Pytorch教程|Pytorch教程[04]torch.nn---Containers 深度学习|人工智能|pytorch

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一、模型创建步骤
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二、构建模型两要素
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三、torch.nn模块
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nn.Module
? parameters : 存储管理nn.Parameter类
? modules : 存储管理nn.Module类
? buffers：存储管理缓冲属性，如BN层中的running_mean
? ***_hooks：存储管理钩子函数

self._parameters = OrderedDict() self._buffers = OrderedDict() self._backward_hooks = OrderedDict() self._forward_hooks = OrderedDict() self._forward_pre_hooks = OrderedDict() self._state_dict_hooks = OrderedDict() self._load_state_dict_pre_hooks = OrderedDict() self._modules = OrderedDict()

例：

class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5)def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x))

四、模型容器——Containers

Pytorch教程|Pytorch教程[04]torch.nn---Containers

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4.1 nn.Sequential
nn.Sequential 是 nn.module的容器，用于按顺序包装一组网络层
? 顺序性：各网络层之间严格按照顺序构建
? 自带forward()：自带的forward里，通过for循环依次执行前向传播运算

# Using Sequential to create a small model. When `model` is run, # input will first be passed to `Conv2d(1,20,5)`. The output of # `Conv2d(1,20,5)` will be used as the input to the first # `ReLU`; the output of the first `ReLU` will become the input # for `Conv2d(20,64,5)`. Finally, the output of # `Conv2d(20,64,5)` will be used as input to the second `ReLU` model = nn.Sequential( nn.Conv2d(1,20,5), nn.ReLU(), nn.Conv2d(20,64,5), nn.ReLU() )# Using Sequential with OrderedDict. This is functionally the # same as the above code model = nn.Sequential(OrderedDict([ ('conv1', nn.Conv2d(1,20,5)), ('relu1', nn.ReLU()), ('conv2', nn.Conv2d(20,64,5)), ('relu2', nn.ReLU()) ]))

4.2 nn.ModuleList
nn.ModuleList是 nn.module的容器，用于包装一组网络层，以迭代方式调用网络层主要方法：
? append()：在ModuleList后面添加网络层
? extend()：拼接两个ModuleList
? insert()：指定在ModuleList中位置插入网络层

class MyModule(nn.Module): def __init__(self): super(MyModule, self).__init__() self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(10)])def forward(self, x): # ModuleList can act as an iterable, or be indexed using ints for i, l in enumerate(self.linears): x = self.linears[i // 2](x) + l(x) return x

4.3 nn.ModuleDict
【Pytorch教程|Pytorch教程[04]torch.nn---Containers】nn.ModuleDict是 nn.module的容器，用于包装一组网络层，以索引方式调用网络层主要方法：
? clear()：清空ModuleDict
? items()：返回可迭代的键值对(key-value pairs)
? keys()：返回字典的键(key)
? values()：返回字典的值(value)
? pop()：返回一对键值，并从字典中删除

class MyModule(nn.Module): def __init__(self): super(MyModule, self).__init__() self.choices = nn.ModuleDict({ 'conv': nn.Conv2d(10, 10, 3), 'pool': nn.MaxPool2d(3) }) self.activations = nn.ModuleDict([ ['lrelu', nn.LeakyReLU()], ['prelu', nn.PReLU()] ])def forward(self, x, choice, act): x = self.choices[choice](x) x = self.activations[act](x) return x

4.4 容器总结