| 1 | /**
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| 2 | * @license
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| 3 | * Copyright 2020 Google LLC. All Rights Reserved.
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| 4 | * Licensed under the Apache License, Version 2.0 (the "License");
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| 5 | * you may not use this file except in compliance with the License.
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| 6 | * You may obtain a copy of the License at
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| 7 | *
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| 8 | * http://www.apache.org/licenses/LICENSE-2.0
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| 9 | *
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| 10 | * Unless required by applicable law or agreed to in writing, software
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| 11 | * distributed under the License is distributed on an "AS IS" BASIS,
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| 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 13 | * See the License for the specific language governing permissions and
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| 14 | * limitations under the License.
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| 15 | * =============================================================================
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| 16 | */
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| 17 | /// <amd-module name="@tensorflow/tfjs-core/dist/ops/batchnorm" />
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| 18 | import { Tensor, Tensor1D } from '../tensor';
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| 19 | import { Rank, TensorLike } from '../types';
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| 20 | /**
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| 21 | * Batch normalization.
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| 22 | *
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| 23 | * As described in
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| 24 | * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).
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| 25 | *
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| 26 | * Mean, variance, scale, and offset can be of two shapes:
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| 27 | * - The same shape as the input.
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| 28 | * - In the common case, the depth dimension is the last dimension of x, so
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| 29 | * the values would be an `tf.Tensor1D` of shape [depth].
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| 30 | *
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| 31 | * Also available are stricter rank-specific methods with the same signature
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| 32 | * as this method that assert that parameters passed are of given rank
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| 33 | * - `tf.batchNorm2d`
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| 34 | * - `tf.batchNorm3d`
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| 35 | * - `tf.batchNorm4d`
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| 36 | *
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| 37 | * @param x The input Tensor.
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| 38 | * @param mean A mean Tensor.
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| 39 | * @param variance A variance Tensor.
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| 40 | * @param offset An offset Tensor.
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| 41 | * @param scale A scale Tensor.
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| 42 | * @param varianceEpsilon A small float number to avoid dividing by 0.
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| 43 | *
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| 44 | * @doc {heading: 'Operations', subheading: 'Normalization'}
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| 45 | */
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| 46 | 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>;
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| 47 | export declare const batchNorm: typeof batchNorm_;
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| 48 | export {};
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