Increased default dimensionality of latent space

deepof/models.py

@@ -133,9 +133,9 @@ class GMVAE:
 
         defaults = {
             "bidirectional_merge": "concat",
-            "clipvalue": 1.0,
+            "clipvalue": 0.75,
             "dense_activation": "relu",
-            "dense_layers_per_branch": 3,
+            "dense_layers_per_branch": 1,
             "dropout_rate": 0.1,
             "learning_rate": 1e-4,
             "units_conv": 64,

deepof_experiments.smk

@@ -19,11 +19,11 @@ warmup_epochs = [15]
 warmup_mode = ["sigmoid"]
 losses = ["ELBO"]  # , "MMD", "ELBO+MMD"]
 overlap_loss = [0.1, 0.2, 0.5, 0.75, 1.]
-encodings = [32]  # [2, 4, 6, 8, 10, 12, 14, 16]
+encodings = [16]  # [2, 4, 6, 8, 10, 12, 14, 16]
 cluster_numbers = [15]  # [1, 5, 10, 15, 20, 25]
 latent_reg = ["variance"]  # ["none", "categorical", "variance", "categorical+variance"]
 entropy_knn = [10]
-next_sequence_pred_weights = [0.15]
+next_sequence_pred_weights = [0.0]
 phenotype_pred_weights = [0.0]
 rule_based_pred_weights = [0.0]
 window_lengths = [22]  # range(11,56,11)

supplementary_notebooks/deepof_model_evaluation.ipynb

@@ -553,11 +553,11 @@
     loss=loss,
     number_of_components=k,
     compile_model=True,
     batch_size=batch_size,
     encoding=encoding,
-    next_sequence_prediction=NextSeqPred,
+    next_sequence_prediction=0.1,
     phenotype_prediction=PhenoPred,
     rule_based_prediction=RuleBasedPred,
 ).build(
     data_prep.shape
 )