运行时错误：预期一维目标张量，不支持多目标 Pytorch

Question

我最近从 keras 转向了 pytorch，我仍在努力了解这一切是如何工作的。 下面是我使用简单的 MLP 对 mnist 数据集进行分类的代码。 就像我以前在 keras 中所做的那样，我将每个 28x28 图像展平为一个向量 784 ，并且我还为我的标签创建了一个单热表示。 在模型中，我希望给定一个 784 的向量，该模型将输出一个具有概率的单热向量，但是一旦我的代码达到计算损失，我就会收到以下错误：

RuntimeError: 1D target tensor expected, multi-target not supported

下面是我的代码：

    import numpy as np
import matplotlib.pyplot as plt
import torch
import time
from torch import nn, optim
from keras.datasets import mnist
from torch.utils.data import Dataset, DataLoader

RANDOM_SEED = 42
np.random.seed(RANDOM_SEED)
torch.manual_seed(RANDOM_SEED)


# ----------------------------------------------------

class MnistDataset(Dataset):

    def __init__(self, data_size=0):

        (x, y), (_, _) = mnist.load_data()

        x = [i.flatten() for i in x]
        x = np.array(x, dtype=np.float32)

        if data_size < 0 or data_size > len(y):
            assert ("Data size should be between 0 to number of files in the dataset")

        if data_size == 0:
            data_size = len(y)

        self.data_size = data_size

        # picking 'data_size' random samples
        self.x = x[:data_size]
        self.y = y[:data_size]

        # scaling between 0-1
        self.x = (self.x / 255)

        # Creating one-hot representation of target
        y_encoded = []
        for label in y:
            encoded = np.zeros(10)
            encoded[label] = 1
            y_encoded.append(encoded)

        self.y = np.array(y_encoded)

    def __len__(self):
        return self.data_size

    def __getitem__(self, index):

        x_sample = self.x[index]
        label = self.y[index]

        return x_sample, label


# ----------------------------------------------------

num_train_samples = 10000
num_test_samples = 2000

# Each generator returns a single
# sample & its label on each iteration.
mnist_train = MnistDataset(data_size=num_train_samples)
mnist_test = MnistDataset(data_size=num_test_samples)

# Each generator returns a batch of samples on each iteration.
train_loader = DataLoader(mnist_train, batch_size=128, shuffle=True)  # 79 batches
test_loader = DataLoader(mnist_test, batch_size=128, shuffle=True)  # 16 batches


# ----------------------------------------------------

# Defining the Model Architecture

class MLP(nn.Module):

    def __init__(self):
        super().__init__()

        self.fc1 = nn.Linear(28 * 28, 100)
        self.act1 = nn.ReLU()
        self.fc2 = nn.Linear(100, 50)
        self.act2 = nn.ReLU()
        self.fc3 = nn.Linear(50, 10)
        self.act3 = nn.Sigmoid()

    def forward(self, x):
        x = self.act1(self.fc1(x))
        x = self.act2(self.fc2(x))
        output = self.act3(self.fc3(x))

        return output


# ----------------------------------------------------

model = MLP()

# Defining optimizer and loss function
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)

# ----------------------------------------------------

# Training the model

epochs = 10

print("Training Started...")

for epoch in range(epochs):
    for batch_index, (inputs, targets) in enumerate(train_loader):

        optimizer.zero_grad()  # Zero the gradients
        outputs = model(inputs)  # Forward pass
        loss = criterion(outputs, targets)  # Compute the Loss
        loss.backward()  # Compute the Gradients
        optimizer.step()  # Update the parameters

        # Evaluating the model
        total = 0
        correct = 0
        with torch.no_grad():
            for batch_idx, (inputs, targets) in enumerate(test_loader):
                outputs = model(inputs)
                _, predicted = torch.max(outputs.data, 1)
                total += targets.size(0)
                correct += predicted.eq(targets.data).cpu().sum()
            print('Epoch : {} Test Acc : {}'.format(epoch, (100. * correct / total)))

print("Training Completed Sucessfully")

# ----------------------------------------------------

我还阅读了一些与同一问题相关的其他帖子，其中大多数人说目标的交叉熵损失必须是一个数字，这完全让我无法理解。有人可以解释一下解决方案吗？谢谢。

Answer 1

对于nn.CrossEntropyLoss ，您不需要标签的单热表示，您只需要传递预测的 logit，其形状是(batch_size, n_class)和一个目标向量(batch_size,)

所以只需传入标签索引向量y而不是 one-hot 向量。

修复了您的代码：

class MnistDataset(Dataset):

    def __init__(self, data_size=0):

        (x, y), (_, _) = mnist.load_data()

        x = [i.flatten() for i in x]
        x = np.array(x, dtype=np.float32)

        if data_size < 0 or data_size > len(y):
            assert ("Data size should be between 0 to number of files in the dataset")

        if data_size == 0:
            data_size = len(y)

        self.data_size = data_size

        # picking 'data_size' random samples
        self.x = x[:data_size]
        self.y = y[:data_size]

        # scaling between 0-1
        self.x = (self.x / 255)

        self.y = y # <--

    def __len__(self):
        return self.data_size

    def __getitem__(self, index):

        x_sample = self.x[index]
        label = self.y[index]

        return x_sample, label

查看 Pytorch 示例以获取更多详细信息： https ://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html