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Core Neural Networks Framework

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/*
 * Copyright (c) 2018 by Andrew Charneski.
 *
 * The author licenses this file to you 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 com.simiacryptus.mindseye.eval;

import com.simiacryptus.mindseye.lang.Layer;
import com.simiacryptus.mindseye.lang.PointSample;
import com.simiacryptus.mindseye.lang.ReferenceCounting;
import com.simiacryptus.mindseye.opt.TrainingMonitor;

/**
 * Base class for an object which can be evaluated using differential weights. This represents a function without inputs
 * and apply only one output. The internal weights, effectively the function's input, are adjusted to minimize this
 * output.
 */
public interface Trainable extends ReferenceCounting {
  /**
   * Cached cached trainable.
   *
   * @return the cached trainable
   */
  default CachedTrainable cached() {
    return new CachedTrainable<>(this);
  }

  /**
   * Measure trainable . point sample.
   *
   * @param monitor the monitor
   * @return the trainable . point sample
   */
  PointSample measure(TrainingMonitor monitor);

  /**
   * Reset sampling boolean.
   *
   * @param seed the seed
   * @return the boolean
   */
  default boolean reseed(final long seed) {
    return false;
  }

  /**
   * Gets key.
   *
   * @return the key
   */
  Layer getLayer();

}