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autoencoder_epoch_1_L1_nonorm_layer_0.pth

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autoencoder_epoch_1_L1_nonorm_layer_1.pth

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autoencoder_epoch_1_L1_nonorm_layer_10.pth

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autoencoder_epoch_1_L1_nonorm_layer_12.pth

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autoencoder_epoch_1_L1_nonorm_layer_36.pth

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autoencoder_epoch_1_L1_nonorm_layer_37.pth

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autoencoder_epoch_1_L1_nonorm_layer_5.pth

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autoencoder_epoch_1_L1_nonorm_layer_7.pth

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model.py

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+    
+class GroupedAutoEncoder(nn.Module):
+    def __init__(self, input_dim, hidden_dim, num_groups):
+        super(GroupedAutoEncoder, self).__init__()
+
+        self.num_groups = num_groups
+        self.group_input_dim = input_dim // num_groups
+        self.group_hidden_dim = hidden_dim // num_groups
+
+        assert input_dim % num_groups == 0, "Input dimension must be divisible by the number of groups."
+        assert hidden_dim % num_groups == 0, "Hidden dimension must be divisible by the number of groups."
+
+        # Define group-wise encoders and decoders
+        self.encoders = nn.ModuleList([
+            nn.Linear(self.group_input_dim, self.group_hidden_dim, bias=False)
+            for _ in range(num_groups)
+        ])
+        '''
+        self.decoders = nn.ModuleList([
+            nn.Linear(self.group_hidden_dim, self.group_input_dim, bias=False)
+            for _ in range(num_groups)
+        ])
+        '''
+        self.decoder = nn.Linear(hidden_dim, input_dim, bias=False)
+
+        self.init_weights()
+
+    def init_weights(self):
+        for encoder in self.encoders:
+            nn.init.xavier_uniform_(encoder.weight)
+        #for decoder in self.decoders:
+        #    nn.init.xavier_uniform_(decoder.weight)
+        nn.init.xavier_uniform_(self.decoder.weight)
+        
+    def forward(self, x):
+        # Split input into groups
+        group_inputs = torch.split(x, self.group_input_dim, dim=1)
+
+        # Apply group-wise encoding
+        encoded_groups = [encoder(group) for group, encoder in zip(group_inputs, self.encoders)]
+
+        # Apply group-wise decoding
+        #decoded_groups = [decoder(group) for group, decoder in zip(encoded_groups, self.decoders)]
+        
+        reconstructed = self.decoder(torch.cat(encoded_groups,dim=1))
+        
+        # Concatenate groups back together
+        # reconstructed = torch.cat(decoded_groups, dim=1)
+        return reconstructed