mirror of
https://github.com/socathie/circomlib-ml.git
synced 2026-04-23 03:00:32 -04:00
30a43a2712
* feat: `Poly` renamed to `ZeLU` with scaling implemented * fix: assertion in `ZeLU` * feat: `AveragePooling2D` with scaling * feat: `BatchNorm` with scaling * feat: `Conv1D` with scaling * feat: `Conv2D` with scaling * feat: `Dense` with scaling * fix: assertion in `Dense` * feat: `GlobalAveragePooling2D` with scaling * feat: input-only `ArgMax` * feat: input-only `Flatten2D` * feat: input-only `GlobalMaxPooling2D` * feat: input-only `MaxPooling2D` * feat: input-only `ReLU` * test: precision up to 36 decimals * chore: clean up * test: model1 with 36 decimals * fix: ReLU should use `p//2` as threshold * test: clean up * test: mnist model with 18 decimals * build: Update package.json version to 2.0.0 * chore: Update README with warning message
32 lines
959 B
Plaintext
32 lines
959 B
Plaintext
pragma circom 2.0.0;
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// BatchNormalization layer for 2D inputs
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// a = gamma/(moving_var+epsilon)**.5
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// b = beta-gamma*moving_mean/(moving_var+epsilon)**.5
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// n = 10 to the power of the number of decimal places
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template BatchNormalization2D(nRows, nCols, nChannels, n) {
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signal input in[nRows][nCols][nChannels];
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signal input a[nChannels];
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signal input b[nChannels];
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signal input out[nRows][nCols][nChannels];
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signal input remainder[nRows][nCols][nChannels];
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for (var i=0; i<nRows; i++) {
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for (var j=0; j<nCols; j++) {
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for (var k=0; k<nChannels; k++) {
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assert(remainder[i][j][k] < n);
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out[i][j][k] * n + remainder[i][j][k] === a[k]*in[i][j][k]+b[k];
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}
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}
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}
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}
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// component main { public [ out ] } = BatchNormalization2D(1, 1, 1, 1000);
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/* INPUT = {
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"in": ["123"],
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"a": ["234"],
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"b": ["345678"],
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"out": ["374"],
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"remainder": ["460"]
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} */ |