diff --git a/mlair/model_modules/abstract_model_class.py b/mlair/model_modules/abstract_model_class.py
index 894ff7ac4e787a8b31f75ff932f60bec8c561094..989f4578f78e6566dfca5a63f671ced8120491d8 100644
--- a/mlair/model_modules/abstract_model_class.py
+++ b/mlair/model_modules/abstract_model_class.py
@@ -82,7 +82,7 @@ class AbstractModelClass(ABC):
self.__custom_objects = value
@property
- def compile_options(self) -> Callable:
+ def compile_options(self) -> Dict:
"""
The compile options property allows the user to use all keras.compile() arguments. They can ether be passed as
dictionary (1), as attribute, without setting compile_options (2) or as mixture (partly defined as instance
@@ -116,7 +116,7 @@ class AbstractModelClass(ABC):
def set_compile_options(self):
self.optimizer = keras.optimizers.SGD()
self.loss = keras.losses.mean_squared_error
- self.compile_options = {"optimizer" = keras.optimizers.Adam(), "metrics": ["mse", "mae"]}
+ self.compile_options = {"optimizer": keras.optimizers.Adam(), "metrics": ["mse", "mae"]}
Note:
* As long as the attribute and the dict value have exactly the same values, the setter method will not raise
diff --git a/mlair/model_modules/fully_connected_networks.py b/mlair/model_modules/fully_connected_networks.py
index 007b8f0de9d2ea6ad6ae64179371a98a56d40447..9fb08cdf6efacab12c2828ed221966586bce1d08 100644
--- a/mlair/model_modules/fully_connected_networks.py
+++ b/mlair/model_modules/fully_connected_networks.py
@@ -10,53 +10,6 @@ from mlair.model_modules.loss import var_loss, custom_loss
import keras
-class FCN_64_32_16(AbstractModelClass):
- """
- A customised model 4 Dense layers (64, 32, 16, window_lead_time), where the last layer is the output layer depending
- on the window_lead_time parameter.
- """
-
- def __init__(self, input_shape: list, output_shape: list):
- """
- Sets model and loss depending on the given arguments.
-
- :param input_shape: list of input shapes (expect len=1 with shape=(window_hist, station, variables))
- :param output_shape: list of output shapes (expect len=1 with shape=(window_forecast))
- """
-
- assert len(input_shape) == 1
- assert len(output_shape) == 1
- super().__init__(input_shape[0], output_shape[0])
-
- # settings
- self.activation = keras.layers.PReLU
-
- # apply to model
- self.set_model()
- self.set_compile_options()
- self.set_custom_objects(loss=self.compile_options['loss'])
-
- def set_model(self):
- """
- Build the model.
- """
- x_input = keras.layers.Input(shape=self._input_shape)
- x_in = keras.layers.Flatten()(x_input)
- x_in = keras.layers.Dense(64, name="Dense_64")(x_in)
- x_in = self.activation()(x_in)
- x_in = keras.layers.Dense(32, name="Dense_32")(x_in)
- x_in = self.activation()(x_in)
- x_in = keras.layers.Dense(16, name="Dense_16")(x_in)
- x_in = self.activation()(x_in)
- x_in = keras.layers.Dense(self._output_shape, name="Dense_output")(x_in)
- out_main = self.activation()(x_in)
- self.model = keras.Model(inputs=x_input, outputs=[out_main])
-
- def set_compile_options(self):
- self.optimizer = keras.optimizers.adam(lr=1e-2)
- self.compile_options = {"loss": [keras.losses.mean_squared_error], "metrics": ["mse", "mae"]}
-
-
class FCN(AbstractModelClass):
"""
A customisable fully connected network (64, 32, 16, window_lead_time), where the last layer is the output layer depending
@@ -66,11 +19,15 @@ class FCN(AbstractModelClass):
_activation = {"relu": keras.layers.ReLU, "tanh": partial(keras.layers.Activation, "tanh"),
"sigmoid": partial(keras.layers.Activation, "sigmoid"),
"linear": partial(keras.layers.Activation, "linear"),
- "selu": partial(keras.layers.Activation, "selu")}
- _initializer = {"selu": keras.initializers.lecun_normal()}
+ "selu": partial(keras.layers.Activation, "selu"),
+ "prelu": partial(keras.layers.PReLU, alpha_initializer=keras.initializers.constant(value=0.25))}
+ _initializer = {"tanh": "glorot_uniform", "sigmoid": "glorot_uniform", "linear": "glorot_uniform",
+ "relu": keras.initializers.he_normal(), "selu": keras.initializers.lecun_normal(),
+ "prelu": keras.initializers.he_normal()}
_optimizer = {"adam": keras.optimizers.adam, "sgd": keras.optimizers.SGD}
_regularizer = {"l1": keras.regularizers.l1, "l2": keras.regularizers.l2, "l1_l2": keras.regularizers.l1_l2}
_requirements = ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad", "momentum", "nesterov", "l1", "l2"]
+ _dropout = {"selu": keras.layers.AlphaDropout}
def __init__(self, input_shape: list, output_shape: list, activation="relu", activation_output="linear",
optimizer="adam", n_layer=1, n_hidden=10, regularizer=None, dropout=None, layer_configuration=None,
@@ -96,12 +53,12 @@ class FCN(AbstractModelClass):
self._update_model_name()
self.kernel_initializer = self._initializer.get(activation, "glorot_uniform")
self.kernel_regularizer = self._set_regularizer(regularizer, **kwargs)
- self.dropout = self._set_dropout(dropout)
+ self.dropout, self.dropout_rate = self._set_dropout(activation, dropout)
# apply to model
self.set_model()
self.set_compile_options()
- self.set_custom_objects(loss=custom_loss([keras.losses.mean_squared_error, var_loss]), var_loss=var_loss)
+ self.set_custom_objects(loss=self.compile_options["loss"][0], var_loss=var_loss)
def _set_activation(self, activation):
try:
@@ -139,12 +96,11 @@ class FCN(AbstractModelClass):
except KeyError:
raise AttributeError(f"Given regularizer {regularizer} is not supported in this model class.")
- @staticmethod
- def _set_dropout(dropout):
- if dropout is None:
- return dropout
- assert 0 <= dropout < 1
- return dropout
+ def _set_dropout(self, activation, dropout_rate):
+ if dropout_rate is None:
+ return None, None
+ assert 0 <= dropout_rate < 1
+ return self._dropout.get(activation, keras.layers.Dropout), dropout_rate
def _update_model_name(self):
n_input = str(reduce(lambda x, y: x * y, self._input_shape))
@@ -168,7 +124,7 @@ class FCN(AbstractModelClass):
kernel_regularizer=self.kernel_regularizer)(x_in)
x_in = self.activation(name=f"{self.activation_name}_{layer + 1}")(x_in)
if self.dropout is not None:
- x_in = keras.layers.Dropout(self.dropout)(x_in)
+ x_in = self.dropout(self.dropout_rate)(x_in)
else:
assert isinstance(self.layer_configuration, list) is True
for layer, n_hidden in enumerate(self.layer_configuration):
@@ -176,7 +132,7 @@ class FCN(AbstractModelClass):
kernel_regularizer=self.kernel_regularizer)(x_in)
x_in = self.activation(name=f"{self.activation_name}_{layer + 1}")(x_in)
if self.dropout is not None:
- x_in = keras.layers.Dropout(self.dropout)(x_in)
+ x_in = self.dropout(self.dropout_rate)(x_in)
x_in = keras.layers.Dense(self._output_shape)(x_in)
out = self.activation_output(name=f"{self.activation_output_name}_output")(x_in)
self.model = keras.Model(inputs=x_input, outputs=[out])
@@ -184,3 +140,30 @@ class FCN(AbstractModelClass):
def set_compile_options(self):
self.compile_options = {"loss": [custom_loss([keras.losses.mean_squared_error, var_loss])],
"metrics": ["mse", "mae", var_loss]}
+
+
+class FCN_64_32_16(FCN):
+ """
+ A customised model 4 Dense layers (64, 32, 16, window_lead_time), where the last layer is the output layer depending
+ on the window_lead_time parameter.
+ """
+
+ _requirements = ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad"]
+
+ def __init__(self, input_shape: list, output_shape: list, **kwargs):
+ """
+ Sets model and loss depending on the given arguments.
+
+ :param input_shape: list of input shapes (expect len=1 with shape=(window_hist, station, variables))
+ :param output_shape: list of output shapes (expect len=1 with shape=(window_forecast))
+ """
+ lr = kwargs.pop("lr", 1e-2)
+ super().__init__(input_shape, output_shape, activation="prelu", activation_output="linear",
+ layer_configuration=[64, 32, 16], optimizer="adam", lr=lr, **kwargs)
+
+ def set_compile_options(self):
+ self.compile_options = {"loss": [keras.losses.mean_squared_error], "metrics": ["mse", "mae"]}
+
+ def _update_model_name(self):
+ self.model_name = "FCN"
+ super()._update_model_name()