/* 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.metrics;

import org.tensorflow.Operand;
import org.tensorflow.framework.losses.Losses;
import org.tensorflow.framework.metrics.impl.LossMetric;
import org.tensorflow.framework.metrics.impl.MeanMetricWrapper;
import org.tensorflow.op.Ops;
import org.tensorflow.types.family.TNumber;

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

/**
 * A metric that computes the sparse categorical cross-entropy loss between true labels and
 * predicted labels.
 *
 * @param <T> The data type for the metric result.
 */
public class SparseCategoricalCrossentropy<T extends TNumber> extends MeanMetricWrapper<T>
    implements LossMetric<T> {

  private final boolean fromLogits;
  private final int axis;

  /**
   * Creates a SparseCategoricalCrossentropy metric
   *
   * @param tf the TensorFlow Ops
   * @param name the name of this metric, if null then metric name is {@link Class#getSimpleName()}.
   * @param fromLogits Whether to interpret predictions as a tensor of logit values as opposed to a
   *     probability distribution.
   * @param axis The dimension along which the entropy is computed.
   * @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 data type.
   * @param type the type for the variables and result
   */
  public SparseCategoricalCrossentropy(
      Ops tf, String name, boolean fromLogits, int axis, long seed, Class<T> type) {
    super(tf, name, seed, type);
    setLoss(this);
    this.fromLogits = fromLogits;
    this.axis = axis;
  }

  /** {@inheritDoc} */
  @Override
  public Operand<T> call(
      Operand<? extends TNumber> labels, Operand<? extends TNumber> predictions) {
    Operand<T> tLabels = cast(getTF(), labels, getResultType());
    Operand<T> tPredictions = cast(getTF(), predictions, getResultType());
    return Losses.sparseCategoricalCrossentropy(getTF(), tLabels, tPredictions, fromLogits, axis);
  }
}
