/* Copyright 2020 The TensorFlow Authors. 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.
=======================================================================*/
package org.tensorflow.framework.activations;

import static org.tensorflow.framework.utils.CastHelper.cast;

import org.tensorflow.Operand;
import org.tensorflow.op.Ops;
import org.tensorflow.types.family.TFloating;

/**
 * Hard sigmoid activation.
 *
 * <p>A faster approximation of the sigmoid activation.
 *
 * <p>Defined as:
 *
 * <ul>
 *   <li><code>if x &lt; -2.5: return 0</code>
 *   <li><code>if x &gt; 2.5: return 1</code>
 *   <li><code>if -2.5 &lt;= x &lt;= 2.5: return 0.2 * x + 0.5</code>
 * </ul>
 *
 * <p>For example:
 *
 * <pre>
 *     Operand&lt;TFloat32&gt; input = tf.constant(
 *              new float[] {-3.0f,-1.0f, 0.0f,1.0f,3.0f});
 *     HardSigmoid&lt;TFloat32&gt; hardSigmoid = new HardSigmoid&lt;&gt;(tf);
 *     Operand&lt;TFloat32&gt; result = hardSigmoid.call(input);
 *     // result is [0.f , 0.3f, 0.5f, 0.7f, 1.f]
 * </pre>
 */
public class HardSigmoid<T extends TFloating> extends AbstractActivation<T> {

  /** Creates Hard sigmoid activation. */
  public HardSigmoid() {
    super();
  }

  /** {@inheritDoc} */
  @Override
  public Operand<T> call(Ops tf, Operand<T> input) {
    Class<T> inputType = input.type();
    Operand<T> point2 = cast(tf, tf.constant(0.2), inputType);
    Operand<T> point5 = cast(tf, tf.constant(0.5), inputType);

    Operand<T> x = tf.math.add(tf.math.mul(input, point2), point5);
    return tf.clipByValue(
        x, cast(tf, tf.constant(0), inputType), cast(tf, tf.constant(1), inputType));
  }
}
