Function argMax

tfjs-backend-cpu/src/kernels/ArgMax.ts:24–71

(
    args: {inputs: ArgMaxInputs, backend: MathBackendCPU, attrs: ArgMaxAttrs})

import {transpose} from './Transpose';

export function argMax(
    args: {inputs: ArgMaxInputs, backend: MathBackendCPU, attrs: ArgMaxAttrs}):
    TensorInfo {
  const {inputs, backend, attrs} = args;
  const {x} = inputs;
  const {axis} = attrs;

  assertNotComplex(x, 'argMax');

  let axes = util.parseAxisParam(axis, x.shape);
  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
  let $x = x;
  const intermediateTensorInfos = [];
  if (permutedAxes != null) {
    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
    intermediateTensorInfos.push($x);
    axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);
  }

  axes = [axes[0]];
  backend_util.assertAxesAreInnerMostDims('argMax', axes, $x.shape.length);
  const [outShape, reduceShape] =
      backend_util.computeOutAndReduceShapes($x.shape, axes);

  const outSize = util.sizeFromShape(outShape);
  const vals = util.makeZerosTypedArray(outSize, 'int32');
  const reduceSize = util.sizeFromShape(reduceShape);

  const aVals = backend.data.get($x.dataId).values as TypedArray;
  for (let i = 0; i < vals.length; ++i) {
    const offset = i * reduceSize;
    let max = aVals[offset];
    let maxIndex = 0;
    for (let j = 0; j < reduceSize; ++j) {
      const value = aVals[offset + j];
      if (value > max) {
        max = value;
        maxIndex = j;
      }
    }
    vals[i] = maxIndex;
  }

  intermediateTensorInfos.forEach(
      t => backend.disposeIntermediateTensorInfo(t));

  return backend.makeTensorInfo(outShape, 'int32', vals);
}

export const argMaxConfig: KernelConfig = {
  kernelName: ArgMax,