@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("../index");
var jasmine_util_1 = require("../jasmine_util");
var test_util_1 = require("../test_util");
jasmine_util_1.describeWithFlags('batchNormalization4D', test_util_1.ALL_ENVS, function () {
it('simple batchnorm4D, no offset or scale, 2x1x1x2', function () {
var x = tf.tensor4d([2, 100, 4, 400], [2, 1, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var varianceEpsilon = .001;
var result = tf.batchNormalization4d(x, mean, variance, varianceEpsilon, undefined, undefined);
test_util_1.expectArraysClose(result, [
(x.get(0, 0, 0, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(0, 0, 0, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon),
(x.get(1, 0, 0, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(1, 0, 0, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('simple batchnorm4D, no offset, 2x1x1x2', function () {
var x = tf.tensor4d([2, 100, 4, 400], [2, 1, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var scale = tf.tensor1d([4, 5]);
var varianceEpsilon = .001;
var result = tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, undefined);
test_util_1.expectArraysClose(result, [
(x.get(0, 0, 0, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(0, 0, 0, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon),
(x.get(1, 0, 0, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(1, 0, 0, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('simple batchnorm4D, no scale, 2x1x1x2', function () {
var x = tf.tensor4d([2, 100, 4, 400], [2, 1, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var offset = tf.tensor1d([4, 5]);
var varianceEpsilon = .001;
var result = tf.batchNormalization4d(x, mean, variance, varianceEpsilon, undefined, offset);
test_util_1.expectArraysClose(result, [
offset.get(0) +
(x.get(0, 0, 0, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(0, 0, 0, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon),
offset.get(0) +
(x.get(1, 0, 0, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(1, 0, 0, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('simple batchnorm4D, 2x1x1x2', function () {
var x = tf.tensor4d([2, 100, 4, 400], [2, 1, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var offset = tf.tensor1d([3, 4]);
var scale = tf.tensor1d([4, 5]);
var varianceEpsilon = .001;
var result = tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset);
test_util_1.expectArraysClose(result, [
offset.get(0) +
(x.get(0, 0, 0, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(0, 0, 0, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon),
offset.get(0) +
(x.get(1, 0, 0, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(1, 0, 0, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('simple batchnorm4D gradients, 2x1x1x2', function () {
var x = tf.tensor4d([2, 100, 4, 400], [2, 1, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var offset = tf.tensor1d([3, 4]);
var scale = tf.tensor1d([2, 5]);
var varianceEpsilon = .001;
var dy = tf.tensor4d([-1, -1, -1, -1], [2, 1, 1, 2]);
var gradX = tf.grad(function (x) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
test_util_1.expectArraysClose(gradX, tf.tensor4d([-1.414, -2.887, -1.414, -2.887], [2, 1, 1, 2]));
var gradMean = tf.grad(function (mean) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
test_util_1.expectArraysClose(gradMean, tf.tensor1d([2.828, 5.773]));
var gradVariance = tf.grad(function (variance) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
test_util_1.expectArraysClose(gradVariance, tf.tensor1d([1.413, 238.519]));
var gradOffset = tf.grad(function (offset) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
test_util_1.expectArraysClose(gradOffset, dy.sum([0, 1, 2]));
var gradScale = tf.grad(function (scale) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
test_util_1.expectArraysClose(gradScale, tf.tensor1d([-2.828, -286.318]));
});
it('batchnorm4D gradients, same shapes in x, mean and variance', function () {
var x = tf.tensor4d([10, 20, 30, 40], [2, 1, 1, 2]);
var mean = tf.tensor4d([0, 5, 10, 15], [2, 1, 1, 2]);
var variance = tf.tensor4d([2, 4, 6, 8], [2, 1, 1, 2]);
var scale = tf.tensor4d([2, 5, 2, 5], [2, 1, 1, 2]);
var offset = tf.tensor4d([0, 0, 0, 0], [2, 1, 1, 2]);
var varianceEpsilon = .001;
var dy = tf.tensor4d([-1, -1, -1, -1], [2, 1, 1, 2]);
var gradX = tf.grad(function (x) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
test_util_1.expectArraysClose(gradX, tf.tensor4d([-1.414, -2.500, -0.816, -1.768], [2, 1, 1, 2]));
var gradMean = tf.grad(function (mean) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
test_util_1.expectArraysClose(gradMean, tf.tensor4d([1.414, 2.500, 0.816, 1.768], [2, 1, 1, 2]));
var gradVariance = tf.grad(function (variance) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
test_util_1.expectArraysClose(gradVariance, tf.tensor4d([3.533, 4.686, 1.360, 2.762], [2, 1, 1, 2]));
var gradOffset = tf.grad(function (offset) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
test_util_1.expectArraysClose(gradOffset, dy);
var gradScale = tf.grad(function (scale) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
test_util_1.expectArraysClose(gradScale, tf.tensor4d([-7.069, -7.499, -8.164, -8.838], [2, 1, 1, 2]));
});
});
jasmine_util_1.describeWithFlags('batchNormalization3D', test_util_1.ALL_ENVS, function () {
it('simple batchnorm3D, no offset or scale, 2x1x2', function () {
var x = tf.tensor3d([2, 100, 4, 400], [2, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var varianceEpsilon = .001;
var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, undefined, undefined);
test_util_1.expectArraysClose(result, [
(x.get(0, 0, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(0, 0, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon),
(x.get(1, 0, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(1, 0, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('simple batchnorm3D, no offset, 2x1x2', function () {
var x = tf.tensor3d([2, 100, 4, 400], [2, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var scale = tf.tensor1d([4, 5]);
var varianceEpsilon = .001;
var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, undefined);
test_util_1.expectArraysClose(result, [
(x.get(0, 0, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(0, 0, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon),
(x.get(1, 0, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(1, 0, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('simple batchnorm3D, no scale, 2x1x2', function () {
var x = tf.tensor3d([2, 100, 4, 400], [2, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var offset = tf.tensor1d([4, 5]);
var varianceEpsilon = .001;
var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, undefined, offset);
test_util_1.expectArraysClose(result, [
offset.get(0) +
(x.get(0, 0, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(0, 0, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon),
offset.get(0) +
(x.get(1, 0, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(1, 0, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('simple batchnorm3D, 2x1x2', function () {
var x = tf.tensor3d([2, 100, 4, 400], [2, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var offset = tf.tensor1d([3, 4]);
var scale = tf.tensor1d([4, 5]);
var varianceEpsilon = .001;
var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset);
test_util_1.expectArraysClose(result, [
offset.get(0) +
(x.get(0, 0, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(0, 0, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon),
offset.get(0) +
(x.get(1, 0, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(1, 0, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('batchnorm3D, x,mean,var,offset,scale are all 3D', function () {
var shape = [2, 1, 2];
var x = tf.tensor3d([2, 100, 4, 400], shape);
var mean = tf.tensor3d([1, 2, 3, 4], shape);
var variance = tf.tensor3d([2, 3, 4, 5], shape);
var offset = tf.tensor3d([3, 4, 5, 6], shape);
var scale = tf.tensor3d([4, 5, 6, 7], shape);
var varianceEpsilon = .001;
var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset);
test_util_1.expectArraysClose(result, [
offset.get(0, 0, 0) +
(x.get(0, 0, 0) - mean.get(0, 0, 0)) * scale.get(0, 0, 0) /
Math.sqrt(variance.get(0, 0, 0) + varianceEpsilon),
offset.get(0, 0, 1) +
(x.get(0, 0, 1) - mean.get(0, 0, 1)) * scale.get(0, 0, 1) /
Math.sqrt(variance.get(0, 0, 1) + varianceEpsilon),
offset.get(1, 0, 0) +
(x.get(1, 0, 0) - mean.get(1, 0, 0)) * scale.get(1, 0, 0) /
Math.sqrt(variance.get(1, 0, 0) + varianceEpsilon),
offset.get(1, 0, 1) +
(x.get(1, 0, 1) - mean.get(1, 0, 1)) * scale.get(1, 0, 1) /
Math.sqrt(variance.get(1, 0, 1) + varianceEpsilon)
]);
});
it('simple batchnorm3D gradients, 2x1x2', function () {
var x = tf.tensor3d([2, 100, 4, 400], [2, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var offset = tf.tensor1d([3, 4]);
var scale = tf.tensor1d([2, 5]);
var varianceEpsilon = .001;
var dy = tf.tensor3d([1, 1, 1, 1], [2, 1, 2]);
var gradX = tf.grad(function (x) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
test_util_1.expectArraysClose(gradX, tf.tensor3d([1.414, 2.887, 1.414, 2.887], [2, 1, 2]));
var gradMean = tf.grad(function (mean) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
test_util_1.expectArraysClose(gradMean, tf.tensor1d([-2.828, -5.773]));
var gradVariance = tf.grad(function (variance) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
test_util_1.expectArraysClose(gradVariance, tf.tensor1d([-1.413, -238.519]));
var gradOffset = tf.grad(function (offset) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
test_util_1.expectArraysClose(gradOffset, tf.onesLike(offset).mul(tf.scalar(2)));
var gradScale = tf.grad(function (scale) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
test_util_1.expectArraysClose(gradScale, tf.tensor1d([2.828, 286.318]));
});
it('batchnorm3D gradients, same shapes in x, mean and variance', function () {
var x = tf.tensor3d([10, 20, 30, 40], [2, 1, 2]);
var mean = tf.tensor3d([0, 5, 10, 15], [2, 1, 2]);
var variance = tf.tensor3d([2, 4, 6, 8], [2, 1, 2]);
var scale = tf.tensor3d([2, 5, 2, 5], [2, 1, 2]);
var offset = tf.tensor3d([0, 0, 0, 0], [2, 1, 2]);
var varianceEpsilon = .001;
var dy = tf.tensor3d([1, 1, 1, 1], [2, 1, 2]);
var gradX = tf.grad(function (x) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
test_util_1.expectArraysClose(gradX, tf.tensor3d([1.414, 2.500, 0.816, 1.768], [2, 1, 2]));
var gradMean = tf.grad(function (mean) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
test_util_1.expectArraysClose(gradMean, tf.tensor3d([-1.414, -2.500, -0.816, -1.768], [2, 1, 2]));
var gradVariance = tf.grad(function (variance) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
test_util_1.expectArraysClose(gradVariance, tf.tensor3d([-3.533, -4.686, -1.360, -2.762], [2, 1, 2]));
var gradOffset = tf.grad(function (offset) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
test_util_1.expectArraysClose(gradOffset, tf.onesLike(offset));
var gradScale = tf.grad(function (scale) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
test_util_1.expectArraysClose(gradScale, tf.tensor3d([7.069, 7.499, 8.164, 8.838], [2, 1, 2]));
});
it('batchnorm matches tensorflow, 2x3x3', function () {
var x = tf.tensor3d([
0.49955603, 0.04158615, -1.09440524, 2.03854165, -0.61578344,
2.87533573, 1.18105987, 0.807462, 1.87888837, 2.26563962, -0.37040935,
1.35848753, -0.75347094, 0.15683117, 0.91925946, 0.34121279,
0.92717143, 1.89683965
], [2, 3, 3]);
var mean = tf.tensor1d([0.39745062, -0.48062894, 0.4847822]);
var variance = tf.tensor1d([0.32375343, 0.67117643, 1.08334653]);
var offset = tf.tensor1d([0.69398749, -1.29056387, 0.9429723]);
var scale = tf.tensor1d([-0.5607271, 0.9878457, 0.25181573]);
var varianceEpsilon = .001;
var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset);
test_util_1.expectArraysClose(result, [
0.59352049, -0.66135202, 0.5610874, -0.92077015, -1.45341019, 1.52106473,
-0.07704776, 0.26144429, 1.28010017, -1.14422404, -1.15776136, 1.15425493,
1.82644104, -0.52249442, 1.04803919, 0.74932291, 0.40568101, 1.2844412
]);
});
});
jasmine_util_1.describeWithFlags('batchNormalization2D', test_util_1.ALL_ENVS, function () {
it('simple batchnorm2D, no offset or scale, 2x2', function () {
var x = tf.tensor2d([2, 100, 4, 400], [2, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var varianceEpsilon = .001;
var result = tf.batchNormalization2d(x, mean, variance, varianceEpsilon, undefined, undefined);
test_util_1.expectArraysClose(result, [
(x.get(0, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(0, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon),
(x.get(1, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(1, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('simple batchnorm2D, no offset, 2x2', function () {
var x = tf.tensor2d([2, 100, 4, 400], [2, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var scale = tf.tensor1d([4, 5]);
var varianceEpsilon = .001;
var result = tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, undefined);
test_util_1.expectArraysClose(result, [
(x.get(0, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(0, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon),
(x.get(1, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
(x.get(1, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('simple batchnorm2D, no scale, 2x2', function () {
var x = tf.tensor2d([2, 100, 4, 400], [2, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var offset = tf.tensor1d([4, 5]);
var varianceEpsilon = .001;
var result = tf.batchNormalization2d(x, mean, variance, varianceEpsilon, undefined, offset);
test_util_1.expectArraysClose(result, [
offset.get(0) +
(x.get(0, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(0, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon),
offset.get(0) +
(x.get(1, 0) - mean.get(0)) * 1 /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(1, 1) - mean.get(1)) * 1 /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('simple batchnorm2D, 2x2', function () {
var x = tf.tensor2d([2, 100, 4, 400], [2, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var offset = tf.tensor1d([3, 4]);
var scale = tf.tensor1d([4, 5]);
var varianceEpsilon = .001;
var result = tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset);
test_util_1.expectArraysClose(result, [
offset.get(0) +
(x.get(0, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(0, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon),
offset.get(0) +
(x.get(1, 0) - mean.get(0)) * scale.get(0) /
Math.sqrt(variance.get(0) + varianceEpsilon),
offset.get(1) +
(x.get(1, 1) - mean.get(1)) * scale.get(1) /
Math.sqrt(variance.get(1) + varianceEpsilon)
]);
});
it('simple batchnorm2D gradients, 2x2', function () {
var x = tf.tensor2d([2, 100, 4, 400], [2, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var offset = tf.tensor1d([3, 4]);
var scale = tf.tensor1d([2, 5]);
var varianceEpsilon = .001;
var dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);
var gradX = tf.grad(function (x) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
test_util_1.expectArraysClose(gradX, tf.tensor2d([1.414, 2.887, 1.414, 2.887], [2, 2]));
var gradMean = tf.grad(function (mean) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
test_util_1.expectArraysClose(gradMean, tf.tensor1d([-2.828, -5.773]));
var gradVariance = tf.grad(function (variance) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
test_util_1.expectArraysClose(gradVariance, tf.tensor1d([-1.413, -238.519]));
var gradOffset = tf.grad(function (offset) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
test_util_1.expectArraysClose(gradOffset, tf.onesLike(offset).mul(tf.scalar(2)));
var gradScale = tf.grad(function (scale) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
test_util_1.expectArraysClose(gradScale, tf.tensor1d([2.828, 286.318]));
});
it('batchnorm2D gradients, same shapes in x, mean and variance', function () {
var x = tf.tensor2d([10, 20, 30, 40], [2, 2]);
var mean = tf.tensor2d([0, 5, 10, 15], [2, 2]);
var variance = tf.tensor2d([2, 4, 6, 8], [2, 2]);
var scale = tf.tensor2d([2, 5, 2, 5], [2, 2]);
var offset = tf.tensor2d([0, 0, 0, 0], [2, 2]);
var varianceEpsilon = .001;
var dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);
var gradX = tf.grad(function (x) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
test_util_1.expectArraysClose(gradX, tf.tensor2d([1.414, 2.500, 0.816, 1.768], [2, 2]));
var gradMean = tf.grad(function (mean) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
test_util_1.expectArraysClose(gradMean, tf.tensor2d([-1.414, -2.500, -0.816, -1.768], [2, 2]));
var gradVariance = tf.grad(function (variance) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
test_util_1.expectArraysClose(gradVariance, tf.tensor2d([-3.533, -4.686, -1.360, -2.762], [2, 2]));
var gradOffset = tf.grad(function (offset) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
test_util_1.expectArraysClose(gradOffset, tf.onesLike(offset));
var gradScale = tf.grad(function (scale) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
test_util_1.expectArraysClose(gradScale, tf.tensor2d([7.069, 7.499, 8.164, 8.838], [2, 2]));
});
it('batchnorm2D matches tensorflow, 3x3', function () {
var x = tf.tensor2d([
0.3136892, 0.92389025, 0.594782, 0.05021042, 0.67545404, 0.93910035,
0.13277993, 0.96474269, 0.88608916
], [3, 3]);
var mean = tf.tensor1d([0.19526312, 0.74857256, 0.45166398]);
var variance = tf.tensor1d([0.22963001, 0.61521992, 0.46623685]);
var offset = tf.tensor1d([0.43098484, 0.77712237, 0.47916298]);
var scale = tf.tensor1d([0.62186907, 0.85673736, 0.19201061]);
var varianceEpsilon = .001;
var result = tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset);
test_util_1.expectArraysClose(result, [
0.58433646, 0.96846228, 0.51936529, 0.24315402, 0.69732157, 0.61608542,
0.35007446, 1.01304821, 0.60119441
]);
});
it('throws when passed x as a non-tensor', function () {
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
expect(function () { return tf.batchNormalization({}, mean, variance); })
.toThrowError(/Argument 'x' passed to 'batchNormalization' must be a Tensor/);
});
it('throws when passed mean as a non-tensor', function () {
var x = tf.tensor4d([2, 100, 4, 400], [2, 1, 1, 2]);
var variance = tf.tensor1d([2, 3]);
expect(function () { return tf.batchNormalization(x, {}, variance); })
.toThrowError(/Argument 'mean' passed to 'batchNormalization' must be a Tensor/);
});
it('throws when passed variance as a non-tensor', function () {
var x = tf.tensor4d([2, 100, 4, 400], [2, 1, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var e = /Argument 'variance' passed to 'batchNormalization' must be a Tensor/;
expect(function () { return tf.batchNormalization(x, mean, {}); })
.toThrowError(e);
});
it('throws when passed scale as a non-tensor', function () {
var x = tf.tensor4d([2, 100, 4, 400], [2, 1, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var epsilon = .001;
expect(function () {
return tf.batchNormalization(x, mean, variance, epsilon, {});
})
.toThrowError(/Argument 'scale' passed to 'batchNormalization' must be a Tensor/);
});
it('throws when passed offset as a non-tensor', function () {
var x = tf.tensor4d([2, 100, 4, 400], [2, 1, 1, 2]);
var mean = tf.tensor1d([1, 2]);
var variance = tf.tensor1d([2, 3]);
var epsilon = .001;
var scale = tf.tensor1d([0.62186907, 0.85673736, 0.19201061]);
var e = /Argument 'offset' passed to 'batchNormalization' must be a Tensor/;
expect(function () { return tf.batchNormalization(x, mean, variance, epsilon, scale, {}); })
.toThrowError(e);
});
});