networks.py
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	import torch.nn as nn
	import torch.nn.functional as F


	class EmbeddingNet(nn.Module):
	def __init__(self,dropout):
	super(EmbeddingNet, self).__init__()
	self.convnet = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=4, kernel_size=3),
	nn.BatchNorm2d(4),
	nn.ReLU(),
	nn.MaxPool2d(2,2),
	nn.Dropout(dropout),
	nn.Conv2d(in_channels=4, out_channels=8, kernel_size=3),
	nn.BatchNorm2d(8),
	nn.ReLU(),
	nn.MaxPool2d(2,2),
	nn.Dropout(dropout),
	nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3),
	nn.BatchNorm2d(16),
	nn.ReLU(),
	nn.MaxPool2d(2,2),
	nn.Dropout(dropout),
	nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3),
	nn.BatchNorm2d(32),
	nn.ReLU(),
	nn.MaxPool2d(2,2),
	nn.Dropout(dropout),
	nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),
	nn.BatchNorm2d(64),
	nn.ReLU(),
	nn.MaxPool2d(2,2),
	nn.Dropout(dropout))

	self.fc = nn.Sequential(nn.Linear(64 8 13, 512),
	nn.ReLU(),
	nn.Dropout(dropout),
	nn.Linear(512, 64),
	nn.ReLU(),
	nn.Dropout(dropout),
	nn.Linear(64, 4)
	)

	def forward(self, x):
	output = self.convnet(x)
	output = output.view(-1, 64 8 13)
	output = self.fc(output)
	return output

	def get_embedding(self, x):
	return self.forward(x)

	class SiameseNet(nn.Module):
	def __init__(self, embedding_net):
	super(SiameseNet, self).__init__()
	self.embedding_net = embedding_net

	def forward(self, x1, x2):
	output1 = self.embedding_net(x1)
	output2 = self.embedding_net(x2)
	return output1, output2

	def get_embedding(self, x):
	return self.embedding_net(x)