@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 2020 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.
* =============================================================================
*/
Object.defineProperty(exports, "__esModule", { value: true });
exports.multinomialConfig = void 0;
var tfjs_1 = require("@tensorflow/tfjs");
var nodejs_kernel_backend_1 = require("../nodejs_kernel_backend");
exports.multinomialConfig = {
kernelName: tfjs_1.Multinomial,
backendName: 'tensorflow',
kernelFunc: function (args) {
var logits = args.inputs.logits;
var backend = args.backend;
var _a = args.attrs, numSamples = _a.numSamples, seed = _a.seed, normalized = _a.normalized;
if (normalized) {
throw new Error('TF Node backend does not support normalized logits ' +
'passed to multinomial');
}
var opAttrs = [
(0, nodejs_kernel_backend_1.createTensorsTypeOpAttr)('T', logits.dtype),
(0, nodejs_kernel_backend_1.createTensorsTypeOpAttr)('output_dtype', 'int32'),
{ name: 'seed', type: backend.binding.TF_ATTR_INT, value: seed },
{ name: 'seed2', type: backend.binding.TF_ATTR_INT, value: seed * seed },
];
var numSamplesTensor = (0, tfjs_1.scalar)(numSamples, 'int32');
var res = backend.executeSingleOutput(tfjs_1.Multinomial, opAttrs, [logits, numSamplesTensor]);
numSamplesTensor.dispose();
return res;
}
};