关键词：神经网络、孪生神经网络、类似度

前语

孪生神经网络是一种先进的深度学习模型，它经过将原始网络和孪生网络结合起来，能够处理图画识别任务中的一些难题。在这篇文章中，咱们将介绍孪生神经网络的组成部分、工作原理以及使用事例。期望经过这篇文章，读者能够更深化地了解孪生神经网络的各个方面，然后更好地把握这种先进的深度学习模型。

组成部分

孪生神经网络经过将原始网络和孪生网络结合起来。具体来说，孪生神经网络经过孪生技能，将原始网络提取出的特征进行安稳化，使得不同光照、视点、尺度的图画中提取出的特征愈加安稳，然后提高了图画识别的准确性。同时，孪生神经网络也提高了模型的可解说性，经过孪生技能，能够将原始网络和孪生网络的参数分隔核算，使得用户的对模型的解说愈加容易和了解。

原始网络：

原始网络首要担任从输入的图画中提取特征。原始网络一般包含多个卷积层和池化层，能够提取出图画中的部分特征。
例如，咱们能够使用经典的resnet系列的网络：

import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
    expansion: int = 1
    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        groups: int = 1,
        base_width: int = 64,
        dilation: int = 1,
        norm_layer: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride
    def forward(self, x: Tensor) -> Tensor:
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out
class Bottleneck(nn.Module):
    expansion: int = 4
    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        groups: int = 1,
        base_width: int = 64,
        dilation: int = 1,
        norm_layer: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
    def forward(self, x: Tensor) -> Tensor:
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out
class ResNet(nn.Module):
    def __init__(
        self,
        block: Type[Union[BasicBlock, Bottleneck]],
        layers: List[int],
        num_classes: int = 1000,
        zero_init_residual: bool = False,
        groups: int = 1,
        width_per_group: int = 64,
        replace_stride_with_dilation: Optional[List[bool]] = None,
        norm_layer: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)  # type: ignore[arg-type]
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)  # type: ignore[arg-type]
    def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
                    stride: int = 1, dilate: bool = False) -> nn.Sequential:
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))
        return nn.Sequential(*layers)
    def _forward_impl(self, x: Tensor) -> Tensor:
        # See note [TorchScript super()]
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        # x = self.fc(x)
        return x
    def forward(self, x: Tensor) -> Tensor:
        return self._forward_impl(x)
def _resnet(
    arch: str,
    block: Type[Union[BasicBlock, Bottleneck]],
    layers: List[int],
    pretrained: bool,
    progress: bool,
    **kwargs: Any
) -> ResNet:
    model = ResNet(block, layers, **kwargs)
    return model
def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
    r"""ResNet-18 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
                   **kwargs)
def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
    r"""ResNet-34 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)
def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)

孪生网络&输出层：

孪生网络首要担任对原始网络提取出的特征进行孪生。孪生网络一般也包含多个卷积层和池化层，但其参数与原始网络不同，以避免对原始网络的特征进行混杂。孪生网络还包含一个全连接层，用于将原始网络的特征与输出成果相连接。

输出层是孪生神经网络的最后一层，用于输出模型的猜测成果。输出层的参数一般与原始网络和孪生网络不同，以避免对输出成果进行混杂。

import torch
import torch.nn as nn
from resnet import resnet50
resnet50 = resnet50(pretrained=False)
resnet = resnet50.features
class SiameseNetwork(nn.Module):
    def __init__(self, input_shape):
        super(SiameseNetwork, self).__init__()
        self.resnet = resnet
        self.fc = nn.Sequential(
            nn.Linear(2048, 1023),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(1023, 1))
    def forward_once(self, x):
        output = self.resnet(x)
        output = torch.flatten(output, 1)
        return output
    def forward(self, input1, input2):
        output1 = self.forward_once(input1)
        output2 = self.forward_once(input2)
        output = output1 - output2
        output = self.fc(output)
        return output

在孪生神经网络中，原始网络和孪生网络的参数是分隔核算的。具体来说，孪生网络的参数是在练习过程中，经过对原始网络进行反向传达，从原始网络的输出成果中核算得出的。这样能够保证孪生网络对原始网络的特征进行安稳化处理，然后提高了模型的准确性和安稳性。

工作原理

孪生神经网络是一种深度学习模型，它经过将原始网络和孪生网络结合起来，能够处理图画识别任务中的一些难题。孪生神经网络的工作原理能够概括为以下几个步骤:

输入数据：孪生神经网络的输入是一张图片，一般是一个三维的张量，其间每个元素代表图片中的一个像素。
原始网络提取特征：原始网络是孪生神经网络中的第一个网络，它首要担任从输入的图片中提取特征。原始网络一般包含多个卷积层和池化层，能够提取出图片中的部分特征。
孪生网络安稳特征：孪生网络是孪生神经网络中的第二个网络，它首要担任对原始网络提取出的特征进行安稳化处理。孪生网络一般也包含多个卷积层和池化层，但其参数与原始网络不同，以避免对原始网络的特征进行混杂。孪生网络还包含一个全连接层，用于将原始网络的特征与输出成果相连接。
孪生网络输出成果：孪生网络经过全连接层将原始网络的特征安稳化处理后，输出一个二维的张量，这个张量代表输入图片的特征向量。
输出网络输出成果：孪生神经网络的最后一层一般是输出层，它首要担任将孪生网络输出的特征向量转换为输出成果。输出层的参数与原始网络和孪生网络不同，以避免对输出成果进行混杂。
模型练习：孪生神经网络的练习过程是经过反向传达算法完成的。在练习过程中，孪生神经网络的输出成果与实在标签进行比较，经过误差函数来辅导模型的调整，使得模型输出的成果愈加准确。

经过上述工作原理，孪生神经网络能够将输入图片中的特征提取并安稳化，然后提高了图画识别模型的准确性和安稳性。

【Siamese】手把手教你搭建一个孪生神经网络，比较两张图像的相似度

前语

组成部分

工作原理

作者信息

【Siamese】手把手教你搭建一个孪生神经网络，比较两张图像的相似度

前语

组成部分

工作原理

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作者信息