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Author: Golbstein

losses.py

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+from keras import backend as K
+import tensorflow as tf
+
+def triplet_loss(y_true, y_pred, cosine = True, alpha = 0.2, embedding_size = 128):
+    ind = int(embedding_size * 2)
+    a_pred = y_pred[:, :embedding_size]
+    p_pred = y_pred[:, embedding_size:ind]
+    n_pred = y_pred[:, ind:]
+    if cosine:
+        positive_distance = 1 - K.sum((a_pred * p_pred), axis=-1)
+        negative_distance = 1 - K.sum((a_pred * n_pred), axis=-1)
+    else:
+        positive_distance = K.sqrt(K.sum(K.square(a_pred - p_pred), axis=-1))
+        negative_distance = K.sqrt(K.sum(K.square(a_pred - n_pred), axis=-1))
+    loss = K.maximum(0.0, positive_distance - negative_distance + alpha)
+    return loss
+
+def attribute_crossentropy(y_true, y_pred):
+    y_true = tf.gather(labels, tf.to_int32(y_true[0]))
+    return K.mean(K.binary_crossentropy(y_true, y_pred), axis=-1)