@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
854 lines • 35.7 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("../index");
var jasmine_util_1 = require("../jasmine_util");
var test_util_1 = require("../test_util");
var sqArr = function (arr) { return arr.map(function (d) { return d * d; }); };
var sumArr = function (arr) { return arr.reduce(function (prev, curr) { return prev + curr; }, 0); };
var flatten = function (arr) {
return arr.reduce(function (prev, curr) {
return prev.concat(Array.isArray(curr) ? flatten(curr) : curr);
}, []);
};
jasmine_util_1.describeWithFlags('localResponseNormalization with Tensor3D', test_util_1.ALL_ENVS, function () {
it('throws error with invalid input', function () {
var x = tf.tensor2d([1, 20, 300, 4], [1, 4]);
var radius = 3;
expect(function () { return x.localResponseNormalization(radius); }).toThrowError();
});
it('throws error with invalid radius', function () {
var x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
var radius = 0.5;
expect(function () { return x.localResponseNormalization(radius); }).toThrowError();
});
it('computes simple normalization across channels', function () {
var x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
var radius = 1;
var bias = 1;
var alpha = 1;
var beta = 0.5;
var result = x.localResponseNormalization(radius, bias, alpha, beta);
var f = function () {
var vals = [];
for (var _i = 0; _i < arguments.length; _i++) {
vals[_i] = arguments[_i];
}
return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
};
test_util_1.expectArraysClose(result, [
x.get(0, 0, 0) * f(x.get(0, 0, 0), x.get(0, 0, 1)),
x.get(0, 0, 1) * f(x.get(0, 0, 0), x.get(0, 0, 1), x.get(0, 0, 2)),
x.get(0, 0, 2) * f(x.get(0, 0, 1), x.get(0, 0, 2), x.get(0, 0, 3)),
x.get(0, 0, 3) * f(x.get(0, 0, 2), x.get(0, 0, 3)),
]);
});
it('uses beta = 1.0 to test GPU optimization', function () {
var x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
var radius = 1;
var bias = 1;
var alpha = 1;
var beta = 1.0;
var result = x.localResponseNormalization(radius, bias, alpha, beta);
var f = function () {
var vals = [];
for (var _i = 0; _i < arguments.length; _i++) {
vals[_i] = arguments[_i];
}
return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
};
test_util_1.expectArraysClose(result, [
x.get(0, 0, 0) * f(x.get(0, 0, 0), x.get(0, 0, 1)),
x.get(0, 0, 1) * f(x.get(0, 0, 0), x.get(0, 0, 1), x.get(0, 0, 2)),
x.get(0, 0, 2) * f(x.get(0, 0, 1), x.get(0, 0, 2), x.get(0, 0, 3)),
x.get(0, 0, 3) * f(x.get(0, 0, 2), x.get(0, 0, 3)),
]);
});
it('uses beta = 0.75 to test GPU optimization', function () {
var x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
var radius = 1;
var bias = 1;
var alpha = 1;
var beta = 0.75;
var result = x.localResponseNormalization(radius, bias, alpha, beta);
var f = function () {
var vals = [];
for (var _i = 0; _i < arguments.length; _i++) {
vals[_i] = arguments[_i];
}
return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
};
test_util_1.expectArraysClose(result, [
x.get(0, 0, 0) * f(x.get(0, 0, 0), x.get(0, 0, 1)),
x.get(0, 0, 1) * f(x.get(0, 0, 0), x.get(0, 0, 1), x.get(0, 0, 2)),
x.get(0, 0, 2) * f(x.get(0, 0, 1), x.get(0, 0, 2), x.get(0, 0, 3)),
x.get(0, 0, 3) * f(x.get(0, 0, 2), x.get(0, 0, 3)),
]);
});
it('computes complex normalization across channels', function () {
var x = tf.tensor3d([1, 20, 300, 4, 5, 15, 24, 200, 1, 20, 300, 4, 5, 15, 24, 200], [2, 2, 4]);
var radius = 1;
var bias = 1;
var alpha = 1;
var beta = 0.5;
var result = x.localResponseNormalization(radius, bias, alpha, beta);
var f = function () {
var vals = [];
for (var _i = 0; _i < arguments.length; _i++) {
vals[_i] = arguments[_i];
}
return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
};
test_util_1.expectArraysClose(result, [
x.get(0, 0, 0) * f(x.get(0, 0, 0), x.get(0, 0, 1)),
x.get(0, 0, 1) * f(x.get(0, 0, 0), x.get(0, 0, 1), x.get(0, 0, 2)),
x.get(0, 0, 2) * f(x.get(0, 0, 1), x.get(0, 0, 2), x.get(0, 0, 3)),
x.get(0, 0, 3) * f(x.get(0, 0, 2), x.get(0, 0, 3)),
x.get(0, 1, 0) * f(x.get(0, 1, 0), x.get(0, 1, 1)),
x.get(0, 1, 1) * f(x.get(0, 1, 0), x.get(0, 1, 1), x.get(0, 1, 2)),
x.get(0, 1, 2) * f(x.get(0, 1, 1), x.get(0, 1, 2), x.get(0, 1, 3)),
x.get(0, 1, 3) * f(x.get(0, 1, 2), x.get(0, 1, 3)),
x.get(1, 0, 0) * f(x.get(1, 0, 0), x.get(1, 0, 1)),
x.get(1, 0, 1) * f(x.get(1, 0, 0), x.get(1, 0, 1), x.get(1, 0, 2)),
x.get(1, 0, 2) * f(x.get(1, 0, 1), x.get(1, 0, 2), x.get(1, 0, 3)),
x.get(1, 0, 3) * f(x.get(1, 0, 2), x.get(1, 0, 3)),
x.get(1, 1, 0) * f(x.get(1, 1, 0), x.get(1, 1, 1)),
x.get(1, 1, 1) * f(x.get(1, 1, 0), x.get(1, 1, 1), x.get(1, 1, 2)),
x.get(1, 1, 2) * f(x.get(1, 1, 1), x.get(1, 1, 2), x.get(1, 1, 3)),
x.get(1, 1, 3) * f(x.get(1, 1, 2), x.get(1, 1, 3)),
]);
});
it('yields same result as tensorflow', function () {
var input = [
[
[
0.95782757, 0.12892687, 0.63624668, 0.70160735, 0.77376258,
0.54166114, 0.71172535, 0.65087497
],
[
0.91872108, 0.38846886, 0.37847793, 0.50477624, 0.42154622,
0.43310916, 0.36253822, 0.07576156
],
[
0.48662257, 0.4154036, 0.81704032, 0.91660416, 0.87671542, 0.64215934,
0.29933751, 0.90671134
]
],
[
[
0.6208992, 0.60847163, 0.41475761, 0.2127713, 0.65306914, 0.13923979,
0.32003641, 0.28183973
],
[
0.04751575, 0.26870155, 0.45150304, 0.58678186, 0.99118924,
0.58878231, 0.30913198, 0.18836617
],
[
0.16166461, 0.56322742, 0.67908955, 0.2269547, 0.38491273, 0.97113752,
0.51210916, 0.69430435
]
],
[
[
0.06625497, 0.13011181, 0.59202921, 0.88871598, 0.6366322, 0.47911358,
0.96530843, 0.74259472
],
[
0.62660718, 0.0445286, 0.18430257, 0.76863647, 0.87511849, 0.53588808,
0.27980685, 0.30281997
],
[
0.73987067, 0.91034842, 0.26241004, 0.72832751, 0.78974342,
0.50751543, 0.05434644, 0.8231523
]
]
];
var expected = [
[
[
0.62630326, 0.07662392, 0.34354961, 0.41885775, 0.42621866,
0.29751951, 0.42365381, 0.4364861
],
[
0.62828875, 0.251122, 0.23605582, 0.36483878, 0.30624411, 0.32672295,
0.29576892, 0.06582346
],
[
0.3376624, 0.24321821, 0.42558169, 0.46646208, 0.45103404, 0.32380751,
0.17021206, 0.59476018
]
],
[
[
0.44719055, 0.43318295, 0.26775005, 0.14921051, 0.49148726,
0.10764983, 0.25084552, 0.25714993
],
[
0.04202608, 0.21094096, 0.27973703, 0.34166718, 0.57487047,
0.35158369, 0.19708875, 0.15495601
],
[
0.12034657, 0.41341963, 0.47968671, 0.13278878, 0.22735766,
0.57154536, 0.30411762, 0.42352781
]
],
[
[
0.05656794, 0.08849642, 0.36951816, 0.53186077, 0.33065733,
0.24236222, 0.54666328, 0.45085984
],
[
0.52425432, 0.03133496, 0.11043368, 0.46954039, 0.5271349, 0.31946796,
0.1876673, 0.25085902
],
[
0.47316891, 0.5277527, 0.13831842, 0.40036613, 0.50113004, 0.28860986,
0.03395459, 0.59127772
]
]
];
var x = tf.tensor3d(flatten(input), [3, 3, 8]);
var radius = 2;
var bias = 1;
var alpha = 1;
var beta = 0.5;
var result = x.localResponseNormalization(radius, bias, alpha, beta);
test_util_1.expectArraysClose(result, flatten(expected));
});
it('accepts a tensor-like object', function () {
var x = [[[1, 20, 300, 4]]];
var radius = 1;
var bias = 1;
var alpha = 1;
var beta = 0.5;
var result = tf.localResponseNormalization(x, radius, bias, alpha, beta);
var f = function () {
var vals = [];
for (var _i = 0; _i < arguments.length; _i++) {
vals[_i] = arguments[_i];
}
return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
};
test_util_1.expectArraysClose(result, [
x[0][0][0] * f(x[0][0][0], x[0][0][1]),
x[0][0][1] * f(x[0][0][0], x[0][0][1], x[0][0][2]),
x[0][0][2] * f(x[0][0][1], x[0][0][2], x[0][0][3]),
x[0][0][3] * f(x[0][0][2], x[0][0][3]),
]);
});
});
jasmine_util_1.describeWithFlags('localResponseNormalization with Tensor4D', test_util_1.ALL_ENVS, function () {
it('throws error with invalid input', function () {
var x = tf.tensor2d([1, 20, 300, 4], [1, 4]);
var radius = 3;
expect(function () { return x.localResponseNormalization(radius); }).toThrowError();
});
it('throws error with invalid radius', function () {
var x = tf.tensor4d([1, 20, 300, 4], [1, 1, 1, 4]);
var radius = 0.5;
expect(function () { return x.localResponseNormalization(radius); }).toThrowError();
});
it('computes simple normalization across channels', function () {
var x = tf.tensor4d([1, 20, 300, 4, 1, 20, 300, 4], [2, 1, 1, 4]);
var radius = 1;
var bias = 1;
var alpha = 1;
var beta = 0.5;
var result = x.localResponseNormalization(radius, bias, alpha, beta);
var f = function () {
var vals = [];
for (var _i = 0; _i < arguments.length; _i++) {
vals[_i] = arguments[_i];
}
return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
};
var b0 = 0;
var b1 = 1;
test_util_1.expectArraysClose(result, [
x.get(b0, 0, 0, 0) * f(x.get(b0, 0, 0, 0), x.get(b0, 0, 0, 1)),
x.get(b0, 0, 0, 1) *
f(x.get(b0, 0, 0, 0), x.get(b0, 0, 0, 1), x.get(b0, 0, 0, 2)),
x.get(b0, 0, 0, 2) *
f(x.get(b0, 0, 0, 1), x.get(b0, 0, 0, 2), x.get(b0, 0, 0, 3)),
x.get(b0, 0, 0, 3) * f(x.get(b0, 0, 0, 2), x.get(b0, 0, 0, 3)),
x.get(b1, 0, 0, 0) * f(x.get(b1, 0, 0, 0), x.get(b1, 0, 0, 1)),
x.get(b1, 0, 0, 1) *
f(x.get(b1, 0, 0, 0), x.get(b1, 0, 0, 1), x.get(b1, 0, 0, 2)),
x.get(b1, 0, 0, 2) *
f(x.get(b1, 0, 0, 1), x.get(b1, 0, 0, 2), x.get(b1, 0, 0, 3)),
x.get(b1, 0, 0, 3) * f(x.get(b1, 0, 0, 2), x.get(b1, 0, 0, 3)),
]);
});
it('yields same result as tensorflow', function () {
var input = [
[
[
[
0.5659827, 0.57000327, 0.75555623, 0.89843333, 0.55120194,
0.53531718, 0.56402838, 0.95481384
],
[
0.57334661, 0.65172958, 0.75794137, 0.80764937, 0.376616,
0.92726362, 0.36422753, 0.60535395
],
[
0.82404268, 0.01054764, 0.4649173, 0.91637003, 0.82287347, 0.043468,
0.44953859, 0.92056584
]
],
[
[
0.68583369, 0.52534163, 0.53325927, 0.39608097, 0.9337523,
0.37397444, 0.81212556, 0.5697
],
[
0.34278774, 0.57656682, 0.2356832, 0.02636456, 0.49111438,
0.17981696, 0.65398049, 0.70132935
],
[
0.14241767, 0.68376505, 0.65419888, 0.69369483, 0.21489143,
0.46235347, 0.0559243, 0.60612857
]
],
[
[
0.59678483, 0.09368539, 0.3017447, 0.36870825, 0.68145788,
0.52048779, 0.46136606, 0.94114387
],
[
0.3156569, 0.75275254, 0.31970251, 0.3154043, 0.61088014,
0.13359487, 0.99048364, 0.33625424
],
[
0.82103574, 0.52066624, 0.63629258, 0.42294252, 0.93214262,
0.57041013, 0.66087878, 0.7019999
]
]
],
[
[
[
0.21894431, 0.43085241, 0.79883206, 0.19462204, 0.68623316,
0.08703053, 0.82380795, 0.85634673
],
[
0.45011401, 0.70312083, 0.86319792, 0.83205295, 0.67109787,
0.82081223, 0.46556532, 0.46408331
],
[
0.07028461, 0.0038743, 0.44619524, 0.0611403, 0.96373355,
0.80561554, 0.42428243, 0.46897113
]
],
[
[
0.21006894, 0.48764861, 0.36842632, 0.23030031, 0.69685507,
0.31707478, 0.68662715, 0.0639503
],
[
0.53940296, 0.50777435, 0.12625301, 0.12324154, 0.89205229,
0.69380629, 0.33191144, 0.81000078
],
[
0.52650976, 0.71220326, 0.07246161, 0.08874547, 0.42528927,
0.36320579, 0.54055619, 0.79342318
]
],
[
[
0.75916636, 0.74499428, 0.76877356, 0.87210917, 0.93040991,
0.49491942, 0.70801985, 0.14901721
],
[
0.27037835, 0.89302075, 0.69147241, 0.23044991, 0.98916364,
0.60161841, 0.63691151, 0.56759977
],
[
0.56307781, 0.92782414, 0.25880754, 0.98518133, 0.04097319,
0.24640906, 0.54566145, 0.99261606
]
]
]
];
var expected = [
[
[
[
0.38019636, 0.32782161, 0.414222, 0.49507114, 0.3040463, 0.28107059,
0.33586296, 0.60191077
],
[
0.37577698, 0.37752095, 0.42895618, 0.4225589, 0.2054275,
0.52219951, 0.23032214, 0.39414096
],
[
0.59856331, 0.00637784, 0.25168711, 0.5541048, 0.48015645,
0.02301128, 0.27214608, 0.6427291
]
],
[
[
0.48127589, 0.35518789, 0.30486941, 0.23976389, 0.52926594,
0.21061926, 0.46920502, 0.39090639
],
[
0.27937523, 0.46979892, 0.17829391, 0.02044933, 0.37045884,
0.12140442, 0.44160855, 0.50198948
],
[
0.10289387, 0.44164398, 0.41853485, 0.42720893, 0.14580171,
0.31817055, 0.043797, 0.48155668
]
],
[
[
0.49458414, 0.07425242, 0.21042404, 0.26262277, 0.46205613,
0.30202535, 0.27406475, 0.61140078
],
[
0.23736385, 0.55076694, 0.2135559, 0.21463785, 0.38077739,
0.08309806, 0.62830603, 0.23137885
],
[
0.5355776, 0.32740855, 0.3451882, 0.24221195, 0.51988536,
0.31387195, 0.37391993, 0.46748781
]
]
],
[
[
[
0.16003507, 0.31178808, 0.51775187, 0.12722474, 0.40769571,
0.05085804, 0.48455271, 0.5505302
],
[
0.2880325, 0.39714804, 0.45591024, 0.4131493, 0.34525412, 0.4554069,
0.29119283, 0.31980222
],
[
0.0640529, 0.00352532, 0.3052578, 0.03666528, 0.56009793,
0.46656418, 0.24587312, 0.32762629
]
],
[
[
0.17643087, 0.40210918, 0.2634095, 0.16233148, 0.4649446,
0.21803913, 0.47819966, 0.05093931
],
[
0.43121469, 0.403974, 0.08191212, 0.07693455, 0.57362044,
0.39671475, 0.19025819, 0.54028469
],
[
0.39356521, 0.53120333, 0.05151648, 0.06554616, 0.33433318,
0.2425479, 0.36161765, 0.5536595
]
],
[
[
0.46011236, 0.39919043, 0.36865807, 0.43511948, 0.46734285,
0.26861796, 0.43624333, 0.11205748
],
[
0.17642327, 0.57622254, 0.37609601, 0.12030836, 0.54640025,
0.34052721, 0.36361033, 0.3926385
],
[
0.37581176, 0.51741964, 0.14429154, 0.57254595, 0.02646073,
0.13531584, 0.35629693, 0.64837402
]
]
]
];
var x = tf.tensor4d(flatten(input), [2, 3, 3, 8]);
var radius = 2;
var result = x.localResponseNormalization(radius);
test_util_1.expectArraysClose(result, flatten(expected));
});
it('throws when passed a non-tensor', function () {
var e = /Argument 'x' passed to 'localResponseNormalization' must be a Tensor/;
expect(function () { return tf.localResponseNormalization({}); })
.toThrowError(e);
});
it('gradient with 3D input', function () {
var input = [
[
[
0.95782757, 0.12892687, 0.63624668, 0.70160735, 0.77376258,
0.54166114, 0.71172535, 0.65087497
],
[
0.91872108, 0.38846886, 0.37847793, 0.50477624, 0.42154622,
0.43310916, 0.36253822, 0.07576156
],
[
0.48662257, 0.4154036, 0.81704032, 0.91660416, 0.87671542, 0.64215934,
0.29933751, 0.90671134
]
],
[
[
0.6208992, 0.60847163, 0.41475761, 0.2127713, 0.65306914, 0.13923979,
0.32003641, 0.28183973
],
[
0.04751575, 0.26870155, 0.45150304, 0.58678186, 0.99118924,
0.58878231, 0.30913198, 0.18836617
],
[
0.16166461, 0.56322742, 0.67908955, 0.2269547, 0.38491273, 0.97113752,
0.51210916, 0.69430435
]
],
[
[
0.06625497, 0.13011181, 0.59202921, 0.88871598, 0.6366322, 0.47911358,
0.96530843, 0.74259472
],
[
0.62660718, 0.0445286, 0.18430257, 0.76863647, 0.87511849, 0.53588808,
0.27980685, 0.30281997
],
[
0.73987067, 0.91034842, 0.26241004, 0.72832751, 0.78974342,
0.50751543, 0.05434644, 0.8231523
]
]
];
var expected = [[
[
[
0.27552658, 0.52414668, 0.11137494, 0.24928074, 0.07215497,
0.16210511, 0.19277242, 0.38672262
],
[
0.23314378, 0.38181645, 0.30470729, 0.35180706, 0.37793165,
0.41450983, 0.60044503, 0.83605933
],
[
0.51801264, 0.38517883, 0.02934788, 0.03102355, 0.08222333,
0.09746625, 0.4151727, 0.29936206
]
],
[
[
0.37059873, 0.32463685, 0.26611608, 0.54228389, 0.30733055,
0.66392428, 0.55629295, 0.79049641
],
[
0.87162501, 0.68129337, 0.35793597, 0.18797961, -0.03660985,
0.23235559, 0.48184156, 0.76417446
],
[
0.65893668, 0.41059417, 0.26254228, 0.40696776, 0.3330358, 0.01789692,
0.3162199, 0.28867012
]
],
[
[
0.83880937, 0.62594998, 0.324698, 0.13046435, 0.09858654, 0.17851587,
0.09067203, 0.30748016
],
[
0.57213897, 0.67710453, 0.45385274, 0.19951296, 0.07371041,
0.20141563, 0.51362634, 0.7163325
],
[
0.33668244, 0.09696329, 0.33500126, 0.08948036, 0.26512182,
0.19593786, 0.59144169, 0.379444
]
]
]];
var radius = 2.0;
var bias = 1.0;
var alpha = 1.0;
var beta = 0.5;
var t = tf.tensor3d(input);
var dy = tf.onesLike(t);
var gradients = tf.grad(function (t) {
return tf.localResponseNormalization(t, radius, bias, alpha, beta);
})(t, dy);
test_util_1.expectArraysEqual(gradients.shape, t.shape);
test_util_1.expectArraysClose(gradients, flatten(expected));
});
it('gradient with 4D input', function () {
var input = [
[
[
[
0.5659827, 0.57000327, 0.75555623, 0.89843333, 0.55120194,
0.53531718, 0.56402838, 0.95481384
],
[
0.57334661, 0.65172958, 0.75794137, 0.80764937, 0.376616,
0.92726362, 0.36422753, 0.60535395
],
[
0.82404268, 0.01054764, 0.4649173, 0.91637003, 0.82287347, 0.043468,
0.44953859, 0.92056584
]
],
[
[
0.68583369, 0.52534163, 0.53325927, 0.39608097, 0.9337523,
0.37397444, 0.81212556, 0.5697
],
[
0.34278774, 0.57656682, 0.2356832, 0.02636456, 0.49111438,
0.17981696, 0.65398049, 0.70132935
],
[
0.14241767, 0.68376505, 0.65419888, 0.69369483, 0.21489143,
0.46235347, 0.0559243, 0.60612857
]
],
[
[
0.59678483, 0.09368539, 0.3017447, 0.36870825, 0.68145788,
0.52048779, 0.46136606, 0.94114387
],
[
0.3156569, 0.75275254, 0.31970251, 0.3154043, 0.61088014,
0.13359487, 0.99048364, 0.33625424
],
[
0.82103574, 0.52066624, 0.63629258, 0.42294252, 0.93214262,
0.57041013, 0.66087878, 0.7019999
]
]
],
[
[
[
0.21894431, 0.43085241, 0.79883206, 0.19462204, 0.68623316,
0.08703053, 0.82380795, 0.85634673
],
[
0.45011401, 0.70312083, 0.86319792, 0.83205295, 0.67109787,
0.82081223, 0.46556532, 0.46408331
],
[
0.07028461, 0.0038743, 0.44619524, 0.0611403, 0.96373355,
0.80561554, 0.42428243, 0.46897113
]
],
[
[
0.21006894, 0.48764861, 0.36842632, 0.23030031, 0.69685507,
0.31707478, 0.68662715, 0.0639503
],
[
0.53940296, 0.50777435, 0.12625301, 0.12324154, 0.89205229,
0.69380629, 0.33191144, 0.81000078
],
[
0.52650976, 0.71220326, 0.07246161, 0.08874547, 0.42528927,
0.36320579, 0.54055619, 0.79342318
]
],
[
[
0.75916636, 0.74499428, 0.76877356, 0.87210917, 0.93040991,
0.49491942, 0.70801985, 0.14901721
],
[
0.27037835, 0.89302075, 0.69147241, 0.23044991, 0.98916364,
0.60161841, 0.63691151, 0.56759977
],
[
0.56307781, 0.92782414, 0.25880754, 0.98518133, 0.04097319,
0.24640906, 0.54566145, 0.99261606
]
]
]
];
var dyVals = [
[
[
[
1.40394282, -1.68962789, -0.21134049, 1.15015793, 1.51244378,
0.42844626, -2.70123291, 0.06449971
],
[
-0.29038581, 0.67567694, 0.95617437, -1.07383668, 0.20920482,
0.39050213, -0.81124371, 2.42158198
],
[
-1.01235235, -0.63514435, -1.49017262, -0.01205151, 0.78492945,
-0.20330679, -2.31419802, -0.31220308
]
],
[
[
0.07061944, -0.46716127, 0.91232526, -1.30444264, -0.07080109,
0.13207501, 0.26701283, -0.48946589
],
[
-0.74995744, -0.79466617, -1.03790498, -0.32234526, 1.33345711,
0.11863081, 1.93010819, 0.47857195
],
[
0.37702683, -0.7804451, 0.45868117, 1.06967258, -0.65336537,
0.3594887, 0.62512684, 0.77009726
]
],
[
[
0.76865023, 1.00893021, -0.24408816, -0.3943336, 0.47094285,
-2.61926222, 1.52929449, 0.7862013
],
[
-1.20878386, -0.26222935, -0.9076528, 0.03079577, -0.01467486,
-0.06949636, 0.05466342, 1.44880533
],
[
0.05611863, 0.15142779, 0.7802065, -1.2623471, 0.09119794,
-0.20110528, 0.17715968, -0.48476508
]
]
],
[
[
[
0.1549256, 0.94472402, -0.70033115, -1.05752802, -0.63035947,
-1.35643113, -0.27211693, 2.33576941
],
[
0.81070906, -0.58353454, -0.3253817, 2.53953528, -1.40062141,
1.7728076, -0.59849483, 1.49650824
],
[
-0.00610052, -2.29434419, -1.77995121, -0.66354084, -0.70676774,
-0.81570011, -1.30821037, 0.40997007
]
],
[
[
-1.02013469, -0.74198806, -0.82677251, -0.00890179, -1.62196338,
-0.5095427, 1.26501179, 0.12931485
],
[
-1.14763546, 0.11011696, -0.23312508, 0.29730096, -0.49138394,
-0.27012363, -0.15987533, -1.84277928
],
[
-0.03816459, -0.73517877, -2.00476885, 0.47192496, -0.27395752,
0.99806124, 1.54439747, -1.02016675
]
],
[
[
-1.27831209, -0.6961385, -0.73713994, -1.97954738, 0.39108652,
-0.46152538, 1.8255372, 2.18119025
],
[
0.56322283, -1.59858179, 1.54127491, -0.57665956, -1.0098567,
0.93239671, 0.25231698, -0.7346009
],
[
0.41614994, -1.20103085, 0.4330301, -1.23348403, -0.46117213,
-0.3780126, 0.35449561, -0.60129249
]
]
]
];
var depthRadius = 1;
var bias = 1;
var alpha = 1;
var beta = 0.75;
var expected = [
[
[
[
0.88732064, -0.98597342, -0.00569269, 0.09561057, 0.42255375,
0.30286378, -1.17104781, 0.44769961
],
[
-0.22329885, 0.19271846, 0.41454071, -0.50674957, 0.14660946,
0.1591837, -0.83707076, 1.19177234
],
[
-0.26728818, -0.3847312, -0.72818488, 0.09040837, 0.24023688,
-0.11545581, -1.09341288, 0.33930668
]
],
[
[
0.10079086, -0.38184536, 0.60918945, -0.7267822, 0.13867335,
0.03526202, 0.17270499, -0.2705338
],
[
-0.38344458, -0.15589149, -0.68160093, -0.27644777, 0.79392856,
-0.14384332, 0.67121017, -0.23130262
],
[
0.31069142, -0.39895257, 0.11755499, 0.39481708, -0.5234766,
0.2511853, 0.40955079, 0.3492966
]
],
[
[
0.32660595, 0.7240563, -0.18117335, -0.2649861, 0.67781603,
-1.46250272, 0.8465963, -0.05466701
],
[
-0.71582067, 0.20831716, -0.50778204, 0.07256755, -0.00893679,
-0.03798783, -0.18604305, 0.75747406
],
[
-0.00540833, -0.07677216, 0.41930205, -0.69235319, 0.20631291,
-0.11946303, 0.19601521, -0.21237698
]
]
],
[
[
[
0.0800111, 0.60922205, -0.31155977, -0.46448132, -0.15912701,
-0.72455585, -0.5727275, 0.71780092
],
[
0.50568235, -0.31544152, -0.40618286, 0.97909468, -1.15286613,
0.8145386, -0.77758539, 0.93794745
],
[
-0.00535599, -1.99269259, -1.15343952, -0.31053686, 0.01680636,
0.10109296, -0.66026396, 0.35474917
]
],
[
[
-0.74113333, -0.20625943, -0.4339568, 0.21517368, -0.5734458,
-0.23481363, 0.53855389, 0.05860626
],
[
-0.61435795, 0.29290834, -0.19639145, 0.20930134, -0.08880179,
0.02209887, 0.21427482, -0.51696646
],
[
0.13036536, -0.19079237, -1.43941545, 0.42789665, -0.29732707,
0.52354813, 0.78893, -0.59992862
]
],
[
[
-0.328383, 0.15830949, 0.13110149, -0.492423, 0.46827313,
-0.58950633, 0.56422544, 1.44929576
],
[
0.46141064, -0.80682266, 0.92562175, -0.28897452, -0.30567497,
0.50646484, 0.16439518, -0.38878182
],
[
0.41004074, -0.38593128, 0.42881966, -0.22443436, -0.24573228,
-0.2941249, 0.31119603, -0.17903978
]
]
]
];
var t = tf.tensor(input);
var dy = tf.tensor(dyVals);
var gradients = tf.grad(function (t) { return tf.localResponseNormalization(t, depthRadius, bias, alpha, beta); })(t, dy);
test_util_1.expectArraysClose(gradients, flatten(expected));
});
});
//# sourceMappingURL=lrn_test.js.map