using System;

using Tensorflow.Keras.ArgsDefinition;

using Tensorflow.Keras.Engine;

using static Tensorflow.Binding;

using static Tensorflow.KerasApi;

namespace Tensorflow.Keras.Metrics

{

/// <summary>

/// Encapsulates metric logic and state.

/// </summary>

public class Metric : Layer, IMetricFunc

{

protected IVariableV1 total;

protected IVariableV1 count;

protected string _reduction;

protected TF_DataType _dtype;

public Metric(string name = null, TF_DataType dtype = TF_DataType.DtInvalid)

: base(new LayerArgs

{

Name = name,

DType = dtype

})

{

stateful = true;

built = true;

}

protected override IVariableV1 add_weight(string name,

Shape shape = null,

TF_DataType dtype = TF_DataType.TF_FLOAT,

IInitializer initializer = null,

IRegularizer regularizer = null,

VariableSynchronization synchronization = VariableSynchronization.OnRead,

VariableAggregation aggregation = VariableAggregation.Sum,

bool trainable = true,

Func<VariableArgs, IVariableV1> getter = null)

{

if (shape == null)

shape = new Shape(new int[0]);

return tf_with(ops.init_scope(), delegate

{

return base.add_weight(name, shape,

dtype: dtype,

trainable: false,

initializer: initializer,

synchronization: synchronization,

aggregation: aggregation);

});

}

public virtual Tensor update_state(Tensor y_true, Tensor y_pred, Tensor sample_weight = null)

=> throw new NotImplementedException("");

public virtual void reset_states()

{

foreach (var v in Weights)

v.assign(0);

}

public virtual Tensor result()

=> throw new NotImplementedException("");

public override string ToString()

=> $"{name} {(float)total.numpy()}/{(float)count.numpy()}";

}

}