如何在PyTorch中进行超参数搜索

在PyTorch中进行超参数搜索通常有两种常用的方法：

1. 使用Grid Search：通过定义一个超参数的候选值列表，对所有可能的组合进行穷举搜索，选择表现最好的超参数组合。可以使用GridSearchCV类来实现这一过程。

from sklearn.model_selection import GridSearchCV
from sklearn.metrics import accuracy_score
from torch import nn, optim
from torch.utils.data import DataLoader

# Define your model
class MyModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(MyModel, self).__init__()
        self.hidden = nn.Linear(input_dim, hidden_dim)
        self.relu = nn.ReLU()
        self.output = nn.Linear(hidden_dim, output_dim)
    
    def forward(self, x):
        x = self.hidden(x)
        x = self.relu(x)
        x = self.output(x)
        return x

# Define your dataset and dataloader
# dataset = ...
# dataloader = DataLoader(dataset, batch_size=64, shuffle=True)

# Define parameter grid
param_grid = {
    'hidden_dim': [64, 128, 256],
    'learning_rate': [0.001, 0.01, 0.1]
}

# Create a GridSearchCV object
grid_search = GridSearchCV(MyModel, param_grid, scoring='accuracy', cv=3)

# Fit the model
grid_search.fit(dataloader)

# Print best parameters
print(grid_search.best_params_)

2. 使用Random Search：与Grid Search不同，Random Search是随机地在指定的参数空间中采样，从而更有效地搜索超参数空间。可以使用RandomizedSearchCV类来实现这一过程。

from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import accuracy_score
from torch import nn, optim
from torch.utils.data import DataLoader

# Define your model
# Define your model
class MyModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(MyModel, self).__init__()
        self.hidden = nn.Linear(input_dim, hidden_dim)
        self.relu = nn.ReLU()
        self.output = nn.Linear(hidden_dim, output_dim)
    
    def forward(self, x):
        x = self.hidden(x)
        x = self.relu(x)
        x = self.output(x)
        return x

# Define your dataset and dataloader
# dataset = ...
# dataloader = DataLoader(dataset, batch_size=64, shuffle=True)

# Define parameter grid
param_dist = {
    'hidden_dim': [64, 128, 256],
    'learning_rate': [0.001, 0.01, 0.1]
}

# Create a RandomizedSearchCV object
random_search = RandomizedSearchCV(MyModel, param_dist, n_iter=10, scoring='accuracy', cv=3)

# Fit the model
random_search.fit(dataloader)

# Print best parameters
print(random_search.best_params_)

无论选择哪种方法，超参数搜索是一个耗时的过程，需要谨慎选择超参数的范围和步长，以及合适的评估指标来评估模型性能。