pytorch保存模型可以保存学习率和batchsize等超参数么

PyTorch 中保存模型通常只保存模型的权重（参数）以及训练状态，如优化器的状态（optimizer.state_dict），它们包含了训练过程的一些信息，比如当前的学习率（learning rate）。学习率和 batch size 等超参数本身并不是模型的一部分，而是配置的一部分，通常会存储在外部脚本或者日志文件中，而不是直接保存到模型文件中。

当你需要记录超参数时，可以在训练完成后将它们一起保存在一个配置文件或字典中，便于后续加载和复现实验。例如，你可以使用 Python 的 JSON 或者 YAML 库来保存这些信息：

import torch
import json

# 超参数
hyperparams = {
    'learning_rate': learning_rate,
    'batch_size': batch_size
}

# 存储到文件
with open('hyperparameters.json', 'w') as f:
    json.dump(hyperparams, f)

# 当需要加载时
with open('hyperparameters.json', 'r') as f:
    loaded_hyperparams = json.load(f)

相关问题

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)

1.使用PyTorch实现LeNet网络 2.使用实验7的 Fashion MNIST 对LeNet进行训练和测试，优化算法采用SGD或Adam，torch.optim.SGD 或 torch.optim.Adam 3.分别绘制训练和测试的损失函数曲线和分类正确率曲线 4.调节BatchSize、学习率，并依据测试损失曲线的拐点确定最佳模型，保存该模型 5.使用测试集测试所保存模型的性能，以混淆矩阵展示

好的，这是一个深度学习的任务，需要使用PyTorch实现LeNet网络并对Fashion MNIST数据集进行训练和测试，我来帮您一步一步完成这个任务。

首先，我们需要导入必要的包：torch、torchvision、numpy和matplotlib。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import numpy as np
import matplotlib.pyplot as plt

接下来，我们定义LeNet网络的结构。

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.pool1 = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.pool2 = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(16 * 4 * 4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = x.view(-1, 16 * 4 * 4)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

这里我们定义了一个包含2个卷积层和3个全连接层的LeNet网络，其中第一个卷积层有6个5x5的卷积核，第二个卷积层有16个5x5的卷积核。每个卷积层后面都跟了一个2x2的最大池化层，然后是3个全连接层，分别有120、84和10个神经元。

接下来，我们加载Fashion MNIST数据集，并将其划分为训练集和验证集。

transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize((0.1307,), (0.3081,))
])

train_dataset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

val_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=False)

这里我们使用了PyTorch内置的Fashion MNIST数据集，并使用了一个Compose对象将ToTensor和Normalize变换组合起来。我们将训练集和验证集分别放入DataLoader中，batch_size设置为64，shuffle设置为True和False，表示训练集需要打乱，而验证集不需要。

接下来，我们定义优化算法和损失函数。

net = LeNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01)

这里我们使用了SGD优化算法和交叉熵损失函数，学习率设置为0.01。

接下来，我们开始训练模型。

train_losses = []
train_accs = []
val_losses = []
val_accs = []

for epoch in range(10):
    train_loss = 0.0
    train_acc = 0.0
    val_loss = 0.0
    val_acc = 0.0

    net.train()
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        train_acc += (predicted == labels).sum().item()

    net.eval()
    with torch.no_grad():
        for inputs, labels in val_loader:
            outputs = net(inputs)
            loss = criterion(outputs, labels)

            val_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            val_acc += (predicted == labels).sum().item()

    train_loss /= len(train_loader)
    train_acc /= len(train_dataset)
    val_loss /= len(val_loader)
    val_acc /= len(val_dataset)

    train_losses.append(train_loss)
    train_accs.append(train_acc)
    val_losses.append(val_loss)
    val_accs.append(val_acc)

    print('Epoch %d: train_loss=%.4f train_acc=%.4f val_loss=%.4f val_acc=%.4f' % (
        epoch+1, train_loss, train_acc, val_loss, val_acc))

这里我们训练了10个epoch，每个epoch分别对训练集进行一次迭代，同时在验证集上计算loss和accuracy。在每个epoch结束时，我们将训练集和验证集的loss和accuracy记录下来。

最后，我们绘制训练和验证的损失函数曲线和分类正确率曲线。

fig, ax = plt.subplots(1, 2, figsize=(12, 6))

ax[0].plot(train_losses, label='train')
ax[0].plot(val_losses, label='val')
ax[0].set_xlabel('epoch')
ax[0].set_ylabel('loss')
ax[0].set_title('Training and validation loss')
ax[0].legend()

ax[1].plot(train_accs, label='train')
ax[1].plot(val_accs, label='val')
ax[1].set_xlabel('epoch')
ax[1].set_ylabel('accuracy')
ax[1].set_title('Training and validation accuracy')
ax[1].legend()

plt.show()

这里我们使用了matplotlib库来绘制图形，包括训练和验证的损失函数曲线和分类正确率曲线。

接下来，我们调节BatchSize和学习率，并依据测试损失曲线的拐点确定最佳模型，并保存该模型。

train_losses = []
train_accs = []
val_losses = []
val_accs = []

best_val_loss = float('inf')
best_model = None

batch_sizes = [16, 32, 64, 128, 256]
learning_rates = [0.001, 0.01, 0.1, 1]

for batch_size in batch_sizes:
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False)

    for learning_rate in learning_rates:
        net = LeNet()
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.SGD(net.parameters(), lr=learning_rate)

        for epoch in range(10):
            train_loss = 0.0
            train_acc = 0.0
            val_loss = 0.0
            val_acc = 0.0

            net.train()
            for i, (inputs, labels) in enumerate(train_loader):
                optimizer.zero_grad()

                outputs = net(inputs)
                loss = criterion(outputs, labels)
                loss.backward()
                optimizer.step()

                train_loss += loss.item()
                _, predicted = torch.max(outputs.data, 1)
                train_acc += (predicted == labels).sum().item()

            net.eval()
            with torch.no_grad():
                for inputs, labels in val_loader:
                    outputs = net(inputs)
                    loss = criterion(outputs, labels)

                    val_loss += loss.item()
                    _, predicted = torch.max(outputs.data, 1)
                    val_acc += (predicted == labels).sum().item()

            train_loss /= len(train_loader)
            train_acc /= len(train_dataset)
            val_loss /= len(val_loader)
            val_acc /= len(val_dataset)

            train_losses.append(train_loss)
            train_accs.append(train_acc)
            val_losses.append(val_loss)
            val_accs.append(val_acc)

            if val_loss < best_val_loss:
                best_val_loss = val_loss
                best_model = net.state_dict()

            print('BatchSize=%d LearningRate=%.3f Epoch %d: train_loss=%.4f train_acc=%.4f val_loss=%.4f val_acc=%.4f' % (
                batch_size, learning_rate, epoch+1, train_loss, train_acc, val_loss, val_acc))

print('Best validation loss:', best_val_loss)

torch.save(best_model, 'best_model.pth')

这里我们使用了两个for循环，分别对BatchSize和学习率进行调节，并在每个epoch结束时记录train_loss、train_acc、val_loss和val_acc。在每次更新最佳模型时，我们将模型的参数保存下来。

最后，我们使用测试集测试所保存模型的性能，并以混淆矩阵展示。

test_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)

net = LeNet()
net.load_state_dict(torch.load('best_model.pth'))

net.eval()
with torch.no_grad():
    all_predicted = []
    all_labels = []

    for inputs, labels in test_loader:
        outputs = net(inputs)
        _, predicted = torch.max(outputs.data, 1)

        all_predicted.extend(predicted.numpy())
        all_labels.extend(labels.numpy())

    all_predicted = np.array(all_predicted)
    all_labels = np.array(all_labels)

    confusion_matrix = np.zeros((10, 10))
    for i in range(len(all_predicted)):
        confusion_matrix[all_labels[i], all_predicted[i]] += 1

    print(confusion_matrix)

这里我们加载保存的最佳模型，并使用测试集进行测试。使用numpy库创建混淆矩阵，将模型预测结果与真实标签进行比对，并将结果输出。