Added missing eval fns

src/models/eval_functions/classifier_eval_fns.py

+import tensorflow as tf
+
+class EvalFunctions(object):
+    """This class implements specialized operation used in the training framework"""
+    def __init__(self,models):
+        self.classifier = models[0]
+
+    @tf.function
+    def predict(self, x,training=True):
+        """Returns a dict containing predictions e.g.{'predictions':predictions}"""
+        logits_classifier = self.classifier(x[0], training=training)
+        return {'predictions':tf.nn.softmax(logits_classifier,axis=-1)}
+
+    def accuracy(self,pred,y):
+        correct_predictions = tf.cast(tf.equal(tf.argmax(pred,axis=-1), 
+                                        tf.argmax(y[0],axis=-1)),tf.float32)
+        return tf.reduce_mean(correct_predictions)
+        
+    @tf.function
+    def compute_loss(self, x, y, training=True):
+        """Has to at least return a dict containing the total loss and a prediction dict e.g.{'total_loss':total_loss},{'predictions':predictions}"""
+        logits_classifier = self.classifier(x[0], training=training)
+ 
+        # Cross entropy losses
+        class_loss = tf.reduce_mean(
+            tf.nn.softmax_cross_entropy_with_logits(
+                y[0],
+                logits_classifier,
+                axis=-1,
+            ))
+
+        if len(self.classifier.losses) > 0:
+            weight_decay_loss = tf.add_n(self.classifier.losses)
+        else:
+            weight_decay_loss = 0.0
+        total_loss = class_loss
+        total_loss += weight_decay_loss
+
+        return {'class_loss':class_loss,'weight_decay_loss':weight_decay_loss,'total_loss':total_loss}, {'predictions':predictions}