| 1 | import { Tensor3D, Tensor4D } from '../tensor';
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| 2 | import * as conv_util from './conv_util';
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| 3 | /**
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| 4 | * Computes the derivative of the filter of a 2D convolution.
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| 5 | *
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| 6 | * @param x The input tensor, of rank 4 or rank 3 of shape
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| 7 | * [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.
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| 8 | * @param dy The dy image, of rank 4 or rank 3, of shape
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| 9 | * [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.
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| 10 | * @param filterShape The shape of the filter, length 4,
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| 11 | * [filterHeight, filterWidth, inDepth, outDepth].
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| 12 | * @param strides The strides of the convolution: [strideHeight,
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| 13 | * strideWidth].
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| 14 | * @param pad A string from: 'same', 'valid'. The type of padding algorithm
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| 15 | * used in the forward prop of the op.
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| 16 | * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
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| 17 | * "NHWC". Specify the data format of the input and output data. With the
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| 18 | * default format "NHWC", the data is stored in the order of: [batch,
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| 19 | * height, width, channels].
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| 20 | * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
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| 21 | * provided, it will default to truncate.
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| 22 | */
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| 23 | declare function conv2DBackpropFilter_<T extends Tensor3D | Tensor4D>(x: T, dy: T, filterShape: [number, number, number, number], strides: [number, number] | number, pad: 'valid' | 'same' | number | conv_util.ExplicitPadding, dataFormat?: 'NHWC' | 'NCHW', dimRoundingMode?: 'floor' | 'round' | 'ceil'): Tensor4D;
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| 24 | export declare const conv2DBackpropFilter: typeof conv2DBackpropFilter_;
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| 25 | export {};
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