diff --git a/mlair/model_modules/fully_connected_networks.py b/mlair/model_modules/fully_connected_networks.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9d577e8166ed912d7bd720a3eeca84dbf40d98a
--- /dev/null
+++ b/mlair/model_modules/fully_connected_networks.py
@@ -0,0 +1,128 @@
+__author__ = "Lukas Leufen"
+__date__ = '2021-02-'
+
+from mlair.model_modules import AbstractModelClass
+from mlair.helpers import select_from_dict
+
+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.dropout_rate = 0.1
+ self.regularizer = keras.regularizers.l2(0.1)
+ 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(name='{}'.format("major"))(x_input)
+ x_in = keras.layers.Dense(64, name='{}_Dense_64'.format("major"))(x_in)
+ x_in = self.activation()(x_in)
+ x_in = keras.layers.Dense(32, name='{}_Dense_32'.format("major"))(x_in)
+ x_in = self.activation()(x_in)
+ x_in = keras.layers.Dense(16, name='{}_Dense_16'.format("major"))(x_in)
+ x_in = self.activation()(x_in)
+ x_in = keras.layers.Dense(self._output_shape, name='{}_Dense'.format("major"))(x_in)
+ out_main = self.activation()(x_in)
+ self.model = keras.Model(inputs=x_input, outputs=[out_main])
+
+ def set_compile_options(self):
+ self.initial_lr = 1e-2
+ self.optimizer = keras.optimizers.adam(lr=self.initial_lr)
+ self.compile_options = {"loss": [keras.losses.mean_squared_error], "metrics": ["mse", "mae"]}
+
+
+class FCN(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.
+ """
+
+ _activation = {"relu": keras.layers.ReLU(), "tanh": keras.layers.Activation("tanh"),
+ "sigmoid": keras.layers.Activation("sigmoid")}
+ _optimizer = {"adam": keras.optimizers.adam, "sgd": keras.optimizers.SGD}
+ _requirements = ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad", "momentum", "nesterov"]
+
+ def __init__(self, input_shape: list, output_shape: list, activation="relu", optimizer="adam",
+ layers=1, neurons=10, **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))
+ """
+
+ assert len(input_shape) == 1
+ assert len(output_shape) == 1
+ super().__init__(input_shape[0], output_shape[0])
+
+ # settings
+ self.activation = self._set_activation(activation)
+ self.optimizer = self._set_optimizer(optimizer, **kwargs)
+ self.layer_configuration = (layers, neurons)
+
+ # apply to model
+ self.set_model()
+ self.set_compile_options()
+ # self.set_custom_objects(loss=self.compile_options['loss'])
+
+ def _set_activation(self, activation):
+ try:
+ return self._activation.get(activation.lower())
+ except KeyError:
+ raise AttributeError(f"Given activation {activation} is not supported in this model class.")
+
+ def _set_optimizer(self, optimizer, **kwargs):
+ try:
+ opt_name = optimizer.lower()
+ opt = self._optimizer.get(opt_name)
+ opt_kwargs = {}
+ if opt_name == "adam":
+ opt_kwargs = select_from_dict(kwargs, ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad"])
+ elif opt_name == "sgd":
+ opt_kwargs = select_from_dict(kwargs, ["lr", "momentum", "decay", "nesterov"])
+ return opt(**opt_kwargs)
+ except KeyError:
+ raise AttributeError(f"Given optimizer {optimizer} is not supported in this model class.")
+
+ def set_model(self):
+ """
+ Build the model.
+ """
+ x_input = keras.layers.Input(shape=self._input_shape)
+ x_in = keras.layers.Flatten()(x_input)
+ n_layer, n_hidden = self.layer_configuration
+ for layer in range(n_layer):
+ x_in = keras.layers.Dense(n_hidden)(x_in)
+ x_in = self.activation(x_in)
+ x_in = keras.layers.Dense(self._output_shape)(x_in)
+ out = self.activation(x_in)
+ self.model = keras.Model(inputs=x_input, outputs=[out])
+
+ def set_compile_options(self):
+ self.compile_options = {"loss": [keras.losses.mean_squared_error], "metrics": ["mse", "mae"]}
diff --git a/mlair/run_modules/model_setup.py b/mlair/run_modules/model_setup.py
index dda18fac5d8546c6e399334f3d89415d246a1975..feaaff9b387dc1b2ec76bdb83223023075303326 100644
--- a/mlair/run_modules/model_setup.py
+++ b/mlair/run_modules/model_setup.py
@@ -56,7 +56,6 @@ class ModelSetup(RunEnvironment):
"""Initialise and run model setup."""
super().__init__()
self.model = None
- # path = self.data_store.get("experiment_path")
exp_name = self.data_store.get("experiment_name")
path = self.data_store.get("model_path")
self.scope = "model"
@@ -138,9 +137,10 @@ class ModelSetup(RunEnvironment):
def build_model(self):
"""Build model using input and output shapes from data store."""
- args_list = ["input_shape", "output_shape"]
- args = self.data_store.create_args_dict(args_list, self.scope)
+ # args_list = ["input_shape", "output_shape"]
model = self.data_store.get("model_class")
+ args_list = model.requirements()
+ args = self.data_store.create_args_dict(args_list, self.scope)
self.model = model(**args)
self.get_model_settings()