/* 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
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=======================================================================*/

package org.tensorflow.framework.initializers;

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

/**
 * The Glorot initializer, also called Xavier initializer.
 *
 * <p>Draws samples from a random distribution.
 *
 * <p>If the distribution is TRUNCATED_NORMAL, then the distribution is centered on 0 with <code>
 *  stddev = Math.sqrt(2. / (fanIn + fanOut))</code> where <code>fanIn</code> is the number of input
 * units in the weight tensor and <code>fanOut</code> is the number of output units in the weight
 * tensor.
 *
 * <p>If the distribution is UNIFORM, then samples are drawn from a uniform distribution within
 * <code>[-limit, limit]</code>, where <code>limit = sqrt(6 / (fanIn + fanOut))</code> ( <code>fanIn
 * </code> is the number of input units in the weight tensor and <code> fanOut</code> is the number
 * of output units).
 *
 * <p>Examples:
 *
 * <p>Glorot Normal:
 *
 * <pre>
 *     long seed = 1001l;
 *     Glorot&lt;TFloat32, TFloat32&gt; initializer =
 *             new org.tensorflow.framework.initializers.Glorot&lt;&gt;(tf,
 *             Distribution.TRUNCATED_NORMAL, seed);
 *     Operand&lt;TFloat32&gt; values =
 *             initializer.call(tf.constant(Shape.of(2,2)), TFloat32.class);
 * </pre>
 *
 * <p>Glorot Uniform:
 *
 * <pre>
 *    long seed = 1001l;
 *    Glorot&lt;TFloat32, TFloat32&gt; initializer =
 *             new org.tensorflow.framework.initializers.Glorot&lt;&gt;(tf,
 *             Distribution.UNIFORM, seed);
 *     Operand&lt;TFloat32&gt; values =
 *             initializer.call(tf.constant(Shape.of(2,2)), TFloat32.class);
 * </pre>
 *
 * <p><b>NOTE:</b>
 * <p>For a GlorotNormal equivalent initializer, use {@link
 * VarianceScaling.Distribution#TRUNCATED_NORMAL} for the distribution parameter.
 * <p>For a GlorotUniform equivalent initializer, use {@link VarianceScaling.Distribution#UNIFORM}
 * for the distribution parameter.
 *
 * @param <T> The TType for the call operation
 * @see VarianceScaling.Distribution
 * @see <a href="http://proceedings.mlr.press/v9/glorot10a.html">Glorot et al., 2010</a>
 */
public class Glorot<T extends TFloating> extends VarianceScaling<T> {

  public static final double SCALE = 1.0;

  /**
   * Creates a Glorot initializer
   *
   * @param tf the TensorFlow Ops
   * @param distribution The distribution type for the Glorot initializer.
   * @param seed the seed for random number generation. An initializer created with a given seed
   *     will always produce the same random tensor for a given shape and dtype.
   * @see VarianceScaling.Distribution
   */
  public Glorot(Ops tf, Distribution distribution, long seed) {
    super(tf, SCALE, Mode.FAN_AVG, distribution, seed);
  }
}
