set_extra_state 设置额外的状态信息。如model.set_extra_state(state),state是一个字典。我们可以使用gets_extra_state()获取添加的额外状态。extra_repr要打印自定义的额外信息,您应该在自己的模块中重新实现此方法。单行和多行字符串都可以接受。def extra_repr(self): res = """ 正在打印: MODEL: ...
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...
extra_repr()[source] Set the extra representation of the module To print customized extra information, you should reimplement this method in your own modules. Both single-line and multi-line strings are acceptable. float()[source] Casts all floating point parameters and buffers to float datatype...
defforward(self,input):#F来自于importnn.functionalasFreturnF.relu(input,inplace=self.inplace)defextra_repr(self):inplace_str='inplace'ifself.inplaceelse''returninplace_str 2、torch.nn.functional.relu()torch.nn.functional.relu_() 其实这两个函数也是调用了torch.relu() and torch.relu_() 代...
extra_repr()[source] Set the extra representation of the module To print customized extra information, you should reimplement this method in your own modules. Both single-line and multi-line strings are acceptable. float()[source] Casts all floating point parameters and buffers to float datatype...
extra_repr() + ', exclusive={exclusive}'.format(**self.__dict__)) # TODO: reduce unused weights, maybe when torch.sparse is stable Example #9Source File: ESNCell.py From EchoTorch with GNU General Public License v3.0 5 votes def to_sparse(m): """ To sparse matrix :param m: :...
激活函数(也称“非线性映射函数”) 不使用激活函数的话,神经网络的每层都只是做线性变换,多层输入叠加后也还是线性变换。因为线性模型的表达能力通常不够,所以这时候就体现了激活函数的作用了,激活函数可以引入非线性因素 激活函数的性质 1. 连续并可导(允许少数点上不可导)的非线性函数。可导的激活函数 可以直接利用...
(self.weight) bound = 1 / math.sqrt(fan_in) init.uniform_(self.bias, -bound, bound) def forward(self, input): return F.linear(input, self.weight, self.bias) def extra_repr(self): return 'in_features={}, out_features={}, bias={}'.format( self.in_features, self.out_features,...
bias is not None else out return out.reshape(out_shape) def extra_repr(self) -> str: return ( "in_features={}, out_features={}, bias={}, w_bit={}, group_size={}".format( self.in_features, self.out_features, self.bias is not None, self.w_bit, self.group_size, ) )...
torch.nn.Module.extra_repr Supported 14 torch.nn.Module.float Supported 15 torch.nn.Module.forward Supported 16 torch.nn.Module.half Supported 17 torch.nn.Module.load_state_dict Supported 18 torch.nn.Module.modules Supported 19 torch.nn.Module.named_buffers Supported 20 torc...