refac naming of concat layers in inception, /close #59

See merge request toar/machinelearningtools!48

src/model_modules/inception_model.py

@@ -153,6 +153,7 @@ class InceptionModelBase:
         self.number_of_blocks += 1
         self.part_of_block = 0
         tower_build = {}
+        block_name = f"Block_{self.number_of_blocks}"
         for part, part_settings in tower_conv_parts.items():
             tower_build[part] = self.create_conv_tower(input_x, **part_settings, **kwargs)
         if 'max_pooling' in tower_pool_parts.keys():
@@ -165,7 +166,8 @@ class InceptionModelBase:
             tower_build['maxpool'] = self.create_pool_tower(input_x, **tower_pool_parts, **kwargs)
             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)
+        block = keras.layers.concatenate(list(tower_build.values()), axis=3,
+                                         name=block_name+"_Co")
         return block
 
 

test/test_model_modules/test_inception_model.py

@@ -152,6 +152,10 @@ class TestInceptionModelBase:
         assert self.step_in(block_1b._keras_history[0]).name == "Block_1b_5x5"
         assert isinstance(self.step_in(block_pool1._keras_history[0], depth=2), keras.layers.pooling.MaxPooling2D)
         assert isinstance(self.step_in(block_pool2._keras_history[0], depth=2), keras.layers.pooling.AveragePooling2D)
+        # check naming of concat layer
+        assert block.name == 'Block_1_Co/concat:0'
+        assert block._keras_history[0].name == 'Block_1_Co'
+        assert isinstance(block._keras_history[0], keras.layers.merge.Concatenate)
         # next block
         opts['input_x'] = block
         opts['tower_pool_parts']['max_pooling'] = True
@@ -166,6 +170,10 @@ class TestInceptionModelBase:
         assert self.step_in(block_2a._keras_history[0]).name == "Block_2a_3x3"
         assert self.step_in(block_2b._keras_history[0]).name == "Block_2b_5x5"
         assert isinstance(self.step_in(block_pool._keras_history[0], depth=2), keras.layers.pooling.MaxPooling2D)
+        # check naming of concat layer
+        assert block.name == 'Block_2_Co/concat:0'
+        assert block._keras_history[0].name == 'Block_2_Co'
+        assert isinstance(block._keras_history[0], keras.layers.merge.Concatenate)
 
     def test_batch_normalisation(self, base, input_x):
         base.part_of_block += 1