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softmax.ts
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softmax.ts
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/**
* @license
* Copyright 2018 Google Inc. 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 {customGrad} from '../gradients';
import {Tensor} from '../tensor';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike} from '../types';
import {op} from './operation';
/**
* Computes the softmax normalized vector given the logits.
*
* ```js
* const a = tf.tensor1d([1, 2, 3]);
*
* a.softmax().print(); // or tf.softmax(a)
* ```
*
* ```js
* const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);
*
* a.softmax().print(); // or tf.softmax(a)
* ```
*
* @param logits The logits array.
* @param dim The dimension softmax would be performed on. Defaults to `-1`
* which indicates the last dimension.
*/
/** @doc {heading: 'Operations', subheading: 'Normalization'} */
function softmax_<T extends Tensor>(logits: T|TensorLike, dim = -1): T {
const $logits = convertToTensor(logits, 'logits', 'softmax');
if (dim === -1) {
dim = $logits.rank - 1;
}
if (dim !== $logits.rank - 1) {
throw Error(
'Softmax along a non-last dimension is not yet supported. ' +
`Logits was rank ${$logits.rank} and dim was ${dim}`);
}
const customOp = customGrad(logits => {
// Do it in log space for numerical stability.
// exp(X - logSumExp(X))
const keepDims = true;
const lse = logits.logSumExp([dim], keepDims);
const logResult = logits.toFloat().sub(lse);
const y = logResult.exp() as T;
const gradFunc = (dy: T) => {
const dyTimesY = dy.mul(y);
const keepDims = true;
return dyTimesY.sub(dyTimesY.sum([dim], keepDims).mul(y));
};
return {value: y, gradFunc};
});
return customOp($logits);
}
/**
* Computes the log softmax.
*
* ```js
* const a = tf.tensor1d([1, 2, 3]);
*
* a.logSoftmax().print(); // or tf.logSoftmax(a)
* ```
*
* ```js
* const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);
*
* a.logSoftmax().print(); // or tf.logSoftmax(a)
* ```
*
* @param logits The logits array.
* @param axis The dimension softmax would be performed on. Defaults to `-1`
* which indicates the last dimension.
*/
/** @doc {heading: 'Operations', subheading: 'Normalization'} */
function logSoftmax_<T extends Tensor>(logits: T|TensorLike, axis = -1): T {
const $logits = convertToTensor(logits, 'logits', 'logSoftmax');
if (axis === -1) {
axis = $logits.rank - 1;
}
if (axis !== $logits.rank - 1) {
throw Error(
'Log Softmax along a non-last dimension is not yet supported. ' +
`Logits was rank ${$logits.rank} and axis was ${axis}`);
}
const customOp = customGrad(logits => {
const keepDims = true;
const xMax = logits.max(axis, true);
const shifted = logits.sub(xMax);
const value =
shifted.toFloat().sub(shifted.exp().sum(axis, keepDims).log()) as T;
const gradFunc = (dy: T) => {
const softmax = value.exp();
return dy.sub(dy.sum(axis, keepDims).mul(softmax));
};
return {value, gradFunc};
});
return customOp($logits);
}
export const softmax = op({softmax_});
export const logSoftmax = op({logSoftmax_});