
21
22 # Calculate Loss: softmax --> cross entropy loss
---> 23 loss = criterion(outputs, labels)
24
25 # Getting gradients w.r.t. parameters
3 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
548 result = self._slow_forward(*input, **kwargs)
549 else:
--> 550 result = self.forward(*input, **kwargs)
551 for hook in self._forward_hooks.values():
552 hook_result = hook(self, input, result)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in forward(self, input, target)
930 def forward(self, input, target):
931 return F.cross_entropy(input, target, weight=self.weight,
--> 932 ignore_index=self.ignore_index, reduction=self.reduction)
933
934
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2315 if size_average is not None or reduce is not None:
2316 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2317 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
2318
2319
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
2113 .format(input.size(0), target.size(0)))
2114 if dim == 2:
-> 2115 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
2116 elif dim == 4:
2117 ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: 1D target tensor expected, multi-target not supported
当我 initialize/use 我的损失函数时，我预计我做错了什么。我该怎么做才能开始训练我的模型？
如果你正在使用交叉熵损失，你不应该一次性编码你的目标变量 y。
Pytorch 交叉熵只期望 class 索引作为目标，而不是它们的单热编码版本。
引用文档 https://pytorch.org/docs/master/generated/torch.nn.CrossEntropyLoss.html :
This criterion expects a class index in the range [0, C-1] as the target for each value of a 1D tensor of size minibatch;
我的目标是使用 Pytorch 并基于 EMNIST 数据集（字母的黑白图片）构建多 class 图像 classifier。
我的训练数据 X_train 的形状是 (124800, 28, 28)。
原始目标变量的形状 y_train 是 (124800, 1)，但是我创建了一个单热编码，所以现在的形状是 (124800, 26)。
我正在构建的模型应该有 26 个输出变量，每个变量代表一个字母的概率。
我读入我的数据如下：
import scipy .io
emnist = scipy.io.loadmat(DATA_DIR + '/emnist-letters.mat')
data = emnist ['dataset']
X_train = data ['train'][0, 0]['images'][0, 0]
X_train = X_train.reshape((-1,28,28), order='F')
y_train = data ['train'][0, 0]['labels'][0, 0]
然后，我创建了一个one-hot-encoding如下：
y_train_one_hot = np.zeros([len(y_train), 27])
for i in range (0, len(y_train)):
y_train_one_hot[i, y_train[i][0]] = 1
y_train_one_hot = np.delete(y_train_one_hot, 0, 1)
我用以下方法创建数据集：
train_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_train), torch.from_numpy(y_train_one_hot))
batch_size = 128
n_iters = 3000
num_epochs = n_iters / (len(train_dataset) / batch_size)
num_epochs = int(num_epochs)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
然后我按如下方式构建我的模型：
class CNNModel(nn.Module):
def __init__(self):
super(CNNModel, self).__init__()
# Convolution 1
self.cnn1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=0)
self.relu1 = nn.ReLU()
# Max pool 1
self.maxpool1 = nn.MaxPool2d(2,2)
# Convolution 2
self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=0)
self.relu2 = nn.ReLU()
# Max pool 2
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
# Fully connected 1 (readout)
self.fc1 = nn.Linear(32 * 4 * 4, 26)
def forward(self, x):
# Convolution 1
out = self.cnn1(x.float())
out = self.relu1(out)
# Max pool 1
out = self.maxpool1(out)
# Convolution 2
out = self.cnn2(out)
out = self.relu2(out)
# Max pool 2
out = self.maxpool2(out)
# Resize
# Original size: (100, 32, 7, 7)
# out.size(0): 100
# New out size: (100, 32*7*7)
out = out.view(out.size(0), -1)
# Linear function (readout)
out = self.fc1(out)
return out
model = CNNModel()
criterion = nn.CrossEntropyLoss()
learning_rate = 0.01
optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
然后我按如下方式训练模型：
iter = 0
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# Add a single channel dimension
# From: [batch_size, height, width]
# To: [batch_size, 1, height, width]
images = images.unsqueeze(1)
# Forward pass to get output/logits
outputs = model(images)
# Clear gradients w.r.t. parameters
optimizer.zero_grad()
# Forward pass to get output/logits
outputs = model(images)
# Calculate Loss: softmax --> cross entropy loss
loss = criterion(outputs, labels)
# Getting gradients w.r.t. parameters
loss.backward()
# Updating parameters
optimizer.step()
iter += 1
if iter % 500 == 0:
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
for images, labels in test_loader:
images = images.unsqueeze(1)
# Forward pass only to get logits/output
outputs = model(images)
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
# Print Loss
print('Iteration: {}. Loss: {}. Accuracy: {}'.format(iter, loss.data[0], accuracy))
然而，当我运行这个时，我得到以下错误：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-11-c26c43bbc32e> in <module>()
21
22 # Calculate Loss: softmax --> cross entropy loss
---> 23 loss = criterion(outputs, labels)
24
25 # Getting gradients w.r.t. parameters
3 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
548 result = self._slow_forward(*input, **kwargs)
549 else:
--> 550 result = self.forward(*input, **kwargs)
551 for hook in self._forward_hooks.values():
552 hook_result = hook(self, input, result)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in forward(self, input, target)
930 def forward(self, input, target):
931 return F.cross_entropy(input, target, weight=self.weight,
--> 932 ignore_index=self.ignore_index, reduction=self.reduction)
933
934
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2315 if size_average is not None or reduce is not None:
2316 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2317 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
2318
2319
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
2113 .format(input.size(0), target.size(0)))
2114 if dim == 2:
-> 2115 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
2116 elif dim == 4:
2117 ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: 1D target tensor expected, multi-target not supported
当我 initialize/use 我的损失函数时，我预计我做错了什么。我该怎么做才能开始训练我的模型？
如果你正在使用交叉熵损失，你不应该一次性编码你的目标变量 y。 Pytorch 交叉熵只期望 class 索引作为目标，而不是它们的单热编码版本。
引用文档 https://pytorch.org/docs/master/generated/torch.nn.CrossEntropyLoss.html :
This criterion expects a class index in the range [0, C-1] as the target for each value of a 1D tensor of size minibatch;