torch.nn save
时间: 2023-11-20 09:51:07 浏览: 25
torch.nn.save()是PyTorch中用于保存模型的函数。它可以将整个模型保存为一个文件,也可以只保存模型的参数。具体来说,它有两个参数:第一个参数是要保存的模型,第二个参数是保存的文件名。如果只想保存模型的参数,可以使用模型的state_dict()方法来获取参数,然后再使用torch.save()函数将其保存。同样地,如果要加载模型,可以使用torch.load()函数来加载整个模型或者只加载模型的参数。
相关问题
实现一个Linear层(torch.nn.Linear)功能,分别基于torch.nn.Module和torch.autograd.Function
好的,下面分别给出基于`torch.nn.Module`和`torch.autograd.Function`的实现示例。
基于`torch.nn.Module`的实现示例:
```python
import torch
import torch.nn as nn
class LinearLayer(nn.Module):
def __init__(self, input_size, output_size):
super(LinearLayer, self).__init__()
self.weight = nn.Parameter(torch.Tensor(output_size, input_size))
self.bias = nn.Parameter(torch.Tensor(output_size))
self.reset_parameters()
def reset_parameters(self):
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
nn.init.uniform_(self.bias, -bound, bound)
def forward(self, input):
return torch.matmul(input, self.weight.t()) + self.bias
```
基于`torch.autograd.Function`的实现示例:
```python
import torch
from torch.autograd import Function
class LinearFunction(Function):
@staticmethod
def forward(ctx, input, weight, bias=None):
ctx.save_for_backward(input, weight, bias)
output = torch.matmul(input, weight.t())
if bias is not None:
output += bias.unsqueeze(0).expand_as(output)
return output
@staticmethod
def backward(ctx, grad_output):
input, weight, bias = ctx.saved_tensors
grad_input = grad_weight = grad_bias = None
if ctx.needs_input_grad[0]:
grad_input = torch.matmul(grad_output, weight)
if ctx.needs_input_grad[1]:
grad_weight = torch.matmul(grad_output.t(), input)
if bias is not None and ctx.needs_input_grad[2]:
grad_bias = grad_output.sum(0)
return grad_input, grad_weight, grad_bias
class LinearLayer(Function):
@staticmethod
def forward(ctx, input, weight, bias=None):
ctx.save_for_backward(input, weight, bias)
output = torch.matmul(input, weight.t())
if bias is not None:
output += bias.unsqueeze(0).expand_as(output)
return output
@staticmethod
def backward(ctx, grad_output):
input, weight, bias = ctx.saved_tensors
grad_input = grad_weight = grad_bias = None
if ctx.needs_input_grad[0]:
grad_input = torch.matmul(grad_output, weight)
if ctx.needs_input_grad[1]:
grad_weight = torch.matmul(grad_output.t(), input)
if bias is not None and ctx.needs_input_grad[2]:
grad_bias = grad_output.sum(0)
return grad_input, grad_weight, grad_bias
```
这两个示例分别基于`torch.nn.Module`和`torch.autograd.Function`实现了一个Linear层。你可以根据需要选择其中一种实现方式。希望对你有所帮助!
基于torch.autograd.Function,实现一个torch.nn.linear功能
好的,以下是基于`torch.autograd.Function`实现`torch.nn.Linear`功能的示例代码:
```python
import torch
from torch.autograd import Function
class LinearFunction(Function):
@staticmethod
def forward(ctx, input, weight, bias=None):
ctx.save_for_backward(input, weight, bias)
output = input.mm(weight.t())
if bias is not None:
output += bias.unsqueeze(0).expand_as(output)
return output
@staticmethod
def backward(ctx, grad_output):
input, weight, bias = ctx.saved_tensors
grad_input = grad_weight = grad_bias = None
if ctx.needs_input_grad[0]:
grad_input = grad_output.mm(weight)
if ctx.needs_input_grad[1]:
grad_weight = grad_output.t().mm(input)
if bias is not None and ctx.needs_input_grad[2]:
grad_bias = grad_output.sum(0)
return grad_input, grad_weight, grad_bias
class LinearLayer(torch.nn.Module):
def __init__(self, input_size, output_size):
super(LinearLayer, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.weight = torch.nn.Parameter(torch.Tensor(output_size, input_size))
self.bias = torch.nn.Parameter(torch.Tensor(output_size))
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
torch.nn.init.uniform_(self.bias, -bound, bound)
def forward(self, input):
return LinearFunction.apply(input, self.weight, self.bias)
```
在这个示例中,我们首先定义了一个名为`LinearFunction`的自定义函数,该函数继承自`torch.autograd.Function`。在这个函数中,我们实现了linear层的前向传播和反向传播逻辑。
接下来,我们定义了`LinearLayer`类,该类继承自`torch.nn.Module`。在类的构造函数中,我们创建了权重和偏置项参数,并使用`reset_parameters`方法对它们进行初始化。
在`forward`方法中,我们调用了`LinearFunction`的`apply`方法来完成linear层的前向传播。通过这种方式,我们可以将`LinearFunction`作为一个可调用的函数使用,并且它具有自动求导的能力。
你可以创建一个`LinearLayer`的实例,并将输入数据传递给它进行前向传播。希望这个示例能够帮助你理解如何基于`torch.autograd.Function`实现linear层的功能!