Merge branch 'felix_issue111_refac_remove-comments' into 'develop'

Resolve "Refac: remove comments"

See merge request toar/machinelearningtools!93

src/model_modules/inception_model.py

@@ -75,9 +75,6 @@ class InceptionModelBase:
                                   name=f'Block_{self.number_of_blocks}{self.block_part_name()}_1x1')(input_x)
             tower = self.act(tower, activation, **act_settings)
 
-            # tower = self.padding_layer(padding)(padding=padding_size,
-            #                                     name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad'
-            #                                     )(tower)
             tower = Padding2D(padding)(padding=padding_size,
                                        name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad'
                                        )(tower)
@@ -111,29 +108,6 @@ class InceptionModelBase:
         else:
             return act_name.__name__
 
-    # @staticmethod
-    # def padding_layer(padding):
-    #     allowed_paddings = {
-    #         'RefPad2D': ReflectionPadding2D, 'ReflectionPadding2D': ReflectionPadding2D,
-    #         'SymPad2D': SymmetricPadding2D, 'SymmetricPadding2D': SymmetricPadding2D,
-    #         'ZeroPad2D': keras.layers.ZeroPadding2D, 'ZeroPadding2D': keras.layers.ZeroPadding2D
-    #     }
-    #     if isinstance(padding, str):
-    #         try:
-    #             pad2d = allowed_paddings[padding]
-    #         except KeyError as einfo:
-    #             raise NotImplementedError(
-    #                 f"`{einfo}' is not implemented as padding. "
-    #                 "Use one of those: i) `RefPad2D', ii) `SymPad2D', iii) `ZeroPad2D'")
-    #     else:
-    #         if padding in allowed_paddings.values():
-    #             pad2d = padding
-    #         else:
-    #             raise TypeError(f"`{padding.__name__}' is not a valid padding layer type. "
-    #                             "Use one of those: "
-    #                             "i) ReflectionPadding2D, ii) SymmetricPadding2D, iii) ZeroPadding2D")
-    #     return pad2d
-
     def create_pool_tower(self, input_x, pool_kernel, tower_filter, activation='relu', max_pooling=True, **kwargs):
         """
         This function creates a "MaxPooling tower block"
@@ -159,7 +133,6 @@ class InceptionModelBase:
             block_type = "AvgPool"
             pooling = layers.AveragePooling2D
 
-        # tower = self.padding_layer(padding)(padding=padding_size, name=block_name+'Pad')(input_x)
         tower = Padding2D(padding)(padding=padding_size, name=block_name+'Pad')(input_x)
         tower = pooling(pool_kernel, strides=(1, 1), padding='valid', name=block_name+block_type)(tower)
 
@@ -215,35 +188,6 @@ class InceptionModelBase:
         return block
 
 
-# if __name__ == '__main__':
-#     from keras.models import Model
-#     from keras.layers import Conv2D, Flatten, Dense, Input
-#     import numpy as np
-#
-#
-#     kernel_1 = (3, 3)
-#     kernel_2 = (5, 5)
-#     x = np.array(range(2000)).reshape(-1, 10, 10, 1)
-#     y = x.mean(axis=(1, 2))
-#
-#     x_input = Input(shape=x.shape[1:])
-#     pad1 = PadUtils.get_padding_for_same(kernel_size=kernel_1)
-#     x_out = InceptionModelBase.padding_layer('RefPad2D')(padding=pad1, name="RefPAD1")(x_input)
-#     # x_out = ReflectionPadding2D(padding=pad1, name="RefPAD")(x_input)
-#     x_out = Conv2D(5, kernel_size=kernel_1, activation='relu')(x_out)
-#
-#     pad2 = PadUtils.get_padding_for_same(kernel_size=kernel_2)
-#     x_out = InceptionModelBase.padding_layer(SymmetricPadding2D)(padding=pad2, name="SymPAD1")(x_out)
-#     # x_out = SymmetricPadding2D(padding=pad2, name="SymPAD")(x_out)
-#     x_out = Conv2D(2, kernel_size=kernel_2, activation='relu')(x_out)
-#     x_out = Flatten()(x_out)
-#     x_out = Dense(1, activation='linear')(x_out)
-#
-#     model = Model(inputs=x_input, outputs=x_out)
-#     model.compile('adam', loss='mse')
-#     model.summary()
-#     # model.fit(x, y, epochs=10)
-
 if __name__ == '__main__':
     print(__name__)
     from keras.datasets import cifar10