请注意，默认情况下，nn.Linear将使用缩放的均匀分布来初始化层的权重，以保持方差较小。而在第二个实...
请注意，默认情况下，nn.Linear将使用缩放的均匀分布来初始化层的权重，以保持方差较小。而在第二个实...
x_linear = nn.Linear(x1_dim, 1, bias=False) self.x_dot_linear = nn.Linear(x1_dim, 1, bias=False) self.y_linear = nn.Linear(x2_dim, 1, bias=False) self.layer_norm_on = opt.get('{}_norm_on'.format(self.prefix), False) self.init = init_wrapper(opt.get('{}_init'....
self_attn.out_proj.bias Adam-mini found the param block with name: transformer_encoder.layers.1.linear1.weight Adam-mini found the param block with name: transformer_encoder.layers.1.linear1.bias Adam-mini found the param block with name: transformer_encoder.layers.1.linear2.weight Adam-mini...
" FlattenConsecutive(2), Linear(n_hidden*2, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh(),\n", " Linear(n_hidden, vocab_size),\n", "])\n", "\n", "# parameter init\n", "with torch.no_grad():\n", " model.layers[-1].weight *= 0.1 # last layer make less c...
当是2分类的时候,,一般常用的激活函数是sigmoid(x),此时有:y^{(1)} = sigmoid(x^{(1)}·w^T+bias) = sigmoid([5*0.1, 4*0.2, 3*0.3, 2*0.4, 1*0.5] + 0.8) “线性加权”的局限和优化 线性加权解决不了“异或”的数据问题\begin{bmatrix} 0& 1\\ 1&0 \end{bmatrix},因为一个线性加权...
= 1.0 if q: tensor_q = F.linear(query, self.q_proj_weight, self.in_proj_bias) tensor_k = F.linear(key, self.k_proj_weight, self.in_proj_bias) tensor_v = F.linear(value, self.v_proj_weight, self.in_proj_bias) else: tensor_q, tensor_k, tensor_v = self._in_proj_qkv(...
1 -> Linear(in_features=2, out_features=2, bias=True) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. named_buffers(prefix='',recurse=True)[source] Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself. ...
Linear(2, 2)) >>> net.apply(init_weights) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[ 1., 1.], [ 1., 1.]]) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[ 1., 1.], [ 1., 1.]]) Sequential( (0):...
self.in_proj_bias = Parameter(torch.empty(3* embed_dim)) else: self.register_parameter('in_proj_bias',None) # 后期会将所有头的注意力拼接在一起然后乘上权重矩阵输出 # out_proj是为了后期准备的 self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) ...