using System;

using System.Collections.Generic;

using System.Text;

using Tensorflow.Keras.Engine;

namespace Tensorflow.Keras

{

public interface IOptimizerApi

{

/// <summary>

/// Adam optimization is a stochastic gradient descent method that is based on

/// adaptive estimation of first-order and second-order moments.

/// </summary>

/// <param name="learning_rate"></param>

/// <param name="beta_1"></param>

/// <param name="beta_2"></param>

/// <param name="epsilon"></param>

/// <param name="amsgrad"></param>

/// <param name="name"></param>

/// <returns></returns>

IOptimizer Adam(float learning_rate = 0.001f,

float beta_1 = 0.9f,

float beta_2 = 0.999f,

float epsilon = 1e-7f,

bool amsgrad = false,

string name = "Adam");

/// <summary>

/// Adam enables L2 weight decay on gradients.

/// </summary>

/// <param name="learning_rate"></param>

/// <param name="weight_decay"></param>

/// <param name="beta_1"></param>

/// <param name="beta_2"></param>

/// <param name="epsilon"></param>

/// <param name="amsgrad"></param>

/// <param name="decay_params"></param>

/// <param name="name"></param>

/// <returns></returns>

IOptimizer AdamW(float learning_rate = 0.001f,

float weight_decay = 0.004f,

float beta_1 = 0.9f,

float beta_2 = 0.999f,

float epsilon = 1e-7f,

bool amsgrad = false,

List<string> no_decay_params = null,

string name = "AdamW");

/// <summary>

/// Construct a new RMSprop optimizer.

/// </summary>

/// <param name="learning_rate"></param>

/// <param name="rho"></param>

/// <param name="momentum"></param>

/// <param name="epsilon"></param>

/// <param name="centered"></param>

/// <param name="name"></param>

/// <returns></returns>

IOptimizer RMSprop(float learning_rate = 0.001f,

float rho = 0.9f,

float momentum = 0.0f,

float epsilon = 1e-7f,

bool centered = false,

string name = "RMSprop");

IOptimizer SGD(float learning_rate);

}

}