/**
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* @license
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* Copyright 2018 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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/* Original Source: losses.py */
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import * as tfc from '@tensorflow/tfjs-core';
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import { tidy, util } from '@tensorflow/tfjs-core';
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import { epsilon } from './backend/common';
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import * as K from './backend/tfjs_backend';
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import { ValueError } from './errors';
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/**
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* Normalizes a tensor wrt the L2 norm alongside the specified axis.
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* @param x
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* @param axis Axis along which to perform normalization.
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*/
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export function l2Normalize(x, axis) {
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return tidy(() => {
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if (x.dtype !== 'float32') {
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x = tfc.cast(x, 'float32');
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}
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const squareSum = tfc.sum(K.square(x), axis, true);
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const epsilonTensor = tfc.fill(squareSum.shape, epsilon());
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const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));
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return tfc.div(x, norm);
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});
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}
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export function meanSquaredError(yTrue, yPred) {
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return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));
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}
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export function meanAbsoluteError(yTrue, yPred) {
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return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));
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}
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export function meanAbsolutePercentageError(yTrue, yPred) {
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return tidy(() => {
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const diff = tfc.sub(yTrue, yPred);
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const clippedTrue = tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);
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const absResult = tfc.abs(tfc.div(diff, clippedTrue));
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return tfc.mul(100, tfc.mean(absResult, -1));
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});
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}
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export function meanSquaredLogarithmicError(yTrue, yPred) {
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return tidy(() => {
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const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);
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const firstLog = tfc.log(tfc.add(1, clippedPred));
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const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);
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const secondLog = tfc.log(tfc.add(1, clippedTrue));
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return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);
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});
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}
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export function squaredHinge(yTrue, yPred) {
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return tidy(() => {
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const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));
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return tfc.mean(K.square(maxResult), -1);
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});
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}
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export function hinge(yTrue, yPred) {
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return tidy(() => {
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const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));
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return tfc.mean(maxResult, -1);
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});
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}
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export function categoricalHinge(yTrue, yPred) {
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return tidy(() => {
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const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);
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const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);
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return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));
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});
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}
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/**
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* Logarithm of the hyperbolic cosine of the prediction error.
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*
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* `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and
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* to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly
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* like the mean squared error, but will not be so strongly affected by the
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* occasional wildly incorrect prediction.
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*/
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export function logcosh(yTrue, yPred) {
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return tidy(() => {
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const log2 = Math.log(2);
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const predictionDiff = tfc.sub(yPred, yTrue);
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const logcoshResult = tfc.sub(tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))), log2);
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return tfc.mean(logcoshResult, -1);
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});
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}
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export function categoricalCrossentropy(target, output, fromLogits = false) {
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return tidy(() => {
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if (fromLogits) {
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output = tfc.softmax(output);
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}
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else {
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// scale preds so that the class probabilities of each sample sum to 1.
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const outputSum = tfc.sum(output, output.shape.length - 1, true);
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output = tfc.div(output, outputSum);
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}
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output = tfc.clipByValue(output, epsilon(), 1 - epsilon());
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return tfc.neg(tfc.sum(tfc.mul(tfc.cast(target, 'float32'), tfc.log(output)), output.shape.length - 1));
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});
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}
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/**
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* Categorical crossentropy with integer targets.
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*
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* @param target An integer tensor.
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* @param output A tensor resulting from a softmax (unless `fromLogits` is
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* `true`, in which case `output` is expected to be the logits).
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* @param fromLogits Boolean, whether `output` is the result of a softmax, or is
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* a tensor of logits.
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*/
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export function sparseCategoricalCrossentropy(target, output, fromLogits = false) {
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return tidy(() => {
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const flatTarget = tfc.cast(tfc.floor(K.flatten(target)), 'int32');
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output = tfc.clipByValue(output, epsilon(), 1 - epsilon());
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const outputShape = output.shape;
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const oneHotTarget = tfc.reshape(tfc.oneHot(flatTarget, outputShape[outputShape.length - 1]), outputShape);
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return categoricalCrossentropy(oneHotTarget, output, fromLogits);
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});
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}
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/**
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* From TensorFlow's implementation in nn_impl.py:
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*
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* For brevity, let `x = logits`, `z = labels`. The logistic loss is
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* z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
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* = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
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* = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
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* = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
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* = (1 - z) * x + log(1 + exp(-x))
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* = x - x * z + log(1 + exp(-x))
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* For x < 0, to avoid overflow in exp(-x), we reformulate the above
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* x - x * z + log(1 + exp(-x))
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* = log(exp(x)) - x * z + log(1 + exp(-x))
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* = - x * z + log(1 + exp(x))
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* Hence, to ensure stability and avoid overflow, the implementation uses this
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* equivalent formulation
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* max(x, 0) - x * z + log(1 + exp(-abs(x)))
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*
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* @param labels The labels.
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* @param logits The logits.
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*/
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export function sigmoidCrossEntropyWithLogits(labels, logits) {
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if (!util.arraysEqual(labels.shape, logits.shape)) {
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throw new ValueError(`logits and labels must have the same shape, but got shapes ` +
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`${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);
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}
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return tidy(() => {
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// The logistic loss formula from above is
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// x - x * z + log(1 + exp(-x))
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// For x < 0, a more numerically stable formula is
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// -x * z + log(1 + exp(x))
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// Note that these two expressions can be combined into the following:
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// max(x, 0) - x * z + log(1 + exp(-abs(x)))
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const reluLogits = tfc.relu(logits);
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const negAbsLogits = tfc.neg(tfc.abs(logits));
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return tfc.add(tfc.sub(reluLogits, tfc.mul(logits, labels)), tfc.log1p(tfc.exp(negAbsLogits)));
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});
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}
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export function binaryCrossentropy(yTrue, yPred) {
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return tidy(() => {
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let y;
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y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());
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y = tfc.log(tfc.div(y, tfc.sub(1, y)));
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return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);
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});
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}
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export function kullbackLeiblerDivergence(yTrue, yPred) {
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return tidy(() => {
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const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);
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const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);
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return tfc.sum(tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);
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});
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}
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export function poisson(yTrue, yPred) {
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return tidy(() => {
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const logPred = tfc.log(tfc.add(epsilon(), yPred));
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return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);
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});
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}
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export function cosineProximity(yTrue, yPred) {
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return tidy(() => {
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const trueNormalized = l2Normalize(yTrue, -1);
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const predNormalized = l2Normalize(yPred, -1);
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const trueXPred = tfc.mul(trueNormalized, predNormalized);
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return tfc.neg(tfc.sum(trueXPred, -1));
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});
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}
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export const mse = meanSquaredError;
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export const MSE = meanSquaredError;
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export const mae = meanAbsoluteError;
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export const MAE = meanAbsoluteError;
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export const mape = meanAbsolutePercentageError;
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export const MAPE = meanAbsolutePercentageError;
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export const msle = meanSquaredLogarithmicError;
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export const MSLE = meanSquaredLogarithmicError;
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export const kld = kullbackLeiblerDivergence;
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export const KLD = kullbackLeiblerDivergence;
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export const cosine = cosineProximity;
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// TODO(michaelterry): Add deserialize() function.
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export const lossesMap = {
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meanSquaredError,
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meanAbsoluteError,
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meanAbsolutePercentageError,
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meanSquaredLogarithmicError,
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squaredHinge,
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hinge,
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categoricalHinge,
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logcosh,
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categoricalCrossentropy,
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sparseCategoricalCrossentropy,
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binaryCrossentropy,
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kullbackLeiblerDivergence,
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poisson,
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cosineProximity
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};
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// Porting note: This diverges from the PyKeras implementation and may need to
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// change based on (de)serialization requirements.
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export function get(identifierOrFn) {
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if (typeof identifierOrFn === 'string') {
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if (identifierOrFn in lossesMap) {
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return lossesMap[identifierOrFn];
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}
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let errMsg = `Unknown loss ${identifierOrFn}`;
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if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {
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errMsg = `Unknown loss ${identifierOrFn}. ` +
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'Use "categoricalCrossentropy" as the string name for ' +
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'tf.losses.softmaxCrossEntropy';
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}
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throw new ValueError(errMsg);
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}
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else {
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return identifierOrFn;
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}
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}
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* @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n\n/* Original Source: losses.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport {Tensor, Tensor1D, tidy, util} from '@tensorflow/tfjs-core';\n\nimport {epsilon} from './backend/common';\nimport * as K from './backend/tfjs_backend';\nimport {ValueError} from './errors';\nimport {LossOrMetricFn} from './types';\n\n/**\n * Normalizes a tensor wrt the L2 norm alongside the specified axis.\n * @param x\n * @param axis Axis along which to perform normalization.\n */\nexport function l2Normalize(x: Tensor, axis?: number): Tensor {\n  return tidy(() => {\n    if (x.dtype !== 'float32') {\n      x = tfc.cast(x, 'float32');\n    }\n    const squareSum = tfc.sum(K.square(x), axis, true);\n    const epsilonTensor = tfc.fill(squareSum.shape, epsilon());\n    const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));\n    return tfc.div(x, norm);\n  });\n}\n\nexport function meanSquaredError(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));\n}\n\nexport function meanAbsoluteError(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));\n}\n\nexport function meanAbsolutePercentageError(\n    yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const diff = tfc.sub(yTrue, yPred);\n    const clippedTrue =\n        tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);\n    const absResult = tfc.abs(tfc.div(diff, clippedTrue));\n    return tfc.mul(100, tfc.mean(absResult, -1));\n  });\n}\n\nexport function meanSquaredLogarithmicError(\n    yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n    const firstLog = tfc.log(tfc.add(1, clippedPred));\n\n    const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n    const secondLog = tfc.log(tfc.add(1, clippedTrue));\n\n    return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);\n  });\n}\n\nexport function squaredHinge(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n    return tfc.mean(K.square(maxResult), -1);\n  });\n}\n\nexport function hinge(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n    return tfc.mean(maxResult, -1);\n  });\n}\n\nexport function categoricalHinge(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);\n    const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);\n    return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));\n  });\n}\n\n/**\n * Logarithm of the hyperbolic cosine of the prediction error.\n *\n * `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and\n * to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly\n * like the mean squared error, but will not be so strongly affected by the\n * occasional wildly incorrect prediction.\n */\nexport function logcosh(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const log2 = Math.log(2);\n    const predictionDiff = tfc.sub(yPred, yTrue);\n    const logcoshResult = tfc.sub(\n        tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))),\n        log2);\n    return tfc.mean(logcoshResult, -1);\n  });\n}\n\nexport function categoricalCrossentropy(\n    target: Tensor, output: Tensor, fromLogits = false): Tensor {\n  return tidy(() => {\n    if (fromLogits) {\n      output = tfc.softmax(output);\n    } else {\n      // scale preds so that the class probabilities of each sample sum to 1.\n      const outputSum = tfc.sum(output, output.shape.length - 1, true);\n      output = tfc.div(output, outputSum);\n    }\n    output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n    return tfc.neg(tfc.sum(\n        tfc.mul(tfc.cast(target, 'float32'), tfc.log(output)),\n        output.shape.length - 1));\n  });\n}\n\n/**\n * Categorical crossentropy with integer targets.\n *\n * @param target An integer tensor.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n */\nexport function sparseCategoricalCrossentropy(\n    target: Tensor, output: Tensor, fromLogits = false): Tensor {\n  return tidy(() => {\n    const flatTarget =\n        tfc.cast(tfc.floor(K.flatten(target)), 'int32') as Tensor1D;\n    output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n    const outputShape = output.shape;\n    const oneHotTarget = tfc.reshape(\n        tfc.oneHot(flatTarget, outputShape[outputShape.length - 1]),\n        outputShape);\n    return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n  });\n}\n\n/**\n * From TensorFlow's implementation in nn_impl.py:\n *\n * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n *      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n *    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n *    = (1 - z) * x + log(1 + exp(-x))\n *    = x - x * z + log(1 + exp(-x))\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n *      x - x * z + log(1 + exp(-x))\n *    = log(exp(x)) - x * z + log(1 + exp(-x))\n *    = - x * z + log(1 + exp(x))\n * Hence, to ensure stability and avoid overflow, the implementation uses this\n * equivalent formulation\n *    max(x, 0) - x * z + log(1 + exp(-abs(x)))\n *\n * @param labels The labels.\n * @param logits The logits.\n */\nexport function sigmoidCrossEntropyWithLogits(\n    labels: Tensor, logits: Tensor): Tensor {\n  if (!util.arraysEqual(labels.shape, logits.shape)) {\n    throw new ValueError(\n        `logits and labels must have the same shape, but got shapes ` +\n        `${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n  }\n  return tidy(() => {\n    // The logistic loss formula from above is\n    //   x - x * z + log(1 + exp(-x))\n    // For x < 0, a more numerically stable formula is\n    //   -x * z + log(1 + exp(x))\n    // Note that these two expressions can be combined into the following:\n    //   max(x, 0) - x * z + log(1 + exp(-abs(x)))\n    const reluLogits = tfc.relu(logits);\n    const negAbsLogits = tfc.neg(tfc.abs(logits));\n    return tfc.add(\n        tfc.sub(reluLogits, tfc.mul(logits, labels)),\n        tfc.log1p(tfc.exp(negAbsLogits)));\n  });\n}\n\nexport function binaryCrossentropy(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    let y: Tensor;\n    y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());\n    y = tfc.log(tfc.div(y, tfc.sub(1, y)));\n    return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n  });\n}\n\nexport function kullbackLeiblerDivergence(\n    yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);\n    const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);\n    return tfc.sum(\n        tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);\n  });\n}\n\nexport function poisson(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const logPred = tfc.log(tfc.add(epsilon(), yPred));\n    return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);\n  });\n}\n\nexport function cosineProximity(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const trueNormalized = l2Normalize(yTrue, -1);\n    const predNormalized = l2Normalize(yPred, -1);\n    const trueXPred = tfc.mul(trueNormalized, predNormalized);\n    return tfc.neg(tfc.sum(trueXPred, -1));\n  });\n}\n\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const msle = meanSquaredLogarithmicError;\nexport const MSLE = meanSquaredLogarithmicError;\nexport const kld = kullbackLeiblerDivergence;\nexport const KLD = kullbackLeiblerDivergence;\nexport const cosine = cosineProximity;\n\n// TODO(michaelterry): Add deserialize() function.\n\nexport const lossesMap: {[functionName: string]: LossOrMetricFn} = {\n  meanSquaredError,\n  meanAbsoluteError,\n  meanAbsolutePercentageError,\n  meanSquaredLogarithmicError,\n  squaredHinge,\n  hinge,\n  categoricalHinge,\n  logcosh,\n  categoricalCrossentropy,\n  sparseCategoricalCrossentropy,\n  binaryCrossentropy,\n  kullbackLeiblerDivergence,\n  poisson,\n  cosineProximity\n};\n\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function get(identifierOrFn: string|LossOrMetricFn): LossOrMetricFn {\n  if (typeof identifierOrFn === 'string') {\n    if (identifierOrFn in lossesMap) {\n      return lossesMap[identifierOrFn];\n    }\n    let errMsg = `Unknown loss ${identifierOrFn}`;\n    if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {\n      errMsg = `Unknown loss ${identifierOrFn}. ` +\n          'Use \"categoricalCrossentropy\" as the string name for ' +\n          'tf.losses.softmaxCrossEntropy';\n    }\n    throw new ValueError(errMsg);\n  } else {\n    return identifierOrFn;\n  }\n}\n"]}
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