Bihemispheric Neuromorphic Network

Private user posted an update — Sep 07, 2025 04:05 AM EDT

import torch.nn as nn

class OptimizedMLP(nn.Module): def init(self, input_size, hidden_sizes, output_size, dropout=0.1): super().init() layers = [] in_features = input_size

    for hidden_size in hidden_sizes:
        layers.extend([
            nn.Linear(in_features, hidden_size),
            nn.LayerNorm(hidden_size),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout)
        ])
        in_features = hidden_size

    # اضافه کردن LayerNorm و Dropout برای لایه خروجی
    layers.extend([
        nn.Linear(in_features, output_size),
        nn.LayerNorm(output_size),
        nn.Dropout(dropout)
    ])

    self.net = nn.Sequential(*layers)

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

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