@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @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.
* =============================================================================
*/
import { Conv2D } from '../kernel_names';
import { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';
import { conv2DBackpropInput } from '../ops/conv2d_backprop_input';
import * as conv_util from '../ops/conv_util';
import * as util from '../util';
export const conv2DGradConfig = {
kernelName: Conv2D,
inputsToSave: ['x', 'filter'],
gradFunc: (dy, saved, attrs) => {
const [x4D, $filter] = saved;
const { dilations, strides, pad, dataFormat } = attrs;
util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +
`are not yet supported in gradients. Got dilations '${dilations}'`);
return {
x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),
filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)
};
}
};
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