@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/).
46 lines (45 loc) • 1.72 kB
TypeScript
/**
* @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.
* =============================================================================
*/
import { Shape } from '@tensorflow/tfjs';
/**
* Node.js-specific tensor type: int64-type scalar.
*
* This class is created for a specific purpose: to support
* writing `step`s to TensorBoard via op-kernel bindings.
* `step` is required to have an int64 dtype, but TensorFlow.js
* (tfjs-core) doesn't have a built-in int64 dtype. This is
* related to a lack of `Int64Array` or `Uint64Array` typed
* array in basic JavaScript.
*
* This class is introduced as a workaround.
*/
export declare class Int64Scalar {
readonly value: number;
readonly dtype: string;
readonly rank: number;
private valueArray_;
private static endiannessOkay_;
constructor(value: number);
get shape(): Shape;
/** Get the Int32Array that represents the int64 value. */
get valueArray(): Int32Array;
}
/**
* This method encodes a Int32Array as Int64 layout in order to create TF_INT64
* tensor through binding.
*/
export declare function encodeInt32ArrayAsInt64(value: Int32Array): Int32Array;