Added parameters in default_model_creator() for on-line data augmentation,...

Added parameters in default_model_creator() for on-line data augmentation, random scale and random shift.

core/model.py

 import tensorflow as tf
-
+import tensorflow.keras.layers as tkl
 from core.layers import RandomShift2DLayer, RandomScale2DLayer
 
 
-def default_model_creator():
+def default_model_creator(model_config):
     model = tf.keras.Sequential([
-        RandomScale2DLayer(minval=-10, maxval=10),
-        RandomShift2DLayer(minval=0, maxval=100, axis=1),
-        tf.keras.layers.Flatten(),
-        tf.keras.layers.Dense(units=320, activation='relu'),
-        tf.keras.layers.Dropout(rate=0.05),
-        tf.keras.layers.Dense(units=160, activation='relu'),
-        tf.keras.layers.Dropout(rate=0.05),
-        tf.keras.layers.Dense(units=80, activation='relu'),
-        tf.keras.layers.Dropout(rate=0.05),
-        tf.keras.layers.Dense(units=40, activation='relu'),
-        tf.keras.layers.Dropout(rate=0.0),
-        tf.keras.layers.Dense(units=10, activation='relu'),
-        tf.keras.layers.Dense(units=10, activation='relu'),
-        tf.keras.layers.Dense(units=10, activation='relu'),
-        tf.keras.layers.Dense(units=10, activation='relu'),
-        tf.keras.layers.Dense(units=10, activation='relu'),
-        tf.keras.layers.Dense(units=1, activation='sigmoid'),
-        tf.keras.layers.experimental.preprocessing.Rescaling(scale=360, offset=0.0)
+        RandomScale2DLayer(minval=model_config['random_scale']['minval'],
+                           maxval=model_config['random_scale']['maxval']),
+        RandomShift2DLayer(minval=model_config['random_shift']['minval'],
+                           maxval=model_config['random_shift']['maxval'],
+                           axis=1),
+        tkl.Flatten(),
+        tkl.Dense(units=320, activation='relu'),
+        tkl.Dropout(rate=0.05),
+        tkl.Dense(units=160, activation='relu'),
+        tkl.Dropout(rate=0.05),
+        tkl.Dense(units=80, activation='relu'),
+        tkl.Dropout(rate=0.05),
+        tkl.Dense(units=40, activation='relu'),
+        tkl.Dropout(rate=0.0),
+        tkl.Dense(units=10, activation='relu'),
+        tkl.Dense(units=10, activation='relu'),
+        tkl.Dense(units=10, activation='relu'),
+        tkl.Dense(units=10, activation='relu'),
+        tkl.Dense(units=10, activation='relu'),
+        tkl.Dense(units=1, activation='sigmoid'),
+        tkl.experimental.preprocessing.Rescaling(scale=360, offset=0.0)
     ])
 
     return model