dlr: fixup argmax & y_true/y_pred

aimodel/src/lib/ai/components/MetricMeanIoU.py

@@ -14,7 +14,6 @@ def one_hot_mean_iou(y_true, y_pred, classes=2):
 	"""
 	
 	y_pred = tf.math.argmax(y_pred, axis=-1)
-	y_true = tf.math.argmax(y_true, axis=-1)
 	y_true = tf.cast(y_true, dtype=tf.float32)
 	y_pred = tf.cast(y_pred, dtype=tf.float32)
 	

aimodel/src/lib/ai/components/MetricSensitivity.py

@@ -4,36 +4,9 @@ import tensorflow as tf
 
 def sensitivity(y_true, y_pred):
 	y_pred = tf.math.argmax(y_pred, axis=-1)
-	y_true = tf.math.argmax(y_true, axis=-1)
 	y_true = tf.cast(y_true, dtype=tf.float32)
 	y_pred = tf.cast(y_pred, dtype=tf.float32)
 	
 	recall = tf.keras.metrics.Recall()
 	recall.update_state(y_true, y_pred)
 	return recall.result()
-
-class MetricSensitivity(tf.keras.metrics.Metric):
-	"""An implementation of the sensitivity.
-	Also known as Recall. In other words, how many of the true positives were accurately predicted.
-	@source 
-	Args:
-		smooth (float): The batch size (currently unused).
-	"""
-
-	def __init__(self, name="sensitivity", **kwargs):
-		super(MetricSensitivity, self).__init__(name=name)
-		
-		self.recall = tf.keras.metrics.Recall(**kwargs)
-
-	def call(self, y_true, y_pred):
-		ground_truth = tf.cast(y_true, dtype=tf.float32)
-		prediction = tf.cast(y_pred, dtype=tf.float32)
-		
-		return self.recall(y_true, y_pred)
-
-	def get_config(self):
-		config = super(MetricSensitivity, self).get_config()
-		config.update({
-			
-		})
-		return config

aimodel/src/lib/ai/components/MetricSpecificity.py

@@ -3,7 +3,7 @@ import math
 import tensorflow as tf
 
 
-def specificity(y_pred, y_true):
+def specificity(y_true, y_pred):
 	"""An implementation of the specificity.
 	In other words, a measure of how many of the true negatives were accurately predicted
 	@source https://datascience.stackexchange.com/a/40746/86851
@@ -14,7 +14,6 @@ def specificity(y_pred, y_true):
 	Specificity score
 	"""
 	y_pred = tf.math.argmax(y_pred, axis=-1)
-	y_true = tf.math.argmax(y_true, axis=-1)
 	y_true = tf.cast(y_true, dtype=tf.float32)
 	y_pred = tf.cast(y_pred, dtype=tf.float32)