@tensorflow/tfjs-node
Version:
This repository provides native TensorFlow execution in backend JavaScript applications under the Node.js runtime, accelerated by the TensorFlow C binary under the hood. It provides the same API as [TensorFlow.js](https://js.tensorflow.org/api/latest/).
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JavaScript
;
/**
* @license
* Copyright 2018 Google LLC. 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) {
function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
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) : adopt(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 (g && (g = 0, op[0] && (_ = 0)), _) 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 };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs");
var tfn = require("../index");
// We still need node-fetch so that we can mock the core
// tf.env().platform.fetch call and return a valid response.
// tslint:disable-next-line:no-require-imports
var fetch = require('node-fetch');
var OCTET_STREAM_TYPE = 'application/octet-stream';
var JSON_TYPE = 'application/json';
// Test data;
var modelTopology1 = {
'class_name': 'Sequential',
'keras_version': '2.1.4',
'config': [{
'class_name': 'Dense',
'config': {
'kernel_initializer': {
'class_name': 'VarianceScaling',
'config': {
'distribution': 'uniform',
'scale': 1.0,
'seed': null,
'mode': 'fan_avg'
}
},
'name': 'dense',
'kernel_constraint': null,
'bias_regularizer': null,
'bias_constraint': null,
'dtype': 'float32',
'activation': 'linear',
'trainable': true,
'kernel_regularizer': null,
'bias_initializer': { 'class_name': 'Zeros', 'config': {} },
'units': 1,
'batch_input_shape': [null, 3],
'use_bias': true,
'activity_regularizer': null
}
}],
'backend': 'tensorflow'
};
describe('nodeHTTPRequest-load', function () {
var requestInits;
var setupFakeWeightFiles = function (fileBufferMap) {
spyOn(tf.env().platform, 'fetch')
.and.callFake(function (path, init) {
return new Promise(function (resolve, reject) {
var contentType = '';
if (path.endsWith('model.json')) {
contentType = JSON_TYPE;
}
else if (path.endsWith('weightfile0') || path.endsWith('weightfile1')) {
contentType = OCTET_STREAM_TYPE;
}
else {
reject(new Error("Invalid path: ".concat(path)));
}
requestInits.push(init);
resolve(new fetch.Response(fileBufferMap[path], { 'headers': { 'Content-Type': contentType } }));
});
});
};
beforeEach(function () {
requestInits = [];
});
it('Constructor', function () {
var handler = tfn.io.nodeHTTPRequest('./foo_model.json');
expect(handler == null).toEqual(false);
expect(typeof handler.load).toEqual('function');
expect(typeof handler.save).toEqual('function');
});
it('Load through NodeHTTPRequest object', function () { return __awaiter(void 0, void 0, void 0, function () {
var weightManifest1, trainingConfig1, floatData, handler, modelArtifacts;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
weightManifest1 = [{
paths: ['weightfile0'],
weights: [
{
name: 'dense/kernel',
shape: [3, 1],
dtype: 'float32',
},
{
name: 'dense/bias',
shape: [1],
dtype: 'float32',
}
]
}];
trainingConfig1 = {
loss: 'categorical_crossentropy',
metrics: ['accuracy'],
optimizer_config: { class_name: 'SGD', config: { learningRate: 0.1 } }
};
floatData = new Float32Array([1, 3, 3, 7]);
setupFakeWeightFiles({
'http://localhost/model.json': JSON.stringify({
modelTopology: modelTopology1,
weightsManifest: weightManifest1,
trainingConfig: trainingConfig1
}),
'http://localhost/weightfile0': floatData,
});
handler = tfn.io.nodeHTTPRequest('http://localhost/model.json', { credentials: 'include', cache: 'no-cache' });
return [4 /*yield*/, handler.load()];
case 1:
modelArtifacts = _a.sent();
expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
expect(modelArtifacts.trainingConfig).toEqual(trainingConfig1);
expect(new Float32Array(tf.io.CompositeArrayBuffer.join(modelArtifacts.weightData))).toEqual(floatData);
expect(requestInits).toEqual([
{ credentials: 'include', cache: 'no-cache' },
{ credentials: 'include', cache: 'no-cache' }
]);
return [2 /*return*/];
}
});
}); });
it('Load through registered handler', function () { return __awaiter(void 0, void 0, void 0, function () {
var weightManifest1, floatData, model;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
weightManifest1 = [{
paths: ['weightfile0'],
weights: [
{
name: 'dense/kernel',
shape: [3, 1],
dtype: 'float32',
},
{
name: 'dense/bias',
shape: [1],
dtype: 'float32',
}
]
}];
floatData = new Float32Array([1, 3, 3, 7]);
setupFakeWeightFiles({
'https://localhost/model.json': JSON.stringify({ modelTopology: modelTopology1, weightsManifest: weightManifest1 }),
'https://localhost/weightfile0': floatData,
});
return [4 /*yield*/, tf.loadLayersModel('https://localhost/model.json')];
case 1:
model = _a.sent();
expect(model.inputs.length).toEqual(1);
expect(model.inputs[0].shape).toEqual([null, 3]);
expect(model.outputs.length).toEqual(1);
expect(model.outputs[0].shape).toEqual([null, 1]);
return [2 /*return*/];
}
});
}); });
});