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NNMF2d.py

@@ -7,7 +7,6 @@ class NNMF2d(torch.nn.Module):
     in_channels: int
     out_channels: int
     weight: torch.Tensor
-    bias: None | torch.Tensor
     iterations: int
     epsilon: float | None
     init_min: float
@@ -16,8 +15,6 @@ class NNMF2d(torch.nn.Module):
     positive_function_type: int
     local_learning: bool
     local_learning_kl: bool
-    use_reconstruction: bool
-    skip_connection: bool
 
     def __init__(
         self,

append_block.py

@@ -9,8 +9,6 @@ def append_block(
     out_channels: int,
     test_image: torch.Tensor,
     parameter_cnn_top: list[torch.nn.parameter.Parameter],
-    parameter_cnn_skip: list[torch.nn.parameter.Parameter],
-    parameter_cnn: list[torch.nn.parameter.Parameter],
     parameter_nnmf: list[torch.nn.parameter.Parameter],
     parameter_norm: list[torch.nn.parameter.Parameter],
     torch_device: torch.device,
@@ -24,8 +22,6 @@ def append_block(
     iterations: int = 20,
     local_learning: bool = False,
     local_learning_kl: bool = False,
-    use_nnmf: bool = True,
-    use_identity: bool = False,
     momentum: float = 0.1,
     track_running_stats: bool = False,
 ) -> torch.Tensor:
@@ -39,7 +35,6 @@ def append_block(
         kernel_size_internal[1] = test_image.shape[-1]
 
     # Main
-
     network.append(torch.nn.ReLU())
     test_image = network[-1](test_image)
 

convert_log_to_numpy.py

@@ -19,6 +19,7 @@ def get_data(path: str = "log_cnn"):
     for id in range(0, len(te)):
         np_temp[id, 0] = te[id].step
         np_temp[id, 1] = te[id].value
+
     print(np_temp[:, 1] / 100)
     return np_temp
 

make_network.py

@@ -1,5 +1,4 @@
 import torch
-from SplitOnOffLayer import SplitOnOffLayer
 from append_block import append_block
 from L1NormLayer import L1NormLayer
 from NNMF2d import NNMF2d
@@ -7,7 +6,6 @@ from append_parameter import append_parameter
 
 
 def make_network(
-    use_nnmf: bool,
     input_dim_x: int,
     input_dim_y: int,
     input_number_of_channel: int,
@@ -67,11 +65,7 @@ def make_network(
         (1, 1),
         (1, 1),
     ],
-    local_learning: list[bool] = [False, False, False, False],
-    local_learning_kl: bool = True,
-    max_pool: bool = True,
     enable_onoff: bool = False,
-    use_identity: bool = False,
 ) -> tuple[
     torch.nn.Sequential,
     list[list[torch.nn.parameter.Parameter]],
@@ -86,14 +80,11 @@ def make_network(
     assert len(number_of_output_channels) == len(stride_pool)
     assert len(number_of_output_channels) == len(padding_pool)
     assert len(number_of_output_channels) == len(dilation_pool)
-    assert len(number_of_output_channels) == len(local_learning)
 
     if enable_onoff:
         input_number_of_channel *= 2
 
     parameter_cnn_top: list[torch.nn.parameter.Parameter] = []
-    parameter_cnn_skip: list[torch.nn.parameter.Parameter] = []
-    parameter_cnn: list[torch.nn.parameter.Parameter] = []
     parameter_nnmf: list[torch.nn.parameter.Parameter] = []
     parameter_norm: list[torch.nn.parameter.Parameter] = []
 
@@ -104,10 +95,6 @@ def make_network(
     network = torch.nn.Sequential()
     network = network.to(torch_device)
 
-    if enable_onoff:
-        network.append(SplitOnOffLayer())
-        test_image = network[-1](test_image)
-
     for block_id in range(0, len(number_of_output_channels)):
 
         test_image = append_block(
@@ -122,16 +109,10 @@ def make_network(
             positive_function_type=positive_function_type,
             beta=beta,
             iterations=iterations,
-            local_learning=local_learning[block_id],
-            local_learning_kl=local_learning_kl,
             torch_device=torch_device,
             parameter_cnn_top=parameter_cnn_top,
-            parameter_cnn_skip=parameter_cnn_skip,
-            parameter_cnn=parameter_cnn,
             parameter_nnmf=parameter_nnmf,
             parameter_norm=parameter_norm,
-            use_nnmf=use_nnmf,
-            use_identity=use_identity,
         )
 
         if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0):
@@ -214,16 +195,12 @@ def make_network(
 
     parameters: list[list[torch.nn.parameter.Parameter]] = [
         parameter_cnn_top,
-        parameter_cnn_skip,
-        parameter_cnn,
         parameter_nnmf,
         parameter_norm,
     ]
 
     name_list: list[str] = [
         "cnn_top",
-        "cnn_skip",
-        "cnn",
         "nnmf",
         "batchnorm2d",
     ]

run_network.py

@@ -23,36 +23,16 @@ from make_optimize import make_optimize
 
 def main(
     lr_initial_nnmf: float = 0.01,
-    lr_initial_cnn: float = 0.001,
     lr_initial_cnn_top: float = 0.001,
-    lr_initial_cnn_skip: float = 0.001,
     lr_initial_norm: float = 0.001,
     iterations: int = 20,
-    use_nnmf: bool = True,
     dataset: str = "CIFAR10",  # "CIFAR10", "FashionMNIST", "MNIST"
-    enable_onoff: bool = False,
-    local_learning_all: bool = False,
-    local_learning_0: bool = False,
-    local_learning_1: bool = False,
-    local_learning_2: bool = False,
-    local_learning_3: bool = False,
-    local_learning_kl: bool = False,
-    max_pool: bool = False,
     only_print_network: bool = False,
-    use_identity: bool = False,
-    da_auto_mode: bool = False,
 ) -> None:
 
-    if local_learning_all:
-        local_learning_0 = True
-        local_learning_1 = True
-        local_learning_2 = True
-        local_learning_3 = True
+    da_auto_mode: bool = False  # Automatic Data Augmentation from TorchVision
     lr_limit: float = 1e-9
 
-    if use_identity:
-        use_nnmf = True
-
     torch_device: torch.device = (
         torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
     )
@@ -63,11 +43,6 @@ def main(
     batch_size_test: int = 50  # 0
     number_of_epoch: int = 5000
 
-    if use_nnmf:
-        prefix: str = "nnmf"
-    else:
-        prefix = "cnn"
-
     loss_mode: int = 0
     loss_coeffs_mse: float = 0.5
     loss_coeffs_kldiv: float = 1.0
@@ -111,22 +86,11 @@ def main(
         parameters,
         name_list,
     ) = make_network(
-        use_nnmf=use_nnmf,
         input_dim_x=input_dim_x,
         input_dim_y=input_dim_y,
         input_number_of_channel=input_number_of_channel,
         iterations=iterations,
-        enable_onoff=enable_onoff,
-        local_learning=[
-            local_learning_0,
-            local_learning_1,
-            local_learning_2,
-            local_learning_3,
-        ],
-        local_learning_kl=local_learning_kl,
-        max_pool=max_pool,
         torch_device=torch_device,
-        use_identity=use_identity,
     )
 
     print(network)
@@ -152,8 +116,6 @@ def main(
         parameters=parameters,
         lr_initial=[
             lr_initial_cnn_top,
-            lr_initial_cnn_skip,
-            lr_initial_cnn,
             lr_initial_nnmf,
             lr_initial_norm,
         ],
@@ -166,9 +128,7 @@ def main(
         else:
             my_string += "-_"
 
-    default_path: str = (
-        f"{prefix}_iter{iterations}{my_string}0{local_learning_0}_1{local_learning_1}_2{local_learning_2}_3{local_learning_3}_kl{local_learning_kl}_max{max_pool}"
-    )
+    default_path: str = f"iter{iterations}{my_string}"
     log_dir: str = f"log_{default_path}"
 
     tb = SummaryWriter(log_dir=log_dir)