@tensorflow/tfjs-core/src/ops/log_sigmoid.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @license
 * Copyright 2018 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 {customGrad} from '../gradients';
import {Tensor} from '../tensor';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike} from '../types';

import {mul} from './mul';
import {neg} from './neg';
import {op} from './operation';
import {sigmoid} from './sigmoid';
import {softplus} from './softplus';

/**
 * Computes log sigmoid of the input `tf.Tensor` element-wise:
 * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.
 *
 * ```js
 * const x = tf.tensor1d([0, 1, -1, .7]);
 *
 * x.logSigmoid().print();  // or tf.logSigmoid(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */
function logSigmoid_<T extends Tensor>(x: T|TensorLike): T {
  const $x = convertToTensor(x, 'x', 'logSigmoid');

  // Use a custom gradient to maintain previous implementation.
  // There is no LogSigmoid kernel in TF so we can't use engine.runKernel
  // directly
  const customOp = customGrad((x: Tensor) => {
    // TODO(yassogba) we can remove the chained softplus call here only
    // after backends have modualrized softplus at which point we can call
    // engine runKernel(..., Sotfplus, ...) directly.
    const value = neg(softplus(neg(x)));

    const gradFunc = (dy: T) => {
      const derX = mul(dy, sigmoid(neg(x)));
      return derX;
    };
    return {value, gradFunc};
  });

  return customOp($x) as T;
}
export const logSigmoid = op({logSigmoid_});