diff --git a/src/model_modules/model_class.py b/src/model_modules/model_class.py
index c16c2ecd2a104679b35edb21d43435822baedc85..19ea629c010221d147290d99ef2a427f7ec1fe1c 100644
--- a/src/model_modules/model_class.py
+++ b/src/model_modules/model_class.py
@@ -18,13 +18,16 @@ class AbstractModelClass(ABC):
"""
def __init__(self) -> None:
+
"""
Predefine internal attributes for model and loss.
"""
+
self._model = None
self._loss = None
def __getattr__(self, name: str) -> Any:
+
"""
Is called if __getattribute__ is not able to find requested attribute. Normally, the model class is saved into
a variable like `model = ModelClass()`. To bypass a call like `model.model` to access the _model attribute,
@@ -34,18 +37,22 @@ class AbstractModelClass(ABC):
:param name: name of the attribute or method to call
:return: attribute or method from self.model namespace
"""
+
return self.model.__getattribute__(name)
@property
def model(self) -> keras.Model:
+
"""
The model property containing a keras.Model instance.
:return: the keras model
"""
+
return self._model
@property
def loss(self) -> Callable:
+
"""
The loss property containing a callable loss function. The loss function can be any keras loss or a customised
function. If the loss is a customised function, it must contain the internal loss(y_true, y_pred) function:
@@ -55,6 +62,7 @@ class AbstractModelClass(ABC):
return loss
:return: the loss function
"""
+
return self._loss
@@ -67,6 +75,7 @@ class MyLittleModel(AbstractModelClass):
"""
def __init__(self, activation, window_history_size, channels, regularizer, dropout_rate, window_lead_time):
+
"""
Sets model and loss depending on the given arguments.
:param activation: activation function
@@ -76,11 +85,13 @@ class MyLittleModel(AbstractModelClass):
: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__()
self.set_model(activation, window_history_size, channels, dropout_rate, window_lead_time)
self.set_loss()
def set_model(self, activation, window_history_size, channels, dropout_rate, window_lead_time):
+
"""
Build the model.
:param activation: activation function
@@ -90,25 +101,28 @@ class MyLittleModel(AbstractModelClass):
:param window_lead_time: number of time steps to forecast in the output layer
:return: built keras model
"""
- 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)
- self._model = keras.Model(inputs=X_input, outputs=[out_main])
+
+ # add 1 to window_size to include current time step t0
+ x_input = keras.layers.Input(shape=(window_history_size + 1, 1, channels))
+ 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)
+ 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
+ self._loss = keras.losses.mean_squared_error
diff --git a/test/test_model_modules/test_model_class.py b/test/test_model_modules/test_model_class.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c05e8bf33611b5e2950faa1d9840823ececf8f7
--- /dev/null
+++ b/test/test_model_modules/test_model_class.py
@@ -0,0 +1,29 @@
+import pytest
+import keras
+
+from src.model_modules.model_class import AbstractModelClass
+
+
+class TestAbstractModelClass:
+
+ @pytest.fixture
+ def amc(self):
+ return AbstractModelClass()
+
+ def test_init(self, amc):
+ assert amc._model is None
+ assert amc._loss is None
+
+ def test_model_property(self, amc):
+ amc._model = keras.Model()
+ assert isinstance(amc.model, keras.Model) is True
+
+ def test_loss_property(self, amc):
+ amc._loss = keras.losses.mean_absolute_error
+ assert amc.loss == keras.losses.mean_absolute_error
+
+ def test_getattr(self, amc):
+ amc._model = keras.Model()
+ assert hasattr(amc, "compile") is True
+ assert hasattr(amc.model, "compile") is True
+ assert amc.compile == amc.model.compile