36 lines
2.1 KiB
Markdown
36 lines
2.1 KiB
Markdown
# Neural Classification
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Classify surface pixels into two classes using a simple two-layer feedforward neural network with sigmoid activations. Features are extracted via multi-scale Gaussian filtering. Equivalent in purpose to Gwyddion's neural.c classifier.
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## Inputs
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| Name | Type | Required | Description |
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|------|------|----------|-------------|
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| field | DATA_FIELD | Yes | Input surface to classify |
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| training_mask | IMAGE | No | Training labels: 0 = class A, 255 = class B. When omitted the network uses unsupervised self-labelling |
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## Outputs
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| Name | Type | Description |
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|------|------|-------------|
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| mask | IMAGE | Binary classification mask (0 or 255) |
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| probability | DATA_FIELD | Per-pixel probability of belonging to class B (values in 0-1) |
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## Controls
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| Name | Type | Default | Description |
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|------|------|---------|-------------|
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| n_gaussians | INT | 4 | Number of Gaussian blur scales for feature extraction (1-10). Each scale uses sigma = 2^i |
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| n_hidden | INT | 16 | Number of neurons in the hidden layer (4-128) |
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| train_steps | INT | 200 | Number of gradient descent iterations (10-5000) |
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| seed | INT | 42 | Random seed for weight initialisation (0-999999) |
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## Notes
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- Feature extraction applies Gaussian blur at multiple scales (sigma = 1, 2, 4, 8, ...) to capture both fine and coarse surface structure. Each feature is normalised to zero mean and unit variance before training.
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- The network architecture is input -> hidden (sigmoid) -> output (sigmoid), trained with binary cross-entropy loss and standard backpropagation.
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- When a training mask is provided, the network learns in supervised mode using all pixels (0 pixels as class A targets, 255 pixels as class B targets).
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- Without a training mask, the node uses an unsupervised approach: the random initial weights produce an initial classification which is then refined by self-training for a small number of steps.
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- Increasing n_hidden or train_steps improves capacity but slows computation. For most surfaces, the defaults work well.
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- The probability output can be fed into a Threshold Mask node for adjustable post-classification thresholding.
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