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network/Adam.py

@@ -151,11 +151,7 @@ class Adam(torch.optim.Optimizer):
                 if sbs_setting[i] is False:
                     param -= step_size * (exp_avg / denom)
                 else:
-                    # delta = torch.exp(-step_size * (exp_avg / denom))
-                    delta = torch.tanh(-step_size * (exp_avg / denom))
-                    delta += 1.0
-                    delta *= 0.5
-                    delta += 0.5
+                    delta = 0.5 * torch.tanh(-step_size * (exp_avg / denom)) + 1.0
                     self._logging.info(
                         f"ADAM: Layer {i} -> dw_min:{float(delta.min()):.4e}  dw_max:{float(delta.max()):.4e} lr:{lr:.4e}"
                     )

network/Dataset.py

@@ -15,6 +15,8 @@ class DatasetMaster(torch.utils.data.Dataset, ABC):
     initial_size: list[int]
     channel_size: int
 
+    alpha: float
+
     # Initialize
     def __init__(
         self,

network/DatasetMix.py

@@ -0,0 +1,90 @@
+import torch
+from network.Dataset import DatasetMNIST, DatasetFashionMNIST, DatasetCIFAR
+import math
+
+
+class DatasetMNISTMix(DatasetMNIST):
+    def __init__(
+        self,
+        train: bool = False,
+        path_pattern: str = "./",
+        path_label: str = "./",
+        alpha: float = 1.0,
+    ) -> None:
+        super().__init__(train, path_pattern, path_label)
+        self.alpha = alpha
+
+    def __getitem__(self, index: int) -> tuple[torch.Tensor, list[int]]:  # type: ignore
+
+        assert self.alpha >= 0.0
+        assert self.alpha <= 1.0
+
+        image_a, target_a = super().__getitem__(index)
+
+        target_b: int = target_a
+        while target_b == target_a:
+            image_b, target_b = super().__getitem__(
+                int(math.floor(self.number_of_pattern * torch.rand((1)).item()))
+            )
+
+        image = self.alpha * image_a + (1.0 - self.alpha) * image_b
+        target = [target_a, target_b]
+        return image, target
+
+
+class DatasetFashionMNISTMix(DatasetFashionMNIST):
+    def __init__(
+        self,
+        train: bool = False,
+        path_pattern: str = "./",
+        path_label: str = "./",
+        alpha: float = 1.0,
+    ) -> None:
+        super().__init__(train, path_pattern, path_label)
+        self.alpha = alpha
+
+    def __getitem__(self, index: int) -> tuple[torch.Tensor, list[int]]:  # type: ignore
+
+        assert self.alpha >= 0.0
+        assert self.alpha <= 1.0
+
+        image_a, target_a = super().__getitem__(index)
+
+        target_b: int = target_a
+        while target_b == target_a:
+            image_b, target_b = super().__getitem__(
+                int(math.floor(self.number_of_pattern * torch.rand((1)).item()))
+            )
+
+        image = self.alpha * image_a + (1.0 - self.alpha) * image_b
+        target = [target_a, target_b]
+        return image, target
+
+
+class DatasetCIFARMix(DatasetCIFAR):
+    def __init__(
+        self,
+        train: bool = False,
+        path_pattern: str = "./",
+        path_label: str = "./",
+        alpha: float = 1.0,
+    ) -> None:
+        super().__init__(train, path_pattern, path_label)
+        self.alpha = alpha
+
+    def __getitem__(self, index: int) -> tuple[torch.Tensor, list[int]]:  # type: ignore
+
+        assert self.alpha >= 0.0
+        assert self.alpha <= 1.0
+
+        image_a, target_a = super().__getitem__(index)
+
+        target_b: int = target_a
+        while target_b == target_a:
+            image_b, target_b = super().__getitem__(
+                int(math.floor(self.number_of_pattern * torch.rand((1)).item()))
+            )
+
+        image = self.alpha * image_a + (1.0 - self.alpha) * image_b
+        target = [target_a, target_b]
+        return image, target

network/HDynamicLayer.py

@@ -443,7 +443,6 @@ class FunctionalSbS(torch.autograd.Function):
             )
 
         elif (parameter_output_layer is True) and (parameter_local_learning is True):
-
             target_one_hot: torch.Tensor = torch.zeros(
                 (
                     labels.shape[0],

network/InputSpikeImage.py

@@ -51,7 +51,13 @@ class InputSpikeImage(torch.nn.Module):
     def forward(self, input: torch.Tensor) -> torch.Tensor:
 
         if self.number_of_spikes < 1:
-            return input
+            output = input
+            output = output.type(dtype=input.dtype)
+            if self._normalize is True:
+                output = output * output.shape[-1] * output.shape[-2] * output.shape[-3]  / output.sum(dim=-1, keepdim=True).sum(
+                    dim=-2, keepdim=True
+                ).sum(dim=-3, keepdim=True)
+            return output
 
         input_shape: list[int] = [
             int(input.shape[0]),
@@ -95,9 +101,10 @@ class InputSpikeImage(torch.nn.Module):
                 )
             )
 
-        if self._normalize is True:
         output = output.type(dtype=input_work.dtype)
-            output = output / output.sum(dim=-1, keepdim=True).sum(
+
+        if self._normalize is True:
+            output = output * output.shape[-1] * output.shape[-2] * output.shape[-3]  / output.sum(dim=-1, keepdim=True).sum(
                 dim=-2, keepdim=True
             ).sum(dim=-3, keepdim=True)
 

network/NNMFLayer.py

@@ -0,0 +1,280 @@
+import torch
+
+from network.calculate_output_size import calculate_output_size
+
+
+class NNMFLayer(torch.nn.Module):
+
+    _epsilon_0: float
+    _weights: torch.nn.parameter.Parameter
+    _weights_exists: bool = False
+    _kernel_size: list[int]
+    _stride: list[int]
+    _dilation: list[int]
+    _padding: list[int]
+    _output_size: torch.Tensor
+    _number_of_neurons: int
+    _number_of_input_neurons: int
+    _h_initial: torch.Tensor | None = None
+    _w_trainable: bool
+    _weight_noise_range: list[float]
+    _input_size: list[int]
+    _output_layer: bool = False
+    _number_of_iterations: int
+    _local_learning: bool = False
+
+    device: torch.device
+    default_dtype: torch.dtype
+
+    _number_of_grad_weight_contributions: float = 0.0
+
+    last_input_store: bool = False
+    last_input_data: torch.Tensor | None = None
+
+    _layer_id: int = -1
+
+    def __init__(
+        self,
+        number_of_input_neurons: int,
+        number_of_neurons: int,
+        input_size: list[int],
+        forward_kernel_size: list[int],
+        number_of_iterations: int,
+        epsilon_0: float = 1.0,
+        weight_noise_range: list[float] = [0.0, 1.0],
+        strides: list[int] = [1, 1],
+        dilation: list[int] = [0, 0],
+        padding: list[int] = [0, 0],
+        w_trainable: bool = False,
+        device: torch.device | None = None,
+        default_dtype: torch.dtype | None = None,
+        layer_id: int = -1,
+        local_learning: bool = False,
+        output_layer: bool = False,
+    ) -> None:
+        super().__init__()
+
+        assert device is not None
+        assert default_dtype is not None
+        self.device = device
+        self.default_dtype = default_dtype
+
+        self._w_trainable = bool(w_trainable)
+        self._stride = strides
+        self._dilation = dilation
+        self._padding = padding
+        self._kernel_size = forward_kernel_size
+        self._number_of_input_neurons = int(number_of_input_neurons)
+        self._number_of_neurons = int(number_of_neurons)
+        self._epsilon_0 = float(epsilon_0)
+        self._number_of_iterations = int(number_of_iterations)
+        self._weight_noise_range = weight_noise_range
+        self._layer_id = layer_id
+        self._local_learning = local_learning
+        self._output_layer = output_layer
+
+        assert len(input_size) == 2
+        self._input_size = input_size
+
+        self._output_size = calculate_output_size(
+            value=input_size,
+            kernel_size=self._kernel_size,
+            stride=self._stride,
+            dilation=self._dilation,
+            padding=self._padding,
+        )
+
+        self.set_h_init_to_uniform()
+
+        # ###############################################################
+        # Initialize the weights
+        # ###############################################################
+
+        assert len(self._weight_noise_range) == 2
+        weights = torch.empty(
+            (
+                int(self._kernel_size[0])
+                * int(self._kernel_size[1])
+                * int(self._number_of_input_neurons),
+                int(self._number_of_neurons),
+            ),
+            dtype=self.default_dtype,
+            device=self.device,
+        )
+
+        torch.nn.init.uniform_(
+            weights,
+            a=float(self._weight_noise_range[0]),
+            b=float(self._weight_noise_range[1]),
+        )
+        self.weights = weights
+
+    @property
+    def weights(self) -> torch.Tensor | None:
+        if self._weights_exists is False:
+            return None
+        else:
+            return self._weights
+
+    @weights.setter
+    def weights(self, value: torch.Tensor):
+        assert value is not None
+        assert torch.is_tensor(value) is True
+        assert value.dim() == 2
+        temp: torch.Tensor = (
+            value.detach()
+            .clone(memory_format=torch.contiguous_format)
+            .type(dtype=self.default_dtype)
+            .to(device=self.device)
+        )
+        temp /= temp.sum(dim=0, keepdim=True, dtype=self.default_dtype)
+        if self._weights_exists is False:
+            self._weights = torch.nn.parameter.Parameter(temp, requires_grad=True)
+            self._weights_exists = True
+        else:
+            self._weights.data = temp
+
+    @property
+    def h_initial(self) -> torch.Tensor | None:
+        return self._h_initial
+
+    @h_initial.setter
+    def h_initial(self, value: torch.Tensor):
+        assert value is not None
+        assert torch.is_tensor(value) is True
+        assert value.dim() == 1
+        assert value.dtype == self.default_dtype
+        self._h_initial = (
+            value.detach()
+            .clone(memory_format=torch.contiguous_format)
+            .type(dtype=self.default_dtype)
+            .to(device=self.device)
+            .requires_grad_(False)
+        )
+
+    def update_pre_care(self):
+
+        if self._weights.grad is not None:
+            assert self._number_of_grad_weight_contributions > 0
+            self._weights.grad /= self._number_of_grad_weight_contributions
+            self._number_of_grad_weight_contributions = 0.0
+
+    def update_after_care(self, threshold_weight: float):
+
+        if self._w_trainable is True:
+            self.norm_weights()
+            self.threshold_weights(threshold_weight)
+            self.norm_weights()
+
+    def set_h_init_to_uniform(self) -> None:
+
+        assert self._number_of_neurons > 2
+
+        self.h_initial: torch.Tensor = torch.full(
+            (self._number_of_neurons,),
+            (1.0 / float(self._number_of_neurons)),
+            dtype=self.default_dtype,
+            device=self.device,
+        )
+
+    def norm_weights(self) -> None:
+        assert self._weights_exists is True
+        temp: torch.Tensor = (
+            self._weights.data.detach()
+            .clone(memory_format=torch.contiguous_format)
+            .type(dtype=self.default_dtype)
+            .to(device=self.device)
+        )
+        temp /= temp.sum(dim=0, keepdim=True, dtype=self.default_dtype)
+        self._weights.data = temp
+
+    def threshold_weights(self, threshold: float) -> None:
+        assert self._weights_exists is True
+        assert threshold >= 0
+
+        torch.clamp(
+            self._weights.data,
+            min=float(threshold),
+            max=None,
+            out=self._weights.data,
+        )
+
+    ####################################################################
+    # Forward                                                          #
+    ####################################################################
+
+    def forward(
+        self,
+        input: torch.Tensor,
+    ) -> torch.Tensor:
+
+        # Are we happy with the input?
+        assert input is not None
+        assert torch.is_tensor(input) is True
+        assert input.dim() == 4
+        assert input.dtype == self.default_dtype
+        assert input.shape[1] == self._number_of_input_neurons
+        assert input.shape[2] == self._input_size[0]
+        assert input.shape[3] == self._input_size[1]
+
+        # Are we happy with the rest of the network?
+        assert self._epsilon_0 is not None
+        assert self._h_initial is not None
+        assert self._weights_exists is True
+        assert self._weights is not None
+
+        # Convolution of the input...
+        # Well, this is a convoltion layer
+        # there needs to be convolution somewhere
+        input_convolved = torch.nn.functional.fold(
+            torch.nn.functional.unfold(
+                input.requires_grad_(True),
+                kernel_size=(int(self._kernel_size[0]), int(self._kernel_size[1])),
+                dilation=(int(self._dilation[0]), int(self._dilation[1])),
+                padding=(int(self._padding[0]), int(self._padding[1])),
+                stride=(int(self._stride[0]), int(self._stride[1])),
+            ),
+            output_size=tuple(self._output_size.tolist()),
+            kernel_size=(1, 1),
+            dilation=(1, 1),
+            padding=(0, 0),
+            stride=(1, 1),
+        )
+
+        # We might need the convolved input for other layers
+        # let us keep it for the future
+        if self.last_input_store is True:
+            self.last_input_data = input_convolved.detach().clone()
+            self.last_input_data /= self.last_input_data.sum(dim=1, keepdim=True)
+        else:
+            self.last_input_data = None
+
+        input_convolved = input_convolved / input_convolved.sum(dim=1, keepdim=True)
+
+        h = torch.tile(
+            self._h_initial.unsqueeze(0).unsqueeze(-1).unsqueeze(-1),
+            dims=[
+                int(input.shape[0]),
+                1,
+                int(self._output_size[0]),
+                int(self._output_size[1]),
+            ],
+        ).requires_grad_(True)
+
+        for _ in range(0, self._number_of_iterations):
+            h_w = h.unsqueeze(1) * self._weights.unsqueeze(0).unsqueeze(-1).unsqueeze(
+                -1
+            )
+            h_w = h_w / (h_w.sum(dim=2, keepdim=True) + 1e-20)
+            h_w = (h_w * input_convolved.unsqueeze(2)).sum(dim=1)
+            if self._epsilon_0 > 0:
+                h = h + self._epsilon_0 * h_w
+            else:
+                h = h_w
+            h = h / (h.sum(dim=1, keepdim=True) + 1e-20)
+
+        self._number_of_grad_weight_contributions += (
+            h.shape[0] * h.shape[-2] * h.shape[-1]
+        )
+
+        return h

network/Parameter.py

@@ -44,7 +44,7 @@ class LearningParameters:
     overload_path: str = field(default="Previous")
 
     weight_noise_range: list[float] = field(default_factory=list)
-    eps_xy_intitial: float = field(default=0.1)
+    eps_xy_intitial: float = field(default=1.0)
 
     disable_scale_grade: bool = field(default=False)
     kepp_last_grad_scale: bool = field(default=True)
@@ -55,7 +55,6 @@ class LearningParameters:
 
     w_trainable: list[bool] = field(default_factory=list)
 
-
 @dataclass
 class Augmentation:
     """Parameters used for data augmentation."""

network/SbSLayer.py

@@ -87,6 +87,8 @@ class SbSLayer(torch.nn.Module):
         spike_full_layer_input_distribution: bool = False,
         force_forward_spike_on_cpu: bool = False,
         force_forward_spike_output_on_cpu: bool = False,
+        local_learning: bool = False,
+        output_layer: bool = False,
     ) -> None:
         super().__init__()
 
@@ -117,6 +119,8 @@ class SbSLayer(torch.nn.Module):
         self._epsilon_xy_use = epsilon_xy_use
         self._force_forward_h_dynamic_on_cpu = force_forward_h_dynamic_on_cpu
         self._spike_full_layer_input_distribution = spike_full_layer_input_distribution
+        self._local_learning = local_learning
+        self._output_layer = output_layer
 
         assert len(input_size) == 2
         self._input_size = input_size

network/SplitOnOffLayer.py

@@ -28,27 +28,28 @@ class SplitOnOffLayer(torch.nn.Module):
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         assert input.ndim == 4
 
-        # self.training is switched by network.eval() and network.train()
-        if self.training is True:
-            mean_temp = (
-                input.mean(dim=0, keepdim=True)
-                .mean(dim=1, keepdim=True)
-                .detach()
-                .clone()
-            )
+#        # self.training is switched by network.eval() and network.train()
+#        if self.training is True:
+#            mean_temp = (
+#                input.mean(dim=0, keepdim=True)
+#                .mean(dim=1, keepdim=True)
+#                .detach()
+#                .clone()
+#            )
+#
+#            if self.mean is None:
+#                self.mean = mean_temp
+#            else:
+#                self.mean = (1.0 - self.epsilon) * self.mean + self.epsilon * mean_temp
+#
+#        assert self.mean is not None
 
-            if self.mean is None:
-                self.mean = mean_temp
-            else:
-                self.mean = (1.0 - self.epsilon) * self.mean + self.epsilon * mean_temp
-
-        assert self.mean is not None
-
-        temp = input - self.mean.detach().clone()
+#        temp = input - self.mean.detach().clone()
+        temp = input - 0.5
         temp_a = torch.nn.functional.relu(temp)
         temp_b = torch.nn.functional.relu(-temp)
         output = torch.cat((temp_a, temp_b), dim=1)
 
-        output /= output.sum(dim=1, keepdim=True) + 1e-20
+        #output /= output.sum(dim=1, keepdim=True) + 1e-20
 
         return output

network/build_datasets.py

@@ -6,6 +6,11 @@ from network.Dataset import (
     DatasetMNIST,
     DatasetFashionMNIST,
 )
+from network.DatasetMix import (
+    DatasetCIFARMix,
+    DatasetMNISTMix,
+    DatasetFashionMNISTMix,
+)
 from network.Parameter import Config
 
 
@@ -42,6 +47,27 @@ def build_datasets(
         the_dataset_test = DatasetFashionMNIST(
             train=False, path_pattern=cfg.data_path, path_label=cfg.data_path
         )
+    elif cfg.data_mode == "MIX_CIFAR10":
+        the_dataset_train = DatasetCIFARMix(
+            train=True, path_pattern=cfg.data_path, path_label=cfg.data_path
+        )
+        the_dataset_test = DatasetCIFARMix(
+            train=False, path_pattern=cfg.data_path, path_label=cfg.data_path
+        )
+    elif cfg.data_mode == "MIX_MNIST":
+        the_dataset_train = DatasetMNISTMix(
+            train=True, path_pattern=cfg.data_path, path_label=cfg.data_path
+        )
+        the_dataset_test = DatasetMNISTMix(
+            train=False, path_pattern=cfg.data_path, path_label=cfg.data_path
+        )
+    elif cfg.data_mode == "MIX_MNIST_FASHION":
+        the_dataset_train = DatasetFashionMNISTMix(
+            train=True, path_pattern=cfg.data_path, path_label=cfg.data_path
+        )
+        the_dataset_test = DatasetFashionMNISTMix(
+            train=False, path_pattern=cfg.data_path, path_label=cfg.data_path
+        )
     else:
         raise Exception("data_mode unknown")
 

network/build_lr_scheduler.py

@@ -39,7 +39,8 @@ def build_lr_scheduler(
             ):
                 lr_scheduler_list.append(
                     torch.optim.lr_scheduler.ReduceLROnPlateau(
-                        optimizer[id_optimizer],eps=1e-14,
+                        optimizer[id_optimizer],
+                        eps=1e-14,
                     )
                 )
             else:

network/build_network.py

@@ -4,6 +4,7 @@ import torch
 from network.calculate_output_size import calculate_output_size
 from network.Parameter import Config
 from network.SbSLayer import SbSLayer
+from network.NNMFLayer import NNMFLayer
 from network.SplitOnOffLayer import SplitOnOffLayer
 from network.Conv2dApproximation import Conv2dApproximation
 from network.SbSReconstruction import SbSReconstruction
@@ -153,6 +154,14 @@ def build_network(
             if cfg.network_structure.layer_type[layer_id].upper().find("POOLING") != -1:
                 is_pooling_layer = True
 
+            local_learning = False
+            if cfg.network_structure.layer_type[layer_id].upper().find("LOCAL") != -1:
+                local_learning = True
+
+            output_layer = False
+            if layer_id == len(cfg.network_structure.layer_type) - 1:
+                output_layer = True
+
             network.append(
                 SbSLayer(
                     number_of_input_neurons=in_channels,
@@ -180,19 +189,13 @@ def build_network(
                     reduction_cooldown=cfg.reduction_cooldown,
                     force_forward_h_dynamic_on_cpu=cfg.force_forward_h_dynamic_on_cpu,
                     spike_full_layer_input_distribution=spike_full_layer_input_distribution,
+                    local_learning=local_learning,
+                    output_layer=output_layer,
                 )
             )
             # Adding the x,y output dimensions
             input_size.append(network[-1]._output_size.tolist())
 
-            network[-1]._output_layer = False
-            if layer_id == len(cfg.network_structure.layer_type) - 1:
-                network[-1]._output_layer = True
-
-            network[-1]._local_learning = False
-            if cfg.network_structure.layer_type[layer_id].upper().find("LOCAL") != -1:
-                network[-1]._local_learning = True
-
         elif (
             cfg.network_structure.layer_type[layer_id]
             .upper()
@@ -276,6 +279,8 @@ def build_network(
         ):
             logging.info(f"Layer: {layer_id} -> RELU Layer")
             network.append(torch.nn.ReLU())
+            network[-1]._w_trainable = False
+
             input_size.append(input_size[-1])
 
         # #############################################################
@@ -296,6 +301,8 @@ def build_network(
                 )
             )
 
+            network[-1]._w_trainable = False
+
             # Calculate the x,y output dimensions
             input_size_temp = calculate_output_size(
                 value=input_size[-1],
@@ -323,6 +330,9 @@ def build_network(
                     padding=(int(padding[0]), int(padding[1])),
                 )
             )
+
+            network[-1]._w_trainable = False
+
             # Calculate the x,y output dimensions
             input_size_temp = calculate_output_size(
                 value=input_size[-1],
@@ -405,8 +415,67 @@ def build_network(
                 )
             )
 
+            network[-1]._w_trainable = False
+
             input_size.append(input_size[-1])
 
+        # #############################################################
+        # NNMF:
+        # #############################################################
+
+        elif (
+            cfg.network_structure.layer_type[layer_id].upper().startswith("NNMF")
+            is True
+        ):
+
+            assert in_channels > 0
+            assert out_channels > 0
+
+            number_of_iterations: int = -1
+            if len(cfg.number_of_spikes) > layer_id:
+                number_of_iterations = cfg.number_of_spikes[layer_id]
+            elif len(cfg.number_of_spikes) == 1:
+                number_of_iterations = cfg.number_of_spikes[0]
+
+            assert number_of_iterations > 0
+
+            logging.info(
+                (
+                    f"Layer: {layer_id} -> NNMF Layer with {number_of_iterations} iterations "
+                )
+            )
+
+            local_learning = False
+            if cfg.network_structure.layer_type[layer_id].upper().find("LOCAL") != -1:
+                local_learning = True
+
+            output_layer = False
+            if layer_id == len(cfg.network_structure.layer_type) - 1:
+                output_layer = True
+
+            network.append(
+                NNMFLayer(
+                    number_of_input_neurons=in_channels,
+                    number_of_neurons=out_channels,
+                    input_size=input_size[-1],
+                    forward_kernel_size=kernel_size,
+                    number_of_iterations=number_of_iterations,
+                    epsilon_0=cfg.epsilon_0,
+                    weight_noise_range=weight_noise_range,
+                    strides=strides,
+                    dilation=dilation,
+                    padding=padding,
+                    w_trainable=w_trainable,
+                    device=device,
+                    default_dtype=default_dtype,
+                    layer_id=layer_id,
+                    local_learning=local_learning,
+                    output_layer=output_layer,
+                )
+            )
+            # Adding the x,y output dimensions
+            input_size.append(network[-1]._output_size.tolist())
+
         # #############################################################
         # Failure becaue we didn't found the selection of layer
         # #############################################################

network/build_optimizer.py

@@ -2,6 +2,8 @@
 import torch
 from network.Parameter import Config
 from network.SbSLayer import SbSLayer
+from network.NNMFLayer import NNMFLayer
+
 from network.Conv2dApproximation import Conv2dApproximation
 from network.Adam import Adam
 
@@ -26,6 +28,12 @@ def build_optimizer(
             parameter_list_weights.append(network[id]._weights)
             parameter_list_sbs.append(True)
 
+        if (isinstance(network[id], NNMFLayer) is True) and (
+            network[id]._w_trainable is True
+        ):
+            parameter_list_weights.append(network[id]._weights)
+            parameter_list_sbs.append(True)
+
         if (isinstance(network[id], torch.nn.modules.conv.Conv2d) is True) and (
             network[id]._w_trainable is True
         ):

network/load_previous_weights.py

@@ -4,6 +4,8 @@ import glob
 import numpy as np
 
 from network.SbSLayer import SbSLayer
+from network.NNMFLayer import NNMFLayer
+
 from network.SplitOnOffLayer import SplitOnOffLayer
 from network.Conv2dApproximation import Conv2dApproximation
 import os
@@ -46,6 +48,23 @@ def load_previous_weights(
                 )
                 logging.info(f"Weights file used for layer {id} : {file_to_load[0]}")
 
+        if isinstance(network[id], NNMFLayer) is True:
+            filename_wilcard = os.path.join(
+                overload_path, f"Weight_L{id}_*{post_fix}.npy"
+            )
+            file_to_load = glob.glob(filename_wilcard)
+
+            if len(file_to_load) > 1:
+                raise Exception(f"Too many previous weights files {filename_wilcard}")
+
+            if len(file_to_load) == 1:
+                network[id].weights = torch.tensor(
+                    np.load(file_to_load[0]),
+                    dtype=default_dtype,
+                    device=device,
+                )
+                logging.info(f"Weights file used for layer {id} : {file_to_load[0]}")
+
         if isinstance(network[id], torch.nn.modules.conv.Conv2d) is True:
 
             # #################################################

network/loop_train_test.py

@@ -4,6 +4,7 @@ from network.Parameter import Config
 from torch.utils.tensorboard import SummaryWriter
 
 from network.SbSLayer import SbSLayer
+from network.NNMFLayer import NNMFLayer
 from network.save_weight_and_bias import save_weight_and_bias
 from network.SbSReconstruction import SbSReconstruction
 
@@ -19,7 +20,9 @@ def add_weight_and_bias_to_histogram(
         # ################################################
         # Log the SbS Weights
         # ################################################
-        if isinstance(network[id], SbSLayer) is True:
+        if (isinstance(network[id], SbSLayer) is True) or (
+            isinstance(network[id], NNMFLayer) is True
+        ):
             if network[id]._w_trainable is True:
 
                 try:
@@ -228,7 +231,9 @@ def run_optimizer(
     cfg: Config,
 ) -> None:
     for id in range(0, len(network)):
-        if isinstance(network[id], SbSLayer) is True:
+        if (isinstance(network[id], SbSLayer) is True) or (
+            isinstance(network[id], NNMFLayer) is True
+        ):
             network[id].update_pre_care()
 
     for optimizer_item in optimizer:
@@ -236,7 +241,9 @@ def run_optimizer(
             optimizer_item.step()
 
     for id in range(0, len(network)):
-        if isinstance(network[id], SbSLayer) is True:
+        if (isinstance(network[id], SbSLayer) is True) or (
+            isinstance(network[id], NNMFLayer) is True
+        ):
             network[id].update_after_care(
                 cfg.learning_parameters.learning_rate_threshold_w
                 / float(
@@ -618,6 +625,94 @@ def loop_test(
     return performance
 
 
+def loop_test_mix(
+    epoch_id: int,
+    cfg: Config,
+    network: torch.nn.modules.container.Sequential,
+    my_loader_test: torch.utils.data.dataloader.DataLoader,
+    the_dataset_test,
+    device: torch.device,
+    default_dtype: torch.dtype,
+    logging,
+    tb: SummaryWriter | None,
+    overwrite_number_of_spikes: int = -1,
+) -> tuple[float, float]:
+
+    test_correct_a_0: int = 0
+    test_correct_a_1: int = 0
+    test_correct_b_0: int = 0
+    test_correct_b_1: int = 0
+
+    test_count: int = 0
+    test_complete: int = the_dataset_test.__len__()
+
+    logging.info("")
+    logging.info("Testing:")
+    mini_batch_id: int = 0
+
+    for h_x, h_x_labels in my_loader_test:
+        assert len(h_x_labels) == 2
+        label_a = h_x_labels[0]
+        label_b = h_x_labels[1]
+        assert label_a.shape[0] == label_b.shape[0]
+        assert h_x.shape[0] == label_b.shape[0]
+
+        time_0 = time.perf_counter()
+
+        h_collection = forward_pass_test(
+            input=h_x,
+            labels=label_a,
+            the_dataset_test=the_dataset_test,
+            cfg=cfg,
+            network=network,
+            device=device,
+            default_dtype=default_dtype,
+            mini_batch_id=mini_batch_id,
+            overwrite_number_of_spikes=overwrite_number_of_spikes,
+        )
+        h_h: torch.Tensor = h_collection[-1].detach().clone().cpu()
+
+        # -------------
+
+        for id in range(0, h_h.shape[0]):
+            temp = h_h[id, ...].squeeze().argsort(descending=True)
+            test_correct_a_0 += float(label_a[id] == int(temp[0]))
+            test_correct_a_1 += float(label_a[id] == int(temp[1]))
+            test_correct_b_0 += float(label_b[id] == int(temp[0]))
+            test_correct_b_1 += float(label_b[id] == int(temp[1]))
+
+        test_count += h_h.shape[0]
+        performance_a_0: float = 100.0 * test_correct_a_0 / test_count
+        performance_a_1: float = 100.0 * test_correct_a_1 / test_count
+        performance_b_0: float = 100.0 * test_correct_b_0 / test_count
+        performance_b_1: float = 100.0 * test_correct_b_1 / test_count
+        time_1 = time.perf_counter()
+        time_measure_a = time_1 - time_0
+
+        logging.info(
+            (
+                f"\t\t{test_count} of {test_complete}"
+                f" with {performance_a_0/100:^6.2%}, "
+                f"{performance_a_1/100:^6.2%}, "
+                f"{performance_b_0/100:^6.2%}, "
+                f"{performance_b_1/100:^6.2%} \t "
+                f"Time used: {time_measure_a:^6.2f}sec"
+            )
+        )
+        mini_batch_id += 1
+
+    logging.info("")
+
+    if tb is not None:
+        tb.add_scalar("Test Error A0", 100.0 - performance_a_0, epoch_id)
+        tb.add_scalar("Test Error A1", 100.0 - performance_a_1, epoch_id)
+        tb.add_scalar("Test Error B0", 100.0 - performance_b_0, epoch_id)
+        tb.add_scalar("Test Error B1", 100.0 - performance_b_1, epoch_id)
+        tb.flush()
+
+    return performance_a_0, performance_a_1, performance_b_0, performance_b_1
+
+
 def loop_test_reconstruction(
     epoch_id: int,
     cfg: Config,

network/save_weight_and_bias.py

@@ -4,6 +4,7 @@ from network.Parameter import Config
 
 import numpy as np
 from network.SbSLayer import SbSLayer
+from network.NNMFLayer import NNMFLayer
 from network.SplitOnOffLayer import SplitOnOffLayer
 from network.Conv2dApproximation import Conv2dApproximation
 
@@ -38,6 +39,21 @@ def save_weight_and_bias(
                     network[id].weights.detach().cpu().numpy(),
                 )
 
+        # ################################################
+        # Save the NNMF Weights
+        # ################################################
+
+        if isinstance(network[id], NNMFLayer) is True:
+            if network[id]._w_trainable is True:
+
+                np.save(
+                    os.path.join(
+                        cfg.weight_path,
+                        f"Weight_L{id}_S{iteration_number}{post_fix}.npy",
+                    ),
+                    network[id].weights.detach().cpu().numpy(),
+                )
+
         # ################################################
         # Save the Conv2 Weights and Biases
         # ################################################
@@ -88,6 +104,7 @@ def save_weight_and_bias(
 
         if isinstance(network[id], SplitOnOffLayer) is True:
 
+            if network[id].mean is not None:
                 np.save(
                     os.path.join(
                         cfg.weight_path, f"Mean_L{id}_S{iteration_number}{post_fix}.npy"