Dropout regularization

README.adoc

@@ -684,6 +684,7 @@ Here is a list of all command line arguments:
 --val_split, Validation split ratio. Defaults to 0.2.
 --learning_rate, Learning rate. Defaults to 0.01.
 --hidden_units, Number of hidden units. Defaults to 0. If set to >0, a two-layer classifier is used.
+--dropout, Dropout rate. Defaults to 0.
 --mixup, Whether to use mixup for training.
 --upsampling_ratio, Balance train data and upsample minority classes. Values between 0 and 1. Defaults to 0.
 --upsampling_mode, Upsampling mode. Can be 'repeat', 'mean' or 'smote'. Defaults to 'repeat'.

config.py

@@ -127,6 +127,9 @@
 # If >0, a two-layer classifier will be trained
 TRAIN_HIDDEN_UNITS: int = 0
 
+# Dropout rate for training
+TRAIN_DROPOUT: float = 0.0
+
 # Whether to use mixup for training
 TRAIN_WITH_MIXUP: bool = False
 

model.py

@@ -135,8 +135,15 @@ def buildLinearClassifier(num_labels, input_size, hidden_units=0):
 
     # Hidden layer
     if hidden_units > 0:
+        # Dropout layer?
+        if cfg.TRAIN_DROPOUT > 0:
+            model.add(keras.layers.Dropout(cfg.TRAIN_DROPOUT))
         model.add(keras.layers.Dense(hidden_units, activation="relu"))
 
+    # Dropout layer?
+    if cfg.TRAIN_DROPOUT > 0:
+        model.add(keras.layers.Dropout(cfg.TRAIN_DROPOUT))
+
     # Classification layer
     model.add(keras.layers.Dense(num_labels))
 

train.py

@@ -176,6 +176,7 @@ def trainModel(on_epoch_end=None):
         default=0,
         help="Number of hidden units. Defaults to 0. If set to >0, a two-layer classifier is used.",
     )
+    parser.add_argument("--dropout", type=float, default=0.0, help="Dropout rate. Defaults to 0.")
     parser.add_argument("--mixup", action=argparse.BooleanOptionalAction, help="Whether to use mixup for training.")
     parser.add_argument("--upsampling_ratio", type=float, default=0.0, help="Balance train data and upsample minority classes. Values between 0 and 1. Defaults to 0.")
     parser.add_argument("--upsampling_mode", default="repeat", help="Upsampling mode. Can be 'repeat', 'mean' or 'smote'. Defaults to 'repeat'.")
@@ -195,6 +196,7 @@ def trainModel(on_epoch_end=None):
     cfg.TRAIN_VAL_SPLIT = args.val_split
     cfg.TRAIN_LEARNING_RATE = args.learning_rate
     cfg.TRAIN_HIDDEN_UNITS = args.hidden_units
+    cfg.TRAIN_DROPOUT = min(max(0, args.dropout), 0.9)
     cfg.TRAIN_WITH_MIXUP = args.mixup
     cfg.UPSAMPLING_RATIO = min(max(0, args.upsampling_ratio), 1)
     cfg.UPSAMPLING_MODE = args.upsampling_mode