@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
"use strict";
/**
* @license
* Copyright 2018 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
Object.defineProperty(exports, "__esModule", { value: true });
var engine_1 = require("../engine");
var globals_1 = require("../globals");
var tensor_util_env_1 = require("../tensor_util_env");
var util = require("../util");
var array_ops_1 = require("./array_ops");
var broadcast_util_1 = require("./broadcast_util");
var operation_1 = require("./operation");
var tensor_ops_1 = require("./tensor_ops");
var unary_ops_1 = require("./unary_ops");
/**
* Batch normalization, strictly for 2D. For the more relaxed version, see
* `tf.batchNorm`.
*
* @param x The input Tensor.
* @param mean A mean Tensor.
* @param variance A variance Tensor.
* @param offset An offset Tensor.
* @param scale A scale Tensor.
* @param varianceEpsilon A small float number to avoid dividing by 0.
*/
function batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'batchNorm');
var $mean = tensor_util_env_1.convertToTensor(mean, 'mean', 'batchNorm');
var $variance = tensor_util_env_1.convertToTensor(variance, 'variance', 'batchNorm');
var $scale;
if (scale != null) {
$scale = tensor_util_env_1.convertToTensor(scale, 'scale', 'batchNorm');
}
var $offset;
if (offset != null) {
$offset = tensor_util_env_1.convertToTensor(offset, 'offset', 'batchNorm');
}
util.assert($x.rank === 2, function () { return "Error in batchNorm3D: x must be rank 3 but got rank " +
($x.rank + "."); });
util.assert($mean.rank === 2 || $mean.rank === 1, function () { return "Error in batchNorm2D: mean must be rank 2 or rank 1 but " +
("got rank " + $mean.rank + "."); });
util.assert($variance.rank === 2 || $variance.rank === 1, function () { return "Error in batchNorm2D: variance must be rank 2 or rank 1 " +
("but got rank " + $variance.rank + "."); });
if ($scale != null) {
util.assert($scale.rank === 2 || $scale.rank === 1, function () { return "Error in batchNorm2D: scale must be rank 2 or rank 1 " +
("but got rank " + $scale.rank + "."); });
}
if ($offset != null) {
util.assert($offset.rank === 2 || $offset.rank === 1, function () { return "Error in batchNorm2D: offset must be rank 2 or rank 1 " +
("but got rank " + $offset.rank + "."); });
}
return batchNorm_($x, $mean, $variance, $offset, $scale, varianceEpsilon);
}
/**
* Batch normalization, strictly for 3D. For the more relaxed version, see
* `tf.batchNorm`.
*
* @param x The input Tensor.
* @param mean A mean Tensor.
* @param variance A variance Tensor.
* @param offset An offset Tensor.
* @param scale A scale Tensor.
* @param varianceEpsilon A small float number to avoid dividing by 0.
*/
function batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'batchNorm');
var $mean = tensor_util_env_1.convertToTensor(mean, 'mean', 'batchNorm');
var $variance = tensor_util_env_1.convertToTensor(variance, 'variance', 'batchNorm');
var $scale;
if (scale != null) {
$scale = tensor_util_env_1.convertToTensor(scale, 'scale', 'batchNorm');
}
var $offset;
if (offset != null) {
$offset = tensor_util_env_1.convertToTensor(offset, 'offset', 'batchNorm');
}
util.assert($x.rank === 3, function () { return "Error in batchNorm3D: x must be rank 3 but got rank " +
($x.rank + "."); });
util.assert($mean.rank === 3 || $mean.rank === 1, function () { return "Error in batchNorm3D: mean must be rank 3 or rank 1 but " +
("got rank " + $mean.rank + "."); });
util.assert($variance.rank === 3 || $variance.rank === 1, function () { return "Error in batchNorm3D: variance must be rank 3 or rank 1 " +
("but got rank " + $variance.rank + "."); });
if ($scale != null) {
util.assert($scale.rank === 3 || $scale.rank === 1, function () { return "Error in batchNorm3D: scale must be rank 3 or rank 1 " +
("but got rank " + $scale.rank + "."); });
}
if ($offset != null) {
util.assert($offset.rank === 3 || $offset.rank === 1, function () { return "Error in batchNorm3D: offset must be rank 3 or rank 1 " +
("but got rank " + $offset.rank + "."); });
}
return batchNorm_($x, $mean, $variance, $offset, $scale, varianceEpsilon);
}
/**
* Batch normalization, strictly for 4D. For the more relaxed version, see
* `tf.batchNorm`.
*
* @param x The input Tensor.
* @param mean A mean Tensor.
* @param variance A variance Tensor.
* @param offset An offset Tensor.
* @param scale A scale Tensor.
* @param varianceEpsilon A small float number to avoid dividing by 0.
*/
function batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'batchNorm');
var $mean = tensor_util_env_1.convertToTensor(mean, 'mean', 'batchNorm');
var $variance = tensor_util_env_1.convertToTensor(variance, 'variance', 'batchNorm');
var $scale;
if (scale != null) {
$scale = tensor_util_env_1.convertToTensor(scale, 'scale', 'batchNorm');
}
var $offset;
if (offset != null) {
$offset = tensor_util_env_1.convertToTensor(offset, 'offset', 'batchNorm');
}
util.assert($x.rank === 4, function () { return "Error in batchNorm4D: x must be rank 4 but got rank " +
($x.rank + "."); });
util.assert($mean.rank === 4 || $mean.rank === 1, function () { return "Error in batchNorm4D: mean must be rank 4 or rank 1 but " +
("got rank " + $mean.rank + "."); });
util.assert($variance.rank === 4 || $variance.rank === 1, function () { return "Error in batchNorm4D: variance must be rank 4 or rank 1 " +
("but got rank " + $variance.rank + "."); });
if ($scale != null) {
util.assert($scale.rank === 4 || $scale.rank === 1, function () { return "Error in batchNorm4D: scale must be rank 4 or rank 1 " +
("but got rank " + $scale.rank + "."); });
}
if ($offset != null) {
util.assert($offset.rank === 4 || $offset.rank === 1, function () { return "Error in batchNorm4D: offset must be rank 4 or rank 1 " +
("but got rank " + $offset.rank + "."); });
}
return batchNorm_($x, $mean, $variance, $offset, $scale, varianceEpsilon);
}
/**
* @deprecated Please use `tf.batchNorm` instead and note the positional
* argument change of scale, offset, and varianceEpsilon.
*/
function batchNormalization_(x, mean, variance, varianceEpsilon, scale, offset) {
if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
warnDeprecation();
return batchNorm_(x, mean, variance, offset, scale, varianceEpsilon);
}
/**
* Batch normalization.
*
* As described in
* [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).
*
* Mean, variance, scale, and offset can be of two shapes:
* - The same shape as the input.
* - In the common case, the depth dimension is the last dimension of x, so
* the values would be an `tf.Tensor1D` of shape [depth].
*
* Also available are stricter rank-specific methods with the same signature
* as this method that assert that parameters passed are of given rank
* - `tf.batchNorm2d`
* - `tf.batchNorm3d`
* - `tf.batchNorm4d`
*
* @param x The input Tensor.
* @param mean A mean Tensor.
* @param variance A variance Tensor.
* @param offset An offset Tensor.
* @param scale A scale Tensor.
* @param varianceEpsilon A small float number to avoid dividing by 0.
*/
/** @doc {heading: 'Operations', subheading: 'Normalization'} */
function batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {
if (varianceEpsilon == null) {
varianceEpsilon = 0.001;
}
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'batchNorm');
var $mean = tensor_util_env_1.convertToTensor(mean, 'mean', 'batchNorm');
var $variance = tensor_util_env_1.convertToTensor(variance, 'variance', 'batchNorm');
var $scale;
if (scale != null) {
$scale = tensor_util_env_1.convertToTensor(scale, 'scale', 'batchNorm');
}
var $offset;
if (offset != null) {
$offset = tensor_util_env_1.convertToTensor(offset, 'offset', 'batchNorm');
}
util.assert($mean.rank === $variance.rank, function () { return 'Batch normalization gradient requires mean and variance to have ' +
'equal ranks.'; });
util.assert($offset == null || $mean.rank === $offset.rank, function () { return 'Batch normalization gradient requires mean and offset to have ' +
'equal ranks.'; });
util.assert($scale == null || $mean.rank === $scale.rank, function () { return 'Batch normalization gradient requires mean and scale to have ' +
'equal ranks.'; });
var x4D;
if ($x.rank === 0 || $x.rank === 1) {
x4D = $x.as4D(1, 1, 1, $x.size);
}
else if ($x.rank === 2) {
x4D = $x.as4D(1, 1, $x.shape[0], $x.shape[1]);
}
else if ($x.rank === 3) {
x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
}
else {
x4D = $x;
}
var der = function (dy, saved) {
var _a = saved, $x = _a[0], $mean = _a[1], $variance = _a[2], $scale = _a[3];
var scaleValue = $scale == null ? tensor_ops_1.scalar(1) : $scale;
var reductionAxes = broadcast_util_1.getReductionAxes($mean.shape, x4D.shape);
var tileShape = [];
if ($mean.rank === 1) {
for (var i = 0; i < x4D.shape.length - 1; ++i) {
tileShape.push(x4D.shape[i]);
}
tileShape.push(1);
}
var xMinusMean = $x.sub($mean);
var dyTimesScaleValue = dy.mul(scaleValue);
var oneOverSqrtVariance = unary_ops_1.rsqrt($variance.add(tensor_ops_1.scalar(varianceEpsilon)));
var minusHalfRCube = oneOverSqrtVariance.mul(oneOverSqrtVariance)
.mul(oneOverSqrtVariance)
.mul(tensor_ops_1.scalar(-0.5));
var derX = function () {
if ($mean.rank === 1) {
return dy
.mul(array_ops_1.tile(oneOverSqrtVariance.as4D(1, 1, 1, $mean.shape[0]), tileShape))
.mul(scaleValue)
.reshape($x.shape);
}
else {
return dy.mul(oneOverSqrtVariance).mul(scaleValue).reshape($x.shape);
}
};
var derMean = function () {
var meanDer = oneOverSqrtVariance.mul(tensor_ops_1.scalar(-1)).mul(dyTimesScaleValue);
if ($mean.rank === 1) {
meanDer = meanDer.sum(reductionAxes);
}
return meanDer.reshape($mean.shape);
};
var derVariance = function () {
var varianceDer = minusHalfRCube.mul(xMinusMean).mul(dyTimesScaleValue);
if ($mean.rank === 1) {
varianceDer = varianceDer.sum(reductionAxes);
}
return varianceDer.reshape($mean.shape);
};
var derScale = function () {
var xMinusMean2TimesRsqrt = xMinusMean.mul(oneOverSqrtVariance);
var scaleDer = dy.mul(xMinusMean2TimesRsqrt);
if ($mean.rank === 1) {
scaleDer = scaleDer.sum(reductionAxes);
}
return scaleDer.reshape($mean.shape);
};
var derOffset = function () {
var offsetDer = dy;
if ($mean.rank === 1) {
offsetDer = offsetDer.sum(reductionAxes);
}
return offsetDer.reshape($mean.shape);
};
return {
x: derX,
mean: derMean,
variance: derVariance,
scale: derScale,
offset: derOffset
};
};
var inputsToSave = [$x, $mean, $variance, $scale];
var res = engine_1.ENGINE.runKernelFunc(function (backend, save) {
var res = backend.batchNormalization(x4D, batchnormReshape4D($mean), batchnormReshape4D($variance), varianceEpsilon, batchnormReshape4D($scale), batchnormReshape4D($offset));
save([$x, $mean, $variance, $scale]);
return res;
}, { x: $x, mean: $mean, variance: $variance, scale: $scale, offset: $offset }, der, 'BatchNormalization', { varianceEpsilon: varianceEpsilon }, inputsToSave);
return res.reshape($x.shape);
}
function batchnormReshape4D(x) {
if (x == null) {
return null;
}
if (x.rank === 0) {
return x.as1D();
}
else if (x.rank === 1) {
return x;
}
else if (x.rank === 2) {
return x.as4D(1, 1, x.shape[0], x.shape[1]);
}
else if (x.rank === 3) {
return x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
}
return x;
}
/**
* @deprecated Please use `tf.batchNorm2d` instead and note the positional
* argument change of scale, offset, and varianceEpsilon.
*/
function batchNormalization2d_(x, mean, variance, varianceEpsilon, scale, offset) {
if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
warnDeprecation();
return batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon);
}
/**
* @deprecated Please use `tf.batchNorm3d` instead and note the positional
* argument change of scale, offset, and varianceEpsilon.
*/
function batchNormalization3d_(x, mean, variance, varianceEpsilon, scale, offset) {
if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
warnDeprecation();
return batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon);
}
/**
* @deprecated Please use `tf.batchNorm4d` instead and note the positional
* argument change of scale, offset, and varianceEpsilon.
*/
function batchNormalization4d_(x, mean, variance, varianceEpsilon, scale, offset) {
if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
warnDeprecation();
return batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon);
}
function warnDeprecation() {
globals_1.deprecationWarn('tf.batchNormalization() is going away. ' +
'Use tf.batchNorm() instead, and note the positional argument change ' +
'of scale, offset, and varianceEpsilon');
}
exports.batchNormalization2d = operation_1.op({ batchNormalization2d_: batchNormalization2d_ });
exports.batchNormalization3d = operation_1.op({ batchNormalization3d_: batchNormalization3d_ });
exports.batchNormalization4d = operation_1.op({ batchNormalization4d_: batchNormalization4d_ });
exports.batchNormalization = operation_1.op({ batchNormalization_: batchNormalization_ });
exports.batchNorm = operation_1.op({ batchNorm_: batchNorm_ });
exports.batchNorm2d = operation_1.op({ batchNorm2d_: batchNorm2d_ });
exports.batchNorm3d = operation_1.op({ batchNorm3d_: batchNorm3d_ });
exports.batchNorm4d = operation_1.op({ batchNorm4d_: batchNorm4d_ });
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