Over the past weeks, I have been diligently working on developing a novel neural network layer, which I've tentatively named idNorm. The core idea of this layer is to perform data compression and expansion, simulating a form of 'learned intelligence' through the manipulation of compacted data layers. My hypothesis is that this mimics a fundament…

idnorm.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class IdNorm(nn.Module):
    def __init__(self, output_dim, depth):
        super(IdNorm, self).__init__()
        self.depth = depth
        self.output_dim = output_dim
        self.embedding = nn.Embedding(output_dim, depth)
        # Assuming MOD_ADLER is a constant, you may want to parameterize it as well
        self.MOD_ADLER = (self.output_dim - 1) // 2

    def forward(self, x):
        # Apply tanh activation and scale
        x = x.tanh().mul(100).round()
        
        # Initialize result container
        res = torch.zeros_like(x, dtype=torch.float32)

        # Adler-like checksum computation
        for i in range(x.size(0)):
            for j in range(x.size(1)):
                data = x[i, j]
                a = 1
                b = 0
                for index in range(data.size(0)):
                    a = (a + data[index].item()) % self.MOD_ADLER
                    b = (b + a) % self.MOD_ADLER
                res[i, j] = b

        # Adjust the result and apply the embedding
        res = res.add(self.MOD_ADLER).round().long()  # Ensure long dtype for embedding input
        result = self.embedding(res)
        
        return result

# Example usage:
# Define the layer with specific output_dim and depth
id_norm_layer = IdNorm(output_dim=32, depth=16)

# Example tensor
input_tensor = torch.randn(10, 20, 30)  # Example shape (batch_size, seq_length, feature_size)
output = id_norm_layer(input_tensor)