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

source/model_utils.py

@@ -171,7 +171,7 @@ 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_loss(-K.mean(silhouette), inputs=[z, hard_labels])
         self.add_metric(silhouette, aggregation="mean", name="silhouette")
 
         return z

visualizations/train_viz_data_generator.py

@@ -219,22 +219,58 @@ coords_dist_angles1 = merge_tables(coords1, distances1, angles1)
 
 input_dict = {
     "coords": coords1.preprocess(
-        window_size=11, window_step=1, scale=True, random_state=42, filter="gauss"
+        window_size=11,
+        window_step=1,
+        scale=True,
+        random_state=42,
+        filter="gaussian",
+        sigma=55,
+        shuffle=True,
     ),
     "dists": distances1.preprocess(
-        window_size=11, window_step=1, scale=True, random_state=42, filter="gauss"
+        window_size=11,
+        window_step=1,
+        scale=True,
+        random_state=42,
+        filter="gaussian",
+        sigma=55,
+        shuffle=True,
     ),
     "angles": angles1.preprocess(
-        window_size=11, window_step=1, scale=True, random_state=42, filter="gauss"
+        window_size=11,
+        window_step=1,
+        scale=True,
+        random_state=42,
+        filter="gaussian",
+        sigma=55,
+        shuffle=True,
     ),
     "coords+dist": coords_distances1.preprocess(
-        window_size=11, window_step=1, scale=True, random_state=42, filter="gauss"
+        window_size=11,
+        window_step=1,
+        scale=True,
+        random_state=42,
+        filter="gaussian",
+        sigma=55,
+        shuffle=True,
     ),
     "coords+angle": coords_angles1.preprocess(
-        window_size=11, window_step=1, scale=True, random_state=42, filter="gauss"
+        window_size=11,
+        window_step=1,
+        scale=True,
+        random_state=42,
+        filter="gaussian",
+        sigma=55,
+        shuffle=True,
     ),
     "coords+dist+angle": coords_dist_angles1.preprocess(
-        window_size=11, window_step=1, scale=True, random_state=42, filter="gauss"
+        window_size=11,
+        window_step=1,
+        scale=True,
+        random_state=42,
+        filter="gaussian",
+        sigma=55,
+        shuffle=True,
     ),
 }