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神经网络层的基础类,我们构建的神经网络类要继承这个类.
模块可以包含其他模块.
| 方法 | 描述 |
|---|---|
| add_module(name, module) | 插入一个子模块 |
| apply(fn) | |
| buffers(recurse=True) | 返回模块的buffers |
| children() | Returns an iterator over immediate children modules. |
| cpu() | 将所有参数和buffer移动到cpu |
| cuda(device=None) | 将所有参数移动到gpu |
| double() | 将单浮点参数转化为双浮点参数 |
| dump_patches = FALSE | |
| eval() | Sets the module in evaluation mode. |
| extra_repr() | Set the extra representation of the module |
| float() | Casts all floating point parameters and buffers to float datatype. |
| forward(*input) | 向前传播方法,子类必须重写 |
| half() | 将所有浮点参数和buffer和转换为half |
| load_state_dict(state_dict, strict=True) | Copies parameters and buffers from state_dict into this module and its descendants. |
| modules() | returns an iterator over all modules in the network. |
| named_buffers(prefix=’’, recurse=True) | Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself. |
| named_children() | Returns an iterator over immediate children modules |
| named_modules(memo=None, prefix=’’) | Returns an iterator over all modules in the network |
| named_parameters(prefix=’’, recurse=True) | Returns an iterator over module parameters |
| parameters(recurse=True) | Returns an iterator over module parameters. |
| register_backward_hook(hook) | Registers a backward hook on the module. |
| register_buffer(name, tensor) | Adds a persistent buffer to the module. |
| register_forward_hook(hook) | Registers a forward hook on the module. |
| register_forward_pre_hook(hook) | Registers a forward pre-hook on the module. |
| register_parameter(name, param) | Adds a parameter to the module. |
| state_dict(destination=None, prefix=’’, keep_vars=False) | Returns a dictionary containing a whole state of the module. |
| to(*args, **kwargs) | Moves and/or casts the parameters and buffers. |
| train(mode=True) | Sets the module in training mode. |
| type(dst_type) | Casts all parameters and buffers to dst_type. |
| zero_grad() | 将所有模型的梯度参数设置为0 |
参考文献:
https://pytorch.org/docs/stable/nn.html#torch.nn.Module