diff --git a/src/model_modules/model_class.py b/src/model_modules/model_class.py
index ebbd7a25cef9031436d932a6502c9726bfe3e318..84f98690754a5d21a579270f4357636db692747d 100644
--- a/src/model_modules/model_class.py
+++ b/src/model_modules/model_class.py
@@ -10,6 +10,7 @@ from typing import Any, Callable
import keras
from src.model_modules.inception_model import InceptionModelBase
from src.model_modules.flatten import flatten_tail
+import src.model_modules.advanced_paddings as adv_pad
class AbstractModelClass(ABC):
@@ -351,3 +352,136 @@ class MyTowerModel(AbstractModelClass):
"""
self.loss = [keras.losses.mean_squared_error]
+
+
+class MyPaperModel(AbstractModelClass):
+
+ def __init__(self, window_history_size, window_lead_time, channels):
+
+ """
+ Sets model and loss depending on the given arguments.
+ :param activation: activation function
+ :param window_history_size: number of historical time steps included in the input data
+ :param channels: number of variables used in input data
+ :param regularizer: <not used here>
+ :param dropout_rate: dropout rate used in the model [0, 1)
+ :param window_lead_time: number of time steps to forecast in the output layer
+ """
+
+ super().__init__()
+
+ # settings
+ self.window_history_size = window_history_size
+ self.window_lead_time = window_lead_time
+ self.channels = channels
+ self.dropout_rate = .3
+ self.regularizer = keras.regularizers.l2(0.001)
+ 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 = 150
+ self.batch_size = int(256 * 2)
+ self.activation = keras.layers.ELU
+ self.padding = "SymPad2D"
+
+ # apply to model
+ self.set_model()
+ self.set_loss()
+
+ def set_model(self):
+
+ """
+ Build the model.
+ :param activation: activation function
+ :param window_history_size: number of historical time steps included in the input data
+ :param channels: number of variables used in input data
+ :param dropout_rate: dropout rate used in the model [0, 1)
+ :param window_lead_time: number of time steps to forecast in the output layer
+ :return: built keras model
+ """
+ activation = self.activation
+ first_kernel = (3,1)
+ first_filters = 16
+
+ conv_settings_dict1 = {
+ 'tower_1': {'reduction_filter': 8, 'tower_filter': 16 * 2, 'tower_kernel': (3, 1),
+ 'activation': activation},
+ 'tower_2': {'reduction_filter': 8, 'tower_filter': 16 * 2, 'tower_kernel': (5, 1),
+ 'activation': activation},
+ 'tower_3': {'reduction_filter': 8, 'tower_filter': 16 * 2, 'tower_kernel': (1, 1),
+ 'activation': activation},
+ # 'tower_4':{'reduction_filter':8, 'tower_filter':8*2, 'tower_kernel':(7,1), 'activation':activation},
+ }
+ pool_settings_dict1 = {'pool_kernel': (3, 1), 'tower_filter': 16, 'activation': activation}
+
+ conv_settings_dict2 = {
+ 'tower_1': {'reduction_filter': 64, 'tower_filter': 32 * 2, 'tower_kernel': (3, 1),
+ 'activation': activation},
+ 'tower_2': {'reduction_filter': 64, 'tower_filter': 32 * 2, 'tower_kernel': (5, 1),
+ 'activation': activation},
+ 'tower_3': {'reduction_filter': 64, 'tower_filter': 32 * 2, 'tower_kernel': (1, 1),
+ 'activation': activation},
+ # 'tower_4':{'reduction_filter':8*2, 'tower_filter':16*2, 'tower_kernel':(7,1), 'activation':activation},
+ }
+ pool_settings_dict2 = {'pool_kernel': (3, 1), 'tower_filter': 32, 'activation': activation}
+
+ conv_settings_dict3 = {
+ 'tower_1': {'reduction_filter': 64 * 2, 'tower_filter': 32 * 4, 'tower_kernel': (3, 1),
+ 'activation': activation},
+ 'tower_2': {'reduction_filter': 64 * 2, 'tower_filter': 32 * 4, 'tower_kernel': (5, 1),
+ 'activation': activation},
+ 'tower_3': {'reduction_filter': 64 * 2, 'tower_filter': 32 * 4, 'tower_kernel': (1, 1),
+ 'activation': activation},
+ # 'tower_4':{'reduction_filter':16*4, 'tower_filter':32, 'tower_kernel':(7,1), 'activation':activation},
+ }
+ pool_settings_dict3 = {'pool_kernel': (3, 1), 'tower_filter': 32, 'activation': activation}
+
+ ##########################################
+ inception_model = InceptionModelBase()
+
+ X_input = keras.layers.Input(
+ shape=(self.window_history_size + 1, 1, self.channels)) # add 1 to window_size to include current time step t0
+
+ pad_size = adv_pad.PadUtils.get_padding_for_same(first_kernel)
+ # 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 = 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,
+ batch_normalisation=True,
+ padding=self.padding)
+ out_minor1 = flatten_tail(X_in, 'minor_1', False, self.dropout_rate, self.window_lead_time,
+ self.activation, 32, 64)
+
+ X_in = keras.layers.Dropout(self.dropout_rate)(X_in)
+
+ X_in = inception_model.inception_block(X_in, conv_settings_dict2, pool_settings_dict2, regularizer=self.regularizer,
+ batch_normalisation=True, padding=self.padding)
+
+ # X_in = keras.layers.Dropout(self.dropout_rate)(X_in)
+ #
+ # X_in = inception_model.inception_block(X_in, conv_settings_dict3, pool_settings_dict3, regularizer=self.regularizer,
+ # batch_normalisation=True)
+ #############################################
+
+ out_main = flatten_tail(X_in, 'Main', activation=activation, bound_weight=False, dropout_rate=self.dropout_rate,
+ reduction_filter=64 * 2, first_dense=64 * 2, window_lead_time=self.window_lead_time)
+
+ self.model = keras.Model(inputs=X_input, outputs=[out_minor1, out_main])
+
+ def set_loss(self):
+
+ """
+ Set the loss
+ :return: loss function
+ """
+
+ self.loss = [keras.losses.mean_squared_error, keras.losses.mean_squared_error]
diff --git a/src/run_modules/model_setup.py b/src/run_modules/model_setup.py
index 32ca0d2e82af32d8164d80ac42731e10f431a458..307fd63018df1e4825fa8fbee1fb07f6c8fef67e 100644
--- a/src/run_modules/model_setup.py
+++ b/src/run_modules/model_setup.py
@@ -12,6 +12,7 @@ from src.model_modules.keras_extensions import HistoryAdvanced, CallbackHandler
# from src.model_modules.model_class import MyBranchedModel as MyModel
from src.model_modules.model_class import MyLittleModel as MyModel
# from src.model_modules.model_class import MyTowerModel as MyModel
+# from src.model_modules.model_class import MyPaperModel as MyModel
from src.run_modules.run_environment import RunEnvironment