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Add SparseCategoricalCrossentropy and SparseCategoricalAccuracy.
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‎src/TensorFlowNET.Core/Keras/Metrics/IMetricsApi.cs‎

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@@ -22,6 +22,20 @@ Tensor categorical_crossentropy(Tensor y_true, Tensor y_pred,
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/// <returns>Sparse categorical accuracy values.</returns>
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Tensor sparse_categorical_accuracy(Tensor y_true, Tensor y_pred);
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/// <summary>
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/// Computes the sparse categorical crossentropy loss.
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/// </summary>
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/// <param name="y_true"></param>
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/// <param name="y_pred"></param>
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/// <param name="from_logits"></param>
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/// <param name="ignore_class"></param>
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/// <param name="axis"></param>
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/// <returns></returns>
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Tensor sparse_categorical_crossentropy(Tensor y_true, Tensor y_pred,
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bool from_logits = false,
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int? ignore_class = null,
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Axis? axis = null);
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/// <summary>
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/// Computes how often targets are in the top `K` predictions.
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/// </summary>
@@ -56,13 +70,30 @@ IMetricFunc CategoricalCrossentropy(string name = "categorical_crossentropy",
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float label_smoothing = 0f,
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Axis? axis = null);
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/// <summary>
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/// Computes the crossentropy metric between the labels and predictions.
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/// </summary>
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/// <returns></returns>
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IMetricFunc SparseCategoricalCrossentropy(string name = "sparse_categorical_crossentropy",
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TF_DataType dtype = TF_DataType.TF_FLOAT,
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bool from_logits = false,
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int? ignore_class = null,
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Axis? axis = null);
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/// <summary>
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/// Computes the crossentropy metric between the labels and predictions.
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/// </summary>
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/// <returns></returns>
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IMetricFunc CategoricalAccuracy(string name = "categorical_accuracy",
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TF_DataType dtype = TF_DataType.TF_FLOAT);
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/// <summary>
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/// Calculates how often predictions match integer labels.
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/// </summary>
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/// <returns></returns>
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IMetricFunc SparseCategoricalAccuracy(string name = "sparse_categorical_accuracy",
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TF_DataType dtype = TF_DataType.TF_FLOAT);
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/// <summary>
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/// Computes the cosine similarity between the labels and predictions.
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/// </summary>
@@ -114,6 +145,15 @@ IMetricFunc TopKCategoricalAccuracy(int k = 5,
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string name = "top_k_categorical_accuracy",
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TF_DataType dtype = TF_DataType.TF_FLOAT);
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/// <summary>
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/// Computes how often integer targets are in the top K predictions.
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/// </summary>
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/// <param name="k"></param>
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/// <returns></returns>
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IMetricFunc SparseTopKCategoricalAccuracy(int k = 5,
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string name = "sparse_top_k_categorical_accuracy",
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TF_DataType dtype = TF_DataType.TF_FLOAT);
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/// <summary>
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/// Computes the precision of the predictions with respect to the labels.
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/// </summary>

‎src/TensorFlowNET.Keras/BackendImpl.cs‎

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@@ -276,6 +276,64 @@ public Tensor categorical_crossentropy(Tensor target, Tensor output, bool from_l
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return -math_ops.reduce_sum(target * math_ops.log(output), new Axis(axis));
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}
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public Tensor sparse_categorical_crossentropy(Tensor target, Tensor output, bool from_logits = false, int axis = -1, int? ignore_class = null)
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{
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target = tf.cast(target, tf.int64);
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if (!from_logits)
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{
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var epsilon_ = constant_op.constant(epsilon(), output.dtype.as_base_dtype());
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output = tf.clip_by_value(output, epsilon_, 1 - epsilon_);
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output = tf.math.log(output);
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}
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var output_rank = output.shape.ndim;
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if (output_rank > -1)
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{
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axis = Math.Abs(axis) % output_rank;
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if (axis != output_rank - 1)
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{
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/*var permutation = list(
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itertools.chain(
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range(axis), range(axis + 1, output_rank), [axis]
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)
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);
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output = tf.transpose(output, perm: permutation);*/
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throw new NotImplementedException("");
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}
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}
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var output_shape = tf.shape(output);
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var target_rank = target.shape.ndim;
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var update_shape = target_rank > -1 && output_rank > -1 && target_rank != output_rank - 1;
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if (update_shape)
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{
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/*var target = flatten(target);
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output = tf.reshape(output, [-1, output_shape[-1]]);*/
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throw new NotImplementedException("");
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}
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if (ignore_class.HasValue)
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{
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throw new NotImplementedException("");
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}
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var res = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: target, logits: output);
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if (ignore_class.HasValue)
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{
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throw new NotImplementedException("");
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}
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if (update_shape && output_rank >= 3)
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{
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// If our output includes timesteps or
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// spatial dimensions we need to reshape
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res = tf.reshape(res, output_shape[":-1"]);
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}
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return res;
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}
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public Tensor binary_crossentropy(Tensor target, Tensor output, bool from_logits = false)
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{
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if (from_logits)

‎src/TensorFlowNET.Keras/Metrics/MetricsApi.cs‎

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@@ -27,6 +27,11 @@ public Tensor categorical_crossentropy(Tensor y_true, Tensor y_pred, bool from_l
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return keras.backend.categorical_crossentropy(y_true, y_pred, from_logits: from_logits, axis: axis);
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}
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public Tensor sparse_categorical_crossentropy(Tensor y_true, Tensor y_pred, bool from_logits = false, int? ignore_class = null, Axis? axis = null)
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{
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return keras.backend.sparse_categorical_crossentropy(y_true, y_pred, from_logits: from_logits, axis: axis ?? -1, ignore_class: ignore_class);
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}
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/// <summary>
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/// Calculates how often predictions matches integer labels.
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/// </summary>
@@ -103,5 +108,14 @@ public IMetricFunc Precision(float thresholds = 0.5f, int top_k = 0, int class_i
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public IMetricFunc Recall(float thresholds = 0.5f, int top_k = 0, int class_id = 0, string name = "recall", TF_DataType dtype = TF_DataType.TF_FLOAT)
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=> new Recall(thresholds: thresholds, top_k: top_k, class_id: class_id, name: name, dtype: dtype);
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public IMetricFunc SparseCategoricalCrossentropy(string name = "sparse_categorical_crossentropy", TF_DataType dtype = TF_DataType.TF_FLOAT, bool from_logits = false, int? ignore_class = null, Axis? axis = null)
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=> new SparseCategoricalCrossentropy(name: name, dtype: dtype, from_logits: from_logits, ignore_class: ignore_class, axis: axis ?? -1);
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public IMetricFunc SparseTopKCategoricalAccuracy(int k = 5, string name = "sparse_top_k_categorical_accuracy", TF_DataType dtype = TF_DataType.TF_FLOAT)
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=> new SparseTopKCategoricalAccuracy(k: k, name: name, dtype: dtype);
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public IMetricFunc SparseCategoricalAccuracy(string name = "sparse_categorical_accuracy", TF_DataType dtype = TF_DataType.TF_FLOAT)
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=> new SparseCategoricalAccuracy(name: name, dtype: dtype);
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}
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}

‎src/TensorFlowNET.Keras/Metrics/SparseCategoricalAccuracy.cs‎

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@@ -0,0 +1,11 @@
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namespace Tensorflow.Keras.Metrics;
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public class SparseCategoricalAccuracy : MeanMetricWrapper
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{
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public SparseCategoricalAccuracy(string name = "sparse_categorical_accuracy", TF_DataType dtype = TF_DataType.TF_FLOAT)
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: base((yt, yp) => metrics_utils.sparse_categorical_matches(yt, yp),
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name: name,
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dtype: dtype)
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{
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}
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}

‎src/TensorFlowNET.Keras/Metrics/SparseCategoricalCrossentropy.cs‎

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@@ -0,0 +1,16 @@
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namespace Tensorflow.Keras.Metrics;
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public class SparseCategoricalCrossentropy : MeanMetricWrapper
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{
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public SparseCategoricalCrossentropy(string name = "sparse_categorical_crossentropy",
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TF_DataType dtype = TF_DataType.TF_FLOAT,
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bool from_logits = false,
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int? ignore_class = null,
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Axis? axis = null)
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: base((yt, yp) => keras.metrics.sparse_categorical_crossentropy(
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yt, yp, from_logits: from_logits, ignore_class: ignore_class, axis: axis ?? -1),
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name: name,
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dtype: dtype)
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{
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}
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}

‎src/TensorFlowNET.Keras/Metrics/SparseTopKCategoricalAccuracy.cs‎

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@@ -0,0 +1,11 @@
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namespace Tensorflow.Keras.Metrics;
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public class SparseTopKCategoricalAccuracy : MeanMetricWrapper
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{
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public SparseTopKCategoricalAccuracy(int k = 5, string name = "sparse_top_k_categorical_accuracy", TF_DataType dtype = TF_DataType.TF_FLOAT)
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: base((yt, yp) => metrics_utils.sparse_top_k_categorical_matches(yt, yp, k),
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name: name,
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dtype: dtype)
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{
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}
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}

‎src/TensorFlowNET.Keras/Metrics/metrics_utils.cs‎

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@@ -73,7 +73,7 @@ public static Tensor sparse_categorical_matches(Tensor y_true, Tensor y_pred)
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y_true = tf.squeeze(y_true, new Shape(-1));
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}
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y_pred = tf.math.argmax(y_pred, axis: -1);
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y_pred = tf.cast(y_pred, y_true.dtype);
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var matches = tf.cast(
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tf.equal(y_true, y_pred),
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dtype: keras.backend.floatx()

‎test/TensorFlowNET.Keras.UnitTest/Metrics/MetricsTest.cs‎

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@@ -74,6 +74,26 @@ public void CategoricalAccuracy()
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Assert.AreEqual(r, 0.3f);
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}
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/// <summary>
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/// https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseCategoricalAccuracy
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/// </summary>
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[TestMethod]
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public void SparseCategoricalAccuracy()
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{
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var y_true = np.array(new[] { 2, 1 });
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var y_pred = np.array(new[,] { { 0.1f, 0.6f, 0.3f }, { 0.05f, 0.95f, 0f } });
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var m = tf.keras.metrics.SparseCategoricalAccuracy();
86+
m.update_state(y_true, y_pred);
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var r = m.result().numpy();
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Assert.AreEqual(r, 0.5f);
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m.reset_states();
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var weights = np.array(new[] { 0.7f, 0.3f });
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m.update_state(y_true, y_pred, sample_weight: weights);
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r = m.result().numpy();
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Assert.AreEqual(r, 0.3f);
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}
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/// <summary>
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/// https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalCrossentropy
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/// </summary>
@@ -94,6 +114,20 @@ public void CategoricalCrossentropy()
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Assert.AreEqual(r, 1.6271976f);
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}
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/// <summary>
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/// https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseCategoricalCrossentropy
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/// </summary>
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[TestMethod]
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public void SparseCategoricalCrossentropy()
122+
{
123+
var y_true = np.array(new[] { 1, 2 });
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var y_pred = np.array(new[,] { { 0.05f, 0.95f, 0f }, { 0.1f, 0.8f, 0.1f } });
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var m = tf.keras.metrics.SparseCategoricalCrossentropy();
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m.update_state(y_true, y_pred);
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var r = m.result().numpy();
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Assert.AreEqual(r, 1.1769392f);
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}
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/// <summary>
98132
/// https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CosineSimilarity
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/// </summary>
@@ -207,6 +241,26 @@ public void TopKCategoricalAccuracy()
207241
Assert.AreEqual(r, 0.3f);
208242
}
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/// <summary>
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/// https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseTopKCategoricalAccuracy
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/// </summary>
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[TestMethod]
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public void SparseTopKCategoricalAccuracy()
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{
250+
var y_true = np.array(new[] { 2, 1 });
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var y_pred = np.array(new[,] { { 0.1f, 0.9f, 0.8f }, { 0.05f, 0.95f, 0f } });
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var m = tf.keras.metrics.SparseTopKCategoricalAccuracy(k: 1);
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m.update_state(y_true, y_pred);
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var r = m.result().numpy();
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Assert.AreEqual(r, 0.5f);
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m.reset_states();
258+
var weights = np.array(new[] { 0.7f, 0.3f });
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m.update_state(y_true, y_pred, sample_weight: weights);
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r = m.result().numpy();
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Assert.AreEqual(r, 0.3f);
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}
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/// <summary>
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/// https://www.tensorflow.org/api_docs/python/tf/keras/metrics/top_k_categorical_accuracy
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/// </summary>

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