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deepakkumar1984deepakkumar1984
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Mteric and losses skeletonized
1 parent 4deb320 commit 2706b79

36 files changed

Lines changed: 423 additions & 34 deletions

‎src/TensorFlowNET.Keras/Losses/Loss.cs‎

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@@ -6,5 +6,36 @@ namespace Tensorflow.Keras.Losses
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{
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public abstract class Loss
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{
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public static Tensor mean_squared_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
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public static Tensor mean_absolute_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
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public static Tensor mean_absolute_percentage_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
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public static Tensor mean_squared_logarithmic_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
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public static Tensor _maybe_convert_labels(Tensor y_true) => throw new NotImplementedException();
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public static Tensor squared_hinge(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
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public static Tensor hinge(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
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public static Tensor categorical_hinge(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
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public static Tensor huber_loss(Tensor y_true, Tensor y_pred, float delta = 1) => throw new NotImplementedException();
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public static Tensor logcosh(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
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public static Tensor categorical_crossentropy(Tensor y_true, Tensor y_pred, bool from_logits = false, float label_smoothing = 0) => throw new NotImplementedException();
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public static Tensor sparse_categorical_crossentropy(Tensor y_true, Tensor y_pred, bool from_logits = false, float axis = -1) => throw new NotImplementedException();
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public static Tensor binary_crossentropy(Tensor y_true, Tensor y_pred, bool from_logits = false, float label_smoothing = 0) => throw new NotImplementedException();
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public static Tensor kullback_leibler_divergence(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
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public static Tensor poisson(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
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public static Tensor cosine_similarity(Tensor y_true, Tensor y_pred, int axis = -1) => throw new NotImplementedException();
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}
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}

‎src/TensorFlowNET.Keras/Metrics/AUC.cs‎

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using System;
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using System.Collections;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow.Keras.Metrics
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{
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class AUC
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public class AUC : Metric
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{
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public AUC(int num_thresholds= 200, string curve= "ROC", string summation_method= "interpolation",
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string name= null, string dtype= null, float thresholds= 0.5f,
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bool multi_label= false, Tensor label_weights= null) : base(name, dtype)
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{
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throw new NotImplementedException();
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}
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private void _build(TensorShape shape) => throw new NotImplementedException();
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public Tensor interpolate_pr_auc() => throw new NotImplementedException();
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public override Tensor result()
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{
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throw new NotImplementedException();
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}
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public override void update_state(Args args, KwArgs kwargs)
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{
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throw new NotImplementedException();
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}
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public override void reset_states()
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{
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throw new NotImplementedException();
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}
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public override Hashtable get_config()
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{
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throw new NotImplementedException();
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}
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}
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}

‎src/TensorFlowNET.Keras/Metrics/Accuracy.cs‎

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namespace Tensorflow.Keras.Metrics
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{
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class Accuracy
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public class Accuracy : MeanMetricWrapper
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{
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public Accuracy(string name = "accuracy", string dtype = null)
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: base(Metric.accuracy, name, dtype)
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{
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}
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}
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}

‎src/TensorFlowNET.Keras/Metrics/BinaryAccuracy.cs‎

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namespace Tensorflow.Keras.Metrics
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{
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class BinaryAccuracy
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public class BinaryAccuracy : MeanMetricWrapper
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{
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public BinaryAccuracy(string name = "binary_accuracy", string dtype = null, float threshold = 0.5f)
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: base(Fn, name, dtype)
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{
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}
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internal static Tensor Fn(Tensor y_true, Tensor y_pred)
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{
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return Metric.binary_accuracy(y_true, y_pred);
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}
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}
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}

‎src/TensorFlowNET.Keras/Metrics/CategoricalAccuracy.cs‎

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namespace Tensorflow.Keras.Metrics
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{
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class CategoricalAccuracy
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public class CategoricalAccuracy : MeanMetricWrapper
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{
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public CategoricalAccuracy(string name = "categorical_accuracy", string dtype = null)
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: base(Metric.categorical_accuracy, name, dtype)
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{
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}
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}
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}

‎src/TensorFlowNET.Keras/Metrics/CategoricalHinge.cs‎

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namespace Tensorflow.Keras.Metrics
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{
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class CategoricalHinge
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public class CategoricalHinge : MeanMetricWrapper
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{
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public CategoricalHinge(string name = "categorical_hinge", string dtype = null)
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: base(Losses.Loss.categorical_hinge, name, dtype)
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{
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}
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}
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}

‎src/TensorFlowNET.Keras/Metrics/CosineSimilarity.cs‎

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namespace Tensorflow.Keras.Metrics
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{
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class CosineSimilarity
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public class CosineSimilarity : MeanMetricWrapper
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{
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public CosineSimilarity(string name = "cosine_similarity", string dtype = null, int axis = -1)
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: base(Fn, name, dtype)
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{
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}
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internal static Tensor Fn(Tensor y_true, Tensor y_pred)
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{
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return Metric.cosine_proximity(y_true, y_pred);
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}
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}
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}

‎src/TensorFlowNET.Keras/Metrics/FalseNegatives.cs‎

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namespace Tensorflow.Keras.Metrics
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{
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class FalseNegatives
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public class FalseNegatives : _ConfusionMatrixConditionCount
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{
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public FalseNegatives(float thresholds = 0.5F, string name = null, string dtype = null)
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: base(Utils.MetricsUtils.ConfusionMatrix.FALSE_NEGATIVES, thresholds, name, dtype)
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{
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}
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}
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}

‎src/TensorFlowNET.Keras/Metrics/FalsePositives.cs‎

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namespace Tensorflow.Keras.Metrics
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{
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class FalsePositives
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public class FalsePositives : _ConfusionMatrixConditionCount
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{
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public FalsePositives(float thresholds = 0.5F, string name = null, string dtype = null)
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: base(Utils.MetricsUtils.ConfusionMatrix.FALSE_POSITIVES, thresholds, name, dtype)
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{
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}
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}
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}

‎src/TensorFlowNET.Keras/Metrics/Hinge.cs‎

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namespace Tensorflow.Keras.Metrics
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{
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class Hinge
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public class Hinge : MeanMetricWrapper
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{
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public Hinge(string name = "hinge", string dtype = null)
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: base(Losses.Loss.hinge, name, dtype)
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{
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}
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}
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}

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