# 3. Construct solver. lr = CustomWarmupCosineDecay(warmup_start_lr=warmup_start_lr, warmup_epochs=warmup_epochs, cosine_base_lr=cosine_base_lr, max_epoch=max_epoch, num_iters=1)
时间: 2024-01-22 19:04:10 浏览: 25
这是一个构建优化器(solver)的代码段,其中使用了自定义的学习率调度器(CustomWarmupCosineDecay),可以解读为:
- 定义一个自定义的学习率调度器,其参数包括:学习率的初始值(warmup_start_lr)、预热期的epoch数(warmup_epochs)、余弦退火策略中的基础学习率(cosine_base_lr)、训练的最大epoch数(max_epoch)和每个epoch中的迭代次数(num_iters);
- 调用这个学习率调度器,生成一个学习率lr对象,用于优化器的构建。
需要注意的是,学习率调度器的选择和参数设置也会对模型的训练和性能产生重要影响。在这里,使用了一种结合了学习率预热和余弦退火两种策略的调度器。预热期是为了在训练开始时,让学习率从较小的值逐渐增大,以避免由于初始学习率过大而导致的训练不稳定。而余弦退火策略则是为了在训练过程中,让学习率逐渐减小,以避免过拟合和局部最优解。
相关问题
if __name__ == '__main__'
This is a Python construct that is often used to allow a module to be both imported and executed as a standalone program.
When a Python module is imported, its code is executed, and any statements that are not inside a function or conditional block are executed immediately. This can be undesirable in some cases, as you may not want the module's code to be executed until you explicitly call a function or method from that module.
To prevent this from happening, you can use the `if __name__ == '__main__'` construct. This code block will only be executed if the module is being run as a standalone program, and not if it is being imported as a module by another program.
For example, consider the following code:
```
def add(x, y):
return x + y
if __name__ == '__main__':
result = add(2, 3)
print(result)
```
If you run this code as a standalone program (i.e. by running `python my_module.py`), it will print the result of adding 2 and 3 (which is 5). However, if you import this module into another program, the `result` variable will not be created, and the `print` statement will not be executed. Instead, you can call the `add` function directly from the importing program.
class LeNet5_2(nn.Cell): # 定义算子 def __init__(self, num_class=10, num_channel=3): super(LeNet5_2, self).__init__() # 卷积层 self.conv1 = nn.Conv2d(num_channel, 64, 3, pad_mode='valid', weight_init=Normal(0.06)) self.conv2 = nn.Conv2d(64, 64, 3, pad_mode='valid', weight_init=Normal(0.06)) self.conv3 = nn.Conv2d(64, 128, 3, pad_mode='valid', weight_init=Normal(0.06)) # 全连接层 self.fc1 = nn.Dense(128* 2* 2, 120, weight_init=Normal(0.06)) self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.06)) self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.06)) # 激活函数 self.relu = nn.ReLU() # 最大池化成 self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) # 网络展开 self.flatten = nn.Flatten() #? self.num_class = num_class # 构建网络 def construct(self, x): x = self.conv1(x) x = self.relu(x) x = self.max_pool2d(x) x = self.conv2(x) x = self.relu(x) x = self.max_pool2d(x) x = self.conv3(x) x = self.relu(x) x = self.max_pool2d(x) x = self.flatten(x) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.relu(x) x = self.fc3(x) return x 添加批量归一层
可以在LeNet5_2类的__init__方法中添加BatchNorm2d层来实现批量归一化操作,如下所示:
```
class LeNet5_2(nn.Cell):
def __init__(self, num_class=10, num_channel=3):
super(LeNet5_2, self).__init__()
# 卷积层
self.conv1 = nn.Conv2d(num_channel, 64, 3, pad_mode='valid', weight_init=Normal(0.06))
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = nn.Conv2d(64, 64, 3, pad_mode='valid', weight_init=Normal(0.06))
self.bn2 = nn.BatchNorm2d(64)
self.conv3 = nn.Conv2d(64, 128, 3, pad_mode='valid', weight_init=Normal(0.06))
self.bn3 = nn.BatchNorm2d(128)
# 全连接层
self.fc1 = nn.Dense(128*2*2, 120, weight_init=Normal(0.06))
self.bn4 = nn.BatchNorm1d(120)
self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.06))
self.bn5 = nn.BatchNorm1d(84)
self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.06))
# 激活函数
self.relu = nn.ReLU()
# 最大池化层
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
# 网络展开
self.flatten = nn.Flatten()
self.num_class = num_class
# 构建网络
def construct(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.bn4(x)
x = self.relu(x)
x = self.fc2(x)
x = self.bn5(x)
x = self.relu(x)
x = self.fc3(x)
return x
```
其中,BatchNorm2d层用于卷积层的批量归一化操作,BatchNorm1d层用于全连接层的批量归一化操作。在构建网络时,需要将批量归一化层添加到对应的卷积层或全连接层后面。