diff --git a/src/model_modules/model_class.py b/src/model_modules/model_class.py
index 1a5dd9da38520d6d732253015e8a67325e24c460..ebbd7a25cef9031436d932a6502c9726bfe3e318 100644
--- a/src/model_modules/model_class.py
+++ b/src/model_modules/model_class.py
@@ -8,6 +8,8 @@ from abc import ABC
from typing import Any, Callable
import keras
+from src.model_modules.inception_model import InceptionModelBase
+from src.model_modules.flatten import flatten_tail
class AbstractModelClass(ABC):
@@ -240,3 +242,112 @@ class MyBranchedModel(AbstractModelClass):
self.loss = [keras.losses.mean_absolute_error] + [keras.losses.mean_squared_error] + \
[keras.losses.mean_squared_error]
+
+
+class MyTowerModel(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 = 1e-2
+ self.regularizer = keras.regularizers.l2(0.1)
+ self.initial_lr = 1e-2
+ self.optimizer = keras.optimizers.adam(lr=self.initial_lr)
+ self.lr_decay = src.model_modules.keras_extensions.LearningRateDecay(base_lr=self.initial_lr, drop=.94, epochs_drop=10)
+ self.epochs = 20
+ self.batch_size = int(256*4)
+ self.activation = keras.layers.PReLU
+
+ # 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
+ conv_settings_dict1 = {
+ 'tower_1': {'reduction_filter': 8, 'tower_filter': 8 * 2, 'tower_kernel': (3, 1), 'activation': activation},
+ 'tower_2': {'reduction_filter': 8, 'tower_filter': 8 * 2, 'tower_kernel': (5, 1), 'activation': activation},
+ 'tower_3': {'reduction_filter': 8, 'tower_filter': 8 * 2, 'tower_kernel': (1, 1), 'activation': activation},
+ }
+
+ pool_settings_dict1 = {'pool_kernel': (3, 1), 'tower_filter': 8 * 2, 'activation': activation}
+
+ conv_settings_dict2 = {
+ 'tower_1': {'reduction_filter': 8 * 2, 'tower_filter': 16 * 2 * 2, 'tower_kernel': (3, 1),
+ 'activation': activation},
+ 'tower_2': {'reduction_filter': 8 * 2, 'tower_filter': 16 * 2 * 2, 'tower_kernel': (5, 1),
+ 'activation': activation},
+ 'tower_3': {'reduction_filter': 8 * 2, 'tower_filter': 16 * 2 * 2, 'tower_kernel': (1, 1),
+ 'activation': activation},
+ }
+ pool_settings_dict2 = {'pool_kernel': (3, 1), 'tower_filter': 16, 'activation': activation}
+
+ conv_settings_dict3 = {'tower_1': {'reduction_filter': 16 * 4, 'tower_filter': 32 * 2, 'tower_kernel': (3, 1),
+ 'activation': activation},
+ 'tower_2': {'reduction_filter': 16 * 4, 'tower_filter': 32 * 2, 'tower_kernel': (5, 1),
+ 'activation': activation},
+ 'tower_3': {'reduction_filter': 16 * 4, 'tower_filter': 32 * 2, 'tower_kernel': (1, 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
+
+ X_in = inception_model.inception_block(X_input, conv_settings_dict1, pool_settings_dict1,
+ regularizer=self.regularizer,
+ batch_normalisation=True)
+
+ 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)
+
+ 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=True, dropout_rate=self.dropout_rate,
+ reduction_filter=64, first_dense=64, window_lead_time=self.window_lead_time)
+
+ self.model = keras.Model(inputs=X_input, outputs=[out_main])
+
+ def set_loss(self):
+
+ """
+ Set the loss
+ :return: loss function
+ """
+
+ self.loss = [keras.losses.mean_squared_error]
diff --git a/src/run_modules/model_setup.py b/src/run_modules/model_setup.py
index c14298d7d21f63cdc4465c1ed8e8bb30868b3c1a..66697e07d68a01ffa203798c11417b8440d54214 100644
--- a/src/run_modules/model_setup.py
+++ b/src/run_modules/model_setup.py
@@ -104,90 +104,3 @@ class ModelSetup(RunEnvironment):
with tf.device("/cpu:0"):
file_name = f"{self.model_name.split(sep='.')[0]}.pdf"
keras.utils.plot_model(self.model, to_file=file_name, show_shapes=True, show_layer_names=True)
-
-
-def my_loss():
- loss = l_p_loss(4)
- keras_loss = losses.mean_squared_error
- loss_all = [loss] + [keras_loss]
- return loss_all
-
-
-def my_little_loss():
- return losses.mean_squared_error
-
-
-def my_little_model(activation, window_history_size, channels, regularizer, dropout_rate, window_lead_time):
-
- X_input = keras.layers.Input(
- shape=(window_history_size + 1, 1, channels)) # add 1 to window_size to include current time step t0
- X_in = keras.layers.Conv2D(32, (1, 1), padding='same', name='{}_Conv_1x1'.format("major"))(X_input)
- X_in = activation(name='{}_conv_act'.format("major"))(X_in)
- X_in = keras.layers.Flatten(name='{}'.format("major"))(X_in)
- X_in = keras.layers.Dropout(dropout_rate, name='{}_Dropout_1'.format("major"))(X_in)
- X_in = keras.layers.Dense(64, name='{}_Dense_64'.format("major"))(X_in)
- X_in = activation()(X_in)
- X_in = keras.layers.Dense(32, name='{}_Dense_32'.format("major"))(X_in)
- X_in = activation()(X_in)
- X_in = keras.layers.Dense(16, name='{}_Dense_16'.format("major"))(X_in)
- X_in = activation()(X_in)
- X_in = keras.layers.Dense(window_lead_time, name='{}_Dense'.format("major"))(X_in)
- out_main = activation()(X_in)
- return keras.Model(inputs=X_input, outputs=[out_main])
-
-
-def my_model(activation, window_history_size, channels, regularizer, dropout_rate, window_lead_time):
-
- conv_settings_dict1 = {
- 'tower_1': {'reduction_filter': 8, 'tower_filter': 8 * 2, 'tower_kernel': (3, 1), 'activation': activation},
- 'tower_2': {'reduction_filter': 8, 'tower_filter': 8 * 2, 'tower_kernel': (5, 1), 'activation': activation},
- 'tower_3': {'reduction_filter': 8, 'tower_filter': 8 * 2, 'tower_kernel': (1, 1), 'activation': activation},
- }
-
- pool_settings_dict1 = {'pool_kernel': (3, 1), 'tower_filter': 8 * 2, 'activation': activation}
-
- conv_settings_dict2 = {'tower_1': {'reduction_filter': 8 * 2, 'tower_filter': 16 * 2 * 2, 'tower_kernel': (3, 1),
- 'activation': activation},
- 'tower_2': {'reduction_filter': 8 * 2, 'tower_filter': 16 * 2 * 2, 'tower_kernel': (5, 1),
- 'activation': activation},
- 'tower_3': {'reduction_filter': 8 * 2, 'tower_filter': 16 * 2 * 2, 'tower_kernel': (1, 1),
- 'activation': activation},
- }
- pool_settings_dict2 = {'pool_kernel': (3, 1), 'tower_filter': 16, 'activation': activation}
-
- conv_settings_dict3 = {'tower_1': {'reduction_filter': 16 * 4, 'tower_filter': 32 * 2, 'tower_kernel': (3, 1),
- 'activation': activation},
- 'tower_2': {'reduction_filter': 16 * 4, 'tower_filter': 32 * 2, 'tower_kernel': (5, 1),
- 'activation': activation},
- 'tower_3': {'reduction_filter': 16 * 4, 'tower_filter': 32 * 2, 'tower_kernel': (1, 1),
- 'activation': activation},
- }
-
- pool_settings_dict3 = {'pool_kernel': (3, 1), 'tower_filter': 32, 'activation': activation}
-
- ##########################################
- inception_model = InceptionModelBase()
-
- X_input = keras.layers.Input(shape=(window_history_size + 1, 1, channels)) # add 1 to window_size to include current time step t0
-
- X_in = inception_model.inception_block(X_input, conv_settings_dict1, pool_settings_dict1, regularizer=regularizer,
- batch_normalisation=True)
-
- out_minor = flatten_tail(X_in, 'Minor_1', bound_weight=True, activation=activation, dropout_rate=dropout_rate,
- reduction_filter=4, first_dense=32, window_lead_time=window_lead_time)
-
- X_in = keras.layers.Dropout(dropout_rate)(X_in)
-
- X_in = inception_model.inception_block(X_in, conv_settings_dict2, pool_settings_dict2, regularizer=regularizer,
- batch_normalisation=True)
-
- X_in = keras.layers.Dropout(dropout_rate)(X_in)
-
- X_in = inception_model.inception_block(X_in, conv_settings_dict3, pool_settings_dict3, regularizer=regularizer,
- batch_normalisation=True)
- #############################################
-
- out_main = flatten_tail(X_in, 'Main', activation=activation, bound_weight=True, dropout_rate=dropout_rate,
- reduction_filter=64, first_dense=64, window_lead_time=window_lead_time)
-
- return keras.Model(inputs=X_input, outputs=[out_minor, out_main])