diff options
Diffstat (limited to 'doc')
| -rw-r--r-- | doc/CNN_V2.md | 11 | ||||
| -rw-r--r-- | doc/HOWTO.md | 4 |
2 files changed, 9 insertions, 6 deletions
diff --git a/doc/CNN_V2.md b/doc/CNN_V2.md index 4612d7a..6242747 100644 --- a/doc/CNN_V2.md +++ b/doc/CNN_V2.md @@ -214,12 +214,15 @@ def compute_static_features(rgb, depth): ```python class CNNv2(nn.Module): - def __init__(self, kernel_size=3, num_layers=3): + def __init__(self, kernel_sizes, num_layers=3): super().__init__() + if isinstance(kernel_sizes, int): + kernel_sizes = [kernel_sizes] * num_layers + self.kernel_sizes = kernel_sizes self.layers = nn.ModuleList() # All layers: 12D input (4 prev + 8 static) → 4D output - for i in range(num_layers): + for kernel_size in kernel_sizes: self.layers.append( nn.Conv2d(12, 4, kernel_size=kernel_size, padding=kernel_size//2, bias=False) @@ -247,7 +250,7 @@ class CNNv2(nn.Module): ```python # Hyperparameters -kernel_size = 3 # Uniform 3×3 kernels +kernel_sizes = [3, 3, 3] # Per-layer kernel sizes (e.g., [1,3,5]) num_layers = 3 # Number of CNN layers learning_rate = 1e-3 batch_size = 16 @@ -278,7 +281,7 @@ for epoch in range(epochs): torch.save({ 'state_dict': model.state_dict(), # f32 weights 'config': { - 'kernel_size': 3, + 'kernel_sizes': [3, 3, 3], # Per-layer kernel sizes 'num_layers': 3, 'features': ['p0', 'p1', 'p2', 'p3', 'uv.x', 'uv.y', 'sin10_x', 'bias'] }, diff --git a/doc/HOWTO.md b/doc/HOWTO.md index e909a5d..9c67106 100644 --- a/doc/HOWTO.md +++ b/doc/HOWTO.md @@ -161,10 +161,10 @@ Config: 100 epochs, 3×3 kernels, 8→4→4 channels, patch-based (harris detect --input training/input/ --target training/target_2/ \ --epochs 100 --batch-size 16 --checkpoint-every 5 -# Custom architecture +# Custom architecture (per-layer kernel sizes) ./training/train_cnn_v2.py \ --input training/input/ --target training/target_2/ \ - --kernel-sizes 1 3 5 --channels 16 8 4 \ + --kernel-sizes 1,3,5 \ --epochs 5000 --batch-size 16 ``` |