SiaNet/Sequential.cs
/// <summary>
/// Before training a model, you need to configure the learning process, which is done via the compile method. It receives three arguments:
/// <para>
/// 1. An optimizer.This could be the OptimizerType enum or an instance of the Optimizer class.
/// </para><para>
/// 2. A loss function.This is the objective that the model will try to minimize.
/// It can be the LossType enum identifier of an existing loss function (such as CategoricalCrossentropy or MeanSquaredError), or it can be an instance of the loss class.
/// </para>
/// <para>
/// 3. A metric function.For any classification problem you will want the Accuracy of model. A metric could be the MetricType enum or instance of metric class.
/// </para>
/// </summary>
/// <param name="optimizer">The optimizer instance.</param>
/// <param name="loss">The loss type.</param>
/// <param name="metric">The metric type.</param>
Before training a model, you need to configure the learning process, which is done via the compile method. It receives three arguments.

1. An optimizer.This could be the OptimizerType enum or an instance of the Optimizer class.

2. A loss function.This is the objective that the model will try to minimize.
It can be the LossType enum identifier of an existing loss function (such as CategoricalCrossentropy or MeanSquaredError), or it can be an instance of the loss class.

3. A metric function.For any classification problem you will want the Accuracy of model. A metric could be the MetricType enum or instance of metric class.
Before training a model you need to configure the learning process which is done via the compile method . It receives three arguments . 1 . An optimizer . This could be the OptimizerType enum or an instance of the Optimizer class . 2 . A loss function . This is the objective that the model will try to minimize . It can be the LossType enum identifier of an existing loss function ( such as CategoricalCrossentropy or MeanSquaredError ) or it can be an instance of the loss class . 3 . A metric function . For any classification problem you will want the Accuracy of model . A metric could be the MetricType enum or instance of metric class .
public void Compile(BaseOptimizer optimizer, LossType loss, MetricType metric)
        {
            OptimizerFn = optimizer;
            LossFn = BaseLoss.Get(loss);
            MetricFn = BaseMetric.Get(metric);
        }
public void Compile ( BaseOptimizer optimizer , LossType loss , MetricType metric ) { OptimizerFn = optimizer ; LossFn = BaseLoss . Get ( loss ) ; MetricFn = BaseMetric . Get ( metric ) ; }
Before training a model, you need to configure the learning process, which is done via the compile method.
Before training a model you need to configure the learning process which is done via the compile method .
optimizer
BaseOptimizer
The optimizer instance.
The optimizer instance .
loss
LossType
The loss type.
The loss type .
metric
MetricType
The metric type.
The metric type .