@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
27 lines • 1.55 kB
JavaScript
import * as broadcast_util from '../../ops/broadcast_util';
var BatchNormProgram = (function () {
function BatchNormProgram(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
this.outputShape = [];
this.supportsBroadcasting = true;
this.variableNames = ['x', 'mean', 'variance'];
broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);
broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);
var offsetSnippet = '0.0';
if (offsetShape != null) {
broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);
this.variableNames.push('offset');
offsetSnippet = 'getOffsetAtOutCoords()';
}
var scaleSnippet = '1.0';
if (scaleShape != null) {
broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);
this.variableNames.push('scale');
scaleSnippet = 'getScaleAtOutCoords()';
}
this.outputShape = xShape;
this.userCode = "\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = " + offsetSnippet + ";\n float scale = " + scaleSnippet + ";\n float inv = scale * inversesqrt(variance + float(" + varianceEpsilon + "));\n setOutput((x - mean) * inv + offset);\n }\n ";
}
return BatchNormProgram;
}());
export { BatchNormProgram };
//# sourceMappingURL=batchnorm_gpu.js.map