diff --git a/mlair/model_modules/fully_connected_networks.py b/mlair/model_modules/fully_connected_networks.py
index a4c61b5b55f56e974d90a83fca32771019778154..313fc837825e108b877b8a48856a26667211764a 100644
--- a/mlair/model_modules/fully_connected_networks.py
+++ b/mlair/model_modules/fully_connected_networks.py
@@ -28,8 +28,6 @@ class FCN_64_32_16(AbstractModelClass):
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
@@ -42,20 +40,19 @@ class FCN_64_32_16(AbstractModelClass):
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 = 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'.format("major"))(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'.format("major"))(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'.format("major"))(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.initial_lr = 1e-2
- self.optimizer = keras.optimizers.adam(lr=self.initial_lr)
+ self.optimizer = keras.optimizers.adam(lr=1e-2)
self.compile_options = {"loss": [keras.losses.mean_squared_error], "metrics": ["mse", "mae"]}
@@ -117,7 +114,7 @@ class FCN(AbstractModelClass):
n_layer, n_hidden = self.layer_configuration
n_input = str(reduce(lambda x, y: x * y, self._input_shape))
n_output = str(self._output_shape)
- self.model_name += "_".join([n_input, *[f"{n_hidden}" for _ in range(n_layer)], n_output])
+ self.model_name += "_".join(["", n_input, *[f"{n_hidden}" for _ in range(n_layer)], n_output])
def set_model(self):
"""
diff --git a/mlair/model_modules/model_class.py b/mlair/model_modules/model_class.py
index f6e979878604ff6e6c22c4f362520dc00acf69d7..f8e3a21a81351ac614e2275749bb85fa82a96e02 100644
--- a/mlair/model_modules/model_class.py
+++ b/mlair/model_modules/model_class.py
@@ -128,58 +128,6 @@ from mlair.model_modules.flatten import flatten_tail
from mlair.model_modules.advanced_paddings import PadUtils, Padding2D, SymmetricPadding2D
-class MyLittleModel(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.lr_decay = mlair.model_modules.keras_extensions.LearningRateDecay(base_lr=self.initial_lr, drop=.94,
- # epochs_drop=10)
- self.compile_options = {"loss": [keras.losses.mean_squared_error], "metrics": ["mse", "mae"]}
-
-
class MyLittleModelHourly(AbstractModelClass):
"""
A customised model with a 1x1 Conv, and 4 Dense layers (64, 32, 16, window_lead_time), where the last layer is the
@@ -529,8 +477,3 @@ class MyPaperModel(AbstractModelClass):
self.optimizer = keras.optimizers.SGD(lr=self.initial_lr, momentum=0.9)
self.compile_options = {"loss": [keras.losses.mean_squared_error, keras.losses.mean_squared_error],
"metrics": ['mse', 'mae']}
-
-
-if __name__ == "__main__":
- model = MyLittleModel([(1, 3, 10)], [2])
- print(model.compile_options)
diff --git a/mlair/run_modules/experiment_setup.py b/mlair/run_modules/experiment_setup.py
index af540fc296f1d4b707b5373fdbcbb14dac1afc7f..30672ecc9206319896205d886157b2f2f8977f39 100644
--- a/mlair/run_modules/experiment_setup.py
+++ b/mlair/run_modules/experiment_setup.py
@@ -20,7 +20,7 @@ from mlair.configuration.defaults import DEFAULT_STATIONS, DEFAULT_VAR_ALL_DICT,
DEFAULT_NUMBER_OF_BOOTSTRAPS, DEFAULT_PLOT_LIST, DEFAULT_SAMPLING, DEFAULT_DATA_ORIGIN, DEFAULT_ITER_DIM
from mlair.data_handler import DefaultDataHandler
from mlair.run_modules.run_environment import RunEnvironment
-from mlair.model_modules.model_class import MyLittleModel as VanillaModel
+from mlair.model_modules.fully_connected_networks import FCN_64_32_16 as VanillaModel
class ExperimentSetup(RunEnvironment):
diff --git a/test/test_data_handler/test_iterator.py b/test/test_data_handler/test_iterator.py
index ade5c19215e61de5e209db900920187294ac9b18..e47d725a4fd78fec98e81a6de9c18869e7b47637 100644
--- a/test/test_data_handler/test_iterator.py
+++ b/test/test_data_handler/test_iterator.py
@@ -1,7 +1,7 @@
-
from mlair.data_handler.iterator import DataCollection, StandardIterator, KerasIterator
from mlair.helpers.testing import PyTestAllEqual
-from mlair.model_modules.model_class import MyLittleModel, MyBranchedModel
+from mlair.model_modules.model_class import MyBranchedModel
+from mlair.model_modules.fully_connected_networks import FCN_64_32_16
import numpy as np
import pytest
@@ -275,7 +275,7 @@ class TestKerasIterator:
def test_get_model_rank_single_output_branch(self):
iterator = object.__new__(KerasIterator)
- iterator.model = MyLittleModel(input_shape=[(14, 1, 2)], output_shape=[(3,)])
+ iterator.model = FCN_64_32_16(input_shape=[(14, 1, 2)], output_shape=[(3,)])
assert iterator._get_model_rank() == 1
def test_get_model_rank_multiple_output_branch(self):
diff --git a/test/test_run_modules/test_model_setup.py b/test/test_run_modules/test_model_setup.py
index bc4421269a3458c0eb551f8f1820a8d46a597afc..7a4378531ca02733af1bbeeac0efbde56203be3a 100644
--- a/test/test_run_modules/test_model_setup.py
+++ b/test/test_run_modules/test_model_setup.py
@@ -8,7 +8,7 @@ from mlair.data_handler import KerasIterator
from mlair.data_handler import DataCollection
from mlair.helpers.datastore import EmptyScope
from mlair.model_modules.keras_extensions import CallbackHandler
-from mlair.model_modules.model_class import MyLittleModel
+from mlair.model_modules.fully_connected_networks import FCN_64_32_16
from mlair.model_modules import AbstractModelClass
from mlair.run_modules.model_setup import ModelSetup
from mlair.run_modules.run_environment import RunEnvironment
@@ -23,7 +23,7 @@ class TestModelSetup:
obj.scope = "general.model"
obj.model = None
obj.callbacks_name = "placeholder_%s_str.pickle"
- obj.data_store.set("model_class", MyLittleModel)
+ obj.data_store.set("model_class", FCN_64_32_16)
obj.data_store.set("lr_decay", "dummy_str", "general.model")
obj.data_store.set("hist", "dummy_str", "general.model")
obj.data_store.set("epochs", 2)