Implemented shuffle parameter in preprocessing; shuffled validation data in model_training.py

source/model_utils.py

@@ -220,6 +220,9 @@ class Gaussian_mixture_overlap(Layer):
                 intercomponent_mmd, aggregation="mean", name="intercomponent_mmd"
             )
 
+            if self.loss:
+                self.add_loss(-intercomponent_mmd, inputs=[target])
+
         elif self.metric == "wasserstein":
             pass
 
@@ -232,9 +235,14 @@ class Latent_space_control(Layer):
     to the metrics compiled by the model
     """
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, loss=False, *args, **kwargs):
+        self.loss = loss
         super(Latent_space_control, self).__init__(*args, **kwargs)
 
+    def get_config(self):
+        config = super().get_config().copy()
+        config.update({"loss": self.loss})
+
     def call(self, z, z_gauss, z_cat, **kwargs):
 
         # Adds metric that monitors dead neurons in the latent space
@@ -245,7 +253,9 @@ class Latent_space_control(Layer):
         # Adds Silhouette score controling overlap between clusters
         hard_labels = tf.math.argmax(z_cat, axis=1)
         silhouette = tf.numpy_function(silhouette_score, [z, hard_labels], tf.float32)
-        self.add_loss(-K.mean(silhouette), inputs=[z, hard_labels])
         self.add_metric(silhouette, aggregation="mean", name="silhouette")
 
+        if self.loss:
+            self.add_loss(-K.mean(silhouette), inputs=[z, hard_labels])
+
         return z

source/models.py

@@ -168,6 +168,7 @@ class SEQ_2_SEQ_GMVAE:
         number_of_components=1,
         predictor=True,
         overlap_metric="mmd",
+        overlap_loss=False,
     ):
         self.input_shape = input_shape
         self.CONV_filters = units_conv
@@ -185,6 +186,7 @@ class SEQ_2_SEQ_GMVAE:
         self.number_of_components = number_of_components
         self.predictor = predictor
         self.overlap_metric = overlap_metric
+        self.overlap_loss = overlap_loss
 
         if self.prior == "standard_normal":
             self.prior = tfd.mixture.Mixture(
@@ -301,7 +303,10 @@ class SEQ_2_SEQ_GMVAE:
 
         z_gauss = Reshape([2 * self.ENCODING, self.number_of_components])(z_gauss)
         z_gauss = Gaussian_mixture_overlap(
-            self.ENCODING, self.number_of_components, metric=self.overlap_metric
+            self.ENCODING,
+            self.number_of_components,
+            metric=self.overlap_metric,
+            loss=self.overlap_loss,
         )(z_gauss)
 
         z = tfpl.DistributionLambda(