@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
;
/**
* @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.
* =============================================================================
*/
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('lstm', jasmine_util_1.ALL_ENVS, function () {
it('MultiRNNCell with 2 BasicLSTMCells', function () { return __awaiter(_this, void 0, void 0, function () {
var lstmKernel1, lstmBias1, lstmKernel2, lstmBias2, forgetBias, lstm1, lstm2, c, h, onehot, output, _a, _b, _c, _d;
return __generator(this, function (_e) {
switch (_e.label) {
case 0:
lstmKernel1 = tf.tensor2d([
0.26242125034332275, -0.8787832260131836, 0.781475305557251,
1.337337851524353, 0.6180247068405151, -0.2760246992111206,
-0.11299663782119751, -0.46332040429115295, -0.1765323281288147,
0.6807947158813477, -0.8326982855796814, 0.6732975244522095
], [3, 4]);
lstmBias1 = tf.tensor1d([1.090713620185852, -0.8282332420349121, 0, 1.0889357328414917]);
lstmKernel2 = tf.tensor2d([
-1.893059492111206, -1.0185645818710327, -0.6270437240600586,
-2.1829540729522705, -0.4583775997161865, -0.5454602241516113,
-0.3114445209503174, 0.8450229167938232
], [2, 4]);
lstmBias2 = tf.tensor1d([0.9906240105628967, 0.6248329877853394, 0, 1.0224634408950806]);
forgetBias = tf.scalar(1.0);
lstm1 = function (data, c, h) {
return tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
};
lstm2 = function (data, c, h) {
return tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
};
c = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
h = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
onehot = tf.buffer([1, 2], 'float32');
onehot.set(1.0, 0, 0);
output = tf.multiRNNCell([lstm1, lstm2], onehot.toTensor(), c, h);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output[0][0].data()];
case 1:
_a.apply(void 0, [_e.sent(), [-0.7440074682235718]]);
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, output[0][1].data()];
case 2:
_b.apply(void 0, [_e.sent(), [0.7460772395133972]]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, output[1][0].data()];
case 3:
_c.apply(void 0, [_e.sent(), [-0.5802832245826721]]);
_d = test_util_1.expectArraysClose;
return [4 /*yield*/, output[1][1].data()];
case 4:
_d.apply(void 0, [_e.sent(), [0.5745711922645569]]);
return [2 /*return*/];
}
});
}); });
it('basicLSTMCell with batch=2', function () { return __awaiter(_this, void 0, void 0, function () {
var lstmKernel, lstmBias, forgetBias, data, batchedData, c, batchedC, h, batchedH, _a, newC, newH, newCVals, newHVals;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
lstmKernel = tf.randomNormal([3, 4]);
lstmBias = tf.randomNormal([4]);
forgetBias = tf.scalar(1.0);
data = tf.randomNormal([1, 2]);
batchedData = tf.concat2d([data, data], 0);
c = tf.randomNormal([1, 1]);
batchedC = tf.concat2d([c, c], 0);
h = tf.randomNormal([1, 1]);
batchedH = tf.concat2d([h, h], 0);
_a = tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH), newC = _a[0], newH = _a[1];
return [4 /*yield*/, newC.array()];
case 1:
newCVals = _b.sent();
return [4 /*yield*/, newH.array()];
case 2:
newHVals = _b.sent();
expect(newCVals[0][0]).toEqual(newCVals[1][0]);
expect(newHVals[0][0]).toEqual(newHVals[1][0]);
return [2 /*return*/];
}
});
}); });
it('basicLSTMCell accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var lstmKernel, lstmBias, forgetBias, data, batchedData, c, batchedC, h, batchedH, _a, newC, newH, newCVals, newHVals;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
lstmKernel = tf.randomNormal([3, 4]);
lstmBias = [0, 0, 0, 0];
forgetBias = 1;
data = [[0, 0]];
batchedData = tf.concat2d([data, data], 0);
c = [[0]];
batchedC = tf.concat2d([c, c], 0);
h = [[0]];
batchedH = tf.concat2d([h, h], 0);
_a = tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH), newC = _a[0], newH = _a[1];
return [4 /*yield*/, newC.array()];
case 1:
newCVals = _b.sent();
return [4 /*yield*/, newH.array()];
case 2:
newHVals = _b.sent();
expect(newCVals[0][0]).toEqual(newCVals[1][0]);
expect(newHVals[0][0]).toEqual(newHVals[1][0]);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('multiRNN throws when passed non-tensor', jasmine_util_1.ALL_ENVS, function () {
it('input: data', function () {
var lstmKernel1 = tf.zeros([3, 4]);
var lstmBias1 = tf.zeros([4]);
var lstmKernel2 = tf.zeros([2, 4]);
var lstmBias2 = tf.zeros([4]);
var forgetBias = tf.scalar(1.0);
var lstm1 = function (data, c, h) {
return tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
};
var lstm2 = function (data, c, h) {
return tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
};
var c = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
var h = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
expect(function () { return tf.multiRNNCell([lstm1, lstm2], {}, c, h); })
.toThrowError(/Argument 'data' passed to 'multiRNNCell' must be a Tensor/);
});
it('input: c', function () {
var lstmKernel1 = tf.zeros([3, 4]);
var lstmBias1 = tf.zeros([4]);
var lstmKernel2 = tf.zeros([2, 4]);
var lstmBias2 = tf.zeros([4]);
var forgetBias = tf.scalar(1.0);
var lstm1 = function (data, c, h) {
return tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
};
var lstm2 = function (data, c, h) {
return tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
};
var h = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
var data = tf.zeros([1, 2]);
expect(function () { return tf.multiRNNCell([lstm1, lstm2], data, [{}], h); })
.toThrowError(/Argument 'c\[0\]' passed to 'multiRNNCell' must be a Tensor/);
});
it('input: h', function () {
var lstmKernel1 = tf.zeros([3, 4]);
var lstmBias1 = tf.zeros([4]);
var lstmKernel2 = tf.zeros([2, 4]);
var lstmBias2 = tf.zeros([4]);
var forgetBias = tf.scalar(1.0);
var lstm1 = function (data, c, h) {
return tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
};
var lstm2 = function (data, c, h) {
return tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
};
var c = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
var data = tf.zeros([1, 2]);
expect(function () { return tf.multiRNNCell([lstm1, lstm2], data, c, [{}]); })
.toThrowError(/Argument 'h\[0\]' passed to 'multiRNNCell' must be a Tensor/);
});
});
jasmine_util_1.describeWithFlags('basicLSTMCell throws with non-tensor', jasmine_util_1.ALL_ENVS, function () {
it('input: forgetBias', function () {
var lstmKernel = tf.randomNormal([3, 4]);
var lstmBias = tf.randomNormal([4]);
var data = tf.randomNormal([1, 2]);
var batchedData = tf.concat2d([data, data], 0); // 2x2
var c = tf.randomNormal([1, 1]);
var batchedC = tf.concat2d([c, c], 0); // 2x1
var h = tf.randomNormal([1, 1]);
var batchedH = tf.concat2d([h, h], 0); // 2x1
expect(function () { return tf.basicLSTMCell({}, lstmKernel, lstmBias, batchedData, batchedC, batchedH); })
.toThrowError(/Argument 'forgetBias' passed to 'basicLSTMCell' must be a Tensor/);
});
it('input: lstmKernel', function () {
var lstmBias = tf.randomNormal([4]);
var forgetBias = tf.scalar(1.0);
var data = tf.randomNormal([1, 2]);
var batchedData = tf.concat2d([data, data], 0); // 2x2
var c = tf.randomNormal([1, 1]);
var batchedC = tf.concat2d([c, c], 0); // 2x1
var h = tf.randomNormal([1, 1]);
var batchedH = tf.concat2d([h, h], 0); // 2x1
expect(function () { return tf.basicLSTMCell(forgetBias, {}, lstmBias, batchedData, batchedC, batchedH); })
.toThrowError(/Argument 'lstmKernel' passed to 'basicLSTMCell' must be a Tensor/);
});
it('input: lstmBias', function () {
var lstmKernel = tf.randomNormal([3, 4]);
var forgetBias = tf.scalar(1.0);
var data = tf.randomNormal([1, 2]);
var batchedData = tf.concat2d([data, data], 0); // 2x2
var c = tf.randomNormal([1, 1]);
var batchedC = tf.concat2d([c, c], 0); // 2x1
var h = tf.randomNormal([1, 1]);
var batchedH = tf.concat2d([h, h], 0); // 2x1
expect(function () { return tf.basicLSTMCell(forgetBias, lstmKernel, {}, batchedData, batchedC, batchedH); })
.toThrowError(/Argument 'lstmBias' passed to 'basicLSTMCell' must be a Tensor/);
});
it('input: data', function () {
var lstmKernel = tf.randomNormal([3, 4]);
var lstmBias = tf.randomNormal([4]);
var forgetBias = tf.scalar(1.0);
var c = tf.randomNormal([1, 1]);
var batchedC = tf.concat2d([c, c], 0); // 2x1
var h = tf.randomNormal([1, 1]);
var batchedH = tf.concat2d([h, h], 0); // 2x1
expect(function () { return tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, {}, batchedC, batchedH); })
.toThrowError(/Argument 'data' passed to 'basicLSTMCell' must be a Tensor/);
});
it('input: c', function () {
var lstmKernel = tf.randomNormal([3, 4]);
var lstmBias = tf.randomNormal([4]);
var forgetBias = tf.scalar(1.0);
var data = tf.randomNormal([1, 2]);
var batchedData = tf.concat2d([data, data], 0); // 2x2
var h = tf.randomNormal([1, 1]);
var batchedH = tf.concat2d([h, h], 0); // 2x1
expect(function () { return tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, {}, batchedH); })
.toThrowError(/Argument 'c' passed to 'basicLSTMCell' must be a Tensor/);
});
it('input: h', function () {
var lstmKernel = tf.randomNormal([3, 4]);
var lstmBias = tf.randomNormal([4]);
var forgetBias = tf.scalar(1.0);
var data = tf.randomNormal([1, 2]);
var batchedData = tf.concat2d([data, data], 0); // 2x2
var c = tf.randomNormal([1, 1]);
var batchedC = tf.concat2d([c, c], 0); // 2x1
expect(function () { return tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, batchedC, {}); })
.toThrowError(/Argument 'h' passed to 'basicLSTMCell' must be a Tensor/);
});
});
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