RuntimeError:需要1D目标张量，不支持多目标Pytorch

Question

我最近从keras转到pytorch，我仍然在试图理解所有这些工作原理。下面是我使用一个简单的MLP对mnist数据集进行分类的代码。就像我过去在keras中所做的那样，我已经将28x28图像的每一幅绘制成一个784的向量，并且我还为我的标签创建了一个单热点表示。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热向量，但一旦我的代码到达计算损失，我就会得到以下错误：

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")

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

我还读了其他一些关于同样问题的帖子&大多数帖子说CrossEntropy损失的目标必须是一个单一的数字，这完全超过了我的head.Can有人请解释一下solution.Thank你。

Answer 1

对于nn.CrossEntropyLoss，您不需要标签的单一热度表示，只需传递预测的logit (形状为(batch_size, n_class) )和目标向量(batch_size,)

因此，只需传入标签索引向量y，而不是one-hot向量。

修复您的代码：

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

有关更多细节，请查看Pytorch示例：https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html