def forward(self,x): q,k,v = self.w_q(x),self.w_k(x),self.w_v(x) pos_code = torch.cat([self.pos_code.unsqueeze(0) for i in range(x.shape[0])]).to(x.device) if self.pos_bias: att_map = torch.matmul(q,k.permute(0,1,3,2)) + pos_code else: att_map = torch.matmul(q,k.permute(0,1,3,2)) + torch.matmul(q,pos_code.permute(0,1,3,2)) am_shape = att_map.shape att_map = self.softmax(att_map.view(am_shape[0],am_shape[1],am_shape[2] * am_shape[3])).view(am_shape) return att_map * v

这是一个自注意力机制的前向传播函数，使用PyTorch实现。参数含义如下：

- `x`: 输入张量
- `w_q`: 用于计算查询向量的线性层
- `w_k`: 用于计算键向量的线性层
- `w_v`: 用于计算值向量的线性层
- `pos_code`: 位置编码张量
- `pos_bias`: 是否使用位置偏置

在函数中，首先通过线性层`w_q`、`w_k`和`w_v`分别计算出查询向量`q`、键向量`k`和值向量`v`。然后将位置编码张量`pos_code`复制多份，使得它的形状与`att_map`相同。如果使用了位置偏置，则将`pos_code`加到`att_map`上，否则将`att_map`分别与`q`和`pos_code`相乘再相加。接着使用softmax函数对`att_map`进行归一化处理，得到注意力权重。最后，将注意力权重与值向量`v`相乘，得到自注意力机制的输出。

相关问题

class Partial_conv3(nn.Module): def __init__(self, dim, n_div, forward): super().__init__() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.partial_conv3(x1) x = torch.cat((x1, x2), 1) return x 在这段代码中插入全局平均池化做通道增强的模块

可以将全局平均池化放在每个 forward 函数的最后，对每个 channel 进行平均池化操作，然后将结果与未被改变的 channel 拼接起来，得到增强后的输出。修改后的代码如下：

class Partial_conv3(nn.Module):
    def __init__(self, dim, n_div, forward):
        super().__init__()
        self.dim_conv3 = dim // n_div
        self.dim_untouched = dim - self.dim_conv3
        self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False)
        self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1))  # 添加全局平均池化层
        if forward == 'slicing':
            self.forward = self.forward_slicing
        elif forward == 'split_cat':
            self.forward = self.forward_split_cat
        else:
            raise NotImplementedError
    
    def forward_slicing(self, x: Tensor) -> Tensor:
        # only for inference
        x = x.clone()  # !!! Keep the original input intact for the residual connection later
        x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :])
        x[:, :self.dim_conv3, :, :] = self.global_avg_pool(x[:, :self.dim_conv3, :, :])  # 添加全局平均池化
        x = torch.cat((x, x[:, self.dim_conv3:, :, :]), 1)
        return x
    
    def forward_split_cat(self, x: Tensor) -> Tensor:
        x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1)
        x1 = self.partial_conv3(x1)
        x1 = self.global_avg_pool(x1)  # 添加全局平均池化
        x = torch.cat((x1, x2), 1)
        return x

怎么用python将下面代码中的dw,db改为私有属性import numpy as npimport matplotlib.pyplot as pltfrom HelperClass.DataReader_1_0 import *file_name = "../../data/ch04.npz"class NeuralNet_0_1(object): def __init__(self, eta): self.eta = eta self.w = 0 self.b = 0 def __forward(self, x): z = x * self.w + self.b return z def __backward(self, x,y,z): dz = z - y db = dz dw = x * dz return dw, db def __update(self, dw, db): self.w = self.w - self.eta * dw self.b = self.b - self.eta * db def train(self, dataReader): for i in range(dataReader.num_train): # get x and y value for one sample x,y = dataReader.GetSingleTrainSample(i) # get z from x,y z = self.__forward(x) # calculate gradient of w and b dw, db = self.__backward(x, y, z) # update w,b self.__update(dw, db) # end for def inference(self, x): return self.__forward(x)# end classdef ShowResult(net, dataReader): X,Y = dataReader.GetWholeTrainSamples() # draw sample data plt.plot(X, Y, "b.") # draw predication data PX = np.linspace(0,1,10) PZ = net.inference(PX) plt.plot(PX, PZ, "r") plt.title("Air Conditioner Power") plt.xlabel("Number of Servers(K)") plt.ylabel("Power of Air Conditioner(KW)") plt.show()if __name__ == '__main__': # read data sdr = DataReader_1_0(file_name) sdr.ReadData() # create net eta = 0.1 net = NeuralNet_0_1(eta) net.train(sdr) # result print("w=%f,b=%f" %(net.w, net.b)) # predication result = net.inference(1.346) print("result=", result) ShowResult(net, sdr)

将dw和db改为私有属性可以在它们的变量名前加上两个下划线"__"，即将dw改为__dw，将db改为__db。修改后的代码如下：

import numpy as np
import matplotlib.pyplot as plt
from HelperClass.DataReader_1_0 import *

file_name = "../../data/ch04.npz"

class NeuralNet_0_1(object):
def __init__(self, eta):
self.eta = eta
self.w = 0
self.b = 0
self.__dw = 0
self.__db = 0

def __forward(self, x):
z = x * self.w + self.b
return z

def __backward(self, x, y, z):
dz = z - y
self.__db = dz
self.__dw = x * dz
return self.__dw, self.__db

def __update(self):
self.w = self.w - self.eta * self.__dw
self.b = self.b - self.eta * self.__db

def train(self, dataReader):
for i in range(dataReader.num_train):
x, y = dataReader.GetBatchTrainSamples(1)
z = self.__forward(x)
dw, db = self.__backward(x, y, z)
self.__update()