x):

“`python # Import necessary libraries import torch import torch.nn as nn import torch.nn.functional as F # Define the multiplicative filter network class class MultiplicativeFilterNetwork(nn.Module): def __init__(self, input_channels, output_channels, kernel_size): super(MultiplicativeFilterNetwork, self).__init__() self.weight = nn.Parameter(torch.ones(output_channels, input_channels, kernel_size, kernel_size)) def forward(self, x): return F.conv2d(x, self.weight, padding=1) # Instantiate and use the multiplicative filter network input_channels = 3 output_channels = 64 kernel_size = 3 model = MultiplicativeFilterNetwork(input_channels, output_channels, kernel_size) input_data = torch.randn(1, input_channels, 32, 32) output = model(input_data) # Print the output size print(output.size()) “`

Alfalfa

January 2, 2024

Python

Machine Learning Papers: Multiplicative Filter Networks Machine Learning Papers: Multiplicative Filter Networks Machine Learning Papers: Multiplicative Filter Networks is a…
“`python import torch import torch.nn as nn import torch.nn.functional as F class NeuralRadianceFields(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim): super(NeuralRadianceFields, self).__init__() self.fc1 = nn.Linear(input_dim, hidden_dim) self.fc2 = nn.Linear(hidden_dim, hidden_dim) self.fc3 = nn.Linear(hidden_dim, hidden_dim) self.fc4 = nn.Linear(hidden_dim, output_dim) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) x = self.fc4(x) return x def main(): input_dim = 3 hidden_dim = 256 output_dim = 1 model = NeuralRadianceFields(input_dim, hidden_dim, output_dim) x = torch.rand(1, input_dim) y = model(x) print(y) if __name__ == “__main__”: main() “`

Alfalfa

December 23, 2023

Python

Neural Radiance Fields | NeRF Neural Radiance Fields | NeRF Neural Radiance Fields (NeRF) is a novel method for synthesizing…