@tensorflow/tfjs-core/dist/ops/batchnorm.d.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @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.
 * =============================================================================
 */
/// <amd-module name="@tensorflow/tfjs-core/dist/ops/batchnorm" />
import { Tensor, Tensor1D } from '../tensor';
import { Rank, TensorLike } from '../types';
/**
 * Batch normalization.
 *
 * As described in
 * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).
 *
 * Mean, variance, scale, and offset can be of two shapes:
 *   - The same shape as the input.
 *   - In the common case, the depth dimension is the last dimension of x, so
 *     the values would be a `tf.Tensor1D` of shape [depth].
 *
 * Also available are stricter rank-specific methods with the same signature
 * as this method that assert that parameters passed are of given rank
 *   - `tf.batchNorm2d`
 *   - `tf.batchNorm3d`
 *   - `tf.batchNorm4d`
 *
 * @param x The input Tensor.
 * @param mean A mean Tensor.
 * @param variance A variance Tensor.
 * @param offset An offset Tensor.
 * @param scale A scale Tensor.
 * @param varianceEpsilon A small float number to avoid dividing by 0.
 *
 * @doc {heading: 'Operations', subheading: 'Normalization'}
 */
declare function batchNorm_<R extends Rank>(x: Tensor<R> | TensorLike, mean: Tensor<R> | Tensor1D | TensorLike, variance: Tensor<R> | Tensor1D | TensorLike, offset?: Tensor<R> | Tensor1D | TensorLike, scale?: Tensor<R> | Tensor1D | TensorLike, varianceEpsilon?: number): Tensor<R>;
export declare const batchNorm: typeof batchNorm_;
export {};