"use strict";
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/**
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* @license
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* Copyright 2018 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
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step((generator = generator.apply(thisArg, _arguments || [])).next());
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});
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};
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var __generator = (this && this.__generator) || function (thisArg, body) {
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var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
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return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
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function verb(n) { return function (v) { return step([n, v]); }; }
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function step(op) {
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if (f) throw new TypeError("Generator is already executing.");
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while (g && (g = 0, op[0] && (_ = 0)), _) try {
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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;
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if (y = 0, t) op = [op[0] & 2, t.value];
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switch (op[0]) {
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case 0: case 1: t = op; break;
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case 4: _.label++; return { value: op[1], done: false };
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case 5: _.label++; y = op[1]; op = [0]; continue;
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case 7: op = _.ops.pop(); _.trys.pop(); continue;
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default:
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if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
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if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
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}
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op = body.call(thisArg, _);
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} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
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}
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("@tensorflow/tfjs");
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var tfn = require("../index");
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// We still need node-fetch so that we can mock the core
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// tf.env().platform.fetch call and return a valid response.
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// tslint:disable-next-line:no-require-imports
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var fetch = require('node-fetch');
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var OCTET_STREAM_TYPE = 'application/octet-stream';
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var JSON_TYPE = 'application/json';
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// Test data;
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var modelTopology1 = {
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'class_name': 'Sequential',
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'keras_version': '2.1.4',
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'config': [{
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'class_name': 'Dense',
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'config': {
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'kernel_initializer': {
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'class_name': 'VarianceScaling',
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'config': {
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'distribution': 'uniform',
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'scale': 1.0,
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'seed': null,
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'mode': 'fan_avg'
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}
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},
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'name': 'dense',
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'kernel_constraint': null,
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'bias_regularizer': null,
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'bias_constraint': null,
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'dtype': 'float32',
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'activation': 'linear',
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'trainable': true,
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'kernel_regularizer': null,
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'bias_initializer': { 'class_name': 'Zeros', 'config': {} },
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'units': 1,
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'batch_input_shape': [null, 3],
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'use_bias': true,
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'activity_regularizer': null
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}
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}],
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'backend': 'tensorflow'
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};
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describe('nodeHTTPRequest-load', function () {
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var requestInits;
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var setupFakeWeightFiles = function (fileBufferMap) {
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spyOn(tf.env().platform, 'fetch')
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.and.callFake(function (path, init) {
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return new Promise(function (resolve, reject) {
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var contentType = '';
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if (path.endsWith('model.json')) {
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contentType = JSON_TYPE;
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}
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else if (path.endsWith('weightfile0') || path.endsWith('weightfile1')) {
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contentType = OCTET_STREAM_TYPE;
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}
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else {
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reject(new Error("Invalid path: ".concat(path)));
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}
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requestInits.push(init);
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resolve(new fetch.Response(fileBufferMap[path], { 'headers': { 'Content-Type': contentType } }));
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});
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});
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};
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beforeEach(function () {
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requestInits = [];
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});
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it('Constructor', function () {
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var handler = tfn.io.nodeHTTPRequest('./foo_model.json');
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expect(handler == null).toEqual(false);
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expect(typeof handler.load).toEqual('function');
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expect(typeof handler.save).toEqual('function');
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});
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it('Load through NodeHTTPRequest object', function () { return __awaiter(void 0, void 0, void 0, function () {
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var weightManifest1, trainingConfig1, floatData, handler, modelArtifacts;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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weightManifest1 = [{
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paths: ['weightfile0'],
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weights: [
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{
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name: 'dense/kernel',
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shape: [3, 1],
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dtype: 'float32',
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},
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{
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name: 'dense/bias',
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shape: [1],
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dtype: 'float32',
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}
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]
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}];
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trainingConfig1 = {
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loss: 'categorical_crossentropy',
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metrics: ['accuracy'],
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optimizer_config: { class_name: 'SGD', config: { learningRate: 0.1 } }
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};
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floatData = new Float32Array([1, 3, 3, 7]);
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setupFakeWeightFiles({
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'http://localhost/model.json': JSON.stringify({
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modelTopology: modelTopology1,
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weightsManifest: weightManifest1,
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trainingConfig: trainingConfig1
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}),
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'http://localhost/weightfile0': floatData,
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});
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handler = tfn.io.nodeHTTPRequest('http://localhost/model.json', { credentials: 'include', cache: 'no-cache' });
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return [4 /*yield*/, handler.load()];
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case 1:
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modelArtifacts = _a.sent();
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expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
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expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
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expect(modelArtifacts.trainingConfig).toEqual(trainingConfig1);
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expect(new Float32Array(tf.io.CompositeArrayBuffer.join(modelArtifacts.weightData))).toEqual(floatData);
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expect(requestInits).toEqual([
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{ credentials: 'include', cache: 'no-cache' },
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{ credentials: 'include', cache: 'no-cache' }
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]);
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return [2 /*return*/];
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}
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});
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}); });
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it('Load through registered handler', function () { return __awaiter(void 0, void 0, void 0, function () {
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var weightManifest1, floatData, model;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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weightManifest1 = [{
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paths: ['weightfile0'],
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weights: [
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{
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name: 'dense/kernel',
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shape: [3, 1],
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dtype: 'float32',
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},
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{
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name: 'dense/bias',
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shape: [1],
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dtype: 'float32',
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}
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]
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}];
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floatData = new Float32Array([1, 3, 3, 7]);
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setupFakeWeightFiles({
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'https://localhost/model.json': JSON.stringify({ modelTopology: modelTopology1, weightsManifest: weightManifest1 }),
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'https://localhost/weightfile0': floatData,
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});
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return [4 /*yield*/, tf.loadLayersModel('https://localhost/model.json')];
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case 1:
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model = _a.sent();
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expect(model.inputs.length).toEqual(1);
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expect(model.inputs[0].shape).toEqual([null, 3]);
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expect(model.outputs.length).toEqual(1);
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expect(model.outputs[0].shape).toEqual([null, 1]);
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return [2 /*return*/];
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}
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});
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}); });
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});
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