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完整教程:深度学习优化器全面指南:核心参数选择与实战策略

在这里插入图片描述

文章目录

    • 1. 优化器概述与分类
      • 1.1 什么是优化器?
      • 1.2 优化器的主要分类
    • 2. 优化器核心参数详解
      • 2.1 学习率(Learning Rate)
      • 2.2 动量(Momentum)
      • 2.3 自适应学习率参数
    • 3. 优化器选择决策流程
    • 4. 学习率调度策略
      • 4.1 常见的学习率调度器
      • 4.2 学习率预热(Warmup)
    • 5. 优化器性能综合比较
      • 5.1 多优化器对比实验
      • 5.2 优化器选择决策树实现
    • 6. 实践建议与最佳实践
      • 6.1 参数调优策略
      • 6.2 优化器选择检查表
    • 7. 总结
      • 关键要点:
      • 实践建议:
      • 未来趋势:

优化器是深度学习模型训练的核心组件,直接影响模型的收敛速度和解的质量。本文将深入探讨优化器的关键参数选择策略,并提供详细的代码实践。

1. 优化器概述与分类

1.1 什么是优化器?

在深度学习中,优化器是通过调整模型参数来最小化损失函数的算法。它的目标是找到使损失函数值最小的参数组合。

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader, TensorDataset
# 创建简单的演示数据
def create_sample_data():
torch.manual_seed(42)
X = torch.randn(1000, 10)
y = torch.randint(0, 2, (1000,))
return X, y
X, y = create_sample_data()
dataset = TensorDataset(X, y)

1.2 优化器的主要分类

# 展示不同类型的优化器
class SimpleModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(SimpleModel, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
model = SimpleModel(10, 50, 2)
# 不同类型的优化器
optimizers = {
'SGD': optim.SGD(model.parameters(), lr=0.01),
'SGD with Momentum': optim.SGD(model.parameters(), lr=0.01, momentum=0.9),
'Adam': optim.Adam(model.parameters(), lr=0.001),
'AdamW': optim.AdamW(model.parameters(), lr=0.001),
'RMSprop': optim.RMSprop(model.parameters(), lr=0.01),
'Adagrad': optim.Adagrad(model.parameters(), lr=0.01)
}
print("Available Optimizers and Their Key Parameters:")
print("=" * 60)
for name, optimizer in optimizers.items():
print(f"{name:20} | Parameters: {len(list(optimizer.param_groups[0].keys()))}")
for key, value in optimizer.param_groups[0].items():
if key != 'params':
print(f"{' ':22} {key}: {value}")
print("-" * 60)

2. 优化器核心参数详解

2.1 学习率(Learning Rate)

学习率是最重要的超参数,控制参数更新的步长。

def test_learning_rates():
"""测试不同学习率对训练的影响"""
learning_rates = [0.0001, 0.001, 0.01, 0.1, 0.5]
results = {}
for lr in learning_rates:
print(f"\nTesting Learning Rate: {lr}")
# 重置模型
model = SimpleModel(10, 50, 2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=lr)
train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
losses = []
# 简单训练循环
for epoch in range(10):
epoch_loss = 0
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
avg_loss = epoch_loss / len(train_loader)
losses.append(avg_loss)
print(f'Epoch {epoch+1}, Loss: {avg_loss:.4f}')
results[lr] = losses
# 绘制结果
plt.figure(figsize=(12, 8))
for lr, losses in results.items():
plt.plot(losses, label=f'LR={lr}')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training Loss with Different Learning Rates')
plt.legend()
plt.grid(True)
plt.show()
test_learning_rates()

2.2 动量(Momentum)

动量帮助优化器在相关方向加速并抑制振荡。

def test_momentum_values():
"""测试不同动量值的效果"""
momentums = [0.0, 0.5, 0.9, 0.99]
results = {}
for momentum in momentums:
print(f"\nTesting Momentum: {momentum}")
model = SimpleModel(10, 50, 2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=momentum)
train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
losses = []
for epoch in range(10):
epoch_loss = 0
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
avg_loss = epoch_loss / len(train_loader)
losses.append(avg_loss)
results[momentum] = losses
# 可视化结果
plt.figure(figsize=(12, 8))
for momentum, losses in results.items():
plt.plot(losses, label=f'Momentum={momentum}')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Effect of Momentum on Training')
plt.legend()
plt.grid(True)
plt.show()
test_momentum_values()

2.3 自适应学习率参数

对于自适应优化器(如Adam),有几个关键参数:

def test_adam_parameters():
"""测试Adam优化器的关键参数"""
betas_combinations = [
(0.9, 0.999),   # 默认值
(0.9, 0.99),    # 调整二阶矩
(0.8, 0.999),   # 调整一阶矩
(0.95, 0.999)   # 更高的一阶矩
]
eps_values = [1e-8, 1e-7, 1e-6]
results = {}
# 测试不同的beta组合
for i, (beta1, beta2) in enumerate(betas_combinations):
model = SimpleModel(10, 50, 2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(beta1, beta2))
train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
losses = []
for epoch in range(10):
epoch_loss = 0
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
avg_loss = epoch_loss / len(train_loader)
losses.append(avg_loss)
results[f'beta=({beta1},{beta2})'] = losses
# 可视化结果
plt.figure(figsize=(12, 8))
for label, losses in results.items():
plt.plot(losses, label=label)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Adam Optimizer: Different Beta Combinations')
plt.legend()
plt.grid(True)
plt.show()
test_adam_parameters()

3. 优化器选择决策流程

计算机视觉
自然语言处理
强化学习
GAN训练
小数据集
大数据集
开始选择优化器
问题类型?
优先选择Adam/AdamW
优先选择AdamW/Adam
优先选择RMSprop/Adam
使用不同的优化器
Generator: Adam
Discriminator: SGD
数据集大小?
使用较小学习率
增加正则化
使用较大学习率
减少正则化
最终优化器选择
需要微调?
调整学习率
动量参数等
使用默认参数
完成优化器配置

4. 学习率调度策略

4.1 常见的学习率调度器

def test_lr_schedulers():
"""比较不同的学习率调度策略"""
model = SimpleModel(10, 50, 2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.1)  # 使用较大的初始学习率
# 不同的学习率调度器
schedulers = {
'StepLR': optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1),
'ExponentialLR': optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9),
'CosineAnnealingLR': optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10),
'ReduceLROnPlateau': optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=2)
}
train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
results = {}
lr_history = {}
for sched_name, scheduler in schedulers.items():
print(f"\nTesting {sched_name}")
# 重置模型和优化器
model = SimpleModel(10, 50, 2)
optimizer = optim.Adam(model.parameters(), lr=0.1)
if sched_name == 'StepLR':
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)
elif sched_name == 'ExponentialLR':
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
elif sched_name == 'CosineAnnealingLR':
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
elif sched_name == 'ReduceLROnPlateau':
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=2)
losses = []
learning_rates = []
for epoch in range(15):
epoch_loss = 0
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
avg_loss = epoch_loss / len(train_loader)
losses.append(avg_loss)
# 更新学习率
if sched_name == 'ReduceLROnPlateau':
scheduler.step(avg_loss)
else:
scheduler.step()
current_lr = optimizer.param_groups[0]['lr']
learning_rates.append(current_lr)
print(f'Epoch {epoch+1}, Loss: {avg_loss:.4f}, LR: {current_lr:.6f}')
results[sched_name] = losses
lr_history[sched_name] = learning_rates
# 绘制损失曲线
plt.figure(figsize=(15, 10))
plt.subplot(2, 1, 1)
for sched_name, losses in results.items():
plt.plot(losses, label=sched_name)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training Loss with Different LR Schedulers')
plt.legend()
plt.grid(True)
plt.subplot(2, 1, 2)
for sched_name, lrs in lr_history.items():
plt.plot(lrs, label=sched_name)
plt.xlabel('Epoch')
plt.ylabel('Learning Rate')
plt.title('Learning Rate Schedule')
plt.legend()
plt.grid(True)
plt.yscale('log')
plt.tight_layout()
plt.show()
test_lr_schedulers()

4.2 学习率预热(Warmup)

class WarmupScheduler:
"""自定义学习率预热调度器"""
def __init__(self, optimizer, warmup_steps, base_lr):
self.optimizer = optimizer
self.warmup_steps = warmup_steps
self.base_lr = base_lr
self.step_count = 0
def step(self):
self.step_count += 1
if self.step_count <= self.warmup_steps:
# 线性预热
lr = self.base_lr * (self.step_count / self.warmup_steps)
for param_group in self.optimizer.param_groups:
param_group['lr'] = lr
def test_warmup_scheduler():
"""测试学习率预热效果"""
model = SimpleModel(10, 50, 2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 有预热和无预热的对比
warmup_steps = 10
warmup_scheduler = WarmupScheduler(optimizer, warmup_steps, 0.001)
normal_optimizer = optim.Adam(SimpleModel(10, 50, 2).parameters(), lr=0.001)
train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
warmup_losses = []
normal_losses = []
warmup_lrs = []
normal_lrs = []
# 有预热的训练
for epoch in range(15):
epoch_loss = 0
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
warmup_scheduler.step()
epoch_loss += loss.item()
avg_loss = epoch_loss / len(train_loader)
warmup_losses.append(avg_loss)
warmup_lrs.append(optimizer.param_groups[0]['lr'])
# 无预热的训练
model_normal = SimpleModel(10, 50, 2)
for epoch in range(15):
epoch_loss = 0
for data, target in train_loader:
normal_optimizer.zero_grad()
output = model_normal(data)
loss = criterion(output, target)
loss.backward()
normal_optimizer.step()
epoch_loss += loss.item()
avg_loss = epoch_loss / len(train_loader)
normal_losses.append(avg_loss)
normal_lrs.append(normal_optimizer.param_groups[0]['lr'])
# 可视化对比
plt.figure(figsize=(15, 10))
plt.subplot(2, 2, 1)
plt.plot(warmup_losses, label='With Warmup')
plt.plot(normal_losses, label='Without Warmup')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training Loss: Warmup vs No Warmup')
plt.legend()
plt.grid(True)
plt.subplot(2, 2, 2)
plt.plot(warmup_lrs, label='With Warmup')
plt.plot(normal_lrs, label='Without Warmup')
plt.xlabel('Epoch')
plt.ylabel('Learning Rate')
plt.title('Learning Rate Schedule')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
test_warmup_scheduler()

5. 优化器性能综合比较

5.1 多优化器对比实验

def comprehensive_optimizer_comparison():
"""综合比较不同优化器的性能"""
optimizers_config = {
'SGD': {'optimizer': optim.SGD, 'lr': 0.01},
'SGD+Momentum': {'optimizer': optim.SGD, 'lr': 0.01, 'momentum': 0.9},
'Adam': {'optimizer': optim.Adam, 'lr': 0.001},
'AdamW': {'optimizer': optim.AdamW, 'lr': 0.001},
'RMSprop': {'optimizer': optim.RMSprop, 'lr': 0.01},
'Adagrad': {'optimizer': optim.Adagrad, 'lr': 0.01}
}
results = {}
train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
for opt_name, config in optimizers_config.items():
print(f"\nTesting {opt_name}")
# 创建模型实例
model = SimpleModel(10, 50, 2)
criterion = nn.CrossEntropyLoss()
# 创建优化器
optimizer_class = config['optimizer']
optimizer_params = {k: v for k, v in config.items() if k != 'optimizer'}
optimizer = optimizer_class(model.parameters(), **optimizer_params)
losses = []
accuracies = []
for epoch in range(20):
# 训练阶段
model.train()
epoch_loss = 0
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
avg_loss = epoch_loss / len(train_loader)
losses.append(avg_loss)
# 评估准确率
model.eval()
correct = 0
total = 0
with torch.no_grad():
for data, target in train_loader:
output = model(data)
_, predicted = torch.max(output.data, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
accuracy = 100 * correct / total
accuracies.append(accuracy)
if (epoch + 1) % 5 == 0:
print(f'Epoch {epoch+1}, Loss: {avg_loss:.4f}, Accuracy: {accuracy:.2f}%')
results[opt_name] = {
'losses': losses,
'accuracies': accuracies,
'final_accuracy': accuracies[-1]
}
return results
# 运行综合比较
optimizer_results = comprehensive_optimizer_comparison()
# 可视化结果
plt.figure(figsize=(15, 10))
plt.subplot(2, 2, 1)
for opt_name, result in optimizer_results.items():
plt.plot(result['losses'], label=opt_name)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training Loss: Optimizer Comparison')
plt.legend()
plt.grid(True)
plt.subplot(2, 2, 2)
for opt_name, result in optimizer_results.items():
plt.plot(result['accuracies'], label=opt_name)
plt.xlabel('Epoch')
plt.ylabel('Accuracy (%)')
plt.title('Training Accuracy: Optimizer Comparison')
plt.legend()
plt.grid(True)
plt.subplot(2, 2, 3)
# 最终准确率比较
names = list(optimizer_results.keys())
final_accs = [optimizer_results[name]['final_accuracy'] for name in names]
plt.barh(names, final_accs)
plt.xlabel('Final Accuracy (%)')
plt.title('Final Training Accuracy by Optimizer')
plt.grid(True)
plt.tight_layout()
plt.show()

5.2 优化器选择决策树实现

def optimizer_selection_guide(problem_type, dataset_size, architecture, requirements):
"""
优化器选择指导工具
"""
recommendations = []
# 基于问题类型的推荐
if problem_type.lower() in ['cv', 'computer vision']:
recommendations.append("Adam/AdamW: 计算机视觉任务的首选")
recommendations.append("SGD with Momentum: 如果需要更好的泛化能力")
elif problem_type.lower() in ['nlp', 'natural language processing']:
recommendations.append("AdamW: 自然语言处理的最佳选择(更好的权重衰减)")
recommendations.append("Adam: 标准选择,表现稳定")
elif problem_type.lower() in ['rl', 'reinforcement learning']:
recommendations.append("RMSprop: 强化学习的传统选择")
recommendations.append("Adam: 现代强化学习算法的常用选择")
elif problem_type.lower() == 'gan':
recommendations.append("Generator: Adam, Discriminator: SGD - GAN训练的经典组合")
# 基于数据集大小的推荐
if dataset_size == 'small':
recommendations.append("使用较小的学习率,避免过拟合")
recommendations.append("考虑使用更强的正则化")
elif dataset_size == 'large':
recommendations.append("可以使用较大的学习率")
recommendations.append("考虑使用学习率预热")
# 基于架构的推荐
if architecture == 'transformer':
recommendations.append("AdamW with Warmup: Transformer架构的标准配置")
elif architecture == 'cnn':
recommendations.append("Adam或SGD with Momentum都适用")
elif architecture == 'rnn':
recommendations.append("考虑使用梯度裁剪,避免梯度爆炸")
# 基于特定需求的推荐
if 'fast_convergence' in requirements:
recommendations.append("Adam: 通常收敛最快")
if 'generalization' in requirements:
recommendations.append("SGD with Momentum: 通常有更好的泛化能力")
if 'stable_training' in requirements:
recommendations.append("Adam: 训练过程相对稳定")
if 'memory_efficient' in requirements:
recommendations.append("Adagrad/RMSprop: 内存使用相对较少")
return recommendations
# 使用示例
problem_info = {
'problem_type': 'computer vision',
'dataset_size': 'large',
'architecture': 'cnn',
'requirements': ['fast_convergence', 'generalization']
}
recommendations = optimizer_selection_guide(**problem_info)
print("优化器选择推荐:")
print("=" * 50)
for i, rec in enumerate(recommendations, 1):
print(f"{i}. {rec}")

6. 实践建议与最佳实践

6.1 参数调优策略

def create_optimizer_tuning_pipeline():
"""创建优化器参数调优管道"""
def tune_optimizer_parameters(model, train_loader, val_loader, optimizer_class, param_grid):
"""
自动调优优化器参数
"""
best_accuracy = 0
best_params = {}
results = {}
# 生成参数组合
from itertools import product
param_names = list(param_grid.keys())
param_values = list(param_grid.values())
param_combinations = list(product(*param_values))
print(f"Testing {len(param_combinations)} parameter combinations...")
for i, combination in enumerate(param_combinations):
params = dict(zip(param_names, combination))
print(f"\nCombination {i+1}: {params}")
# 创建新的模型实例
current_model = type(model)()  # 创建相同架构的新模型
current_model.load_state_dict(model.state_dict())  # 相同的初始化
# 创建优化器
optimizer = optimizer_class(current_model.parameters(), **params)
criterion = nn.CrossEntropyLoss()
# 训练模型
for epoch in range(5):  # 快速训练进行评估
current_model.train()
for data, target in train_loader:
optimizer.zero_grad()
output = current_model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
# 评估模型
current_model.eval()
correct = 0
total = 0
with torch.no_grad():
for data, target in val_loader:
output = current_model(data)
_, predicted = torch.max(output.data, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
accuracy = 100 * correct / total
results[str(params)] = accuracy
if accuracy > best_accuracy:
best_accuracy = accuracy
best_params = params
print(f"Validation Accuracy: {accuracy:.2f}%")
return best_params, best_accuracy, results
# 示例使用
model = SimpleModel(10, 50, 2)
train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(dataset, batch_size=32, shuffle=False)
# 定义参数网格
adam_param_grid = {
'lr': [0.0001, 0.001, 0.01],
'betas': [(0.9, 0.999), (0.8, 0.999), (0.9, 0.99)],
'eps': [1e-8, 1e-7]
}
best_params, best_acc, all_results = tune_optimizer_parameters(
model, train_loader, val_loader, optim.Adam, adam_param_grid
)
print(f"\nBest Parameters: {best_params}")
print(f"Best Accuracy: {best_acc:.2f}%")
return best_params, all_results
# 运行参数调优
best_params, tuning_results = create_optimizer_tuning_pipeline()

6.2 优化器选择检查表

def optimizer_selection_checklist():
"""优化器选择检查表"""
checklist = {
'问题类型': {
'计算机视觉': ['Adam', 'AdamW', 'SGD with Momentum'],
'自然语言处理': ['AdamW', 'Adam'],
'强化学习': ['RMSprop', 'Adam'],
'生成对抗网络': ['Adam (Generator)', 'SGD (Discriminator)']
},
'数据集大小': {
'小数据集 (<10K样本)': ['较小的学习率', '更强的正则化'],
'中等数据集 (10K-100K)': ['适中的学习率', '标准正则化'],
'大数据集 (>100K)': ['较大的学习率', '学习率预热']
},
'模型架构': {
'CNN': ['Adam', 'SGD with Momentum'],
'RNN/LSTM': ['Adam', 'RMSprop + 梯度裁剪'],
'Transformer': ['AdamW + Warmup'],
'ResNet': ['SGD with Momentum', 'Adam']
},
'训练目标': {
'快速收敛': ['Adam', 'RMSprop'],
'最佳泛化': ['SGD with Momentum'],
'训练稳定性': ['Adam', 'Adagrad'],
'内存效率': ['Adagrad', 'RMSprop']
}
}
return checklist
# 显示检查表
checklist = optimizer_selection_checklist()
print("优化器选择检查表")
print("=" * 60)
for category, subitems in checklist.items():
print(f"\n{category}:")
print("-" * 40)
for subcategory, recommendations in subitems.items():
print(f"  {subcategory}:")
for rec in recommendations:
print(f"    • {rec}")
# 保存最佳实践总结
best_practices = """
优化器选择最佳实践总结:
1. 学习率是最重要的参数,总是需要仔细调优
2. 对于新任务,从Adam/AdamW开始,它们对超参数不太敏感
3. 如果需要最佳泛化能力,考虑使用SGD with Momentum
4. 使用学习率调度器来改善训练动态
5. 对于Transformer架构,使用AdamW配合学习率预热
6. 在训练过程中监控梯度范数,避免梯度爆炸/消失
7. 考虑使用梯度裁剪,特别是对于RNN和深度网络
8. 对于大数据集,使用较大的batch size和学习率
9. 始终在验证集上评估优化器选择的效果
10. 记录完整的优化器配置,确保实验可复现
"""
print("\n" + "=" * 60)
print("最佳实践总结")
print("=" * 60)
print(best_practices)

7. 总结

优化器选择是深度学习模型训练成功的关键因素。通过本文的详细分析和代码实践,我们了解到:

关键要点:

  1. 学习率:最重要的参数,需要仔细调优
  2. 动量:帮助加速收敛并减少振荡
  3. 自适应参数:对于Adam类优化器,beta和epsilon影响显著
  4. 学习率调度:动态调整学习率可以显著改善性能

实践建议:

  • 默认起点:从Adam/AdamW开始,因为它们对超参数相对鲁棒
  • 精细调优:如果需要最佳性能,仔细调优SGD with Momentum
  • 监控分析:始终监控训练动态,使用可视化工具分析优化器行为
  • 实验记录:详细记录所有优化器配置和实验结果

未来趋势:

通过掌握优化器的核心原理和实践技巧,您将能够为不同的深度学习任务选择最合适的优化策略,从而提高模型性能和训练效率。

记住:没有 universally best 的优化器,最佳选择取决于您的具体任务、数据和资源约束。 持续实验和迭代是找到最佳配置的关键!
在这里插入图片描述

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