@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
"use strict";
/**
* @license
* Copyright 2017 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.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
var _this = this;
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('prelu', jasmine_util_1.ALL_ENVS, function () {
it('basic', function () { return __awaiter(_this, void 0, void 0, function () {
var x, a, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
x = tf.tensor1d([0, 1, -2, -4]);
a = tf.tensor1d([0.15, 0.2, 0.25, 0.15]);
result = tf.prelu(x, a);
expect(result.shape).toEqual(x.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0, 1, -0.5, -0.6]]);
return [2 /*return*/];
}
});
}); });
it('basic TensorLike', function () { return __awaiter(_this, void 0, void 0, function () {
var x, a, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
x = [0, 1, -2, -4];
a = [0.15, 0.2, 0.25, 0.15];
result = tf.prelu(x, a);
expect(result.shape).toEqual([4]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0, 1, -0.5, -0.6]]);
return [2 /*return*/];
}
});
}); });
it('basic TensorLike chained', function () { return __awaiter(_this, void 0, void 0, function () {
var x, a, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
x = tf.tensor1d([0, 1, -2, -4]);
a = [0.15, 0.2, 0.25, 0.15];
result = x.prelu(a);
expect(result.shape).toEqual(x.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0, 1, -0.5, -0.6]]);
return [2 /*return*/];
}
});
}); });
it('derivative', function () { return __awaiter(_this, void 0, void 0, function () {
var x, a, dy, dx, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
x = tf.tensor1d([0.5, 3, -0.1, -4]);
a = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
dy = tf.tensor1d([1, 1, 1, 1]);
dx = tf.grad(function (x) { return tf.prelu(x, a); })(x, dy);
expect(dx.shape).toEqual(x.shape);
expect(dx.dtype).toEqual('float32');
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, dx.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 1, 0.25, 0.15]]);
return [2 /*return*/];
}
});
}); });
it('gradient with clones', function () { return __awaiter(_this, void 0, void 0, function () {
var x, a, dx, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
x = tf.tensor1d([0.5, 3, -0.1, -4]);
a = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
dx = tf.grad(function (x) { return tf.prelu(x.clone(), a).clone(); })(x);
expect(dx.shape).toEqual(x.shape);
expect(dx.dtype).toEqual('float32');
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, dx.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 1, 0.25, 0.15]]);
return [2 /*return*/];
}
});
}); });
it('derivative where alpha got broadcasted', function () { return __awaiter(_this, void 0, void 0, function () {
var x, a, dy, da, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
x = tf.tensor2d([[0.5, 3, -0.1, -4]]);
a = tf.tensor2d([[0.2]]);
dy = tf.tensor2d([[1, 1, 1, 1]]);
da = tf.grad(function (a) { return tf.prelu(x, a); })(a, dy);
expect(da.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_a.apply(void 0, [_b.sent(), [-4.1]]);
return [2 /*return*/];
}
});
}); });
it('throws when passed x as a non-tensor', function () {
expect(function () { return tf.prelu({}, tf.scalar(1)); })
.toThrowError(/Argument 'x' passed to 'prelu' must be a Tensor/);
});
it('throws when passed alpha as a non-tensor', function () {
expect(function () { return tf.prelu(tf.scalar(1), {}); })
.toThrowError(/Argument 'alpha' passed to 'prelu' must be a Tensor/);
});
it('throws for string tensor', function () {
expect(function () { return tf.prelu(['a'], 0.1); })
.toThrowError(/Argument 'x' passed to 'prelu' must be numeric tensor/);
});
});
jasmine_util_1.describeWithFlags('maximum', jasmine_util_1.ALL_ENVS, function () {
it('float32 and float32', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 3, -0.1, -4]);
b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
result = tf.maximum(a, b);
expect(result.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.5, 3, 0.25, 0.15]]);
return [2 /*return*/];
}
});
}); });
it('TensorLike', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = [0.5, 3, -0.1, -4];
b = [0.2, 0.4, 0.25, 0.15];
result = tf.maximum(a, b);
expect(result.shape).toEqual([4]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.5, 3, 0.25, 0.15]]);
return [2 /*return*/];
}
});
}); });
it('TensorLike chained', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 3, -0.1, -4]);
b = [0.2, 0.4, 0.25, 0.15];
result = a.maximum(b);
expect(result.shape).toEqual([4]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.5, 3, 0.25, 0.15]]);
return [2 /*return*/];
}
});
}); });
it('int32 and int32', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([1, 5, 2, 3], 'int32');
b = tf.tensor1d([2, 3, 1, 4], 'int32');
result = tf.maximum(a, b);
expect(result.shape).toEqual(a.shape);
expect(result.dtype).toBe('int32');
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [2, 5, 2, 4]]);
return [2 /*return*/];
}
});
}); });
it('bool and bool', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([true, false, false, true], 'bool');
b = tf.tensor1d([false, false, true, true], 'bool');
result = tf.maximum(a, b);
expect(result.shape).toEqual(a.shape);
expect(result.dtype).toBe('int32');
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 0, 1, 1]]);
return [2 /*return*/];
}
});
}); });
it('upcasts when dtypes dont match', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, res, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([1, 0, 0, 1], 'float32');
b = tf.tensor1d([0, 0, 1, 1], 'int32');
res = tf.maximum(a, b);
expect(res.shape).toEqual(a.shape);
expect(res.dtype).toBe('float32');
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, res.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 0, 1, 1]]);
return [2 /*return*/];
}
});
}); });
it('propagates NaN', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, -0.1, NaN]);
b = tf.tensor1d([0.2, 0.3, 0.25]);
result = tf.maximum(a, b);
expect(result.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.5, 0.3, NaN]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts Tensor1D and scalar', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 3, -0.1, -4]);
b = tf.scalar(0.6);
result = tf.maximum(a, b);
expect(result.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.6, 3, 0.6, 0.6]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts scalar and Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.scalar(0.6);
b = tf.tensor1d([0.5, 3, -0.1, -4]);
result = tf.maximum(a, b);
expect(result.shape).toEqual(b.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.6, 3, 0.6, 0.6]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts Tensor1D and Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 0.3]);
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
result = tf.maximum(a, b);
expect(result.shape).toEqual(b.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.5, 0.4, 0.6, 0.3]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor2d([0.5, 0.3], [2, 1]);
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
result = tf.maximum(a, b);
expect(result.shape).toEqual(b.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.5, 0.5, 0.6, 0.3]]);
return [2 /*return*/];
}
});
}); });
it('gradients: Scalar', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, dy, grads, _a, da, db, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
a = tf.scalar(5.2);
b = tf.scalar(0.6);
dy = tf.scalar(3);
grads = tf.grads(function (a, b) { return tf.maximum(a, b); });
_a = grads([a, b], dy), da = _a[0], db = _a[1];
expect(da.shape).toEqual(a.shape);
expect(db.shape).toEqual(b.shape);
expect(da.dtype).toEqual('float32');
expect(db.dtype).toEqual('float32');
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_b.apply(void 0, [_d.sent(), [3 * 1]]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, db.data()];
case 2:
_c.apply(void 0, [_d.sent(), [3 * 0]]);
return [2 /*return*/];
}
});
}); });
it('gradient with clones', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, dy, grads, _a, da, db, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
a = tf.scalar(5.2);
b = tf.scalar(0.6);
dy = tf.scalar(3);
grads = tf.grads(function (a, b) { return tf.maximum(a.clone(), b.clone()).clone(); });
_a = grads([a, b], dy), da = _a[0], db = _a[1];
expect(da.shape).toEqual(a.shape);
expect(db.shape).toEqual(b.shape);
expect(da.dtype).toEqual('float32');
expect(db.dtype).toEqual('float32');
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_b.apply(void 0, [_d.sent(), [3 * 1]]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, db.data()];
case 2:
_c.apply(void 0, [_d.sent(), [3 * 0]]);
return [2 /*return*/];
}
});
}); });
it('gradients: Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, dy, grads, _a, da, db, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
dy = tf.tensor1d([1, 2, 3, 4]);
grads = tf.grads(function (a, b) { return tf.maximum(a, b); });
_a = grads([a, b], dy), da = _a[0], db = _a[1];
expect(da.shape).toEqual(a.shape);
expect(db.shape).toEqual(b.shape);
expect(da.dtype).toEqual('float32');
expect(db.dtype).toEqual('float32');
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_b.apply(void 0, [_d.sent(), [1 * 1, 2 * 0, 3 * 1, 4 * 1]]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, db.data()];
case 2:
_c.apply(void 0, [_d.sent(), [1 * 0, 2 * 1, 3 * 0, 4 * 0]]);
return [2 /*return*/];
}
});
}); });
it('gradients: Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, dy, grads, _a, da, db, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
a = tf.tensor2d([0.5, 0.3, 0.7, 0.9], [2, 2]);
b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
grads = tf.grads(function (a, b) { return tf.maximum(a, b); });
_a = grads([a, b], dy), da = _a[0], db = _a[1];
expect(da.shape).toEqual(a.shape);
expect(db.shape).toEqual(b.shape);
expect(da.dtype).toEqual('float32');
expect(db.dtype).toEqual('float32');
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_b.apply(void 0, [_d.sent(), [1 * 1, 2 * 0, 3 * 1, 4 * 1]]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, db.data()];
case 2:
_c.apply(void 0, [_d.sent(), [1 * 0, 2 * 1, 3 * 0, 4 * 0]]);
return [2 /*return*/];
}
});
}); });
it('throws when passed a as a non-tensor', function () {
expect(function () { return tf.maximum({}, tf.scalar(1)); })
.toThrowError(/Argument 'a' passed to 'maximum' must be a Tensor/);
});
it('throws when passed b as a non-tensor', function () {
expect(function () { return tf.maximum(tf.scalar(1), {}); })
.toThrowError(/Argument 'b' passed to 'maximum' must be a Tensor/);
});
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = [[0.5, 3], [-0.1, -4]];
b = [[0.2, 0.4], [0.25, 0.15]];
result = tf.maximum(a, b);
expect(result.shape).toEqual([2, 2]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.5, 3, 0.25, 0.15]]);
return [2 /*return*/];
}
});
}); });
it('throws for string tensor', function () {
expect(function () { return tf.maximum('q', 3); })
.toThrowError(/Argument 'a' passed to 'maximum' must be numeric tensor/);
expect(function () { return tf.maximum(3, 'q'); })
.toThrowError(/Argument 'b' passed to 'maximum' must be numeric tensor/);
});
});
jasmine_util_1.describeWithFlags('squaredDifference', jasmine_util_1.ALL_ENVS, function () {
it('float32 and float32', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 3, -0.1, -4]);
b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
result = tf.squaredDifference(a, b);
expect(result.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
Math.pow(-4 - 0.15, 2)
]]);
return [2 /*return*/];
}
});
}); });
it('TensorLike', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = [0.5, 3, -0.1, -4];
b = [0.2, 0.4, 0.25, 0.15];
result = tf.squaredDifference(a, b);
expect(result.shape).toEqual([4]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
Math.pow(-4 - 0.15, 2)
]]);
return [2 /*return*/];
}
});
}); });
it('TensorLike chained', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 3, -0.1, -4]);
b = [0.2, 0.4, 0.25, 0.15];
result = a.squaredDifference(b);
expect(result.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
Math.pow(-4 - 0.15, 2)
]]);
return [2 /*return*/];
}
});
}); });
it('int32 and int32', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([1, 5, 2, 3], 'int32');
b = tf.tensor1d([2, 3, 1, 4], 'int32');
result = tf.squaredDifference(a, b);
expect(result.shape).toEqual(a.shape);
expect(result.dtype).toBe('int32');
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.pow(1 - 2, 2), Math.pow(5 - 3, 2), Math.pow(2 - 1, 2),
Math.pow(3 - 4, 2)
]]);
return [2 /*return*/];
}
});
}); });
it('upcasts when dtypes dont match', function () { return __awaiter(_this, void 0, void 0, function () {
var res, _a, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
res = tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(2, 'float32'));
expect(res.dtype).toBe('float32');
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, res.data()];
case 1:
_a.apply(void 0, [_d.sent(), [9]]);
res = tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(true, 'bool'));
expect(res.dtype).toBe('int32');
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, res.data()];
case 2:
_b.apply(void 0, [_d.sent(), [16]]);
res = tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(false, 'bool'));
expect(res.dtype).toBe('int32');
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, res.data()];
case 3:
_c.apply(void 0, [_d.sent(), [25]]);
return [2 /*return*/];
}
});
}); });
it('propagates NaN', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, -0.1, NaN]);
b = tf.tensor1d([0.2, 0.3, 0.25]);
result = tf.squaredDifference(a, b);
expect(result.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(),
[Math.pow(0.5 - 0.2, 2), Math.pow(-0.1 - 0.3, 2), NaN]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts Tensor1D and scalar', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 3, -0.1, -4]);
b = tf.scalar(0.6);
result = tf.squaredDifference(a, b);
expect(result.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.pow(0.5 - 0.6, 2), Math.pow(3 - 0.6, 2), Math.pow(-0.1 - 0.6, 2),
Math.pow(-4 - 0.6, 2)
]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts scalar and Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.scalar(0.6);
b = tf.tensor1d([0.5, 3, -0.1, -4]);
result = tf.squaredDifference(a, b);
expect(result.shape).toEqual(b.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.pow(0.6 - 0.5, 2), Math.pow(0.6 - 3, 2), Math.pow(0.6 - (-0.1), 2),
Math.pow(0.6 - (-4), 2)
]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts Tensor1D and Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 0.3]);
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
result = tf.squaredDifference(a, b);
expect(result.shape).toEqual(b.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.pow(0.5 - 0.2, 2), Math.pow(0.3 - 0.4, 2), Math.pow(0.5 - 0.6, 2),
Math.pow(0.3 - 0.15, 2)
]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor2d([0.5, 0.3], [2, 1]);
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
result = tf.squaredDifference(a, b);
expect(result.shape).toEqual(b.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.pow(0.5 - 0.2, 2), Math.pow(0.5 - 0.4, 2), Math.pow(0.3 - 0.6, 2),
Math.pow(0.3 - 0.15, 2)
]]);
return [2 /*return*/];
}
});
}); });
it('gradients: Scalar', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, dy, grads, _a, da, db, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
a = tf.scalar(5.2);
b = tf.scalar(0.6);
dy = tf.scalar(3);
grads = tf.grads(function (a, b) { return tf.squaredDifference(a, b); });
_a = grads([a, b], dy), da = _a[0], db = _a[1];
expect(da.shape).toEqual(a.shape);
expect(db.shape).toEqual(b.shape);
expect(da.dtype).toEqual('float32');
expect(db.dtype).toEqual('float32');
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_b.apply(void 0, [_d.sent(), [3 * 2 * (5.2 - 0.6)]]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, db.data()];
case 2:
_c.apply(void 0, [_d.sent(), [3 * 2 * (0.6 - 5.2)]]);
return [2 /*return*/];
}
});
}); });
it('gradient with clones', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, dy, grads, _a, da, db, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
a = tf.scalar(5.2);
b = tf.scalar(0.6);
dy = tf.scalar(3);
grads = tf.grads(function (a, b) { return tf.squaredDifference(a.clone(), b.clone()).clone(); });
_a = grads([a, b], dy), da = _a[0], db = _a[1];
expect(da.shape).toEqual(a.shape);
expect(db.shape).toEqual(b.shape);
expect(da.dtype).toEqual('float32');
expect(db.dtype).toEqual('float32');
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_b.apply(void 0, [_d.sent(), [3 * 2 * (5.2 - 0.6)]]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, db.data()];
case 2:
_c.apply(void 0, [_d.sent(), [3 * 2 * (0.6 - 5.2)]]);
return [2 /*return*/];
}
});
}); });
it('gradients: Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, dy, grads, _a, da, db, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
dy = tf.tensor1d([1, 2, 3, 1]);
grads = tf.grads(function (a, b) { return tf.squaredDifference(a, b); });
_a = grads([a, b], dy), da = _a[0], db = _a[1];
expect(da.shape).toEqual(a.shape);
expect(db.shape).toEqual(b.shape);
expect(da.dtype).toEqual('float32');
expect(db.dtype).toEqual('float32');
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_b.apply(void 0, [_d.sent(), [
1 * 2 * (1.1 - 1.0), 2 * 2 * (2.6 - 2.7), 3 * 2 * (3 - 3),
1 * 2 * (5.9 - 5.8)
]]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, db.data()];
case 2:
_c.apply(void 0, [_d.sent(), [
1 * 2 * (1.0 - 1.1), 2 * 2 * (2.7 - 2.6), 3 * 2 * (3 - 3),
1 * 2 * (5.8 - 5.9)
]]);
return [2 /*return*/];
}
});
}); });
it('gradients: Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, dy, grads, _a, da, db, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
a = tf.tensor2d([0.5, 0.3, 0.7, 0.9], [2, 2]);
b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
grads = tf.grads(function (a, b) { return tf.squaredDifference(a, b); });
_a = grads([a, b], dy), da = _a[0], db = _a[1];
expect(da.shape).toEqual(a.shape);
expect(db.shape).toEqual(b.shape);
expect(da.dtype).toEqual('float32');
expect(db.dtype).toEqual('float32');
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, da.data()];
case 1:
_b.apply(void 0, [_d.sent(), [
1 * 2 * (0.5 - 0.2), 2 * 2 * (0.3 - 0.4), 3 * 2 * (0.7 - 0.7),
4 * 2 * (0.9 - 0.15)
]]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, db.data()];
case 2:
_c.apply(void 0, [_d.sent(), [
1 * 2 * (0.2 - 0.5), 2 * 2 * (0.4 - 0.3), 3 * 2 * (0.7 - 0.7),
4 * 2 * (0.15 - 0.9)
]]);
return [2 /*return*/];
}
});
}); });
it('throws when passed a as a non-tensor', function () {
expect(function () { return tf.squaredDifference({}, tf.scalar(1)); })
.toThrowError(/Argument 'a' passed to 'squaredDifference' must be a Tensor/);
});
it('throws when passed b as a non-tensor', function () {
expect(function () { return tf.squaredDifference(tf.scalar(1), {}); })
.toThrowError(/Argument 'b' passed to 'squaredDifference' must be a Tensor/);
});
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = [[0.5, 3], [-0.1, -4]];
b = 0.6;
result = tf.squaredDifference(a, b);
expect(result.shape).toEqual([2, 2]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.pow(0.5 - 0.6, 2), Math.pow(3 - 0.6, 2), Math.pow(-0.1 - 0.6, 2),
Math.pow(-4 - 0.6, 2)
]]);
return [2 /*return*/];
}
});
}); });
it('throws for string tensor', function () {
expect(function () { return tf.squaredDifference('q', 3); })
.toThrowError(/Argument 'a' passed to 'squaredDifference' must be numeric/);
expect(function () { return tf.squaredDifference(3, 'q'); })
.toThrowError(/Argument 'b' passed to 'squaredDifference' must be numeric/);
});
});
jasmine_util_1.describeWithFlags('minimum', jasmine_util_1.ALL_ENVS, function () {
it('float32 and float32', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 3, -0.1, -4]);
b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
result = tf.minimum(a, b);
expect(result.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.2, 0.4, -0.1, -4]]);
return [2 /*return*/];
}
});
}); });
it('TensorLike', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = [0.5, 3, -0.1, -4];
b = [0.2, 0.4, 0.25, 0.15];
result = tf.minimum(a, b);
expect(result.shape).toEqual([4]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.2, 0.4, -0.1, -4]]);
return [2 /*return*/];
}
});
}); });
it('TensorLike chained', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 3, -0.1, -4]);
b = [0.2, 0.4, 0.25, 0.15];
result = a.minimum(b);
expect(result.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.2, 0.4, -0.1, -4]]);
return [2 /*return*/];
}
});
}); });
it('int32 and int32', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([1, 5, 2, 3], 'int32');
b = tf.tensor1d([2, 3, 1, 4], 'int32');
result = tf.minimum(a, b);
expect(result.shape).toEqual(a.shape);
expect(result.dtype).toBe('int32');
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 3, 1, 3]]);
return [2 /*return*/];
}
});
}); });
it('bool and bool', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([true, false, false, true], 'bool');
b = tf.tensor1d([false, false, true, true], 'bool');
result = tf.minimum(a, b);
expect(result.shape).toEqual(a.shape);
expect(result.dtype).toBe('int32');
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0, 0, 0, 1]]);
return [2 /*return*/];
}
});
}); });
it('upcasts when dtypes dont match', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, res, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([1, 0, 0, 1], 'float32');
b = tf.tensor1d([0, 0, 1, 1], 'int32');
res = tf.minimum(a, b);
expect(res.shape).toEqual(a.shape);
expect(res.dtype).toBe('float32');
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, res.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0, 0, 0, 1]]);
return [2 /*return*/];
}
});
}); });
it('propagates NaN', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, -0.1, NaN]);
b = tf.tensor1d([0.2, 0.3, 0.25]);
result = tf.minimum(a, b);
expect(result.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.2, -0.1, NaN]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts Tensor1D and scalar', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 3, -0.1, -4]);
b = tf.scalar(0.6);
result = tf.minimum(a, b);
expect(result.shape).toEqual(a.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.5, 0.6, -0.1, -4]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts scalar and Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.scalar(0.6);
b = tf.tensor1d([0.5, 3, -0.1, -4]);
result = tf.minimum(a, b);
expect(result.shape).toEqual(b.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.5, 0.6, -0.1, -4]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts Tensor1D and Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor1d([0.5, 0.3]);
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
result = tf.minimum(a, b);
expect(result.shape).toEqual(b.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.2, 0.3, 0.5, 0.15]]);
return [2 /*return*/];
}
});
}); });
it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
a = tf.tensor2d([0.5, 0.3], [2, 1]);
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
result = tf.minimum(a, b);
expect(result.shape).toEqual(b.shape);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.2, 0.4, 0.3, 0.15]]);
return [2 /*return*/];
}
});
}); });
it('gradients: Scalar', function () { return __awaiter(_this, void 0, void 0, function () {
var a, b, dy, grads, _a, da, db, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
a = tf.scalar(5.2);
b = tf.scalar(0.6);
dy = tf.scalar(3);
grads = tf.grads(function (a, b) { return tf.minimum(a, b); });
_a = grads([a, b], dy), da = _a[0], db = _a[1];
expect(da.shape).toEqual(a.shape);
expect(db.shape).toEqual(b.shape);
expect(da.dtype).toEqual('float32');
expect(db.dtype).toEqual('float32');
_b = test_util_1.e