diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index eacbe3e26323e0a0bf1579cba53e2e12ecfd27c0..4a59b5b91edbe7a918a80884cf9e38a5d70a8826 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -21,6 +21,7 @@ version:
artifacts:
name: pages
when: always
+ expire_in: 1 week
paths:
- badges/
@@ -54,6 +55,7 @@ tests (from scratch):
artifacts:
name: pages
when: always
+ expire_in: 1 week
paths:
- badges/
- test_results/
@@ -107,6 +109,7 @@ tests:
artifacts:
name: pages
when: always
+ expire_in: 1 week
paths:
- badges/
- test_results/
@@ -131,6 +134,7 @@ coverage:
artifacts:
name: pages
when: always
+ expire_in: 1 week
paths:
- badges/
- coverage/
@@ -155,6 +159,7 @@ sphinx docs:
artifacts:
name: pages
when: always
+ expire_in: 1 week
paths:
- badges/
- webpage/
@@ -189,6 +194,7 @@ pages:
artifacts:
name: pages
when: always
+ expire_in: 1 week
paths:
- public
- badges/
diff --git a/mlair/helpers/logger.py b/mlair/helpers/logger.py
index 51ecde41192cb3a2838e443c3c338c5ac4e29b4d..d960ee6f0b0f1f3b76662817cd1bbf5f68772084 100644
--- a/mlair/helpers/logger.py
+++ b/mlair/helpers/logger.py
@@ -19,6 +19,10 @@ class Logger:
# define shared logger format
self.formatter = '%(asctime)s - %(levelname)s: %(message)s [%(filename)s:%(funcName)s:%(lineno)s]'
+ # assure defaults
+ level_stream = level_stream or logging.INFO
+ level_file = level_file or logging.DEBUG
+
# set log path
self.log_file = self.setup_logging_path(log_path)
# set root logger as file handler
diff --git a/mlair/model_modules/recurrent_networks.py b/mlair/model_modules/recurrent_networks.py
index 6ec920c1cde08c0d2fc6064528eea800fbdde2a7..e909ae7696bdf90d4e9a95e020b75a97e15dfd50 100644
--- a/mlair/model_modules/recurrent_networks.py
+++ b/mlair/model_modules/recurrent_networks.py
@@ -33,7 +33,7 @@ class RNN(AbstractModelClass): # pragma: no cover
def __init__(self, input_shape: list, output_shape: list, activation="relu", activation_output="linear",
activation_rnn="tanh", dropout_rnn=0,
optimizer="adam", n_layer=1, n_hidden=10, regularizer=None, dropout=None, layer_configuration=None,
- batch_normalization=False, rnn_type="lstm", add_dense_layer=False, **kwargs):
+ batch_normalization=False, rnn_type="lstm", add_dense_layer=False, dense_layer_configuration=None, **kwargs):
"""
Sets model and loss depending on the given arguments.
@@ -64,6 +64,15 @@ class RNN(AbstractModelClass): # pragma: no cover
is added if set to false. (Default false)
:param rnn_type: define which kind of recurrent network should be applied. Chose from either lstm or gru. All
units will be of this kind. (Default lstm)
+ :param add_dense_layer: set True to use additional dense layers between last recurrent layer and output layer.
+ If no further specification is made on the exact dense_layer_configuration, a single layer as added with n
+ neurons where n is equal to min(n_previous_layer, n_output**2). If set to False, the output layer directly
+ follows after the last recurrent layer.
+ :param dense_layer_configuration: specify the number of dense layers and the number of neurons given as list
+ where each element corresponds to the number of neurons to add. The position / length of the list specifies
+ the number of layers to add. The last layer is followed by the output layer. In case a value is given for
+ the number of neurons that is less than the number of output neurons, the addition of dense layers is
+ stopped immediately.
"""
assert len(input_shape) == 1
@@ -80,6 +89,7 @@ class RNN(AbstractModelClass): # pragma: no cover
self.optimizer = self._set_optimizer(optimizer.lower(), **kwargs)
self.bn = batch_normalization
self.add_dense_layer = add_dense_layer
+ self.dense_layer_configuration = dense_layer_configuration or []
self.layer_configuration = (n_layer, n_hidden) if layer_configuration is None else layer_configuration
self.RNN = self._rnn.get(rnn_type.lower())
self._update_model_name(rnn_type)
@@ -119,9 +129,22 @@ class RNN(AbstractModelClass): # pragma: no cover
x_in = self.dropout(self.dropout_rate)(x_in)
if self.add_dense_layer is True:
- x_in = keras.layers.Dense(min(self._output_shape ** 2, conf[-1]), name=f"Dense_{len(conf) + 1}",
- kernel_initializer=self.kernel_initializer, )(x_in)
- x_in = self.activation(name=f"{self.activation_name}_{len(conf) + 1}")(x_in)
+ if len(self.dense_layer_configuration) == 0:
+ x_in = keras.layers.Dense(min(self._output_shape ** 2, conf[-1]), name=f"Dense_{len(conf) + 1}",
+ kernel_initializer=self.kernel_initializer, )(x_in)
+ x_in = self.activation(name=f"{self.activation_name}_{len(conf) + 1}")(x_in)
+ if self.dropout is not None:
+ x_in = self.dropout(self.dropout_rate)(x_in)
+ else:
+ for layer, n_hidden in enumerate(self.dense_layer_configuration):
+ if n_hidden < self._output_shape:
+ break
+ x_in = keras.layers.Dense(n_hidden, name=f"Dense_{len(conf) + layer + 1}",
+ kernel_initializer=self.kernel_initializer, )(x_in)
+ x_in = self.activation(name=f"{self.activation_name}_{len(conf) + layer + 1}")(x_in)
+ if self.dropout is not None:
+ 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])
diff --git a/mlair/run_modules/run_environment.py b/mlair/run_modules/run_environment.py
index 5414b21cb0cb26674c699a02c22400959e11f1aa..df34345b4fb67e764f6e4d8d6570a5fafb762304 100644
--- a/mlair/run_modules/run_environment.py
+++ b/mlair/run_modules/run_environment.py
@@ -92,12 +92,12 @@ class RunEnvironment(object):
logger = None
tracker_list = []
- def __init__(self, name=None):
+ def __init__(self, name=None, log_level_stream=None):
"""Start time tracking automatically and logs as info."""
if RunEnvironment.data_store is None:
RunEnvironment.data_store = DataStoreObject()
if RunEnvironment.logger is None:
- RunEnvironment.logger = Logger()
+ RunEnvironment.logger = Logger(level_stream=log_level_stream)
self._name = name if name is not None else self.__class__.__name__
self.time = TimeTracking(name=name)
logging.info(f"{self._name} started")
diff --git a/mlair/workflows/abstract_workflow.py b/mlair/workflows/abstract_workflow.py
index c969aa35ebca60aa749a294bcaa5de727407a461..adb718b7a45dfbec60f88765b5a9f869c177b73b 100644
--- a/mlair/workflows/abstract_workflow.py
+++ b/mlair/workflows/abstract_workflow.py
@@ -13,9 +13,10 @@ class Workflow:
method is sufficient. It must be taken care for inter-stage dependencies, this workflow class only handles the
execution but not the dependencies (workflow would probably fail in this case)."""
- def __init__(self, name=None):
+ def __init__(self, name=None, log_level_stream=None):
self._registry_kwargs = {}
self._registry = []
+ self._log_level_stream = log_level_stream
self._name = name if name is not None else self.__class__.__name__
def add(self, stage, **kwargs):
@@ -25,6 +26,6 @@ class Workflow:
def run(self):
"""Run workflow embedded in a run environment and according to the stage's ordering."""
- with RunEnvironment(name=self._name):
+ with RunEnvironment(name=self._name, log_level_stream=self._log_level_stream):
for pos, stage in enumerate(self._registry):
stage(**self._registry_kwargs[pos])
diff --git a/mlair/workflows/default_workflow.py b/mlair/workflows/default_workflow.py
index 961979cb774e928bda96d4cd1a3a7b0f8565e968..3c75d9809f59ed8e5e970ba1b2c3245adbc0459e 100644
--- a/mlair/workflows/default_workflow.py
+++ b/mlair/workflows/default_workflow.py
@@ -36,8 +36,9 @@ class DefaultWorkflow(Workflow):
batch_size=None,
epochs=None,
data_handler=None,
+ log_level_stream=None,
**kwargs):
- super().__init__()
+ super().__init__(log_level_stream=log_level_stream)
# extract all given kwargs arguments
params = remove_items(inspect.getfullargspec(self.__init__).args, "self")