/**
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* @license
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* Copyright 2018 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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/**
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* Normalization layers.
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*/
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import * as tfc from '@tensorflow/tfjs-core';
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import { moments, reshape, serialization, tidy, util } from '@tensorflow/tfjs-core';
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import { getConstraint, serializeConstraint } from '../constraints';
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import { InputSpec, Layer } from '../engine/topology';
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import { NotImplementedError, ValueError } from '../errors';
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import { getInitializer, serializeInitializer } from '../initializers';
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import { getRegularizer, serializeRegularizer } from '../regularizers';
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import * as generic_utils from '../utils/generic_utils';
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import * as math_utils from '../utils/math_utils';
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import { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';
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/**
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* Applies batch normalization on x given mean, var, beta and gamma.
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*
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* I.e. returns:
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* `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`
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*
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* @param x Input tensor.
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* @param mean Mean of batch.
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* @param variance Variance of batch.
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* @param beta Tensor with which to center the input.
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* @param gamma Tensor by which to scale the input.
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* @param epsilon Fuzz factor.
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* @returns The result of the batch normalization.
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*/
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export function batchNormalization(x, mean, variance, beta, gamma, epsilon = 1e-3) {
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let out;
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if (x.rank === 2) {
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out = tfc.batchNorm2d(x, mean, variance, beta, gamma, epsilon);
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}
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else if (x.rank === 3) {
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// TODO(cais): Check rank; give proper error message.
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out = tfc.batchNorm3d(x, mean, variance, beta, gamma, epsilon);
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}
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else if (x.rank === 4) {
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out = tfc.batchNorm4d(x, mean, variance, beta, gamma, epsilon);
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}
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else {
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throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} ` +
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`yet`);
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}
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return out;
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}
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/**
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* Non-broadcasting batch normalization for use in training (not inference).
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*
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* The input is normalized to zero mean and unit variance along the
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* `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.
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* The result of that is returned as the first element
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* of the returned `Array`. The other two elements are the mean and variance,
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* respectively.
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*
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* @param x Input tensor to be normalized.
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* @param gamma Tensor by which to scale the input.
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* @param beta Tensor by which to center the input.
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* @param reductionAxes Axes over which to normalize.
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* @param epsilon Fuzz factor.
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* @returns An `Array` of three `Tensors`:
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* [normalized tensor, mean of input, variance of input].
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*/
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function regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {
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return tidy(() => {
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const meanAndVariance = tfc.moments(x, reductionAxes);
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const mean = meanAndVariance.mean;
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const variance = meanAndVariance.variance;
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const normed = batchNormalization(x, mean, variance, beta, gamma, epsilon);
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return [normed, mean, variance];
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});
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}
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/**
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* Broadcasting batch normalization for use in training (not inference).
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*
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* The input is normalized to zero mean and unit variance along the
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* `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.
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* The result of that is returned as the first element
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* of the returned `Array`. The other two elements are the mean and variance,
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* respectively.
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*
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* @param x Input tensor to be normalized.
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* @param gamma Tensor by which to scale the input.
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* @param beta Tensor by which to center the input.
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* @param reductionAxes Axes over which to normalize.
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* @param epsilon Fuzz factor.
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* @returns An `Array` of three `Tensors`:
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* [normalized tensor, mean of input, variance of input].
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*/
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function broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {
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return tidy(() => {
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const meanAndVariance = tfc.moments(x, reductionAxes);
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const mean = meanAndVariance.mean;
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const variance = meanAndVariance.variance;
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const targetShape = [];
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for (const axis of math_utils.range(0, x.rank)) {
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if (reductionAxes.indexOf(axis) !== -1) {
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targetShape.push(1);
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}
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else {
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targetShape.push(x.shape[axis]);
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}
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}
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const broadcastMean = reshape(mean, targetShape);
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const broadcastVariance = reshape(variance, targetShape);
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const broadcastGamma = gamma == null ? null : reshape(gamma, targetShape);
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const broadcastBeta = beta == null ? null : reshape(beta, targetShape);
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const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon);
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return [normed, mean, variance];
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});
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}
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/**
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* Batch normalization for use in training (not inference).
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*
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* @param x Input tensor to be normalized.
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* @param gamma Tensor by which to scale the input.
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* @param beta Tensor by which to center the input.
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* @param reductionAxes Axes over which to normalize.
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* @param epsilon Fuzz factor.
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* @returns An `Array` of three `Tensors`:
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* [normalized tensor, mean of input, variance of input].
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*/
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export function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {
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if (util.arraysEqual(reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {
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return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);
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}
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else {
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return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);
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}
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}
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class BatchNormalization extends Layer {
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constructor(args) {
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if (args == null) {
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args = {};
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}
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super(args);
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this.supportsMasking = true;
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this.axis = args.axis == null ? -1 : args.axis;
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this.momentum = args.momentum == null ? 0.99 : args.momentum;
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this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
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this.center = args.center == null ? true : args.center;
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this.scale = args.scale == null ? true : args.scale;
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this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');
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this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');
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this.movingMeanInitializer =
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getInitializer(args.movingMeanInitializer || 'zeros');
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this.movingVarianceInitializer =
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getInitializer(args.movingVarianceInitializer || 'ones');
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this.betaConstraint = getConstraint(args.betaConstraint);
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this.gammaConstraint = getConstraint(args.gammaConstraint);
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this.betaRegularizer = getRegularizer(args.betaRegularizer);
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this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
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}
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build(inputShape) {
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inputShape = getExactlyOneShape(inputShape);
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const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);
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const dim = inputShape[axis];
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if (dim == null) {
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throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but ` +
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`the layer received an input with shape ` +
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`${JSON.stringify(inputShape)}.`);
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}
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this.inputSpec =
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[new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];
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const shape = [dim];
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if (this.scale) {
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this.gamma = this.addWeight('gamma', shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);
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}
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if (this.center) {
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this.beta = this.addWeight('beta', shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);
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}
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this.movingMean = this.addWeight('moving_mean', shape, null, this.movingMeanInitializer, null, false);
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this.movingVariance = this.addWeight('moving_variance', shape, null, this.movingVarianceInitializer, null, false);
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this.built = true;
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}
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call(inputs, kwargs) {
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return tidy(() => {
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const training = kwargs['training'] == null ? false : kwargs['training'];
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const input = getExactlyOneTensor(inputs);
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const inputShape = input.shape;
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const ndim = inputShape.length;
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const reductionAxes = math_utils.range(0, ndim);
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const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);
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reductionAxes.splice(axis, 1);
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const broadcastShape = generic_utils.pyListRepeat(1, ndim);
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broadcastShape[axis] = inputShape[axis];
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const sortedReductionAxes = reductionAxes.slice();
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sortedReductionAxes.sort();
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const needsBroadcasting = !util.arraysEqual(sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));
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const normalizeInference = () => {
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if (needsBroadcasting) {
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const broadcastMovingMean = reshape(this.movingMean.read(), broadcastShape);
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const broadcastMovingVariance = reshape(this.movingVariance.read(), broadcastShape);
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const broadcastBeta = this.center ? reshape(this.beta.read(), broadcastShape) : null;
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const broadcastGamma = this.scale ? reshape(this.gamma.read(), broadcastShape) : null;
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return batchNormalization(input, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);
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}
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else {
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return batchNormalization(input, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
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}
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};
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if (!training) {
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return normalizeInference();
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}
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const [normedTraining, mean, variance] = normalizeBatchInTraining(input, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);
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const doMovingAverage = (variable, value, momentum) => {
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tfc.tidy(() => {
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const decay = 1 - momentum;
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const origValue = variable.read();
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const updateDelta = tfc.mul(tfc.sub(origValue, value), decay);
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variable.write(tfc.sub(origValue, updateDelta));
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});
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};
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// Perform updates to moving mean and moving variance for training.
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// Porting Note: In PyKeras, these updates to `movingMean` and
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// `movingAverage` are done as a deferred Graph, added to the `Layer`'s
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// `update`s using the `add_update()` method. Here we do it imperatively
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// and encapsulate the updates in a function that is invoked
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// immediately.
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const updateMovingMeanAndVariance = () => {
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doMovingAverage(this.movingMean, mean, this.momentum);
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doMovingAverage(this.movingVariance, variance, this.momentum);
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};
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updateMovingMeanAndVariance();
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return normedTraining;
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});
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}
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getConfig() {
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const config = {
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axis: this.axis,
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momentum: this.momentum,
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epsilon: this.epsilon,
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center: this.center,
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scale: this.scale,
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betaInitializer: serializeInitializer(this.betaInitializer),
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gammaInitializer: serializeInitializer(this.gammaInitializer),
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movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),
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movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),
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betaRegularizer: serializeRegularizer(this.betaRegularizer),
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gammaRegularizer: serializeRegularizer(this.gammaRegularizer),
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betaConstraint: serializeConstraint(this.betaConstraint),
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gammaConstraint: serializeConstraint(this.gammaConstraint)
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};
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const baseConfig = super.getConfig();
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Object.assign(config, baseConfig);
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return config;
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}
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}
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/** @nocollapse */
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BatchNormalization.className = 'BatchNormalization';
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export { BatchNormalization };
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serialization.registerClass(BatchNormalization);
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class LayerNormalization extends Layer {
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constructor(args) {
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if (args == null) {
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args = {};
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}
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super(args);
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this.axis = args.axis == null ? -1 : args.axis;
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if (typeof this.axis === 'number') {
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if (!Number.isInteger(this.axis)) {
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throw new Error(`Expected axis to be an integer, but received ${this.axis}`);
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}
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}
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else if (Array.isArray(this.axis)) {
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for (const axis of this.axis) {
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if (!Number.isInteger(axis)) {
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throw new Error(`Expected axis to be an array of integers, ` +
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`but received ${JSON.stringify(this.axis)}`);
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}
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}
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}
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else {
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throw new Error(`Expected axis to be an integer or an array of integers, ` +
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`but received ${JSON.stringify(this.axis)}`);
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}
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this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
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this.center = args.center == null ? true : args.center;
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this.scale = args.scale == null ? true : args.scale;
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this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');
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this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');
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this.betaRegularizer = getRegularizer(args.betaRegularizer);
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this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
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this.supportsMasking = true;
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}
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build(inputShape) {
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inputShape = getExactlyOneShape(inputShape);
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const nDims = inputShape.length;
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// Convert axis to array and resolve negatives.
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if (typeof this.axis === 'number') {
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this.axis = [this.axis];
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}
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for (let i = 0; i < this.axis.length; ++i) {
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if (this.axis[i] < 0) {
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this.axis[i] += nDims;
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}
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}
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// Further validate axes.
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for (const axis of this.axis) {
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if (axis < 0 || axis >= nDims) {
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throw new Error(`Invalid axis: ${axis}`);
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}
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}
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if (this.axis.length !== generic_utils.unique(this.axis).length) {
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throw new Error(`Found duplicate axes in: ${this.axis}`);
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}
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const paramShape = this.axis.map(axis => inputShape[axis]);
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const trainable = true;
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if (this.scale) {
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this.gamma = this.addWeight('gamma', paramShape, 'float32', this.gammaInitializer, this.gammaRegularizer, trainable);
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}
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else {
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this.gamma = null;
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}
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if (this.center) {
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this.beta = this.addWeight('beta', paramShape, 'float32', this.betaInitializer, this.betaRegularizer, trainable);
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}
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else {
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this.beta = null;
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}
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this.built = true;
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}
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call(inputs, kwargs) {
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const input = getExactlyOneTensor(inputs);
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const inputShape = input.shape;
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const nDims = inputShape.length;
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return tidy(() => {
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const keepDims = true;
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let { mean, variance } = moments(input, this.axis, keepDims);
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const broadcastShape = generic_utils.pyListRepeat(1, nDims);
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for (const dim of this.axis) {
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broadcastShape[dim] = inputShape[dim];
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}
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const broadcast = (v) => {
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if (v != null && v.shape.length !== nDims) {
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return tfc.reshape(v, broadcastShape);
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}
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else {
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return v;
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}
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};
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let scale = this.scale ? broadcast(this.gamma.read()) : null;
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let offset = this.center ? broadcast(this.beta.read()) : null;
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// TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below
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// is a workaround for the limitation of core's batchNormalization?d don't
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// support broadcasting in their gradients. In addition, the tiling is
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// necessary to ensure correctness on the browser CPU backend regardless
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// of forward or backward computation. Remove this workaround once the
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// limitation is addressed. See .
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const momentsTiling = [];
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const scaleOffsetTiling = [];
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for (let i = 0; i < nDims; ++i) {
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if (this.axis.indexOf(i) !== -1) {
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momentsTiling.push(inputShape[i]);
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scaleOffsetTiling.push(1);
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}
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else {
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momentsTiling.push(1);
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scaleOffsetTiling.push(inputShape[i]);
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}
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}
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mean = tfc.tile(mean, momentsTiling);
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variance = tfc.tile(variance, momentsTiling);
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if (scale != null) {
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scale = tfc.tile(scale, scaleOffsetTiling);
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}
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if (offset != null) {
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offset = tfc.tile(offset, scaleOffsetTiling);
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}
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return batchNormalization(input, mean, variance, offset, scale, this.epsilon);
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});
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}
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getConfig() {
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const config = {
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axis: this.axis,
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epsilon: this.epsilon,
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center: this.center,
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scale: this.scale,
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betaInitializer: serializeInitializer(this.betaInitializer),
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gammaInitializer: serializeInitializer(this.gammaInitializer),
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betaRegularizer: serializeRegularizer(this.betaRegularizer),
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gammaRegularizer: serializeRegularizer(this.gammaRegularizer)
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};
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const baseConfig = super.getConfig();
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Object.assign(config, baseConfig);
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return config;
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}
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}
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/** @nocollapse */
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LayerNormalization.className = 'LayerNormalization';
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export { LayerNormalization };
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serialization.registerClass(LayerNormalization);
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//# 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* @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n\n/**\n * Normalization layers.\n */\n\nimport * as tfc from '@tensorflow/tfjs-core';\nimport {moments, reshape, serialization, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, tidy, util} from '@tensorflow/tfjs-core';\n\nimport {Constraint, ConstraintIdentifier, getConstraint, serializeConstraint} from '../constraints';\nimport {InputSpec, Layer, LayerArgs} from '../engine/topology';\nimport {NotImplementedError, ValueError} from '../errors';\nimport {getInitializer, Initializer, InitializerIdentifier, serializeInitializer} from '../initializers';\nimport {Shape} from '../keras_format/common';\nimport {getRegularizer, Regularizer, RegularizerIdentifier, serializeRegularizer} from '../regularizers';\nimport {Kwargs} from '../types';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport {getExactlyOneShape, getExactlyOneTensor} from '../utils/types_utils';\nimport {LayerVariable} from '../variables';\n\n/**\n * Applies batch normalization on x given mean, var, beta and gamma.\n *\n * I.e. returns:\n *   `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`\n *\n * @param x Input tensor.\n * @param mean Mean of batch.\n * @param variance Variance of batch.\n * @param beta Tensor with which to center the input.\n * @param gamma Tensor by which to scale the input.\n * @param epsilon Fuzz factor.\n * @returns The result of the batch normalization.\n */\nexport function batchNormalization(\n    x: Tensor, mean: Tensor, variance: Tensor, beta?: Tensor, gamma?: Tensor,\n    epsilon = 1e-3): Tensor {\n  let out: Tensor;\n  if (x.rank === 2) {\n    out = tfc.batchNorm2d(\n        x as Tensor2D, mean as Tensor2D | Tensor1D,\n        variance as Tensor2D | Tensor1D, beta as Tensor2D | Tensor1D,\n        gamma as Tensor2D | Tensor1D, epsilon);\n  } else if (x.rank === 3) {\n    // TODO(cais): Check rank; give proper error message.\n    out = tfc.batchNorm3d(\n        x as Tensor3D, mean as Tensor3D | Tensor1D,\n        variance as Tensor3D | Tensor1D, beta as Tensor3D | Tensor1D,\n        gamma as Tensor3D | Tensor1D, epsilon);\n  } else if (x.rank === 4) {\n    out = tfc.batchNorm4d(\n        x as Tensor4D, mean as Tensor4D | Tensor1D,\n        variance as Tensor4D | Tensor1D, beta as Tensor4D | Tensor1D,\n        gamma as Tensor4D | Tensor1D, epsilon);\n  } else {\n    throw new NotImplementedError(\n        `batchNormalization is not implemented for array of rank ${x.rank} ` +\n        `yet`);\n  }\n  return out;\n}\n\n/**\n * Non-broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction regularNormalizeBatchInTraining(\n    x: Tensor, gamma: Tensor, beta: Tensor, reductionAxes: number[],\n    epsilon = 1e-3): [Tensor, Tensor, Tensor] {\n  return tidy(() => {\n           const meanAndVariance = tfc.moments(x, reductionAxes);\n           const mean = meanAndVariance.mean;\n           const variance = meanAndVariance.variance;\n           const normed =\n               batchNormalization(x, mean, variance, beta, gamma, epsilon);\n           return [normed, mean, variance];\n         }) as [Tensor, Tensor, Tensor];\n}\n\n/**\n * Broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction broadcastNormalizeBatchInTraining(\n    x: Tensor, gamma: Tensor, beta: Tensor, reductionAxes: number[],\n    epsilon = 1e-3): [Tensor, Tensor, Tensor] {\n  return tidy(() => {\n           const meanAndVariance = tfc.moments(x, reductionAxes);\n           const mean = meanAndVariance.mean;\n           const variance = meanAndVariance.variance;\n           const targetShape: number[] = [];\n           for (const axis of math_utils.range(0, x.rank)) {\n             if (reductionAxes.indexOf(axis) !== -1) {\n               targetShape.push(1);\n             } else {\n               targetShape.push(x.shape[axis]);\n             }\n           }\n           const broadcastMean = reshape(mean, targetShape);\n           const broadcastVariance = reshape(variance, targetShape);\n           const broadcastGamma =\n               gamma == null ? null : reshape(gamma, targetShape);\n           const broadcastBeta =\n               beta == null ? null : reshape(beta, targetShape);\n           const normed = batchNormalization(\n               x, broadcastMean, broadcastVariance, broadcastBeta,\n               broadcastGamma, epsilon);\n           return [normed, mean, variance];\n         }) as [Tensor, Tensor, Tensor];\n}\n\n/**\n * Batch normalization for use in training (not inference).\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nexport function normalizeBatchInTraining(\n    x: Tensor, gamma: Tensor, beta: Tensor, reductionAxes: number[],\n    epsilon = 1e-3): [Tensor, Tensor, Tensor] {\n  if (util.arraysEqual(\n          reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {\n    return regularNormalizeBatchInTraining(\n        x, gamma, beta, reductionAxes, epsilon);\n  } else {\n    return broadcastNormalizeBatchInTraining(\n        x, gamma, beta, reductionAxes, epsilon);\n  }\n}\n\nexport declare interface BatchNormalizationLayerArgs extends LayerArgs {\n  /**\n   * The integer axis that should be normalized (typically the features axis).\n   * Defaults to -1.\n   *\n   * For instance, after a `Conv2D` layer with `data_format=\"channels_first\"`,\n   * set `axis=1` in `batchNormalization`.\n   */\n  axis?: number;\n\n  /**\n   * Momentum of the moving average. Defaults to 0.99.\n   */\n  momentum?: number;\n\n  /**\n   * Small float added to the variance to avoid dividing by zero. Defaults to\n   * 1e-3.\n   */\n  epsilon?: number;\n\n  /**\n   * If `true`, add offset of `beta` to normalized tensor.\n   * If `false`, `beta` is ignored.\n   * Defaults to `true`.\n   */\n  center?: boolean;\n\n  /**\n   * If `true`, multiply by `gamma`.\n   * If `false`, `gamma` is not used.\n   * When the next layer is linear (also e.g. `nn.relu`),\n   * this can be disabled since the scaling will be done by the next layer.\n   * Defaults to `true`.\n   */\n  scale?: boolean;\n\n  /**\n   * Initializer for the beta weight.\n   *  Defaults to 'zeros'.\n   */\n  betaInitializer?: InitializerIdentifier|Initializer;\n\n  /**\n   * Initializer for the gamma weight.\n   *  Defaults to `ones`.\n   */\n  gammaInitializer?: InitializerIdentifier|Initializer;\n\n  /**\n   * Initializer for the moving mean.\n   * Defaults to `zeros`\n   */\n  movingMeanInitializer?: InitializerIdentifier|Initializer;\n\n  /**\n   * Initializer for the moving variance.\n   *  Defaults to 'Ones'.\n   */\n  movingVarianceInitializer?: InitializerIdentifier|Initializer;\n\n  /**\n   * Constraint for the beta weight.\n   */\n  betaConstraint?: ConstraintIdentifier|Constraint;\n\n  /**\n   * Constraint for gamma weight.\n   */\n  gammaConstraint?: ConstraintIdentifier|Constraint;\n\n  /**\n   * Regularizer for the beta weight.\n   */\n  betaRegularizer?: RegularizerIdentifier|Regularizer;\n\n  /**\n   * Regularizer for the gamma weight.\n   */\n  gammaRegularizer?: RegularizerIdentifier|Regularizer;\n}\n\nexport class BatchNormalization extends Layer {\n  /** @nocollapse */\n  static className = 'BatchNormalization';\n  private readonly axis: number;\n  private readonly momentum: number;\n  private readonly epsilon: number;\n  private readonly center: boolean;\n  private readonly scale: boolean;\n  private readonly betaInitializer: Initializer;\n  private readonly gammaInitializer: Initializer;\n  private readonly movingMeanInitializer: Initializer;\n  private readonly movingVarianceInitializer: Initializer;\n  private readonly betaConstraint: Constraint;\n  private readonly gammaConstraint: Constraint;\n  private readonly betaRegularizer: Regularizer;\n  private readonly gammaRegularizer: Regularizer;\n  private gamma: LayerVariable;\n  private beta: LayerVariable;\n  private movingMean: LayerVariable;\n  private movingVariance: LayerVariable;\n\n  constructor(args?: BatchNormalizationLayerArgs) {\n    if (args == null) {\n      args = {};\n    }\n    super(args);\n\n    this.supportsMasking = true;\n    this.axis = args.axis == null ? -1 : args.axis;\n    this.momentum = args.momentum == null ? 0.99 : args.momentum;\n    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n    this.center = args.center == null ? true : args.center;\n    this.scale = args.scale == null ? true : args.scale;\n    this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n    this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n    this.movingMeanInitializer =\n        getInitializer(args.movingMeanInitializer || 'zeros');\n    this.movingVarianceInitializer =\n        getInitializer(args.movingVarianceInitializer || 'ones');\n    this.betaConstraint = getConstraint(args.betaConstraint);\n    this.gammaConstraint = getConstraint(args.gammaConstraint);\n    this.betaRegularizer = getRegularizer(args.betaRegularizer);\n    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n  }\n\n  public override build(inputShape: Shape|Shape[]): void {\n    inputShape = getExactlyOneShape(inputShape);\n    const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);\n    const dim = inputShape[axis];\n    if (dim == null) {\n      throw new ValueError(\n          `Axis ${axis} of input tensor should have a defined dimension but ` +\n          `the layer received an input with shape ` +\n          `${JSON.stringify(inputShape)}.`);\n    }\n    this.inputSpec =\n        [new InputSpec({ndim: inputShape.length, axes: {[axis]: dim}})];\n    const shape = [dim];\n    if (this.scale) {\n      this.gamma = this.addWeight(\n          'gamma', shape, null, this.gammaInitializer, this.gammaRegularizer,\n          true, this.gammaConstraint);\n    }\n    if (this.center) {\n      this.beta = this.addWeight(\n          'beta', shape, null, this.betaInitializer, this.betaRegularizer, true,\n          this.betaConstraint);\n    }\n    this.movingMean = this.addWeight(\n        'moving_mean', shape, null, this.movingMeanInitializer, null, false);\n    this.movingVariance = this.addWeight(\n        'moving_variance', shape, null, this.movingVarianceInitializer, null,\n        false);\n    this.built = true;\n  }\n\n  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {\n    return tidy(() => {\n      const training = kwargs['training'] == null ? false : kwargs['training'];\n      const input = getExactlyOneTensor(inputs);\n      const inputShape = input.shape;\n      const ndim = inputShape.length;\n      const reductionAxes = math_utils.range(0, ndim);\n      const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);\n      reductionAxes.splice(axis, 1);\n      const broadcastShape = generic_utils.pyListRepeat(1, ndim);\n      broadcastShape[axis] = inputShape[axis];\n\n      const sortedReductionAxes = reductionAxes.slice();\n      sortedReductionAxes.sort();\n      const needsBroadcasting = !util.arraysEqual(\n          sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));\n\n      const normalizeInference: () => Tensor = () => {\n        if (needsBroadcasting) {\n          const broadcastMovingMean =\n              reshape(this.movingMean.read(), broadcastShape);\n          const broadcastMovingVariance =\n              reshape(this.movingVariance.read(), broadcastShape);\n          const broadcastBeta =\n              this.center ? reshape(this.beta.read(), broadcastShape) : null;\n          const broadcastGamma =\n              this.scale ? reshape(this.gamma.read(), broadcastShape) : null;\n          return batchNormalization(\n              input, broadcastMovingMean, broadcastMovingVariance,\n              broadcastBeta, broadcastGamma, this.epsilon);\n        } else {\n          return batchNormalization(\n              input, this.movingMean.read(), this.movingVariance.read(),\n              this.beta == null ? null : this.beta.read(),\n              this.gamma == null ? null : this.gamma.read(), this.epsilon);\n        }\n      };\n\n      if (!training) {\n        return normalizeInference();\n      }\n\n      const [normedTraining, mean, variance] = normalizeBatchInTraining(\n          input, this.gamma.read(), this.beta.read(), reductionAxes,\n          this.epsilon);\n\n      const doMovingAverage =\n          (variable: LayerVariable, value: Tensor, momentum: number): void => {\n            tfc.tidy(() => {\n              const decay = 1 - momentum;\n              const origValue = variable.read();\n              const updateDelta = tfc.mul(tfc.sub(origValue, value), decay);\n              variable.write(tfc.sub(origValue, updateDelta));\n            });\n          };\n\n      // Perform updates to moving mean and moving variance for training.\n      // Porting Note: In PyKeras, these updates to `movingMean` and\n      //   `movingAverage` are done as a deferred Graph, added to the `Layer`'s\n      //   `update`s using the `add_update()` method. Here we do it imperatively\n      //   and encapsulate the updates in a function that is invoked\n      //   immediately.\n      const updateMovingMeanAndVariance = () => {\n        doMovingAverage(this.movingMean, mean, this.momentum);\n        doMovingAverage(this.movingVariance, variance, this.momentum);\n      };\n      updateMovingMeanAndVariance();\n\n      return normedTraining;\n    });\n  }\n\n  override getConfig(): serialization.ConfigDict {\n    const config: serialization.ConfigDict = {\n      axis: this.axis,\n      momentum: this.momentum,\n      epsilon: this.epsilon,\n      center: this.center,\n      scale: this.scale,\n      betaInitializer: serializeInitializer(this.betaInitializer),\n      gammaInitializer: serializeInitializer(this.gammaInitializer),\n      movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n      movingVarianceInitializer:\n          serializeInitializer(this.movingVarianceInitializer),\n      betaRegularizer: serializeRegularizer(this.betaRegularizer),\n      gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n      betaConstraint: serializeConstraint(this.betaConstraint),\n      gammaConstraint: serializeConstraint(this.gammaConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n}\nserialization.registerClass(BatchNormalization);\n\nexport interface LayerNormalizationLayerArgs extends LayerArgs {\n  /**\n   * The axis or axes that should be normalized (typically, the feature axis).\n   * Defaults to -1 (the last axis).\n   */\n  axis?: number|number[];\n\n  /**\n   * A small positive float added to variance to avoid divison by zero.\n   * Defaults to 1e-3.\n   */\n  epsilon?: number;\n\n  /**\n   * If `true`, add offset of `beta` to normalized tensor.\n   * If `false`, `beta` is ignored.\n   * Default: `true`.\n   */\n  center?: boolean;\n\n  /**\n   * If `true`, multiply output by `gamma`.\n   * If `false`, `gamma` is not used.\n   * When the next layer is linear, this can be disabled since scaling will\n   * be done by the next layer.\n   * Default: `true`.\n   */\n  scale?: boolean;\n\n  /**\n   * Initializer for the beta weight.\n   * Default: `'zeros'`.\n   */\n  betaInitializer?: InitializerIdentifier|Initializer;\n\n  /**\n   * Initializer for the gamma weight.\n   * Default: `'ones'`.\n   */\n  gammaInitializer?: InitializerIdentifier|Initializer;\n\n  /** Regularizer for the beta weight. */\n  betaRegularizer?: RegularizerIdentifier|Regularizer;\n\n  /** Regularizer for the gamma weight. */\n  gammaRegularizer?: RegularizerIdentifier|Regularizer;\n}\n\nexport class LayerNormalization extends Layer {\n  /** @nocollapse */\n  static className = 'LayerNormalization';\n\n  private axis: number|number[];\n  readonly epsilon: number;\n  readonly center: boolean;\n  readonly scale: boolean;\n  readonly betaInitializer: Initializer;\n  readonly gammaInitializer: Initializer;\n  readonly betaRegularizer: Regularizer;\n  readonly gammaRegularizer: Regularizer;\n\n  private gamma: LayerVariable;\n  private beta: LayerVariable;\n\n  constructor(args?: LayerNormalizationLayerArgs) {\n    if (args == null) {\n      args = {};\n    }\n    super(args);\n\n    this.axis = args.axis == null ? -1 : args.axis;\n    if (typeof this.axis === 'number') {\n      if (!Number.isInteger(this.axis)) {\n        throw new Error(\n            `Expected axis to be an integer, but received ${this.axis}`);\n      }\n    } else if (Array.isArray(this.axis)) {\n      for (const axis of this.axis) {\n        if (!Number.isInteger(axis)) {\n          throw new Error(\n              `Expected axis to be an array of integers, ` +\n              `but received ${JSON.stringify(this.axis)}`);\n        }\n      }\n    } else {\n      throw new Error(\n          `Expected axis to be an integer or an array of integers, ` +\n          `but received ${JSON.stringify(this.axis)}`);\n    }\n\n    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n    this.center = args.center == null ? true : args.center;\n    this.scale = args.scale == null ? true : args.scale;\n    this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n    this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n    this.betaRegularizer = getRegularizer(args.betaRegularizer);\n    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n\n    this.supportsMasking = true;\n  }\n\n  public override build(inputShape: Shape|Shape[]): void {\n    inputShape = getExactlyOneShape(inputShape);\n    const nDims = inputShape.length;\n\n    // Convert axis to array and resolve negatives.\n    if (typeof this.axis === 'number') {\n      this.axis = [this.axis];\n    }\n    for (let i = 0; i < this.axis.length; ++i) {\n      if (this.axis[i] < 0) {\n        this.axis[i] += nDims;\n      }\n    }\n\n    // Further validate axes.\n    for (const axis of this.axis) {\n      if (axis < 0 || axis >= nDims) {\n        throw new Error(`Invalid axis: ${axis}`);\n      }\n    }\n    if (this.axis.length !== generic_utils.unique(this.axis).length) {\n      throw new Error(`Found duplicate axes in: ${this.axis}`);\n    }\n\n    const paramShape = this.axis.map(axis => inputShape[axis]) as number[];\n\n    const trainable = true;\n    if (this.scale) {\n      this.gamma = this.addWeight(\n          'gamma', paramShape, 'float32', this.gammaInitializer,\n          this.gammaRegularizer, trainable);\n    } else {\n      this.gamma = null;\n    }\n    if (this.center) {\n      this.beta = this.addWeight(\n          'beta', paramShape, 'float32', this.betaInitializer,\n          this.betaRegularizer, trainable);\n    } else {\n      this.beta = null;\n    }\n\n    this.built = true;\n  }\n\n  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {\n    const input = getExactlyOneTensor(inputs);\n    const inputShape = input.shape;\n    const nDims = inputShape.length;\n\n    return tidy(() => {\n      const keepDims = true;\n      let {mean, variance} = moments(input, this.axis, keepDims);\n      const broadcastShape = generic_utils.pyListRepeat(1, nDims);\n      for (const dim of this.axis as number[]) {\n        broadcastShape[dim] = inputShape[dim];\n      }\n\n      const broadcast = (v: Tensor) => {\n        if (v != null && v.shape.length !== nDims) {\n          return tfc.reshape(v, broadcastShape);\n        } else {\n          return v;\n        }\n      };\n\n      let scale = this.scale ? broadcast(this.gamma.read()) : null;\n      let offset = this.center ? broadcast(this.beta.read()) : null;\n\n      // TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below\n      // is a workaround for the limitation of core's batchNormalization?d don't\n      // support broadcasting in their gradients. In addition, the tiling is\n      // necessary to ensure correctness on the browser CPU backend regardless\n      // of forward or backward computation. Remove this workaround once the\n      // limitation is addressed. See .\n      const momentsTiling: number[] = [];\n      const scaleOffsetTiling: number[] = [];\n      for (let i = 0; i < nDims; ++i) {\n        if ((this.axis as number[]).indexOf(i) !== -1) {\n          momentsTiling.push(inputShape[i]);\n          scaleOffsetTiling.push(1);\n        } else {\n          momentsTiling.push(1);\n          scaleOffsetTiling.push(inputShape[i]);\n        }\n      }\n      mean = tfc.tile(mean, momentsTiling);\n      variance = tfc.tile(variance, momentsTiling);\n      if (scale != null) {\n        scale = tfc.tile(scale, scaleOffsetTiling);\n      }\n      if (offset != null) {\n        offset = tfc.tile(offset, scaleOffsetTiling);\n      }\n\n      return batchNormalization(\n          input, mean, variance, offset, scale, this.epsilon);\n    });\n  }\n\n  override getConfig(): serialization.ConfigDict {\n    const config: serialization.ConfigDict = {\n      axis: this.axis,\n      epsilon: this.epsilon,\n      center: this.center,\n      scale: this.scale,\n      betaInitializer: serializeInitializer(this.betaInitializer),\n      gammaInitializer: serializeInitializer(this.gammaInitializer),\n      betaRegularizer: serializeRegularizer(this.betaRegularizer),\n      gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n}\nserialization.registerClass(LayerNormalization);\n"]}
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