Added latent regularization control to deepof.data.coordinates.deep_unsupervised_embedding()

deepof/train_model.py

@@ -278,7 +278,7 @@ project_coords = project(
     animal_ids=tuple([animal_id]),
     arena="circular",
     arena_dims=tuple([arena_dims]),
-    enable_iterative_imputation=True,
+    enable_iterative_imputation=False,
     exclude_bodyparts=exclude_bodyparts,
     exp_conditions=treatment_dict,
     path=train_path,
@@ -350,6 +350,7 @@ if not tune:
 
     trained_models = project_coords.deep_unsupervised_embedding(
         (X_train, y_train, X_val, y_val),
+        epochs=1,
         batch_size=batch_size,
         encoding_size=encoding_size,
         hparams=hparams,

deepof/train_utils.py

@@ -430,7 +430,11 @@ def autoencoder_fitting(
             )
 
             if save_weights:
-                ae.save_weights("{}{}_final_weights.h5".format(output_path, run_ID))
+                ae.save_weights(
+                    os.path.join(
+                        "{}".format(output_path), "{}_final_weights.h5".format(run_ID)
+                    )
+                )
 
             if log_hparams:
                 # Logparams to tensorboard