bias) 115 116 def extra_repr(self) -> str: RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x10 and 5x10) 如何修改上述错误?可以手动设置in_features=10第二个nn.Linear(),或者我们可以尝试 PyTorch 的新功能之一,“惰性”层。 PyTorch 惰性层(自动推断输入形状) nn.LazyXPyTorch 中的...
return -gmm.log_prob(x) def extra_repr(self) -> str: info = f" n_features={self.n_features}, n_components={self.n_components}, [init_scale={self.init_scale}]" return info @property def device(self): return next(self.parameters()).device 该模型将返回落在模型的混合高斯分布域中的...
init.uniform_(self.bias,-bound, bound)defforward(self, input: Tensor) ->Tensor:returnF.linear(input, self.weight, self.bias)defextra_repr(self) ->str:returnf"in_features={self.in_features}, out_features={self.out_features}, bias={self.bias is not None}" Weight Initialization defweights...
returnMyConv2dFunc.apply(x, self.weight) defextra_repr(self): return'MyConv2d: in_channels={}, out_channels={}, kernel_size={}'.format( self.in_channels, self.out_channels, self.kernel_size ) 基于MNIST的测试 使用的卷积神经网络模型为LeNet: ...
def extra_repr(self) -> str: info = f" n_features={self.n_features}, n_components={self.n_components}, [init_scale={self.init_scale}]" return info @property def device(self): return next(self.parameters()).device 1. 2.
extra_repr = self.extra_repr() if extra_repr: # empty string will be split into list [''] extra_lines = extra_repr.split('\n') child_lines = [] for key, module in self._modules.items(): mod_str = repr(module) mod_str = _addindent(mod_str, 2) ...
extra_repr:设置module的额外表示。你应该在自己的modules中重新实现此方法。 测试代码如下: importtorchimporttorch.nnasnnimporttorch.nn.functionalasF# nn.functional.py中存放激活函数等的实现@torch.no_grad()definit_weights(m):print("xxxx:", m)iftype(m) == nn.Linear: ...
(self.stdevs)),1)gmm = torch.distributions.MixtureSameFamily( blend_weight, comp)return-gmm.log_prob(x)defextra_repr(self)->str:info = f" n_features={self.n_features}, n_components={self.n_components}, [init_scale={self.init_scale}]"retur...
(5))ifself.biasisnotNone: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)defforward(self,input):returnF.linear(input,self.weight,self.bias)defextra_repr(self):return'in_features={}, out_features={}, ...
线性层的构造函数接受输入特征大小、输出特征大小以及是否包含偏置参数。初始化时，会创建权重和偏置参数，并通过reset_parameters方法初始化。forward方法应用F.linear函数执行前向传播。extra_repr方法提供额外信息，用于打印模块时显示。线性层的主要功能是创建全连接层，常用于神经网络中。权重矩阵维度由输入...