Function randomNormal_

tfjs-core/src/ops/random_normal.ts:41–55

* Creates a `tf.Tensor` with values sampled from a normal distribution. * * ```js * tf.randomNormal([2, 2]).print(); * ``` * * @param shape An array of integers defining the output tensor shape. * @param mean The mean of the normal distribution. * @param stdDev The standard deviation of the

(
    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',
    seed?: number)

 * @doc {heading: 'Tensors', subheading: 'Random'}
 */
function randomNormal_<R extends Rank>(
    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',
    seed?: number): Tensor<R> {
  assertNonNegativeIntegerDimensions(shape);
  if (dtype != null && (dtype as DataType) === 'bool') {
    throw new Error(`Unsupported data type ${dtype}`);
  }
  const randGauss =
      new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);
  const res = buffer(shape, dtype);
  for (let i = 0; i < res.values.length; i++) {
    res.values[i] = randGauss.nextValue();
  }
  return res.toTensor();
}

export const randomNormal = /* @__PURE__ */ op({randomNormal_});