Merge pull request #118 from lvapeab/master

Written some docs/fixed some code flaws

Author: lvapeab

keras_wrapper/cnn_model.py

@@ -3350,7 +3350,7 @@ def conv_layer(self, x, nb_filter, nb_row, nb_col, dim_ordering,
                           dim_ordering=dim_ordering)(x)
 
         if padding:
-            for i in range(padding):
+            for _ in range(padding):
                 x = ZeroPadding2D(padding=(1, 1), dim_ordering=dim_ordering)(x)
 
         return x
@@ -3367,8 +3367,6 @@ def GoogLeNet_FunctionalAPI(self, nOutput, input):
         CONCAT_AXIS = 1
         NB_CLASS = nOutput  # number of classes (default 1000)
         DROPOUT = 0.4
-        WEIGHT_DECAY = 0.0005  # L2 regularization factor
-        USE_BN = True  # whether to use batch normalization
         # Theano - 'th' (channels, width, height)
         # Tensorflow - 'tf' (width, height, channels)
         DIM_ORDERING = 'th'

keras_wrapper/extra/regularize.py

@@ -19,6 +19,11 @@ def Regularize(layer, params,
     :param params: Params specifying the regularizations to apply
     :param shared_layers: Boolean indicating if we want to get the used layers for applying to a shared-layers model.
     :param name: Name prepended to regularizer layer
+    :param apply_noise: If False, noise won't be applied, independently of params
+    :param apply_dropout: If False, dropout won't be applied, independently of params
+    :param apply_prelu: If False, prelu won't be applied, independently of params
+    :param apply_batch_normalization: If False, batch normalization won't be applied, independently of params
+    :param apply_l2: If False, l2 normalization won't be applied, independently of params
     :return: Regularized layer
     """
     shared_layers_list = []