diff --git a/aimodel/src/lib/ai/components/LossContrastive.py b/aimodel/src/lib/ai/components/LossContrastive.py
index 43e6e74..697e517 100644
--- a/aimodel/src/lib/ai/components/LossContrastive.py
+++ b/aimodel/src/lib/ai/components/LossContrastive.py
@@ -17,14 +17,17 @@ class LossContrastive(tf.keras.losses.Loss):
 		# rainfall = tf.reshape(rainfall, [self.batch_size, rainfall.shape[1]])
 		# water = tf.reshape(water, [self.batch_size, water.shape[1]])
 		
-		
-		logits = tf.linalg.matmul(rainfall, tf.transpose(water)) * tf.clip_by_value(tf.math.exp(self.weight_temperature), 0, 100)
+		# logits = tf.linalg.matmul(rainfall, tf.transpose(water)) * tf.clip_by_value(tf.math.exp(self.weight_temperature), 0, 100)
+		logits = tf.linalg.matmul(rainfall, tf.transpose(water)) * tf.math.exp(self.weight_temperature)
 		
 		# print("LOGITS", logits)
 		
-		labels			= tf.eye(self.batch_size, dtype=tf.int32)
-		loss_rainfall	= tf.keras.metrics.binary_crossentropy(labels, logits, from_logits=True, axis=0)
-		loss_water		= tf.keras.metrics.binary_crossentropy(labels, logits, from_logits=True, axis=1)
+		# labels			= tf.eye(self.batch_size, dtype=tf.int32) # we *would* do this if we were using mean squared error...
+		labels			= tf.range(self.batch_size, dtype=tf.int32) # each row is a different category we think
+		loss_rainfall	= tf.keras.metrics.sparse_categorical_crossentropy(labels, logits, from_logits=True, axis=0)
+		loss_water		= tf.keras.metrics.sparse_categorical_crossentropy(labels, logits, from_logits=True, axis=1)
+		# loss_rainfall	= tf.keras.metrics.binary_crossentropy(labels, logits, from_logits=True, axis=0)
+		# loss_water		= tf.keras.metrics.binary_crossentropy(labels, logits, from_logits=True, axis=1)
 		
 		loss = (loss_rainfall + loss_water) / 2