refactor(NeuralNetwork): remove unused workers parameter

- Removed workers parameter in NeuralNetwork class.
- Updated related documentation and files to reflect this change.

aucmedi/automl/block_pred.py

@@ -74,7 +74,6 @@ def block_predict(config):
     # Define neural network parameters
     nn_paras = {"n_labels": 1,                                  # placeholder
                 "channels": 1,                                  # placeholder
-                "workers": config["workers"],
                 "batch_queue_size": 4,
                 "multiprocessing": False,
     }

aucmedi/automl/block_train.py

@@ -136,7 +136,6 @@ def block_train(config):
     # Define neural network parameters
     nn_paras = {"n_labels": class_n,
                 "channels": 3,
-                "workers": config["workers"],
                 "batch_queue_size": 4,
                 "loss": loss,
                 "metrics": [AUC(100), F1Score(average="macro")],

aucmedi/ensemble/bagging.py

@@ -187,7 +187,6 @@ def train(self, training_generator, epochs=20, iterations=None,
                 "meta_variables": self.model_template.meta_variables,
                 "learning_rate": self.model_template.learning_rate,
                 "batch_queue_size": self.model_template.batch_queue_size,
-                "workers": self.model_template.workers,
                 "multiprocessing": self.model_template.multiprocessing,
             }
 
@@ -325,7 +324,6 @@ def predict(self, prediction_generator, aggregate="mean",
                 "meta_variables": self.model_template.meta_variables,
                 "learning_rate": self.model_template.learning_rate,
                 "batch_queue_size": self.model_template.batch_queue_size,
-                "workers": self.model_template.workers,
                 "multiprocessing": self.model_template.multiprocessing,
             }
 

aucmedi/ensemble/composite.py

@@ -244,7 +244,6 @@ def train(self, training_generator, epochs=20, iterations=None,
                 "meta_variables": self.model_list[i].meta_variables,
                 "learning_rate": self.model_list[i].learning_rate,
                 "batch_queue_size": self.model_list[i].batch_queue_size,
-                "workers": self.model_list[i].workers,
                 "multiprocessing": self.model_list[i].multiprocessing,
             }
 
@@ -355,7 +354,6 @@ def train_metalearner(self, training_generator):
                 "meta_variables": self.model_list[i].meta_variables,
                 "learning_rate": self.model_list[i].learning_rate,
                 "batch_queue_size": self.model_list[i].batch_queue_size,
-                "workers": self.model_list[i].workers,
                 "multiprocessing": self.model_list[i].multiprocessing,
             }
 
@@ -469,7 +467,6 @@ def predict(self, prediction_generator, return_ensemble=False):
                 "meta_variables": self.model_list[i].meta_variables,
                 "learning_rate": self.model_list[i].learning_rate,
                 "batch_queue_size": self.model_list[i].batch_queue_size,
-                "workers": self.model_list[i].workers,
                 "multiprocessing": self.model_list[i].multiprocessing,
             }
 

aucmedi/ensemble/stacking.py

@@ -232,7 +232,6 @@ def train(self, training_generator, epochs=20, iterations=None,
                 "meta_variables": self.model_list[i].meta_variables,
                 "learning_rate": self.model_list[i].learning_rate,
                 "batch_queue_size": self.model_list[i].batch_queue_size,
-                "workers": self.model_list[i].workers,
                 "multiprocessing": self.model_list[i].multiprocessing,
             }
 
@@ -343,7 +342,6 @@ def train_metalearner(self, training_generator):
                 "meta_variables": self.model_list[i].meta_variables,
                 "learning_rate": self.model_list[i].learning_rate,
                 "batch_queue_size": self.model_list[i].batch_queue_size,
-                "workers": self.model_list[i].workers,
                 "multiprocessing": self.model_list[i].multiprocessing,
             }
 
@@ -456,7 +454,6 @@ def predict(self, prediction_generator, return_ensemble=False):
                 "meta_variables": self.model_list[i].meta_variables,
                 "learning_rate": self.model_list[i].learning_rate,
                 "batch_queue_size": self.model_list[i].batch_queue_size,
-                "workers": self.model_list[i].workers,
                 "multiprocessing": self.model_list[i].multiprocessing,
             }
 

aucmedi/neural_network/model.py

@@ -138,7 +138,7 @@ def __init__(self, n_labels, channels, input_shape=None, architecture=None,
                  pretrained_weights=False, loss="categorical_crossentropy",
                  metrics=["categorical_accuracy"], activation_output="softmax",
                  fcl_dropout=True, meta_variables=None, learning_rate=0.0001,
-                 batch_queue_size=10, workers=1, multiprocessing=False,
+                 batch_queue_size=10, multiprocessing=False,
                  verbose=1):
         """ Initialization function for creating a Neural Network (model) object.
 
@@ -169,7 +169,6 @@ def __init__(self, n_labels, channels, input_shape=None, architecture=None,
                                                     ([Classifier][aucmedi.neural_network.architectures.classifier]).
             learning_rate (float):                  Learning rate in which weights of the neural network will be updated.
             batch_queue_size (int):                 The batch queue size is the number of previously prepared batches in the cache during runtime.
-            workers (int):                          Number of workers/threads which preprocess batches during runtime.
             multiprocessing (bool):                 Option whether to utilize multi-processing for workers instead of threading .
             verbose (int):                          Option (0/1) how much information should be written to stdout.
 
@@ -192,7 +191,6 @@ def __init__(self, n_labels, channels, input_shape=None, architecture=None,
         self.metrics = metrics
         self.learning_rate = learning_rate
         self.batch_queue_size = batch_queue_size
-        self.workers = workers
         self.multiprocessing = multiprocessing
         self.pretrained_weights = pretrained_weights
         self.activation_output = activation_output
@@ -298,7 +296,6 @@ def train(self, training_generator, validation_generator=None, epochs=20,
                                      callbacks=callbacks, epochs=epochs,
                                      steps_per_epoch=iterations,
                                      class_weight=class_weights,
-                                     workers=self.workers,
                                      use_multiprocessing=self.multiprocessing,
                                      max_queue_size=self.batch_queue_size,
                                      verbose=self.verbose)
@@ -321,7 +318,6 @@ def train(self, training_generator, validation_generator=None, epochs=20,
                                            epochs=self.tf_epochs,
                                            steps_per_epoch=iterations,
                                            class_weight=class_weights,
-                                           workers=self.workers,
                                            use_multiprocessing=self.multiprocessing,
                                            max_queue_size=self.batch_queue_size,
                                            verbose=self.verbose)
@@ -338,7 +334,6 @@ def train(self, training_generator, validation_generator=None, epochs=20,
                                          initial_epoch=self.tf_epochs,
                                          steps_per_epoch=iterations,
                                          class_weight=class_weights,
-                                         workers=self.workers,
                                          use_multiprocessing=self.multiprocessing,
                                          max_queue_size=self.batch_queue_size,
                                          verbose=self.verbose)
@@ -368,7 +363,7 @@ def predict(self, prediction_generator):
             preds (numpy.ndarray):                  A NumPy array of predictions formatted with shape (n_samples, n_labels).
         """
         # Run inference process with the Keras predict function
-        preds = self.model.predict(prediction_generator, workers=self.workers,
+        preds = self.model.predict(prediction_generator,
                                    max_queue_size=self.batch_queue_size,
                                    use_multiprocessing=self.multiprocessing,
                                    verbose=self.verbose)