Removed outdated non-variational autoencoder model

deepof/train_model.py

@@ -9,9 +9,11 @@ usage: python -m examples.model_training -h
 
 """
 
-from deepof.data import *
-from deepof.train_utils import *
-from deepof.utils import *
+import argparse
+import os
+import deepof.data
+import deepof.train_utils
+import deepof.utils
 
 parser = argparse.ArgumentParser(
     description="Autoencoder training for DeepOF animal pose recognition"
@@ -63,7 +65,7 @@ parser.add_argument(
     "--gaussian-filter",
     "-gf",
     help="Convolves each training instance with a Gaussian filter before feeding it to the autoencoder model",
-    type=str2bool,
+    type=deepof.utils.str2bool,
     default=False,
 )
 parser.add_argument(
@@ -175,7 +177,7 @@ parser.add_argument(
     "--overlap-loss",
     "-ol",
     help="If True, adds the negative MMD between all components of the latent Gaussian mixture to the loss function",
-    type=str2bool,
+    type=deepof.utils.str2bool,
     default=False,
 )
 parser.add_argument(
@@ -287,7 +289,7 @@ assert input_type in [
 ], "Invalid input type. Type python model_training.py -h for help."
 
 # Loads model hyperparameters and treatment conditions, if available
-treatment_dict = load_treatments(train_path)
+treatment_dict = deepof.train_utils.load_treatments(train_path)
 
 # Logs hyperparameters  if specified on the --logparam CLI argument
 logparam = {
@@ -303,7 +305,7 @@ if rule_based_prediction:
     logparam["rule_based_prediction_weight"] = rule_based_prediction
 
 # noinspection PyTypeChecker
-project_coords = project(
+project_coords = deepof.data.project(
     animal_ids=tuple([animal_id]),
     arena="circular",
     arena_dims=tuple([arena_dims]),
@@ -334,10 +336,10 @@ coords = project_coords.get_coords(
 )
 distances = project_coords.get_distances()
 angles = project_coords.get_angles()
-coords_distances = merge_tables(coords, distances)
-coords_angles = merge_tables(coords, angles)
-dists_angles = merge_tables(distances, angles)
-coords_dist_angles = merge_tables(coords, distances, angles)
+coords_distances = deepof.data.merge_tables(coords, distances)
+coords_angles = deepof.data.merge_tables(coords, angles)
+dists_angles = deepof.data.merge_tables(distances, angles)
+coords_dist_angles = deepof.data.merge_tables(coords, distances, angles)
 
 
 def batch_preprocess(tab_dict):
@@ -410,7 +412,7 @@ if not tune:
 
 else:
     # Runs hyperparameter tuning with the specified parameters and saves the results
-    run_ID, tensorboard_callback, entropy, onecycle = get_callbacks(
+    run_ID, tensorboard_callback, entropy, onecycle = deepof.train_utils.get_callbacks(
         X_train=X_train,
         batch_size=batch_size,
         phenotype_prediction=phenotype_prediction,
@@ -429,7 +431,7 @@ else:
         run=run,
     )
 
-    best_hyperparameters, best_model = tune_search(
+    best_hyperparameters, best_model = deepof.train_utils.tune_search(
         data=[X_train, y_train, X_val, y_val],
         encoding_size=encoding_size,
         hypertun_trials=hypertun_trials,
@@ -447,7 +449,7 @@ else:
             tensorboard_callback,
             onecycle,
             entropy,
-            CustomStopper(
+            deepof.train_utils.CustomStopper(
                 monitor="val_loss",
                 patience=5,
                 restore_best_weights=True,

tests/test_build_hypermodels.py

@@ -18,6 +18,7 @@ import tensorflow as tf
 
 tf.config.experimental_run_functions_eagerly(True)
 
+
 @settings(deadline=None, max_examples=10)
 @given(
     encoding_size=st.integers(min_value=2, max_value=16),

tests/test_build_models.py

@@ -17,6 +17,7 @@ import tensorflow as tf
 
 tf.config.experimental_run_functions_eagerly(True)
 
+
 @settings(deadline=None, max_examples=10)
 @given(
     loss=st.one_of(st.just("ELBO"), st.just("MMD"), st.just("ELBO+MMD")),

tests/test_model_utils.py

@@ -247,24 +247,6 @@ def test_MMDiscrepancyLayer(annealing_mode):
     assert isinstance(fit, tf.keras.callbacks.History)
 
 
-# noinspection PyUnresolvedReferences
-def test_dead_neuron_control():
-    X = np.random.uniform(0, 10, [1500, 5])
-    y = np.random.randint(0, 2, [1500, 1])
-
-    test_model = tf.keras.Sequential()
-    test_model.add(tf.keras.layers.Dense(1))
-    test_model.add(deepof.model_utils.Dead_neuron_control())
-
-    test_model.compile(
-        loss=tf.keras.losses.binary_crossentropy,
-        optimizer=tf.keras.optimizers.SGD(),
-    )
-
-    fit = test_model.fit(X, y, epochs=10, batch_size=100, verbose=0)
-    assert isinstance(fit, tf.keras.callbacks.History)
-
-
 def test_find_learning_rate():
     X = np.random.uniform(0, 10, [1500, 5])
     y = np.random.randint(0, 2, [1500, 1])