一、Siamese Network是什么
Siamese Network是一种深度学习网络,使用对称结构来进行相似度比较和验证。Siamese Network最初被用于人脸验证和特定项目中的图像识别,随后被应用在文本、语音和其他领域中。Siamese Network的核心思想是使用两个相同的神经网络,对两个输入进行处理,并汇总结果进行比较。该网络不需要标记数据,因此非常适合在训练数据较少的情况下进行相似度比较。
二、Siamese Network的结构
Siamese Network的核心思想是使用两个相同的神经网络,对输入进行处理,然后比较结果。下面是一个简单的Siamese Network模型:
class SiameseNetwork(nn.Module):
def __init__(self):
super(SiameseNetwork, self).__init__()
self.cnn1 = nn.Sequential(
nn.Conv2d(1, 96, kernel_size=11, stride=1),
nn.ReLU(inplace=True),
nn.LocalResponseNorm(5, alpha=0.0001, beta=0.75),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(96, 256, kernel_size=5, stride=1),
nn.ReLU(inplace=True),
nn.LocalResponseNorm(5, alpha=0.0001, beta=0.75),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(256, 384, kernel_size=3, stride=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, stride=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.fc1 = nn.Sequential(
nn.Linear(256 * 6 * 6, 4096),
nn.Sigmoid()
)
self.fc2 = nn.Sequential(
nn.Linear(4096, 1024),
nn.Sigmoid()
)
self.fc3 = nn.Linear(1024, 1)
def forward_once(self, x):
out = self.cnn1(x)
out = out.view(out.size()[0], -1)
out = self.fc1(out)
out = self.fc2(out)
return out
def forward(self, input1, input2):
output1 = self.forward_once(input1)
output2 = self.forward_once(input2)
distance = torch.sqrt(torch.sum(torch.pow(output1 - output2, 2), 1))
return self.fc3(distance)
在这个模型中,Siamese Network由三个主要组件组成:卷积神经网络、全连接层和距离度量层。网络采用两个相同的卷积神经网络,每个神经网络包含卷积层和全连接层。这两个网络处理两个输入,然后使用距离度量层比较两个结果的相似度。为避免梯度消失问题,在全连接层中使用Sigmoid激活函数。
三、Siamese Network的应用
1. 相似度度量
Siamese Network被广泛应用在相似度度量中,在OCR、特定领域的搜索场景中得到了成功的应用。以下是一个使用Siamese Network进行文本相似度比较的示例:
class TextSiamese(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers, dropout, use_gpu=False):
super(TextSiamese, self).__init__()
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.use_gpu = use_gpu
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers, dropout=dropout, bidirectional=True)
self.fc1 = nn.Linear(hidden_dim * 4, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, 1)
self.dropout = nn.Dropout(dropout)
def init_hidden(self, batch_size):
if self.use_gpu:
h0 = Variable(torch.zeros(self.num_layers * 2, batch_size, self.hidden_dim).cuda())
c0 = Variable(torch.zeros(self.num_layers * 2, batch_size, self.hidden_dim).cuda())
else:
h0 = Variable(torch.zeros(self.num_layers * 2, batch_size, self.hidden_dim))
c0 = Variable(torch.zeros(self.num_layers * 2, batch_size, self.hidden_dim))
return (h0, c0)
def forward_once(self, input, hidden):
emb = self.dropout(self.embedding(input))
out, hidden = self.lstm(emb, hidden)
return out[:, -1, :]
def forward(self, input1, input2):
hidden1 = self.init_hidden(input1.size()[0])
hidden2 = self.init_hidden(input2.size()[0])
output1 = self.forward_once(input1, hidden1)
output2 = self.forward_once(input2, hidden2)
distance = torch.abs(output1 - output2)
distance = self.fc1(distance)
distance = self.dropout(distance)
distance = self.fc2(distance)
return distance
在此模型中,我们使用了一个双向的LSTM网络作为文本的编码器,并在全连接层中使用了Sigmoid激活函数来预测文本对之间的相似度。
2. 图像检索
Siamese Network也被广泛应用于图像检索,其核心思想是使用CNN网络对图像进行编码,然后使用距离度量层比较两张图像的相似度。以下是一个示例代码:
class ImageSiamese(nn.Module):
def __init__(self, pretrained_model):
super(ImageSiamese, self).__init__()
self.cnn = nn.Sequential(*list(pretrained_model.children())[:-1])
self.fc = nn.Sequential(
nn.Linear(in_features=512, out_features=1024),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(in_features=1024, out_features=1)
)
def forward_once(self, x):
x = self.cnn(x)
x = x.view(x.size()[0], -1)
x = self.fc(x)
return x
def forward(self, input1, input2):
output1 = self.forward_once(input1)
output2 = self.forward_once(input2)
distance = torch.sqrt(torch.sum(torch.pow(output1 - output2, 2), 1))
return distance
在图像检索中,我们使用了预训练CNN网络对图像进行编码,并在全连接层中使用了ReLU激活函数和Dropout层来提高模型的泛化能力。与文本相似度比较类似,图像相似度比较可以使用距离度量层进行计算。
3. 对话建模
Siamese Network也被广泛应用于对话建模,其核心思想是使用LSTM网络对对话进行编码,然后使用距离度量层比较两个对话之间的相似度。以下是一个示例代码:
class DialogSiamese(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers, dropout):
super(DialogSiamese, self).__init__()
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers, dropout=dropout, bidirectional=True)
self.fc = nn.Sequential(
nn.Linear(hidden_dim * 4, hidden_dim),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(hidden_dim, 1)
)
def forward_once(self, input):
emb = self.embedding(input)
_, (h, c) = self.lstm(emb)
h = torch.cat([h[0], h[1]], dim=1)
c = torch.cat([c[0], c[1]], dim=1)
out = torch.cat([h, c], dim=1)
out = self.fc(out)
return out
def forward(self, input1, input2):
output1 = self.forward_once(input1)
output2 = self.forward_once(input2)
distance = torch.sqrt(torch.sum(torch.pow(output1 - output2, 2), 1))
return distance
在对话建模中,我们使用了一个双向LSTM网络对对话进行编码,并在全连接层中使用ReLU激活函数和Dropout层来增强模型的泛化能力。在前向计算中,使用距离度量层计算两个对话之间的相似度。