moved tower model to model class

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]

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])