@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
396 lines • 18.1 kB
JavaScript
;
/**
* @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 tape_1 = require("./tape");
var test_util_1 = require("./test_util");
jasmine_util_1.describeWithFlags('getFilteredNodesXToY', jasmine_util_1.ALL_ENVS, function () {
it('no paths from x to y', function () {
var x = tf.scalar(1);
var intermediate1 = tf.scalar(0);
var intermediate2 = tf.scalar(0);
var y = tf.scalar(2);
var tape = [
{
id: 0,
name: 'node0',
inputs: { x: x },
outputs: [intermediate1],
gradient: null
},
{
id: 1,
name: 'node1',
inputs: { intermediate2: intermediate2 },
outputs: [y],
gradient: null
}
];
var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x], y);
expect(filteredTapeNodes.length).toBe(0);
expect(filteredTapeNodes).toEqual([]);
});
it('one operation x => y', function () {
var x = tf.scalar(1);
var y = tf.scalar(2);
var tape = [{ id: 0, name: 'node0', inputs: { x: x }, outputs: [y], gradient: null }];
var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x], y);
expect(filteredTapeNodes.length).toBe(1);
expect(filteredTapeNodes).toEqual(tape);
});
it('1 operation [x0, x1] => y, all input paths', function () {
var x0 = tf.scalar(0);
var x1 = tf.scalar(1);
var y = tf.scalar(2);
var tape = [
{ id: 0, name: 'node0', inputs: { x0: x0, x1: x1 }, outputs: [y], gradient: null }
];
var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x0, x1], y);
expect(filteredTapeNodes.length).toBe(1);
expect(filteredTapeNodes).toEqual(tape);
});
it('one operation [x0, x1] => y, one input paths', function () {
var x0 = tf.scalar(0);
var x1 = tf.scalar(1);
var y = tf.scalar(2);
var tape = [
{ id: 0, name: 'node0', inputs: { x0: x0, x1: x1 }, outputs: [y], gradient: null }
];
var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x0], y);
expect(filteredTapeNodes.length).toBe(1);
// x1 input should be pruned, we don't ask for the gradient of x1.
expect(filteredTapeNodes[0])
.toEqual({ id: 0, name: 'node0', inputs: { x0: x0 }, outputs: [y], gradient: null });
});
it('two operations x => intermediate => y', function () {
var x = tf.scalar(1);
var intermediate = tf.scalar(0);
var y = tf.scalar(2);
var tape = [
{
id: 0,
name: 'node0',
inputs: { x: x },
outputs: [intermediate],
gradient: null
},
{
id: 1,
name: 'node1',
inputs: { intermediate: intermediate },
outputs: [y],
gradient: null
}
];
var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x], y);
expect(filteredTapeNodes.length).toBe(2);
expect(filteredTapeNodes).toEqual(tape);
});
it('two operations [x0, x1], [x2] => ' +
'intermediate => y', function () {
var x0 = tf.scalar(1);
var x1 = tf.scalar(2);
var x2 = tf.scalar(3);
var intermediate = tf.scalar(4);
var y = tf.scalar(2);
var tape = [
{
id: 0,
name: 'node0',
inputs: { x0: x0, x1: x1 },
outputs: [intermediate],
gradient: null
},
{
id: 1,
name: 'node1',
inputs: { x2: x2, intermediate: intermediate },
outputs: [y],
gradient: null
}
];
var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x0, x1, x2], y);
expect(filteredTapeNodes.length).toBe(2);
expect(filteredTapeNodes).toEqual(tape);
});
it('x => y and x => orphan', function () {
var x = tf.scalar(1);
var orphan = tf.scalar(0);
var y = tf.scalar(2);
var tape = [
{ id: 0, name: 'node0', inputs: { x: x }, outputs: [orphan], gradient: null },
{ id: 1, name: 'node1', inputs: { x: x }, outputs: [y], gradient: null }
];
var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x], y);
expect(filteredTapeNodes.length).toBe(1);
// The orphan should be removed.
expect(filteredTapeNodes[0]).toEqual(tape[1]);
});
it('x => y and orphan => y', function () {
var x = tf.scalar(1);
var orphan = tf.scalar(0);
var y = tf.scalar(2);
var tape = [
{ id: 0, name: 'node0', inputs: { x: x, orphan: orphan }, outputs: [y], gradient: null }
];
var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x], y);
expect(filteredTapeNodes.length).toBe(1);
// The orphan should be pruned from the node's input.
expect(filteredTapeNodes[0])
.toEqual({ id: 0, name: 'node0', inputs: { x: x }, outputs: [y], gradient: null });
});
it('1 op with 3 outputs x => y1, y2, y3', function () {
var x = tf.scalar(1);
var y1 = tf.scalar(2);
var y2 = tf.scalar(2);
var y3 = tf.scalar(2);
var tape = [
{ id: 0, name: 'node0', inputs: { x: x }, outputs: [y1, y2, y3], gradient: null }
];
var filteredNodes1 = tape_1.getFilteredNodesXToY(tape, [x], y1);
expect(filteredNodes1.length).toBe(1);
expect(filteredNodes1).toEqual(tape);
var filteredNodes2 = tape_1.getFilteredNodesXToY(tape, [x], y2);
expect(filteredNodes2.length).toBe(1);
expect(filteredNodes2).toEqual(tape);
var filteredNodes3 = tape_1.getFilteredNodesXToY(tape, [x], y3);
expect(filteredNodes3.length).toBe(1);
expect(filteredNodes3).toEqual(tape);
});
});
jasmine_util_1.describeWithFlags('backpropagateGradients', jasmine_util_1.ALL_ENVS, function () {
it('Throws if gradient is not defined', function () {
var x = tf.scalar(0);
var y = tf.scalar(1);
var dy = tf.scalar(1);
var accumulatedGradientsMap = {};
accumulatedGradientsMap[y.id] = dy;
var tape = [{ id: 0, name: 'node0', inputs: { x: x }, outputs: [y], gradient: null }];
expect(function () { return tape_1.backpropagateGradients(accumulatedGradientsMap, tape, function (f) { return tf.tidy(f); }); })
.toThrowError();
});
it('basic backprop with 1 node', function () { return __awaiter(_this, void 0, void 0, function () {
var x, y, dy, accumulatedGradientsMap, tape, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
x = tf.scalar(0);
y = tf.scalar(1);
dy = tf.scalar(1);
accumulatedGradientsMap = {};
accumulatedGradientsMap[y.id] = dy;
tape = [{
id: 0,
name: 'node0',
inputs: { x: x },
outputs: [y],
gradient: function (dy) {
return { x: function () { return dy.add(tf.scalar(1)); } };
}
}];
tape_1.backpropagateGradients(accumulatedGradientsMap, tape, function (f) { return tf.tidy(f); });
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, accumulatedGradientsMap[x.id].data()];
case 1:
_a.apply(void 0, [_b.sent(), [2]]);
return [2 /*return*/];
}
});
}); });
it('basic backprop with 2 nodes', function () { return __awaiter(_this, void 0, void 0, function () {
var x, intermediate, y, dy, accumulatedGradientsMap, tape, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
x = tf.scalar(0);
intermediate = tf.scalar(1);
y = tf.scalar(2);
dy = tf.scalar(1);
accumulatedGradientsMap = {};
accumulatedGradientsMap[y.id] = dy;
tape = [
{
id: 0,
name: 'node0',
inputs: { x: x },
outputs: [intermediate],
gradient: function (dy) {
return { x: function () { return dy.add(tf.scalar(1)); } };
}
},
{
id: 1,
name: 'node1',
inputs: { intermediate: intermediate },
outputs: [y],
gradient: function (dy) {
return { intermediate: function () { return dy.add(tf.scalar(1)); } };
}
}
];
tape_1.backpropagateGradients(accumulatedGradientsMap, tape, function (f) { return tf.tidy(f); });
// dx = dy + 1 + 1
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, accumulatedGradientsMap[x.id].data()];
case 1:
// dx = dy + 1 + 1
_a.apply(void 0, [_b.sent(), [3]]);
return [2 /*return*/];
}
});
}); });
it('basic backprop with a split node accumulates gradients', function () { return __awaiter(_this, void 0, void 0, function () {
var x, intermediate1, intermediate2, y, dy, accumulatedGradientsMap, tape, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
x = tf.scalar(0);
intermediate1 = tf.scalar(1);
intermediate2 = tf.scalar(2);
y = tf.scalar(3);
dy = tf.scalar(1);
accumulatedGradientsMap = {};
accumulatedGradientsMap[y.id] = dy;
tape = [
{
id: 0,
name: 'node0',
inputs: { x: x },
outputs: [intermediate1],
gradient: function (dy) {
return { x: function () { return dy.add(tf.scalar(1)); } };
}
},
{
id: 1,
name: 'node1',
inputs: { x: x },
outputs: [intermediate2],
gradient: function (dy) {
return { x: function () { return dy.add(tf.scalar(1)); } };
}
},
{
id: 2,
name: 'node2',
inputs: { intermediate1: intermediate1, intermediate2: intermediate2 },
outputs: [y],
gradient: function (dy) {
return {
intermediate1: function () { return dy.add(tf.scalar(1)); },
intermediate2: function () { return dy.add(tf.scalar(1)); }
};
}
}
];
tape_1.backpropagateGradients(accumulatedGradientsMap, tape, function (f) { return tf.tidy(f); });
// dx = dy + 1 + 1 + 1 + 1 + 1
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, accumulatedGradientsMap[x.id].data()];
case 1:
_b = [_c.sent()];
return [4 /*yield*/, dy.data()];
case 2:
// dx = dy + 1 + 1 + 1 + 1 + 1
_a.apply(void 0, _b.concat([[(_c.sent())[0] + 5]]));
return [2 /*return*/];
}
});
}); });
it('backprop over 1 node with 3 outputs, w.r.t to the 2nd output', function () { return __awaiter(_this, void 0, void 0, function () {
var x, y1, y2, y3, accumulatedGradientsMap, dy2, dys, tape, _a, _b, _c, _d;
return __generator(this, function (_e) {
switch (_e.label) {
case 0:
x = tf.tensor1d([1, 1, 1]);
y1 = tf.scalar(1);
y2 = tf.scalar(1);
y3 = tf.scalar(1);
accumulatedGradientsMap = {};
dy2 = tf.scalar(5);
accumulatedGradientsMap[y2.id] = dy2;
tape = [{
id: 0,
name: 'node0',
inputs: { x: x },
outputs: [y1, y2, y3],
gradient: function (dys_) {
dys = dys_;
return { x: function () { return tf.stack(dys_); } };
}
}];
tape_1.backpropagateGradients(accumulatedGradientsMap, tape, function (f) { return tf.tidy(f); });
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, accumulatedGradientsMap[x.id].data()];
case 1:
_a.apply(void 0, [_e.sent(), [0, 5, 0]]);
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, dys[0].data()];
case 2:
_b.apply(void 0, [_e.sent(), [0]]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, dys[1].data()];
case 3:
_c.apply(void 0, [_e.sent(), [5]]);
_d = test_util_1.expectArraysClose;
return [4 /*yield*/, dys[2].data()];
case 4:
_d.apply(void 0, [_e.sent(), [0]]);
return [2 /*return*/];
}
});
}); });
});
//# sourceMappingURL=tape_test.js.map