/**
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* @license
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* Copyright 2017 Google Inc. 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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import { ArrayMap, BackendValues, DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap, TensorLike, TensorLike1D, TensorLike3D, TensorLike4D } from './types';
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export interface TensorData<D extends DataType> {
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dataId?: DataId;
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values?: DataTypeMap[D];
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}
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export interface Backend {
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}
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/**
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* A mutable object, similar to `tf.Tensor`, that allows users to set values
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* at locations before converting to an immutable `tf.Tensor`.
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*
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* See `tf.buffer` for creating a tensor buffer.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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export declare class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {
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dtype: D;
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size: number;
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shape: ShapeMap[R];
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strides: number[];
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values: DataTypeMap[D];
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constructor(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]);
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/**
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* Sets a value in the buffer at a given location.
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*
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* @param value The value to set.
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* @param locs The location indices.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Creation'} */
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set(value: SingleValueMap[D], ...locs: number[]): void;
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/**
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* Returns the value in the buffer at the provided location.
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*
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* @param locs The location indices.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Creation'} */
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get(...locs: number[]): SingleValueMap[D];
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locToIndex(locs: number[]): number;
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indexToLoc(index: number): number[];
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readonly rank: number;
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/**
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* Creates an immutable `tf.Tensor` object from the buffer.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Creation'} */
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toTensor(): Tensor<R>;
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}
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export interface TensorTracker {
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makeTensor(values: DataValues, shape: number[], dtype: DataType, backend?: Backend): Tensor;
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makeVariable(initialValue: Tensor, trainable?: boolean, name?: string, dtype?: DataType): Variable;
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incRef(a: Tensor, backend: Backend): void;
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disposeTensor(t: Tensor): void;
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disposeVariable(v: Variable): void;
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read(dataId: DataId): Promise<BackendValues>;
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readSync(dataId: DataId): BackendValues;
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}
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/**
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* The Tensor class calls into this handler to delegate chaining operations.
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*/
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export interface OpHandler {
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cast<T extends Tensor>(x: T, dtype: DataType): T;
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buffer<R extends Rank, D extends DataType>(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]): TensorBuffer<R, D>;
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print<T extends Tensor>(x: T, verbose: boolean): void;
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reshape<R2 extends Rank>(x: Tensor, shape: ShapeMap[R2]): Tensor<R2>;
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expandDims<R2 extends Rank>(x: Tensor, axis: number): Tensor<R2>;
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cumsum<T extends Tensor>(x: Tensor, axis: number, exclusive: boolean, reverse: boolean): T;
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squeeze<T extends Tensor>(x: Tensor, axis?: number[]): T;
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clone<T extends Tensor>(x: T): T;
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oneHot(x: Tensor | TensorLike, depth: number, onValue?: number, offValue?: number): Tensor;
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tile<T extends Tensor>(x: T, reps: number[]): T;
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gather<T extends Tensor>(x: T, indices: Tensor | TensorLike, axis: number): T;
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matMul<T extends Tensor>(a: T, b: T | TensorLike, transposeA: boolean, transposeB: boolean): T;
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dot(t1: Tensor, t2: Tensor | TensorLike): Tensor;
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norm(x: Tensor, ord: number | 'euclidean' | 'fro', axis: number | number[], keepDims: boolean): Tensor;
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slice<R extends Rank, T extends Tensor<R>>(x: T, begin: number | number[], size?: number | number[]): T;
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split<T extends Tensor>(x: T, numOrSizeSplits: number[] | number, axis?: number): T[];
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reverse<T extends Tensor>(x: T, axis?: number | number[]): T;
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concat<T extends Tensor>(tensors: Array<T | TensorLike>, axis: number): T;
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stack<T extends Tensor>(tensors: Array<T | TensorLike>, axis: number): Tensor;
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unstack<T extends Tensor>(value: T, axis: number): Tensor[];
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pad<T extends Tensor>(x: T, paddings: Array<[number, number]>, constantValue: number): T;
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batchNorm<R extends Rank>(x: Tensor<R>, mean: Tensor<R> | Tensor1D | TensorLike, variance: Tensor<R> | Tensor1D | TensorLike, offset?: Tensor<R> | Tensor1D | TensorLike, scale?: Tensor<R> | Tensor1D | TensorLike, varianceEpsilon?: number): Tensor<R>;
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all<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
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any<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
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logSumExp<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
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sum<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
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prod<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
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mean<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
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min<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
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max<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
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argMin<T extends Tensor>(x: Tensor, axis: number): T;
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argMax<T extends Tensor>(x: Tensor, axis: number): T;
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add<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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addStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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atan2<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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sub<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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subStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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pow<T extends Tensor>(base: T, exp: Tensor | TensorLike): T;
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powStrict<T extends Tensor>(base: T, exp: Tensor | TensorLike): T;
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mul<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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mulStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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div<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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divNoNan<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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floorDiv<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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divStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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mod<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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modStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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minimum<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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minimumStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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maximum<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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maximumStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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squaredDifferenceStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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transpose<T extends Tensor>(x: T, perm?: number[]): T;
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logicalNot<T extends Tensor>(x: T): T;
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logicalAnd<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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logicalOr<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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logicalXor<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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where<T extends Tensor>(condition: Tensor | TensorLike, a: T, b: T | TensorLike): T;
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notEqual<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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notEqualStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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less<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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lessStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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equal<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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equalStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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lessEqual<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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lessEqualStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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greater<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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greaterStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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greaterEqual<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
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greaterEqualStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
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neg<T extends Tensor>(x: T): T;
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ceil<T extends Tensor>(x: T): T;
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floor<T extends Tensor>(x: T): T;
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sign<T extends Tensor>(x: T): T;
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isNaN<T extends Tensor>(x: T): T;
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isInf<T extends Tensor>(x: T): T;
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isFinite<T extends Tensor>(x: T): T;
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round<T extends Tensor>(x: T): T;
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exp<T extends Tensor>(x: T): T;
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expm1<T extends Tensor>(x: T): T;
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log<T extends Tensor>(x: T): T;
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log1p<T extends Tensor>(x: T): T;
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sqrt<T extends Tensor>(x: T): T;
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rsqrt<T extends Tensor>(x: T): T;
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square<T extends Tensor>(x: T): T;
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reciprocal<T extends Tensor>(x: T): T;
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abs<T extends Tensor>(x: T): T;
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clipByValue<T extends Tensor>(x: T, clipValueMin: number, clipValueMax: number): T;
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sigmoid<T extends Tensor>(x: T): T;
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logSigmoid<T extends Tensor>(x: T): T;
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softplus<T extends Tensor>(x: T): T;
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zerosLike<T extends Tensor>(x: T): T;
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onesLike<T extends Tensor>(x: T): T;
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sin<T extends Tensor>(x: T): T;
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cos<T extends Tensor>(x: T): T;
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tan<T extends Tensor>(x: T): T;
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asin<T extends Tensor>(x: T): T;
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acos<T extends Tensor>(x: T): T;
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atan<T extends Tensor>(x: T): T;
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sinh<T extends Tensor>(x: T): T;
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cosh<T extends Tensor>(x: T): T;
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tanh<T extends Tensor>(x: T): T;
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asinh<T extends Tensor>(x: T): T;
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acosh<T extends Tensor>(x: T): T;
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atanh<T extends Tensor>(x: T): T;
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erf<T extends Tensor>(x: T): T;
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step<T extends Tensor>(x: T, alpha: number): T;
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relu<T extends Tensor>(x: T): T;
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relu6<T extends Tensor>(x: T): T;
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elu<T extends Tensor>(x: T): T;
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selu<T extends Tensor>(x: T): T;
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leakyRelu<T extends Tensor>(x: T, alpha: number): T;
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prelu<T extends Tensor>(x: T, alpha: T | TensorLike): T;
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softmax<T extends Tensor>(logits: T, dim: number): T;
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logSoftmax<T extends Tensor>(logits: T, axis: number): T;
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image: {
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resizeBilinear<T extends Tensor3D | Tensor4D>(images: T, size: [number, number], alignCorners: boolean): T;
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resizeNearestNeighbor<T extends Tensor3D | Tensor4D>(images: T, size: [number, number], alignCorners: boolean): T;
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};
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conv1d<T extends Tensor2D | Tensor3D>(x: T, filter: Tensor3D | TensorLike3D, stride: number, pad: 'valid' | 'same' | number, dataFormat: 'NWC' | 'NCW', dilation: number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
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conv2d<T extends Tensor3D | Tensor4D>(x: T, filter: Tensor4D | TensorLike4D, strides: [number, number] | number, pad: 'valid' | 'same' | number, dataFormat: 'NHWC' | 'NCHW', dilations: [number, number] | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
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conv2dTranspose<T extends Tensor3D | Tensor4D>(x: T, filter: Tensor4D | TensorLike4D, outputShape: [number, number, number, number] | [number, number, number], strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
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depthwiseConv2d<T extends Tensor3D | Tensor4D>(x: T, filter: Tensor4D | TensorLike4D, strides: [number, number] | number, pad: 'valid' | 'same' | number, dataFormat: 'NHWC' | 'NCHW', dilations: [number, number] | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
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separableConv2d<T extends Tensor3D | Tensor4D>(x: T | TensorLike, depthwiseFilter: Tensor4D | TensorLike4D, pointwiseFilter: Tensor4D | TensorLike, strides: [number, number] | number, pad: 'valid' | 'same', dilation: [number, number] | number, dataFormat: 'NHWC' | 'NCHW'): T;
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maxPool<T extends Tensor3D | Tensor4D>(x: T, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
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avgPool<T extends Tensor3D | Tensor4D>(x: T, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
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pool<T extends Tensor3D | Tensor4D>(input: T, windowShape: [number, number] | number, poolingType: 'avg' | 'max', padding: 'valid' | 'same' | number, diationRate?: [number, number] | number, strides?: [number, number] | number): T;
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localResponseNormalization<T extends Tensor3D | Tensor4D>(x: T, depthRadius: number, bias: number, alpha: number, beta: number): T;
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unsortedSegmentSum<T extends Tensor>(x: T, segmentIds: Tensor1D | TensorLike1D, numSegments: number): T;
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batchToSpaceND<T extends Tensor>(x: T, blockShape: number[], crops: number[][]): T;
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spaceToBatchND<T extends Tensor>(x: T, blockShape: number[], paddings: number[][]): T;
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topk<T extends Tensor>(x: T, k: number, sorted: boolean): {
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values: T;
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indices: T;
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};
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stridedSlice(x: Tensor, begin: number[], end: number[], strides: number[], beginMask: number, endMask: number, ellipsisMask: number, newAxisMask: number, shrinkAxisMask: number): Tensor;
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depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D;
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spectral: {
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fft(x: Tensor): Tensor;
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ifft(x: Tensor): Tensor;
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rfft(x: Tensor): Tensor;
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irfft(x: Tensor): Tensor;
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};
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}
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/**
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* An external consumer can register itself as the tensor tracker. This way
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* the Tensor class can notify the tracker for every tensor created and
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* disposed.
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*/
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export declare function setTensorTracker(fn: () => TensorTracker): void;
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/**
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* An external consumer can register itself as the op handler. This way the
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* Tensor class can have chaining methods that call into ops via the op
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* handler.
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*/
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export declare function setOpHandler(handler: OpHandler): void;
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/**
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* Sets the deprecation warning function to be used by this file. This way the
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* Tensor class can be a leaf but still use the environment.
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*/
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export declare function setDeprecationWarningFn(fn: (msg: string) => void): void;
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/**
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* We wrap data id since we use weak map to avoid memory leaks.
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* Since we have our own memory management, we have a reference counter
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* mapping a tensor to its data, so there is always a pointer (even if that
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* data is otherwise garbage collectable).
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* See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/
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* Global_Objects/WeakMap
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*/
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export declare type DataId = object;
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export declare namespace Tensor { }
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/**
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* A `tf.Tensor` object represents an immutable, multidimensional array of
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* numbers that has a shape and a data type.
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*
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* See `tf.tensor` for details on how to create a `tf.Tensor`.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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export declare class Tensor<R extends Rank = Rank> {
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/** Unique id of this tensor. */
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readonly id: number;
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/**
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* Id of the bucket holding the data for this tensor. Multiple arrays can
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* point to the same bucket (e.g. when calling array.reshape()).
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*/
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dataId: DataId;
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/** The shape of the tensor. */
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readonly shape: ShapeMap[R];
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/** Number of elements in the tensor. */
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readonly size: number;
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/** The data type for the array. */
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readonly dtype: DataType;
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/** The rank type for the array (see `Rank` enum). */
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readonly rankType: R;
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/** Whether this tensor has been globally kept. */
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kept: boolean;
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/** The id of the scope this tensor is being tracked in. */
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scopeId: number;
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/**
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* Number of elements to skip in each dimension when indexing. See
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* https://docs.scipy.org/doc/numpy/reference/generated/\
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* numpy.ndarray.strides.html
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*/
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readonly strides: number[];
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constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number);
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/** Flatten a Tensor to a 1D array. */
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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flatten(): Tensor1D;
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/** Converts a size-1 `tf.Tensor` to a `tf.Scalar`. */
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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asScalar(): Scalar;
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/** Converts a `tf.Tensor` to a `tf.Tensor1D`. */
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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as1D(): Tensor1D;
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/**
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* Converts a `tf.Tensor` to a `tf.Tensor2D`.
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*
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* @param rows Number of rows in `tf.Tensor2D`.
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* @param columns Number of columns in `tf.Tensor2D`.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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as2D(rows: number, columns: number): Tensor2D;
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/**
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* Converts a `tf.Tensor` to a `tf.Tensor3D`.
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*
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* @param rows Number of rows in `tf.Tensor3D`.
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* @param columns Number of columns in `tf.Tensor3D`.
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* @param depth Depth of `tf.Tensor3D`.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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as3D(rows: number, columns: number, depth: number): Tensor3D;
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/**
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* Converts a `tf.Tensor` to a `tf.Tensor4D`.
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*
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* @param rows Number of rows in `tf.Tensor4D`.
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* @param columns Number of columns in `tf.Tensor4D`.
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* @param depth Depth of `tf.Tensor4D`.
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* @param depth2 4th dimension of `tf.Tensor4D`.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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as4D(rows: number, columns: number, depth: number, depth2: number): Tensor4D;
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/**
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* Converts a `tf.Tensor` to a `tf.Tensor5D`.
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*
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* @param rows Number of rows in `tf.Tensor5D`.
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* @param columns Number of columns in `tf.Tensor5D`.
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* @param depth Depth of `tf.Tensor5D`.
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* @param depth2 4th dimension of `tf.Tensor5D`.
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* @param depth3 5th dimension of 'tf.Tensor5D'
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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as5D(rows: number, columns: number, depth: number, depth2: number, depth3: number): Tensor5D;
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/**
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* Casts a `tf.Tensor` to a specified dtype.
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*
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* @param dtype Data-type to cast the tensor to.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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asType<T extends this>(this: T, dtype: DataType): T;
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readonly rank: number;
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/**
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* Returns a promise of `tf.TensorBuffer` that holds the underlying data.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>>;
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/** Returns a `tf.TensorBuffer` that holds the underlying data. */
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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bufferSync<D extends DataType = 'float32'>(): TensorBuffer<R, D>;
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/**
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* Returns the tensor data as a nested array. The transfer of data is done
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* asynchronously.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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array(): Promise<ArrayMap[R]>;
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/**
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* Returns the tensor data as a nested array. The transfer of data is done
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* synchronously.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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arraySync(): ArrayMap[R];
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/**
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* Asynchronously downloads the values from the `tf.Tensor`. Returns a
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* promise of `TypedArray` that resolves when the computation has finished.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
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/**
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* Synchronously downloads the values from the `tf.Tensor`. This blocks the
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* UI thread until the values are ready, which can cause performance issues.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
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/** Returns the underlying bytes of the tensor's data. */
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bytes(): Promise<Uint8Array[] | Uint8Array>;
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/**
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* Disposes `tf.Tensor` from memory.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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dispose(): void;
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protected isDisposedInternal: boolean;
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readonly isDisposed: boolean;
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private throwIfDisposed;
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/** Casts the array to type `float32` */
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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toFloat<T extends this>(this: T): T;
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/** Casts the array to type `int32` */
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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toInt(): this;
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/** Casts the array to type `bool` */
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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toBool(): this;
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/**
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* Prints the `tf.Tensor`. See `tf.print` for details.
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*
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* @param verbose Whether to print verbose information about the tensor,
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* including dtype and size.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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print(verbose?: boolean): void;
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/**
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* Reshapes the tensor into the provided shape.
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* See `tf.reshape` for more details.
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*
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* @param newShape An array of integers defining the output tensor shape.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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reshape<R2 extends Rank>(newShape: ShapeMap[R2]): Tensor<R2>;
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/**
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* Reshapes the tensor into the shape of the provided tensor.
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*
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* @param x The tensor of required shape.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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reshapeAs<T extends Tensor>(x: T): T;
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/**
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* Returns a `tf.Tensor` that has expanded rank, by inserting a dimension
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* into the tensor's shape. See `tf.expandDims` for details.
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*
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* @param axis The dimension index at which to insert shape of 1. Defaults to
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* 0 (the first dimension).
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*/
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/** @doc {heading: 'Tensors', subheading: 'Classes'} */
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expandDims<R2 extends Rank>(axis?: number): Tensor<R2>;
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/**
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* Returns the cumulative sum of the `tf.Tensor` along `axis`.
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*
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* @param axis The axis along which to sum. Optional. Defaults to 0.
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* @param exclusive Whether to perform exclusive cumulative sum. Defaults to
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* false. If set to true then the sum of each tensor entry does not
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* include its own value, but only the values previous to it along the
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* specified axis.
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* @param reverse Whether to sum in the opposite direction. Defaults to
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* false.
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*/
|
/** @doc {heading: 'Tensors', subheading: 'Classes'} */
|
cumsum<T extends Tensor>(axis?: number, exclusive?: boolean, reverse?: boolean): T;
|
/**
|
* Returns a `tf.Tensor` with dimensions of size 1 removed from the shape.
|
* See `tf.squeeze` for more details.
|
*
|
* @param axis A list of numbers. If specified, only squeezes the
|
* dimensions listed. The dimension index starts at 0. It is an error to
|
* squeeze a dimension that is not 1.
|
*/
|
/** @doc {heading: 'Tensors', subheading: 'Classes'} */
|
squeeze<T extends Tensor>(axis?: number[]): T;
|
/** Returns a copy of the tensor. See `tf.clone` for details. */
|
/** @doc {heading: 'Tensors', subheading: 'Classes'} */
|
clone<T extends Tensor>(this: T): T;
|
oneHot(this: Tensor, depth: number, onValue?: number, offValue?: number): Tensor;
|
/**
|
* Returns a human-readable description of the tensor. Useful for logging.
|
*/
|
/** @doc {heading: 'Tensors', subheading: 'Classes'} */
|
toString(verbose?: boolean): string;
|
tile<T extends this>(this: T, reps: number[]): T;
|
gather<T extends this>(this: T, indices: Tensor | TensorLike, axis?: number): T;
|
matMul<T extends Tensor>(this: T, b: T | TensorLike, transposeA?: boolean, transposeB?: boolean): T;
|
dot(b: Tensor | TensorLike): Tensor;
|
norm(ord?: number | 'euclidean' | 'fro', axis?: number | number[], keepDims?: boolean): Tensor;
|
slice<T extends Tensor<R>>(this: T, begin: number | number[], size?: number | number[]): T;
|
reverse<T extends Tensor>(this: T, axis?: number | number[]): T;
|
concat<T extends Tensor>(this: T, x: T | Array<T | TensorLike>, axis?: number): T;
|
split<T extends Tensor>(this: T, numOrSizeSplits: number[] | number, axis?: number): T[];
|
stack(x: Tensor, axis?: number): Tensor;
|
unstack(axis?: number): Tensor[];
|
pad<T extends Tensor>(this: T, paddings: Array<[number, number]>, constantValue?: number): T;
|
/**
|
* @deprecated Use `tf.batchNorm` instead, and note the positional argument
|
* change of scale, offset, and varianceEpsilon.
|
*/
|
batchNormalization(mean: Tensor<R> | Tensor1D | TensorLike, variance: Tensor<R> | Tensor1D | TensorLike, varianceEpsilon?: number, scale?: Tensor<R> | Tensor1D | TensorLike, offset?: Tensor<R> | Tensor1D | TensorLike): Tensor<R>;
|
batchNorm(mean: Tensor<R> | Tensor1D | TensorLike, variance: Tensor<R> | Tensor1D | TensorLike, offset?: Tensor<R> | Tensor1D | TensorLike, scale?: Tensor<R> | Tensor1D | TensorLike, varianceEpsilon?: number): Tensor<R>;
|
all<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
|
any<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
|
logSumExp<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
|
sum<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
|
prod<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
|
mean<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
|
min<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
|
max<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
|
argMin<T extends Tensor>(axis?: number): T;
|
argMax<T extends Tensor>(axis?: number): T;
|
cast<T extends this>(dtype: DataType): T;
|
add<T extends Tensor>(x: Tensor | TensorLike): T;
|
addStrict<T extends this>(this: T, x: T | TensorLike): T;
|
atan2<T extends this>(this: T, x: T | TensorLike): T;
|
sub<T extends Tensor>(x: Tensor | TensorLike): T;
|
subStrict<T extends this>(this: T, x: T | TensorLike): T;
|
pow<T extends Tensor>(this: T, exp: Tensor | TensorLike): T;
|
powStrict(exp: Tensor | TensorLike): Tensor<R>;
|
mul<T extends Tensor>(x: Tensor | TensorLike): T;
|
mulStrict<T extends this>(this: T, x: T | TensorLike): T;
|
div<T extends Tensor>(x: Tensor | TensorLike): T;
|
divNoNan<T extends Tensor>(x: Tensor | TensorLike): T;
|
floorDiv<T extends Tensor>(x: Tensor | TensorLike): T;
|
divStrict<T extends this>(this: T, x: T | TensorLike): T;
|
minimum<T extends Tensor>(x: Tensor | TensorLike): T;
|
minimumStrict<T extends this>(this: T, x: T | TensorLike): T;
|
maximum<T extends Tensor>(x: Tensor | TensorLike): T;
|
maximumStrict<T extends this>(this: T, x: T | TensorLike): T;
|
mod<T extends Tensor>(x: Tensor | TensorLike): T;
|
modStrict<T extends this>(this: T, x: T | TensorLike): T;
|
squaredDifferenceStrict<T extends this>(this: T, x: T | TensorLike): T;
|
transpose<T extends Tensor>(this: T, perm?: number[]): T;
|
notEqual<T extends Tensor>(x: Tensor | TensorLike): T;
|
notEqualStrict<T extends this>(this: T, x: T | TensorLike): T;
|
less<T extends Tensor>(x: Tensor | TensorLike): T;
|
lessStrict<T extends this>(this: T, x: T | TensorLike): T;
|
equal<T extends Tensor>(x: Tensor | TensorLike): T;
|
equalStrict<T extends this>(this: T, x: T | TensorLike): T;
|
lessEqual<T extends Tensor>(x: Tensor | TensorLike): T;
|
lessEqualStrict<T extends this>(this: T, x: T | TensorLike): T;
|
greater<T extends Tensor>(x: Tensor | TensorLike): T;
|
greaterStrict<T extends this>(this: T, x: T | TensorLike): T;
|
greaterEqual<T extends Tensor>(x: Tensor | TensorLike): T;
|
greaterEqualStrict<T extends this>(this: T, x: T | TensorLike): T;
|
logicalAnd(x: Tensor | TensorLike): Tensor;
|
logicalOr(x: Tensor | TensorLike): Tensor;
|
logicalNot<T extends Tensor>(this: T): T;
|
logicalXor(x: Tensor | TensorLike): Tensor;
|
where(condition: Tensor | TensorLike, x: Tensor | TensorLike): Tensor;
|
neg<T extends Tensor>(this: T): T;
|
ceil<T extends Tensor>(this: T): T;
|
floor<T extends Tensor>(this: T): T;
|
sign<T extends Tensor>(this: T): T;
|
isNaN<T extends Tensor>(this: T): T;
|
isInf<T extends Tensor>(this: T): T;
|
isFinite<T extends Tensor>(this: T): T;
|
exp<T extends Tensor>(this: T): T;
|
expm1<T extends Tensor>(this: T): T;
|
log<T extends Tensor>(this: T): T;
|
log1p<T extends Tensor>(this: T): T;
|
sqrt<T extends Tensor>(this: T): T;
|
rsqrt<T extends Tensor>(this: T): T;
|
square<T extends Tensor>(this: T): T;
|
reciprocal<T extends Tensor>(this: T): T;
|
abs<T extends Tensor>(this: T): T;
|
clipByValue(min: number, max: number): Tensor<R>;
|
relu<T extends Tensor>(this: T): T;
|
relu6<T extends Tensor>(this: T): T;
|
elu<T extends Tensor>(this: T): T;
|
selu<T extends Tensor>(this: T): T;
|
leakyRelu(alpha?: number): Tensor<R>;
|
prelu(alpha: Tensor<R> | TensorLike): Tensor<R>;
|
sigmoid<T extends Tensor>(this: T): T;
|
logSigmoid<T extends Tensor>(this: T): T;
|
softplus<T extends Tensor>(this: T): T;
|
zerosLike<T extends Tensor>(this: T): T;
|
onesLike<T extends Tensor>(this: T): T;
|
sin<T extends Tensor>(this: T): T;
|
cos<T extends Tensor>(this: T): T;
|
tan<T extends Tensor>(this: T): T;
|
asin<T extends Tensor>(this: T): T;
|
acos<T extends Tensor>(this: T): T;
|
atan<T extends Tensor>(this: T): T;
|
sinh<T extends Tensor>(this: T): T;
|
cosh<T extends Tensor>(this: T): T;
|
tanh<T extends Tensor>(this: T): T;
|
asinh<T extends Tensor>(this: T): T;
|
acosh<T extends Tensor>(this: T): T;
|
atanh<T extends Tensor>(this: T): T;
|
erf<T extends Tensor>(this: T): T;
|
round<T extends Tensor>(this: T): T;
|
step<T extends Tensor>(this: T, alpha?: number): T;
|
softmax<T extends this>(this: T, dim?: number): T;
|
logSoftmax<T extends this>(this: T, axis?: number): T;
|
resizeBilinear<T extends Tensor3D | Tensor4D>(this: T, newShape2D: [number, number], alignCorners?: boolean): T;
|
resizeNearestNeighbor<T extends Tensor3D | Tensor4D>(this: T, newShape2D: [number, number], alignCorners?: boolean): T;
|
conv1d<T extends Tensor2D | Tensor3D>(this: T, filter: Tensor3D | TensorLike3D, stride: number, pad: 'valid' | 'same' | number, dataFormat?: 'NWC' | 'NCW', dilation?: number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
|
conv2d<T extends Tensor3D | Tensor4D>(this: T, filter: Tensor4D | TensorLike4D, strides: [number, number] | number, pad: 'valid' | 'same' | number, dataFormat?: 'NHWC' | 'NCHW', dilations?: [number, number] | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
|
conv2dTranspose<T extends Tensor3D | Tensor4D>(this: T, filter: Tensor4D | TensorLike4D, outputShape: [number, number, number, number] | [number, number, number], strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
|
depthwiseConv2D<T extends Tensor3D | Tensor4D>(this: T, filter: Tensor4D | TensorLike4D, strides: [number, number] | number, pad: 'valid' | 'same' | number, dataFormat?: 'NHWC' | 'NCHW', dilations?: [number, number] | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
|
separableConv2d<T extends Tensor3D | Tensor4D>(this: T | TensorLike, depthwiseFilter: Tensor4D | TensorLike4D, pointwiseFilter: Tensor4D | TensorLike, strides: [number, number] | number, pad: 'valid' | 'same', dilation?: [number, number] | number, dataFormat?: 'NHWC' | 'NCHW'): T;
|
avgPool<T extends Tensor3D | Tensor4D>(this: T, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
|
maxPool<T extends Tensor3D | Tensor4D>(this: T, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
|
localResponseNormalization<T extends Tensor3D | Tensor4D>(this: T, radius?: number, bias?: number, alpha?: number, beta?: number): T;
|
pool<T extends Tensor3D | Tensor4D>(this: T, windowShape: [number, number] | number, poolingType: 'max' | 'avg', padding: 'valid' | 'same' | number, dilationRate?: [number, number] | number, strides?: [number, number] | number): T;
|
variable(trainable?: boolean, name?: string, dtype?: DataType): Variable<R>;
|
unsortedSegmentSum<T extends Tensor>(this: T, segmentIds: Tensor1D | TensorLike1D, numSegments: number): T;
|
batchToSpaceND<T extends Tensor>(this: T, blockShape: number[], crops: number[][]): T;
|
spaceToBatchND<T extends Tensor>(this: T, blockShape: number[], paddings: number[][]): T;
|
topk<T extends Tensor>(this: T, k?: number, sorted?: boolean): {
|
values: T;
|
indices: T;
|
};
|
stridedSlice(this: Tensor, begin: number[], end: number[], strides: number[], beginMask?: number, endMask?: number, ellipsisMask?: number, newAxisMask?: number, shrinkAxisMask?: number): Tensor;
|
depthToSpace(this: Tensor4D, blockSize: number, dataFormat: 'NHWC' | 'NCHW'): Tensor4D;
|
fft(this: Tensor): Tensor;
|
ifft(this: Tensor): Tensor;
|
rfft(this: Tensor): Tensor;
|
irfft(this: Tensor): Tensor;
|
}
|
export interface NumericTensor<R extends Rank = Rank> extends Tensor<R> {
|
dtype: NumericDataType;
|
dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
|
data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
|
}
|
export interface StringTensor<R extends Rank = Rank> extends Tensor<R> {
|
dtype: 'string';
|
dataSync<D extends DataType = 'string'>(): DataTypeMap[D];
|
data<D extends DataType = 'string'>(): Promise<DataTypeMap[D]>;
|
}
|
/** @doclink Tensor */
|
export declare type Scalar = Tensor<Rank.R0>;
|
/** @doclink Tensor */
|
export declare type Tensor1D = Tensor<Rank.R1>;
|
/** @doclink Tensor */
|
export declare type Tensor2D = Tensor<Rank.R2>;
|
/** @doclink Tensor */
|
export declare type Tensor3D = Tensor<Rank.R3>;
|
/** @doclink Tensor */
|
export declare type Tensor4D = Tensor<Rank.R4>;
|
/** @doclink Tensor */
|
export declare type Tensor5D = Tensor<Rank.R5>;
|
/** @doclink Tensor */
|
export declare type Tensor6D = Tensor<Rank.R6>;
|
/**
|
* A mutable `tf.Tensor`, useful for persisting state, e.g. for training.
|
*/
|
/** @doc {heading: 'Tensors', subheading: 'Classes'} */
|
export declare class Variable<R extends Rank = Rank> extends Tensor<R> {
|
trainable: boolean;
|
name: string;
|
constructor(initialValue: Tensor<R>, trainable: boolean, name: string, tensorId: number);
|
/**
|
* Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have
|
* the same shape and dtype as the old `tf.Tensor`.
|
*
|
* @param newValue New tensor to be assigned to this variable.
|
*/
|
/** @doc {heading: 'Tensors', subheading: 'Classes'} */
|
assign(newValue: Tensor<R>): void;
|
dispose(): void;
|
}
|
