update model_class

src/model_modules/model_class.py

@@ -376,11 +376,11 @@ class MyPaperModel(AbstractModelClass):
         self.channels = channels
         self.dropout_rate = .3
         self.regularizer = keras.regularizers.l2(0.001)
-        self.initial_lr = 1e-2
+        self.initial_lr = 1e-3
         # self.optimizer = keras.optimizers.adam(lr=self.initial_lr, amsgrad=True)
         self.optimizer = keras.optimizers.SGD(lr=self.initial_lr, momentum=0.9)
         self.lr_decay = src.model_modules.keras_extensions.LearningRateDecay(base_lr=self.initial_lr, drop=.94, epochs_drop=10)
-        self.epochs = 1
+        self.epochs = 150
         self.batch_size = int(256 * 2)
         self.activation = keras.layers.ELU
         self.padding = "SymPad2D"
@@ -447,11 +447,12 @@ class MyPaperModel(AbstractModelClass):
         # X_in = adv_pad.SymmetricPadding2D(padding=pad_size)(X_input)
         # X_in = inception_model.padding_layer("SymPad2D")(padding=pad_size, name="SymPad")(X_input)  # adv_pad.SymmetricPadding2D(padding=pad_size)(X_input)
         X_in = inception_model.padding_layer("SymPad2D")(padding=pad_size, name="SymPad")(X_input)
-        X_in = self.activation(name='FirstAct')(X_in)
         X_in = keras.layers.Conv2D(filters=first_filters,
                                    kernel_size=first_kernel,
                                    kernel_regularizer=self.regularizer,
                                    name="First_conv_{}x{}".format(first_kernel[0], first_kernel[1]))(X_in)
+        X_in = self.activation(name='FirstAct')(X_in)
+
 
         X_in = inception_model.inception_block(X_in, conv_settings_dict1, pool_settings_dict1,
                                                regularizer=self.regularizer,