@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 { convertToTensor } from '../../tensor_util_env';
import { assertShapesMatch } from '../../util';
import { abs } from '../abs';
import { add } from '../add';
import { exp } from '../exp';
import { log1p } from '../log1p';
import { Reduction } from '../loss_ops_utils';
import { mul } from '../mul';
import { neg } from '../neg';
import { op } from '../operation';
import { relu } from '../relu';
import { scalar } from '../scalar';
import { sub } from '../sub';
import { computeWeightedLoss } from './compute_weighted_loss';
function sigmoidCrossEntropyWithLogits_(labels, logits) {
const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');
const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');
assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');
/**
* Implementation Details:
*
* For brevity, let `x = logits`, `z = labels`. The logistic loss is
* z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
* = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
* = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
* = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
* = (1 - z) * x + log(1 + exp(-x))
* = x - x * z + log(1 + exp(-x))
*
* For x < 0, to avoid overflow in exp(-x), we reformulate the above
* x - x * z + log(1 + exp(-x))
* = log(exp(x)) - x * z + log(1 + exp(-x))
* = - x * z + log(1 + exp(x))
*
* Hence, to ensure stability and avoid overflow, the implementation uses
* this equivalent formulation:
* max(x, 0) - x * z + log(1 + exp(-abs(x)))
*/
const maxOutput = relu($logits);
const outputXTarget = mul($logits, $labels);
const sigmoidOutput = log1p(exp(neg(abs($logits))));
return add(sub(maxOutput, outputXTarget), sigmoidOutput);
}
/**
* Computes the sigmoid cross entropy loss between two tensors.
*
* If labelSmoothing is nonzero, smooth the labels towards 1/2:
*
* newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)
* + 0.5 * labelSmoothing
*
* @param multiClassLabels The ground truth output tensor of shape
* [batch_size, num_classes], same dimensions as 'predictions'.
* @param logits The predicted outputs.
* @param weights Tensor whose rank is either 0, or the same rank as
* `labels`, and must be broadcastable to `labels` (i.e., all dimensions
* must be either `1`, or the same as the corresponding `losses`
* dimension).
* @param labelSmoothing If greater than 0, then smooth the labels.
* @param reduction Type of reduction to apply to loss. Should be of type
* `Reduction`
*
* @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }
*/
function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');
const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');
let $weights = null;
if (weights != null) {
$weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');
}
assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');
if (labelSmoothing > 0) {
const labelSmoothingScalar = scalar(labelSmoothing);
const one = scalar(1);
const half = scalar(0.5);
$multiClassLabels =
add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));
}
const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
return computeWeightedLoss(losses, $weights, reduction);
}
export const sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });
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