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// Targeted by JavaCPP version 1.5.8: DO NOT EDIT THIS FILE

package org.bytedeco.tensorflow;

import org.bytedeco.tensorflow.Allocator;
import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;

import static org.bytedeco.javacpp.presets.javacpp.*;

import static org.bytedeco.tensorflow.global.tensorflow.*;


/** Update '*var' according to the centered RMSProp algorithm.
 * 
 *  The centered RMSProp algorithm uses an estimate of the centered second moment
 *  (i.e., the variance) for normalization, as opposed to regular RMSProp, which
 *  uses the (uncentered) second moment. This often helps with training, but is
 *  slightly more expensive in terms of computation and memory.
 * 
 *  Note that in dense implementation of this algorithm, mg, ms, and mom will
 *  update even if the grad is zero, but in this sparse implementation, mg, ms,
 *  and mom will not update in iterations during which the grad is zero.
 * 
 *  mean_square = decay * mean_square + (1-decay) * gradient ** 2
 *  mean_grad = decay * mean_grad + (1-decay) * gradient
 *  Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
 * 
 *  ms <- rho * ms_{t-1} + (1-rho) * grad * grad
 *  mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
 *  var <- var - mom
 * 
 *  Arguments:
 *  * scope: A Scope object
 *  * var: Should be from a Variable().
 *  * mg: Should be from a Variable().
 *  * ms: Should be from a Variable().
 *  * mom: Should be from a Variable().
 *  * lr: Scaling factor. Must be a scalar.
 *  * rho: Decay rate. Must be a scalar.
 *  * epsilon: Ridge term. Must be a scalar.
 *  * grad: The gradient.
 *  * indices: A vector of indices into the first dimension of var, ms and mom.
 * 
 *  Optional attributes (see {@code Attrs}):
 *  * use_locking: If {@code True}, updating of the var, mg, ms, and mom tensors is
 *  protected by a lock; otherwise the behavior is undefined, but may exhibit less
 *  contention.
 * 
 *  Returns:
 *  * the created {@code Operation} */
@Namespace("tensorflow::ops") @NoOffset @Properties(inherit = org.bytedeco.tensorflow.presets.tensorflow.class)
public class ResourceSparseApplyCenteredRMSProp extends Pointer {
    static { Loader.load(); }
    /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
    public ResourceSparseApplyCenteredRMSProp(Pointer p) { super(p); }

  /** Optional attribute setters for ResourceSparseApplyCenteredRMSProp */
  public static class Attrs extends Pointer {
      static { Loader.load(); }
      /** Default native constructor. */
      public Attrs() { super((Pointer)null); allocate(); }
      /** Native array allocator. Access with {@link Pointer#position(long)}. */
      public Attrs(long size) { super((Pointer)null); allocateArray(size); }
      /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
      public Attrs(Pointer p) { super(p); }
      private native void allocate();
      private native void allocateArray(long size);
      @Override public Attrs position(long position) {
          return (Attrs)super.position(position);
      }
      @Override public Attrs getPointer(long i) {
          return new Attrs((Pointer)this).offsetAddress(i);
      }
  
    /** If {@code True}, updating of the var, mg, ms, and mom tensors is
     *  protected by a lock; otherwise the behavior is undefined, but may exhibit less
     *  contention.
     * 
     *  Defaults to false */
    public native @ByVal Attrs UseLocking(@Cast("bool") boolean x);

    public native @Cast("bool") boolean use_locking_(); public native Attrs use_locking_(boolean setter);
  }
  public ResourceSparseApplyCenteredRMSProp(@Const @ByRef Scope scope,
                                     @ByVal Input var, @ByVal Input mg, @ByVal Input ms,
                                     @ByVal Input mom, @ByVal Input lr, @ByVal Input rho,
                                     @ByVal Input momentum,
                                     @ByVal Input epsilon,
                                     @ByVal Input grad,
                                     @ByVal Input indices) { super((Pointer)null); allocate(scope, var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices); }
  private native void allocate(@Const @ByRef Scope scope,
                                     @ByVal Input var, @ByVal Input mg, @ByVal Input ms,
                                     @ByVal Input mom, @ByVal Input lr, @ByVal Input rho,
                                     @ByVal Input momentum,
                                     @ByVal Input epsilon,
                                     @ByVal Input grad,
                                     @ByVal Input indices);
  public ResourceSparseApplyCenteredRMSProp(@Const @ByRef Scope scope,
                                     @ByVal Input var, @ByVal Input mg, @ByVal Input ms,
                                     @ByVal Input mom, @ByVal Input lr, @ByVal Input rho,
                                     @ByVal Input momentum,
                                     @ByVal Input epsilon,
                                     @ByVal Input grad,
                                     @ByVal Input indices, @Const @ByRef Attrs attrs) { super((Pointer)null); allocate(scope, var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, attrs); }
  private native void allocate(@Const @ByRef Scope scope,
                                     @ByVal Input var, @ByVal Input mg, @ByVal Input ms,
                                     @ByVal Input mom, @ByVal Input lr, @ByVal Input rho,
                                     @ByVal Input momentum,
                                     @ByVal Input epsilon,
                                     @ByVal Input grad,
                                     @ByVal Input indices, @Const @ByRef Attrs attrs);
  public native @ByVal @Name("operator tensorflow::Operation") Operation asOperation();

  public static native @ByVal Attrs UseLocking(@Cast("bool") boolean x);

  public native @ByRef Operation operation(); public native ResourceSparseApplyCenteredRMSProp operation(Operation setter);
}