Prototyped KNN_purity callback

deepof/model_utils.py

@@ -208,8 +208,8 @@ class knn_cluster_purity(tf.keras.callbacks.Callback):
         """ Passes samples through the encoder and computes cluster purity on the latent embedding """
 
         # Get encoer and grouper from full model
-        encoder =
-        grouper =
+        encoder = 0
+        grouper = 0
 
         # Use encoder and grouper to predict on trial data
         encoding = encoder.predict(self.trial_data)

deepof/models.py

@@ -557,6 +557,7 @@ class SEQ_2_SEQ_GMVAE:
         # encoding_shuffle = deepof.model_utils.MCDropout(self.DROPOUT_RATE)(encoder)
         z_cat = Dense(
             self.number_of_components,
+            name="cluster_assignment",
             activation="softmax",
             kernel_regularizer=(
                 tf.keras.regularizers.l1_l2(l1=0.01, l2=0.01)