问题

在代码的以下部分出现了错误：

correct += (predicted == labels).sum().item()

错误信息提示了问题：张量 a 的大小（19）必须与张量 b 的大小（64）在非单例维度 1 处匹配。这意味着你的模型输出（predicted）的大小在维度 1 上是 19，而标签（labels）的大小在相同的维度上是 64。这是导致错误的原因。

问题可能出现在以下几个地方：

模型的输出层大小：检查你的模型输出层（self.fc2）的大小是否正确。根据你的目标数据，输出层应该有 64 个神经元。
损失函数：你在使用损失函数时，nn.CrossEntropyLoss() 应该用于多类别分类问题，而不是回归问题。确保你的问题是分类问题，如果是回归问题，使用适当的损失函数。
标签的格式：确保标签（labels）的格式正确。标签应该是整数形式，而不是浮点数。你可以使用 labels.long() 来将标签转换为整数类型。
输出的处理：你在处理模型的输出时，可以尝试添加 softmax 操作，以确保输出是类别概率。例如，可以使用 outputs = F.softmax(model(inputs), dim=1) 来获得概率分布。

检查并确保上述问题没有出现，应该能够解决这个错误。如果问题仍然存在，可以提供更多关于数据和模型结构的信息，以便更详细地排查问题。

英文:

I am trying to train a model with the data of the size: torch.Size([280652, 87]) and the target: torch.Size([280652, 64]) with 80% in the training data and 20 in the test data.
My code:

#split the data in train and test
X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2)
# convert to torch tensors
train = torch.tensor(X_train.values, dtype=torch.float32)
test = torch.tensor(X_test.values, dtype=torch.float32)
train_target = torch.tensor(y_train.values, dtype=torch.float32)
test_target = torch.tensor(y_test.values, dtype=torch.float32)
# inizializing and forward propagation
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(87, 50)# layer 1
        self.fc2 = nn.Linear(50, 64)# layer 2
        self.relu = nn.ReLU()# aktivation method

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x
#print(shapes)
print(train.shape)
print(train_target.shape)
print(test.shape)
print(test_target.shape)
#trainings datasets
train_dataset = TensorDataset(train, train_target)
train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)
#train_dataloader = DataLoader(train_dataset, batch_size=len(train_dataset), shuffle=False)
test_dataset = TensorDataset(test, test_target)
test_dataloader = DataLoader(test_dataset, batch_size=64, shuffle=False)
#test_dataloader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)
model = MyModel()
#opimizer (ajust weights) for large amounts
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
print(train.shape)
print(train_target.shape)
print(test.shape)
print(test_target.shape)

#train = F.one_hot(train_target.to(torch.int64))

# Train the model
for epoch in range(10):
    for i, (inputs, labels) in enumerate(train_dataloader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels.float())
        loss.backward()
        optimizer.step()

        # Print the loss every 1000 iterations
        if i % 1000 == 0:
            print(train.shape)
            print(train_target.shape)
            print(test.shape)
            print(test_target.shape)
            print(f&quot;Epoch {epoch+1}, Iteration {i+1}, Loss {loss.item():.4f}&quot;)
correct = 0
total = 0
with torch.no_grad():
    for inputs, labels in test_dataloader:
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        predicted = torch.argmax(outputs, dim=1)
        #correct += (torch.argmax(predicted, dim=1) == labels).sum().item()
        #print(len(labels))
        #print(len(predicted))
        #print(predicted)
        #print(labels)
        correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print(f&quot;Test Accuracy: {accuracy:.2f}%&quot;)

the error accures in the line correct += (predicted == labels).sum().item() with the error: The size of tensor a (19) must match the size of tensor b (64) at non-singleton dimension 1
I have no idea where the 19 comes from and i thought that the batch size shouldnt matter.
Did i forget something or is there a mager error in my code?

I tryed adapting the batch size and the layer sizes and tried some diffrent methods but nothing seems to work right.

答案1

得分: 0

你已经两次应用了argmax函数。
请记住，因为我认为它们是独热编码的，所以你还必须对标签应用argmax。

我尝试过这个，它有效。我没有你的数据，所以我创建了一些虚拟数据：

import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import train_test_split
import numpy as np
from torch.utils.data import TensorDataset, DataLoader
from sklearn.preprocessing import OneHotEncoder

#虚拟数据
data = np.random.randn(10000, 87)
target = np.random.randint(0, 64, (10000, 1))
encoder = OneHotEncoder(sparse=False)
target = encoder.fit_transform(target)

#这部分基本上与你的代码相同
#将数据拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2)
# 转换为torch张量
train = torch.tensor(X_train, dtype=torch.float32)
test = torch.tensor(X_test, dtype=torch.float32)
train_target = torch.tensor(y_train, dtype=torch.float32)
test_target = torch.tensor(y_test, dtype=torch.float32)

#初始化和前向传播
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(87, 50)  # 第一层
        self.fc2 = nn.Linear(50, 64)  # 第二层
        self.relu = nn.ReLU()  # 激活函数

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

#打印形状
print(train.shape)
print(train_target.shape)
print(test.shape)
print(test_target.shape)

#训练数据集
train_dataset = TensorDataset(train, train_target)
train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)

#测试数据集
test_dataset = TensorDataset(test, test_target)
test_dataloader = DataLoader(test_dataset, batch_size=64, shuffle=False)

#创建模型
model = MyModel()

#优化器（调整权重）用于大规模数据
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

#打印形状
print(train.shape)
print(train_target.shape)
print(test.shape)
print(test_target.shape)

#训练模型
for epoch in range(10):
    for i, (inputs, labels) in enumerate(train_dataloader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels.float())
        loss.backward()
        optimizer.step()

        #每1000次迭代打印损失
        if i % 1000 == 0:
            print(train.shape)
            print(train_target.shape)
            print(test.shape)
            print(test_target.shape)
            print(f"Epoch {epoch + 1}, Iteration {i + 1}, Loss {loss.item():.4f}")

#现在是更改的部分
correct = 0
total = 0
with torch.no_grad():
    for inputs, labels in test_dataloader:
        outputs = model(inputs)
        #print(outputs.shape)
        _, predicted = torch.max(outputs.data, 1)
        print(predicted.shape)
        total += labels.size(0)
        labels = torch.argmax(labels, dim=1)
        correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print(f"Test Accuracy: {accuracy:.2f}%")

英文:

You have applied the argmax function twice.
Keep in mind, you also have to do apply an argmax for labels because I believe they are one-hot encoded.

I tried this and worked. I don't have your data so I created some dummy data:

import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import train_test_split
import numpy as np
from torch.utils.data import TensorDataset, DataLoader
from sklearn.preprocessing import OneHotEncoder
#dummy data
data = np.random.randn(10000,87)
target = np.random.randint(0, 64, (10000,1))
encoder = OneHotEncoder(sparse=False)
target = encoder.fit_transform(target)
#this part is basically identical to yours
#split the data in train and test
X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2)
# convert to torch tensors
train = torch.tensor(X_train, dtype=torch.float32)
test = torch.tensor(X_test, dtype=torch.float32)
train_target = torch.tensor(y_train, dtype=torch.float32)
test_target = torch.tensor(y_test, dtype=torch.float32)
# inizializing and forward propagation
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.fc1 = nn.Linear(87, 50)# layer 1
self.fc2 = nn.Linear(50, 64)# layer 2
self.relu = nn.ReLU()# aktivation method
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.fc2(x)
return x
#print(shapes)
print(train.shape)
print(train_target.shape)
print(test.shape)
print(test_target.shape)
#trainings datasets
train_dataset = TensorDataset(train, train_target)
train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)
#train_dataloader = DataLoader(train_dataset, batch_size=len(train_dataset), shuffle=False)
test_dataset = TensorDataset(test, test_target)
test_dataloader = DataLoader(test_dataset, batch_size=64, shuffle=False)
#test_dataloader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)
model = MyModel()
#opimizer (ajust weights) for large amounts
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
print(train.shape)
print(train_target.shape)
print(test.shape)
print(test_target.shape)
# Train the model
for epoch in range(10):
for i, (inputs, labels) in enumerate(train_dataloader):
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels.float())
loss.backward()
optimizer.step()
# Print the loss every 1000 iterations
if i % 1000 == 0:
print(train.shape)
print(train_target.shape)
print(test.shape)
print(test_target.shape)
print(f&quot;Epoch {epoch+1}, Iteration {i+1}, Loss {loss.item():.4f}&quot;)
#now here is the change
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in test_dataloader:
outputs = model(inputs)
#print(outputs.shape)
_, predicted = torch.max(outputs.data, 1)
print(predicted.shape)
total += labels.size(0)
#predicted = torch.argmax(outputs, dim=1)
#correct += (torch.argmax(predicted, dim=1) == labels).sum().item()
#print(len(labels))
#print(len(predicted))
#print(predicted)
#print(labels)
labels = torch.argmax(labels, dim=1)
correct += (predicted == labels).sum().item()
accuracy = 100 * correct / total
print(f&quot;Test Accuracy: {accuracy:.2f}%&quot;)

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