Added nose2body to rule_based_annotation()

deepof/model_utils.py

@@ -367,10 +367,10 @@ class MMDiscrepancyLayer(Layer):
         return z
 
 
-class Gaussian_mixture_overlap(Layer):
+class Cluster_overlap(Layer):
     """
     Identity layer that measures the overlap between the components of the latent Gaussian Mixture
-    using a specified metric (MMD, Wasserstein, Fischer-Rao)
+    using a specified metric (KNN-purity, MMD)
     """
 
     def __init__(self, lat_dims, n_components, loss=False, samples=10, *args, **kwargs):
@@ -378,7 +378,7 @@ class Gaussian_mixture_overlap(Layer):
         self.n_components = n_components
         self.loss = loss
         self.samples = samples
-        super(Gaussian_mixture_overlap, self).__init__(*args, **kwargs)
+        super(Cluster_overlap, self).__init__(*args, **kwargs)
 
     def get_config(self):  # pragma: no cover
         """Updates Constraint metadata"""

deepof/models.py

@@ -334,15 +334,15 @@ class SEQ_2_SEQ_GMVAE:
         """Sets the default parameters for the model. Overwritable with a dictionary"""
 
         defaults = {
-            "bidirectional_merge": "concat",
+            "bidirectional_merge": "ave",
             "clipvalue": 1.0,
             "dense_activation": "relu",
-            "dense_layers_per_branch": 5,
-            "dropout_rate": 1e-3,
-            "learning_rate": 1e-3,
-            "units_conv": 32,
+            "dense_layers_per_branch": 1,
+            "dropout_rate": 0.15,
+            "learning_rate": 1e-4,
+            "units_conv": 64,
             "units_dense2": 32,
-            "units_lstm": 300,
+            "units_lstm": 128,
         }
 
         for k, v in params.items():
@@ -599,7 +599,7 @@ class SEQ_2_SEQ_GMVAE:
             z_gauss = deepof.model_utils.Dead_neuron_control()(z_gauss)
 
         if self.overlap_loss:
-            z_gauss = deepof.model_utils.Gaussian_mixture_overlap(
+            z_gauss = deepof.model_utils.Cluster_overlap(
                 self.ENCODING,
                 self.number_of_components,
                 loss=self.overlap_loss,

deepof/train_model.py

@@ -222,7 +222,7 @@ hypertun_trials = args.hpt_trials
 encoding_size = args.encoding_size
 exclude_bodyparts = [i for i in args.exclude_bodyparts.split(",") if i]
 gaussian_filter = args.gaussian_filter
-hparams = args.hyperparameters
+hparams = args.hyperparameters if args.hyperparameters is not None else {}
 input_type = args.input_type
 k = args.components
 kl_wu = args.kl_warmup
@@ -261,7 +261,6 @@ assert input_type in [
 ], "Invalid input type. Type python model_training.py -h for help."
 
 # Loads model hyperparameters and treatment conditions, if available
-hparams = load_hparams(hparams)
 treatment_dict = load_treatments(train_path)
 
 # Logs hyperparameters  if specified on the --logparam CLI argument
@@ -352,7 +351,7 @@ if not tune:
         (X_train, y_train, X_val, y_val),
         batch_size=batch_size,
         encoding_size=encoding_size,
-        hparams=hparams,
+        hparams={},
         kl_warmup=kl_wu,
         log_history=True,
         log_hparams=True,

deepof/train_utils.py

@@ -46,28 +46,6 @@ class CustomStopper(tf.keras.callbacks.EarlyStopping):
             super().on_epoch_end(epoch, logs)
 
 
-def load_hparams(hparams):
-    """Loads hyperparameters from a custom dictionary pickled on disc.
-    Thought to be used with the output of hyperparameter_tuning.py"""
-
-    if hparams is not None:
-        with open(hparams, "rb") as handle:
-            hparams = pickle.load(handle)
-    else:
-        hparams = {
-            "bidirectional_merge": "ave",
-            "clipvalue": 1.0,
-            "dense_activation": "relu",
-            "dense_layers_per_branch": 1,
-            "dropout_rate": 1e-3,
-            "learning_rate": 1e-3,
-            "units_conv": 160,
-            "units_dense2": 120,
-            "units_lstm": 300,
-        }
-    return hparams
-
-
 def load_treatments(train_path):
     """Loads a dictionary containing the treatments per individual,
     to be loaded as metadata in the coordinates class"""

tests/test_train_utils.py

@@ -21,24 +21,6 @@ import os
 import tensorflow as tf
 
 
-def test_load_hparams():
-    assert type(deepof.train_utils.load_hparams(None)) == dict
-    assert (
-        type(
-            deepof.train_utils.load_hparams(
-                os.path.join(
-                    "tests",
-                    "test_examples",
-                    "test_single_topview",
-                    "Others",
-                    "test_hparams.pkl",
-                )
-            )
-        )
-        == dict
-    )
-
-
 def test_load_treatments():
     assert deepof.train_utils.load_treatments(".") is None
     assert (