refac but now docs yet

src/model_modules/flatten.py

@@ -9,7 +9,20 @@ import keras
 def flatten_tail(input_X: keras.layers, name: str, bound_weight: bool = False, dropout_rate: float = 0.0,
                  window_lead_time: int = 4, activation: Callable = keras.activations.relu,
                  reduction_filter: int = 64, first_dense: int = 64):
-
+    """
+    Flatten output of
+
+    :param input_X:
+    :param name:
+    :param bound_weight:
+    :param dropout_rate:
+    :param window_lead_time:
+    :param activation:
+    :param reduction_filter:
+    :param first_dense:
+
+    :return:
+    """
     X_in = keras.layers.Conv2D(reduction_filter, (1, 1), padding='same', name='{}_Conv_1x1'.format(name))(input_X)
 
     X_in = activation(name='{}_conv_act'.format(name))(X_in)

src/model_modules/inception_model.py

@@ -5,7 +5,8 @@ import logging
 
 import keras
 import keras.layers as layers
-from src.model_modules.advanced_paddings import PadUtils, ReflectionPadding2D, SymmetricPadding2D, Padding2D
+
+from src.model_modules.advanced_paddings import PadUtils, ReflectionPadding2D, Padding2D
 
 
 class InceptionModelBase:
@@ -22,6 +23,7 @@ class InceptionModelBase:
     def block_part_name(self):
         """
         Use unicode due to some issues of keras with normal strings
+
         :return:
         """
         return chr(self.ord_base + self.part_of_block)
@@ -41,6 +43,7 @@ class InceptionModelBase:
         """
         This function creates a "convolution tower block" containing a 1x1 convolution to reduce filter size followed by
         convolution with given filter and kernel size
+
         :param input_x: Input to network part
         :param reduction_filter: Number of filters used in 1x1 convolution to reduce overall filter size before conv.
         :param tower_filter: Number of filters for n x m convolution
@@ -79,8 +82,8 @@ class InceptionModelBase:
             #                                     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)
+                                       name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad'
+                                       )(tower)
 
             tower = layers.Conv2D(tower_filter,
                                   tower_kernel,
@@ -137,6 +140,7 @@ class InceptionModelBase:
     def create_pool_tower(self, input_x, pool_kernel, tower_filter, activation='relu', max_pooling=True, **kwargs):
         """
         This function creates a "MaxPooling tower block"
+
         :param input_x: Input to network part
         :param pool_kernel: size of pooling kernel
         :param tower_filter: Number of filters used in 1x1 convolution to reduce filter size
@@ -160,11 +164,11 @@ class InceptionModelBase:
             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)
+        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)
 
         # convolution block
-        tower = layers.Conv2D(tower_filter, (1, 1), padding='valid', name=block_name+"1x1")(tower)
+        tower = layers.Conv2D(tower_filter, (1, 1), padding='valid', name=block_name + "1x1")(tower)
         tower = self.act(tower, activation, **act_settings)
 
         return tower
@@ -172,6 +176,7 @@ class InceptionModelBase:
     def inception_block(self, input_x, tower_conv_parts, tower_pool_parts, **kwargs):
         """
         Crate a inception block
+
         :param input_x: Input to block
         :param tower_conv_parts: dict containing settings for parts of inception block; Example:
                                  tower_conv_parts = {'tower_1': {'reduction_filter': 32,
@@ -211,7 +216,7 @@ class InceptionModelBase:
             tower_build['avgpool'] = self.create_pool_tower(input_x, **tower_pool_parts, **kwargs, max_pooling=False)
 
         block = keras.layers.concatenate(list(tower_build.values()), axis=3,
-                                         name=block_name+"_Co")
+                                         name=block_name + "_Co")
         return block
 
 
@@ -258,7 +263,7 @@ if __name__ == '__main__':
     conv_settings_dict = {'tower_1': {'reduction_filter': 64,
                                       'tower_filter': 64,
                                       'tower_kernel': (3, 3),
-                                      'activation': LeakyReLU,},
+                                      'activation': LeakyReLU, },
                           'tower_2': {'reduction_filter': 64,
                                       'tower_filter': 64,
                                       'tower_kernel': (5, 5),
@@ -295,12 +300,10 @@ if __name__ == '__main__':
     # compile
     epochs = 1
     lrate = 0.01
-    decay = lrate/epochs
+    decay = lrate / epochs
     sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
     model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
     print(X_train.shape)
     keras.utils.plot_model(model, to_file='model.pdf', show_shapes=True, show_layer_names=True)
     # model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test))
     print('test')
-
-