pytorch 按batch_size计算交叉熵损失如何编写代码

在PyTorch中，计算交叉熵损失使用的函数是nn.CrossEntropyLoss()，在这个函数中，我们可以设置参数size_average和reduce来控制loss的计算方式。其中，size_average和reduce这两个参数默认为True，表示对所有的loss值求平均值，当我们需要对每个样本单独计算loss时，这两个参数需要设置为False。对于batch_size计算交叉熵损失，我们可以先将输入数据与标签数据打包成一个batch，然后将其传给nn.CrossEntropyLoss()函数即可。具体代码实现如下：

import torch
import torch.nn as nn

input_size = 10
batch_size = 32
num_classes = 5

# 生成模拟数据
input_data = torch.randn(batch_size, input_size)
target_data = torch.randint(size=(batch_size,), low=0, high=num_classes)

# 定义模型和损失函数
model = nn.Linear(input_size, num_classes)
criterion = nn.CrossEntropyLoss()

# 前向传播计算loss
output = model(input_data)
loss = criterion(output, target_data)

print(loss.item())

相关问题

中文字体类别识别-行书和隶书，用pytorch编写代码

首先，需要准备好行书和隶书的数据集，并将其分为训练集和测试集。可以使用 PIL 库读取图片，并将其转换为 PyTorch 的张量。接着，可以使用 torchvision 库的 transforms 模块进行数据增强，如将图片进行随机旋转、缩放、裁剪等操作，以增加模型的泛化能力。

然后，需要搭建一个卷积神经网络模型，用于对中文字体进行分类。可以使用 PyTorch 的 nn 模块来定义模型的结构，如卷积层、池化层、全连接层等。对于中文字体的分类问题，可以使用交叉熵损失函数，并选择合适的优化器进行参数更新，如 Adam 或 SGD。

最后，进行模型的训练和测试。在训练时，可以使用 DataLoader 将数据集进行批处理，以提高训练效率。在测试时，可以使用模型对测试集进行预测，并计算准确率和召回率等指标来评估模型的性能。

下面是一个简单的行书和隶书分类器的 PyTorch 代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets

# 定义数据增强操作
transform_train = transforms.Compose([
    transforms.RandomRotation(15),
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
])

transform_test = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
])

# 加载数据集
trainset = datasets.ImageFolder('train/', transform=transform_train)
testset = datasets.ImageFolder('test/', transform=transform_test)

# 定义模型结构
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(256 * 28 * 28, 1024)
        self.fc2 = nn.Linear(1024, 2)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.pool(x)
        x = self.relu(self.conv2(x))
        x = self.pool(x)
        x = self.relu(self.conv3(x))
        x = self.pool(x)
        x = x.view(-1, 256 * 28 * 28)
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义训练参数
batch_size = 32
num_epochs = 10
learning_rate = 0.001

trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=2)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(trainloader, 0):
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 50 == 49:    # 每 50 个 batch 输出一次 loss
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 50))
            running_loss = 0.0

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for images, labels in testloader:
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the test images: %d %%' % (
    100 * correct / total))

需要注意的是，这只是一个简单的示例代码，实际中还需要根据具体情况进行调整和优化。

编写pytorch代码，定义LSTMAttention模型，在参数空间定义学习率、batch_size、权重衰减、神经元个数、dropout、自注意力等超参数，定义一个CPSO优化算法，定义一个适应度函数fitness_function，用于评估LSTMAttention模型在给定超参数下的性能，用CPSO算法优化模型中超参数，找到最优的超参数组合，并将最优的超参数传递给模型

好的，我来解答这个问题。首先，我们需要定义LSTMAttention模型，代码如下：

import torch
import torch.nn as nn
import torch.nn.functional as F

class LSTMAttention(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(LSTMAttention, self).__init__()
        self.lstm = nn.LSTM(input_dim, hidden_dim, batch_first=True)
        self.attention_fc = nn.Linear(hidden_dim, 1)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        out, (h_n, c_n) = self.lstm(x)
        attention_weights = F.softmax(self.attention_fc(out), dim=1)
        context_vector = torch.sum(attention_weights * out, dim=1)
        output = self.fc(context_vector)
        return output

接下来，我们需要定义模型的超参数空间和优化算法。这里我们使用CPSO算法来优化模型的超参数，代码如下：

import random

class CPSO:
    def __init__(self, num_particles, dim, max_iter, lr):
        self.num_particles = num_particles
        self.dim = dim
        self.max_iter = max_iter
        self.lr = lr
        self.particles = [[random.uniform(0, 1) for _ in range(dim)] for _ in range(num_particles)]
        self.velocities = [[0 for _ in range(dim)] for _ in range(num_particles)]
        self.pbest_positions = self.particles.copy()
        self.pbest_fitnesses = [float('inf') for _ in range(num_particles)]
        self.gbest_position = [0 for _ in range(dim)]
        self.gbest_fitness = float('inf')

    def update_position(self, x, v):
        return [xi + vi for xi, vi in zip(x, v)]

    def update_velocity(self, x, v, pbest, gbest):
        w = 0.7
        c1 = 1.4
        c2 = 1.4
        r1 = random.uniform(0, 1)
        r2 = random.uniform(0, 1)
        v_new = [w * vi + c1 * r1 * (pbest[i] - xi) + c2 * r2 * (gbest[i] - xi) for i, xi, vi in zip(range(self.dim), x, v)]
        return v_new

    def evaluate_fitness(self, fitness_function):
        fitnesses = []
        for particle in self.particles:
            fitness = fitness_function(particle)
            fitnesses.append(fitness)
            if fitness < self.pbest_fitnesses[self.particles.index(particle)]:
                self.pbest_fitnesses[self.particles.index(particle)] = fitness
                self.pbest_positions[self.particles.index(particle)] = particle
                if fitness < self.gbest_fitness:
                    self.gbest_fitness = fitness
                    self.gbest_position = particle
        return fitnesses

    def optimize(self, fitness_function):
        for i in range(self.max_iter):
            fitnesses = self.evaluate_fitness(fitness_function)
            for particle, velocity in zip(self.particles, self.velocities):
                velocity = self.update_velocity(particle, velocity, self.pbest_positions[self.particles.index(particle)], self.gbest_position)
                particle = self.update_position(particle, velocity)
            self.velocities = [self.update_velocity(particle, velocity, self.pbest_positions[self.particles.index(particle)], self.gbest_position) for particle, velocity in zip(self.particles, self.velocities)]
            self.particles = [self.update_position(particle, velocity) for particle, velocity in zip(self.particles, self.velocities)]
        return self.gbest_position

接下来，我们需要定义适应度函数fitness_function，用于评估LSTMAttention模型在给定超参数下的性能。这里我们使用交叉熵损失函数和Adam优化算法来训练模型，代码如下：

import torch.optim as optim
import torch.utils.data as data

def fitness_function(hyperparameters):
    # set hyperparameters
    learning_rate = hyperparameters[0]
    batch_size = int(hyperparameters[1] * 128)
    weight_decay = hyperparameters[2]
    hidden_dim = int(hyperparameters[3] * 256)
    dropout = hyperparameters[4]
    num_heads = int(hyperparameters[5] * 8)
    
    # define model
    model = LSTMAttention(input_dim=10, hidden_dim=hidden_dim, output_dim=2)
    
    # define optimizer and loss function
    optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
    criterion = nn.CrossEntropyLoss()
    
    # train model
    for epoch in range(10):
        for i, (x, y) in enumerate(train_loader):
            optimizer.zero_grad()
            output = model(x)
            loss = criterion(output, y)
            loss.backward()
            optimizer.step()
    
    # evaluate model
    correct = 0
    total = 0
    with torch.no_grad():
        for x, y in test_loader:
            output = model(x)
            _, predicted = torch.max(output.data, 1)
            total += y.size(0)
            correct += (predicted == y).sum().item()
    accuracy = correct / total
    return accuracy

最后，我们可以使用CPSO算法来优化模型的超参数，找到最优的超参数组合，并将最优的超参数传递给模型，代码如下：

# define train and test data loaders
train_loader = data.DataLoader(train_data, batch_size=128, shuffle=True)
test_loader = data.DataLoader(test_data, batch_size=128, shuffle=True)

# define hyperparameters space and CPSO optimizer
hyperparameters_space = [(1e-5, 1e-1), (0.1, 1), (1e-5, 1e-1), (0.1, 1), (0, 0.5), (0.1, 1)]
num_particles = 20
dim = len(hyperparameters_space)
max_iter = 50
lr = 0.1
cpso = CPSO(num_particles, dim, max_iter, lr)

# optimize hyperparameters using CPSO algorithm
best_hyperparameters = cpso.optimize(fitness_function)

# set best hyperparameters to model
learning_rate = best_hyperparameters[0]
batch_size = int(best_hyperparameters[1] * 128)
weight_decay = best_hyperparameters[2]
hidden_dim = int(best_hyperparameters[3] * 256)
dropout = best_hyperparameters[4]
num_heads = int(best_hyperparameters[5] * 8)
model = LSTMAttention(input_dim=10, hidden_dim=hidden_dim, output_dim=2)
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)