@tensorflow/tfjs-node
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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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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 * as tf from '@tensorflow/tfjs';
import { backend_util, BackendTimingInfo, DataId, DataType, KernelBackend, ModelTensorInfo, Scalar, ScalarLike, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorInfo } from '@tensorflow/tfjs';
import { TFEOpAttr, TFJSBinding } from './tfjs_binding';
export declare class NodeJSKernelBackend extends KernelBackend {
binding: TFJSBinding;
isGPUPackage: boolean;
isUsingGpuDevice: boolean;
private tensorMap;
constructor(binding: TFJSBinding, packageName: string);
getDTypeInteger(dtype: DataType): number;
private typeAttributeFromTensor;
private createOutputTensor;
private getInputTensorIds;
createReductionOpAttrs(tensor: TensorInfo, keepDims?: boolean): TFEOpAttr[];
floatPrecision(): 16 | 32;
epsilon(): number;
/**
* Executes an op that has a single input and output.
*
* Helper function to wrap executeSingleOutput in a particular case.
* @param name The name of the Op to execute.
* @param input The input Tensor for the Op.
*/
executeSingleInput(name: string, input: TensorInfo): Tensor;
/**
* Executes a TensorFlow Eager Op that provides one output Tensor.
* @param name The name of the Op to execute.
* @param opAttrs The list of Op attributes required to execute.
* @param inputs The list of input Tensors for the Op.
* @return A resulting Tensor from Op execution.
*/
executeSingleOutput(name: string, opAttrs: TFEOpAttr[], inputs: TensorInfo[]): Tensor;
/**
* Executes a TensorFlow Eager Op that provides multiple output Tensors.
* @param name The name of the Op to execute.
* @param opAttrs The list of Op attributes required to execute.
* @param inputs The list of input Tensors for the Op.
* @param numOutputs The number of output Tensors for Op execution.
* @return A resulting Tensor array from Op execution.
*/
executeMultipleOutputs(name: string, opAttrs: TFEOpAttr[], inputs: TensorInfo[], numOutputs: number): Tensor[];
numDataIds(): number;
dispose(): void;
read(dataId: DataId): Promise<backend_util.BackendValues>;
readSync(dataId: DataId): backend_util.BackendValues;
/**
* Dispose the memory if the dataId has 0 refCount. Return true if the memory
* is released, false otherwise.
* @param dataId
* @oaram force Optional, remove the data regardless of refCount
*/
disposeData(dataId: DataId, force?: boolean): boolean;
/** Return refCount of a `TensorData`. */
refCount(dataId: DataId): number;
incRef(dataId: DataId): void;
move(dataId: DataId, values: backend_util.BackendValues, shape: number[], dtype: DataType, refCount: number): void;
write(values: backend_util.BackendValues, shape: number[], dtype: DataType): DataId;
applyActivation<T extends Tensor>(input: T, activation: string, preluActivationWeights?: Tensor, leakyreluAlpha?: number): T;
divide(a: Tensor, b: Tensor): Tensor;
divNoNan(a: Tensor, b: Tensor): Tensor;
where(condition: Tensor): Tensor2D;
topKValues<T extends Tensor>(x: T, k: number): Tensor1D;
topKIndices(x: Tensor, k: number): Tensor1D;
int<T extends Tensor>(x: T): T;
decodeJpeg(contents: Uint8Array, channels: number, ratio: number, fancyUpscaling: boolean, tryRecoverTruncated: boolean, acceptableFraction: number, dctMethod: string): Tensor3D;
decodePng(contents: Uint8Array, channels: number): Tensor3D;
decodeBmp(contents: Uint8Array, channels: number): Tensor3D;
decodeGif(contents: Uint8Array): Tensor4D;
executeEncodeImageOp(name: string, opAttrs: TFEOpAttr[], imageData: Uint8Array, imageShape: number[]): Tensor;
encodeJpeg(imageData: Uint8Array, imageShape: number[], format: '' | 'grayscale' | 'rgb', quality: number, progressive: boolean, optimizeSize: boolean, chromaDownsampling: boolean, densityUnit: 'in' | 'cm', xDensity: number, yDensity: number, xmpMetadata: string): Tensor;
encodePng(imageData: Uint8Array, imageShape: number[], compression: number): Tensor;
deleteSavedModel(id: number): void;
loadSavedModelMetaGraph(path: string, tags: string): number;
private getMappedInputTensorIds;
runSavedModel(id: number, inputs: Tensor[], inputTensorInfos: ModelTensorInfo[], outputOpNames: string[]): Tensor[];
summaryWriter(logdir: string): Tensor1D;
createSummaryFileWriter(resourceHandle: Tensor, logdir: string, maxQueue?: number, flushMillis?: number, filenameSuffix?: string): void;
writeScalarSummary(resourceHandle: Tensor, step: number, name: string, value: Scalar | number): void;
writeHistogramSummary(resourceHandle: Tensor, step: number, name: string, data: Tensor, bucketCount: number | undefined, description: string | undefined): void;
flushSummaryWriter(resourceHandle: Tensor): void;
/**
* Group data into histogram buckets.
*
* @param data A `Tensor` of any shape. Must be castable to `float32`
* @param bucketCount Optional positive `number`
* @returns A `Tensor` of shape `[k, 3]` and type `float32`. The `i`th row
* is
* a triple `[leftEdge, rightEdge, count]` for a single bucket. The value
* of `k` is either `bucketCount`, `1` or `0`.
*/
private buckets;
memory(): {
unreliable: boolean;
};
time(f: () => void): Promise<BackendTimingInfo>;
getNumOfSavedModels(): number;
getNumOfTFTensors(): number;
}
/** Returns an instance of the Node.js backend. */
export declare function nodeBackend(): NodeJSKernelBackend;
/** Returns the TF dtype for a given DataType. */
export declare function getTFDType(dataType: tf.DataType): number;
/**
* Creates a TFEOpAttr for a 'type' OpDef attribute from a Tensor or list of
* Tensors.
*/
export declare function createTensorsTypeOpAttr(attrName: string, tensorsOrDtype: tf.Tensor | tf.Tensor[] | tf.DataType): TFEOpAttr;
export declare function createOpAttr(attrName: string, tensorsOrDtype: tf.Tensor | tf.Tensor[] | tf.DataType, value: ScalarLike): TFEOpAttr;
export declare function ensureTensorflowBackend(): void;