def forward(self, x): x = self.pool1(torch.sigmoid(self.conv1(x))) x = self.pool2(torch.sigmoid(self.conv2(x))) x = x.view(-1, 16 * 4 * 4) x = torch.sigmoid(self.fc1(x)) x = torch.sigmoid(self.fc2(x)) x = self.fc3(x) return x
时间: 2023-06-20 22:04:32 浏览: 98
这是一个神经网络中的前向传播函数。具体来说,这个函数包含了卷积层、池化层和全连接层,以及激活函数的应用。
输入x首先通过第一个卷积层self.conv1进行卷积操作,然后经过一个sigmoid激活函数和一个池化层self.pool1。这个过程会将输入的x的大小从[batch_size, channel_in, height, width]变为[batch_size, channel_out, height/2, width/2],其中channel_out是卷积核的数量,height和width对应的是卷积后的图像大小。
接下来,经过同样的过程,x会再次通过第二个卷积层self.conv2、sigmoid激活函数和池化层self.pool2。这个过程会将输入的x的大小从[batch_size, channel_out, height/2, width/2]变为[batch_size, channel_out, height/4, width/4]。
接着,x被展平成一个一维向量,大小为[batch_size, channel_out * height/4 * width/4],并输入到第一个全连接层self.fc1中,经过一个sigmoid激活函数后,再输入到第二个全连接层self.fc2中,同样经过一个sigmoid激活函数。最后,x输入到最后一个全连接层self.fc3中,不再经过激活函数,输出大小为[batch_size, num_classes],其中num_classes是分类的类别数。
最终,函数返回x,即输出的结果。
相关问题
def forward(self, x): x = self.pool1(torch.sigmoid(self.conv1(x))) x = self.pool2(torch.sigmoid(self.conv2(x))) x = x.view(-1, 16 * 4 * 4) x = torch.sigmoid(self.fc1(x)) x = torch.sigmoid(self.fc2(x)) x = self.fc3(x) return xdef forward(self, x): x = self.pool1(torch.sigmoid(self.conv1(x))) x = self.pool2(torch.sigmoid(self.conv2(x))) x = x.view(-1, 16 * 4 * 4) x = torch.sigmoid(self.fc1(x)) x = torch.sigmoid(self.fc2(x)) x = self.fc3(x) return xdef forward(self, x): x = self.pool1(torch.sigmoid(self.conv1(x))) x = self.pool2(torch.sigmoid(self.conv2(x))) x = x.view(-1, 16 * 4 * 4) x = torch.sigmoid(self.fc1(x)) x = torch.sigmoid(self.fc2(x)) x = self.fc3(x) return xdef forward(self, x): x = self.pool1(torch.sigmoid(self.conv1(x))) x = self.pool2(torch.sigmoid(self.conv2(x))) x = x.view(-1, 16 * 4 * 4) x = torch.sigmoid(self.fc1(x)) x = torch.sigmoid(self.fc2(x)) x = self.fc3(x) return x
这是一个神经网络的前向传播函数,通常用于对输入数据进行预测或分类。该网络包括两个卷积层和三个全连接层,并使用了sigmoid激活函数和池化操作。具体来说,输入数据x经过conv1和pool1后,再经过conv2和pool2,最后通过多层全连接层生成输出。在全连接层中使用sigmoid激活函数,最后一层没有激活函数。
更改import torch import torchvision.models as models import torch.nn as nn import torch.nn.functional as F class eca_Resnet50(nn.Module): def init(self): super().init() self.model = models.resnet50(pretrained=True) self.model.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.model.fc = nn.Linear(2048, 1000) self.eca = ECA_Module(2048, 8) def forward(self, x): x = self.model.conv1(x) x = self.model.bn1(x) x = self.model.relu(x) x = self.model.maxpool(x) x = self.model.layer1(x) x = self.model.layer2(x) x = self.model.layer3(x) x = self.model.layer4(x) x = self.eca(x) x = self.model.avgpool(x) x = torch.flatten(x, 1) x = self.model.fc(x) return x class ECA_Module(nn.Module): def init(self, channel, k_size=3): super(ECA_Module, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x) y = self.conv(y.squeeze(-1).transpose(-1,-2)).transpose(-1,-2).unsqueeze(-1) y = self.sigmoid(y) return x * y.expand_as(x) class ImageDenoising(nn.Module): def init(self): super().init() self.model = eca_Resnet50() self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = self.conv3(x) x = F.relu(x) return x,使最后输出为[16,1,50,50,]。
更改后的代码如下:
```
import torch
import torchvision.models as models
import torch.nn as nn
import torch.nn.functional as F
class eca_Resnet50(nn.Module):
def __init__(self):
super().__init__()
self.model = models.resnet50(pretrained=True)
self.model.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.model.fc = nn.Linear(2048, 1000)
self.eca = ECA_Module(2048, 8)
def forward(self, x):
x = self.model.conv1(x)
x = self.model.bn1(x)
x = self.model.relu(x)
x = self.model.maxpool(x)
x = self.model.layer1(x)
x = self.model.layer2(x)
x = self.model.layer3(x)
x = self.model.layer4(x)
x = self.eca(x)
x = self.model.avgpool(x)
x = torch.flatten(x, 1)
x = self.model.fc(x)
return x
class ECA_Module(nn.Module):
def __init__(self, channel, k_size=3):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x)
y = self.conv(y.squeeze(-1).transpose(-1,-2)).transpose(-1,-2).unsqueeze(-1)
y = self.sigmoid(y)
return x * y.expand_as(x)
class ImageDenoising(nn.Module):
def __init__(self):
super().__init__()
self.model = eca_Resnet50()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = self.conv3(x)
x = F.relu(x)
x = x.view(-1, 1, 50, 50)
return x
```
在修改的过程中,主要是将最后一层卷积层的输出通道数改为 1,并在 forward 方法中添加了 x.view(-1, 1, 50, 50),将输出的大小修改为 [16,1,50,50]。
阅读全文