Here is an example of how you could write a neural net using PyTorch that ensembles multiple convnet outputs before the final layer:
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`import torch import torch.nn as nn class EnsembleConvNet(nn.Module): def __init__(self, num_convnets): super(EnsembleConvNet, self).__init__() self.convnets = nn.ModuleList([nn.Conv2d(3, 10, 3) for _ in range(num_convnets)]) self.final_conv = nn.Conv2d(num_convnets, 10, 1) self.fc = nn.Linear(10, 10) def forward(self, x): conv_outputs = [] for conv in self.convnets: conv_outputs.append(conv(x)) ensembled = torch.cat(conv_outputs, dim=1) ensembled = self.final_conv(ensembled) ensembled = ensembled.view(ensembled.size(0), -1) return self.fc(ensembled)`
This neural net has a list of `num_convnets` convolutional layers, and a final convolutional layer that takes the concatenated output of all the previous convnets as input. The output of the final convolutional layer is then passed through a fully-connected layer and returned as the output of the model.
I hope this helps! Let me know if you have any questions.
