Added ranking losses

ML/Algorithms/Losses/Ranking losses/Pair wise ranking loss/pair_wise_ranking_loss.py

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+import tensorflow as tf
+from typing import Tuple
+
+def pairwise_ranking_loss(y_true: tf.Tensor, y_pred: tf.Tensor, margin: float = 1.0) -> tf.Tensor:
+    """
+    Computes the pairwise ranking loss for a batch of pairs.
+
+    Args:
+        y_true: Tensor of true labels (0 for negative pairs, 1 for positive pairs).
+        y_pred: Tensor of predicted similarities/distances, expected to be a tensor of shape (batch_size, 2, embedding_dim) where 
+                y_pred[:, 0] is the anchor and y_pred[:, 1] is the positive/negative.
+        margin: Margin parameter for the pairwise ranking loss.
+
+    Returns:
+        loss: Computed pairwise ranking loss as a scalar tensor.
+    """
+    anchor, positive_or_negative = y_pred[:, 0], y_pred[:, 1]
+
+    distances = tf.reduce_sum(tf.square(anchor - positive_or_negative), axis=-1)
+    positive_loss = y_true * distances
+    negative_loss = (1 - y_true) * tf.maximum(margin - distances, 0.0)
+
+    loss = positive_loss + negative_loss
+    return tf.reduce_mean(loss)
+
+# Example usage:
+# model.compile(optimizer='adam', loss=pairwise_ranking_loss)

ML/Algorithms/Losses/Ranking losses/Triplet Loss/Triplet_loss.py

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+import tensorflow as tf
+from typing import Tuple
+
+def triplet_loss_func(y_true: tf.Tensor, y_pred: tf.Tensor, alpha: float = 0.3) -> tf.Tensor:
+    """
+    Computes the triplet loss for a batch of triplets.
+
+    Args:
+        y_true: True values of classification (unused in this implementation, typically required for compatibility with Keras).
+        y_pred: Predicted values, expected to be a tensor of shape (batch_size, 3, embedding_dim) where 
+                y_pred[:, 0] is the anchor, y_pred[:, 1] is the positive, and y_pred[:, 2] is the negative.
+        alpha: Margin parameter for the triplet loss.
+
+    Returns:
+        loss: Computed triplet loss as a scalar tensor.
+    """
+    anchor, positive, negative = y_pred[:, 0], y_pred[:, 1], y_pred[:, 2]
+
+    positive_dist = tf.reduce_sum(tf.square(anchor - positive), axis=-1)
+    negative_dist = tf.reduce_sum(tf.square(anchor - negative), axis=-1)
+
+    loss = tf.maximum(positive_dist - negative_dist + alpha, 0.0)
+    return tf.reduce_mean(loss)
+
+# Example usage:
+# model.compile(optimizer='adam', loss=triplet_loss_func)