diff --git a/mlair/data_handler/data_handler_mixed_sampling.py b/mlair/data_handler/data_handler_mixed_sampling.py
index 6c256f86c6289578030bd0f116315fd724eb9b7a..0bdd9b216073bd6d045233afb3fd945718117a98 100644
--- a/mlair/data_handler/data_handler_mixed_sampling.py
+++ b/mlair/data_handler/data_handler_mixed_sampling.py
@@ -283,21 +283,6 @@ class DataHandlerMixedSamplingWithClimateFirFilter(DataHandlerClimateFirFilter):
dh_transformation=dh_transformation[1], **kwargs)
return {"filtered": transformation_filtered, "unfiltered": transformation_unfiltered}
- def get_X_original(self):
- if self.use_filter_branches is True:
- X = []
- for data in self._collection:
- if hasattr(data, "filter_dim"):
- X_total = data.get_X()
- filter_dim = data.filter_dim
- for filter_name in data.filter_dim_order:
- X.append(X_total.sel({filter_dim: filter_name}, drop=True))
- else:
- X.append(data.get_X())
- return X
- else:
- return super().get_X_original()
-
class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWithClimateFirFilter):
# data_handler = DataHandlerMixedSamplingWithClimateFirFilterSingleStation
@@ -457,18 +442,3 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
else: # if no unfiltered meteo branch
transformation_res["filtered_meteo"] = transformation_meteo
return transformation_res if len(transformation_res) > 0 else None
-
- def get_X_original(self):
- if self.use_filter_branches is True:
- X = []
- for data in self._collection:
- if hasattr(data, "filter_dim"):
- X_total = data.get_X()
- filter_dim = data.filter_dim
- for filter_name in data.filter_dim_order:
- X.append(X_total.sel({filter_dim: filter_name}, drop=True))
- else:
- X.append(data.get_X())
- return X
- else:
- return super().get_X_original()
diff --git a/mlair/data_handler/data_handler_with_filter.py b/mlair/data_handler/data_handler_with_filter.py
index 997ecbf51740cce159d2339b589728b5e708de53..47ccc5510c8135745c518611504cd02900a1f883 100644
--- a/mlair/data_handler/data_handler_with_filter.py
+++ b/mlair/data_handler/data_handler_with_filter.py
@@ -116,6 +116,21 @@ class DataHandlerFilter(DefaultDataHandler):
self.use_filter_branches = use_filter_branches
super().__init__(*args, **kwargs)
+ def get_X_original(self):
+ if self.use_filter_branches is True:
+ X = []
+ for data in self._collection:
+ if hasattr(data, "filter_dim"):
+ X_total = data.get_X()
+ filter_dim = data.filter_dim
+ for filter_name in data.filter_dim_order:
+ X.append(X_total.sel({filter_dim: filter_name}, drop=True))
+ else:
+ X.append(data.get_X())
+ return X
+ else:
+ return super().get_X_original()
+
class DataHandlerFirFilterSingleStation(DataHandlerFilterSingleStation):
"""Data handler for a single station to be used by a superior data handler. Inputs are FIR filtered."""
diff --git a/mlair/model_modules/branched_input_networks.py b/mlair/model_modules/branched_input_networks.py
index 2c62c3cafc1537979e4a21bdb3bb6aa798e6e193..a7841f6aab031647e0a3a6a8af6b7c648179cbc3 100644
--- a/mlair/model_modules/branched_input_networks.py
+++ b/mlair/model_modules/branched_input_networks.py
@@ -1,11 +1,71 @@
from functools import partial, reduce
+import copy
from tensorflow import keras as keras
from mlair import AbstractModelClass
-from mlair.helpers import select_from_dict
+from mlair.helpers import select_from_dict, to_list
from mlair.model_modules.loss import var_loss
from mlair.model_modules.recurrent_networks import RNN
+from mlair.model_modules.convolutional_networks import CNNfromConfig
+
+
+class BranchedInputCNN(CNNfromConfig): # pragma: no cover
+ """A convolutional neural network with multiple input branches."""
+
+ def __init__(self, input_shape: list, output_shape: list, layer_configuration: list, optimizer="adam", **kwargs):
+
+ super().__init__([input_shape], output_shape, layer_configuration, optimizer=optimizer, **kwargs)
+
+ def set_model(self):
+
+ x_input = []
+ x_in = []
+ stop_pos = None
+
+ for branch in range(len(self._input_shape)):
+ print(branch)
+ shape_b = self._input_shape[branch]
+ x_input_b = keras.layers.Input(shape=shape_b, name=f"input_branch{branch + 1}")
+ x_input.append(x_input_b)
+ x_in_b = x_input_b
+ b_conf = copy.deepcopy(self.conf)
+
+ for pos, layer_opts in enumerate(b_conf):
+ print(layer_opts)
+ if layer_opts.get("type") == "Concatenate":
+ if stop_pos is None:
+ stop_pos = pos
+ else:
+ assert pos == stop_pos
+ break
+ layer, layer_kwargs, follow_up_layer = self._extract_layer_conf(layer_opts)
+ x_in_b = layer(**layer_kwargs, name=f"{layer.__name__}_branch{branch + 1}_{pos + 1}")(x_in_b)
+ if follow_up_layer is not None:
+ for follow_up in to_list(follow_up_layer):
+ x_in_b = follow_up(name=f"{follow_up.__name__}_branch{branch + 1}_{pos + 1}")(x_in_b)
+ self._layer_save.append({"layer": layer, **layer_kwargs, "follow_up_layer": follow_up_layer,
+ "branch": branch})
+ x_in.append(x_in_b)
+
+ print("concat")
+ x_concat = keras.layers.Concatenate()(x_in)
+
+ if stop_pos is not None:
+ for pos, layer_opts in enumerate(self.conf[stop_pos + 1:]):
+ print(layer_opts)
+ layer, layer_kwargs, follow_up_layer = self._extract_layer_conf(layer_opts)
+ x_concat = layer(**layer_kwargs, name=f"{layer.__name__}_{pos + stop_pos + 1}")(x_concat)
+ if follow_up_layer is not None:
+ for follow_up in to_list(follow_up_layer):
+ x_concat = follow_up(name=f"{follow_up.__name__}_{pos + stop_pos + 1}")(x_concat)
+ self._layer_save.append({"layer": layer, **layer_kwargs, "follow_up_layer": follow_up_layer,
+ "branch": "concat"})
+
+ x_concat = keras.layers.Dense(self._output_shape)(x_concat)
+ out = self.activation_output(name=f"{self.activation_output_name}_output")(x_concat)
+ self.model = keras.Model(inputs=x_input, outputs=[out])
+ print(self.model.summary())
class BranchedInputRNN(RNN): # pragma: no cover
@@ -15,11 +75,6 @@ class BranchedInputRNN(RNN): # pragma: no cover
super().__init__([input_shape], output_shape, *args, **kwargs)
- # apply to model
- # self.set_model()
- # self.set_compile_options()
- # self.set_custom_objects(loss=self.compile_options["loss"][0], var_loss=var_loss)
-
def set_model(self):
"""
Build the model.
diff --git a/mlair/model_modules/convolutional_networks.py b/mlair/model_modules/convolutional_networks.py
index d8eb6eb3403db13932a51274fdc1f563dbfb6ef3..3e87bb7c595e4a0f709ca73449043ad74e9b6e9d 100644
--- a/mlair/model_modules/convolutional_networks.py
+++ b/mlair/model_modules/convolutional_networks.py
@@ -4,20 +4,163 @@ __date__ = '2021-02-'
from functools import reduce, partial
from mlair.model_modules import AbstractModelClass
-from mlair.helpers import select_from_dict
+from mlair.helpers import select_from_dict, to_list
from mlair.model_modules.loss import var_loss, custom_loss
from mlair.model_modules.advanced_paddings import PadUtils, Padding2D, SymmetricPadding2D
import tensorflow.keras as keras
+class CNNfromConfig(AbstractModelClass):
+ _activation = {"relu": keras.layers.ReLU, "tanh": partial(keras.layers.Activation, "tanh"),
+ "sigmoid": partial(keras.layers.Activation, "sigmoid"),
+ "linear": partial(keras.layers.Activation, "linear"),
+ "prelu": partial(keras.layers.PReLU, alpha_initializer=keras.initializers.constant(value=0.25)),
+ "leakyrelu": partial(keras.layers.LeakyReLU)}
+ _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"]
+
+ """
+ Use this class like the following. Note that all keys must match the corresponding tf/keras keys of the layer
+
+ ```python
+ input_shape = [(65,1,9)]
+ output_shape = [(4, )]
+ layer_configuration=[
+ {"type": "Conv2D", "activation": "relu", "kernel_size": (1, 1), "filters": 8},
+ {"type": "Dropout", "rate": 0.2},
+ {"type": "Conv2D", "activation": "relu", "kernel_size": (5, 1), "filters": 16},
+ {"type": "Dropout", "rate": 0.2},
+ {"type": "MaxPooling2D", "pool_size": (8, 1), "strides": (1, 1)},
+ {"type": "Conv2D", "activation": "relu", "kernel_size": (1, 1), "filters": 16},
+ {"type": "Dropout", "rate": 0.2},
+ {"type": "Conv2D", "activation": "relu", "kernel_size": (5, 1), "filters": 32},
+ {"type": "Dropout", "rate": 0.2},
+ {"type": "MaxPooling2D", "pool_size": (8, 1), "strides": (1, 1)},
+ {"type": "Conv2D", "activation": "relu", "kernel_size": (1, 1), "filters": 32},
+ {"type": "Dropout", "rate": 0.2},
+ {"type": "Conv2D", "activation": "relu", "kernel_size": (5, 1), "filters": 64},
+ {"type": "Dropout", "rate": 0.2},
+ {"type": "MaxPooling2D", "pool_size": (8, 1), "strides": (1, 1)},
+ {"type": "Conv2D", "activation": "relu", "kernel_size": (1, 1), "filters": 64},
+ {"type": "Dropout", "rate": 0.2},
+ {"type": "Flatten"},
+ # {"type": "Dense", "units": 128, "activation": "relu"}
+ ]
+ model = CNNfromConfig(input_shape, output_shape, layer_configuration)
+ ```
+
+ """
+
+ def __init__(self, input_shape: list, output_shape: list, layer_configuration: list, optimizer="adam",
+ batch_normalization=False, **kwargs):
+
+ assert len(input_shape) == 1
+ assert len(output_shape) == 1
+ super().__init__(input_shape[0], output_shape[0])
+
+ self.conf = layer_configuration
+ activation_output = kwargs.pop("activation_output", "linear")
+ self.activation_output = self._activation.get(activation_output)
+ self.activation_output_name = activation_output
+ self.kwargs = kwargs
+ self.bn = batch_normalization
+ self.optimizer = self._set_optimizer(optimizer, **kwargs)
+ self._layer_save = []
+
+ # 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)
+
+ def set_model(self):
+ x_input = keras.layers.Input(shape=self._input_shape)
+ x_in = x_input
+
+ for layer_opts in self.conf:
+ print(layer_opts)
+ layer, layer_kwargs, follow_up_layer = self._extract_layer_conf(layer_opts)
+ x_in = layer(**layer_kwargs)(x_in)
+ if follow_up_layer is not None:
+ for follow_up in to_list(follow_up_layer):
+ x_in = follow_up()(x_in)
+ self._layer_save.append({"layer": layer, **layer_kwargs, "follow_up_layer": follow_up_layer})
+
+ 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])
+ print(self.model.summary())
+
+ 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_regularizer(self, regularizer, **kwargs):
+ if regularizer is None or (isinstance(regularizer, str) and regularizer.lower() == "none"):
+ return None
+ try:
+ reg_name = regularizer.lower()
+ reg = self._regularizer.get(reg_name)
+ reg_kwargs = {}
+ if reg_name in ["l1", "l2"]:
+ reg_kwargs = select_from_dict(kwargs, reg_name, remove_none=True)
+ if reg_name in reg_kwargs:
+ reg_kwargs["l"] = reg_kwargs.pop(reg_name)
+ elif reg_name == "l1_l2":
+ reg_kwargs = select_from_dict(kwargs, ["l1", "l2"], remove_none=True)
+ return reg(**reg_kwargs)
+ except KeyError:
+ raise AttributeError(f"Given regularizer {regularizer} is not supported in this model class.")
+
+ def set_compile_options(self):
+ # self.compile_options = {"loss": [custom_loss([keras.losses.mean_squared_error, var_loss])],
+ # "metrics": ["mse", "mae", var_loss]}
+ self.compile_options = {"loss": [keras.losses.mean_squared_error],
+ "metrics": ["mse", "mae", var_loss]}
+
+ def _extract_layer_conf(self, layer_opts):
+ follow_up_layer = None
+ layer_type = layer_opts.pop("type")
+ layer = getattr(keras.layers, layer_type, None)
+ activation_type = layer_opts.pop("activation", None)
+ if activation_type is not None:
+ activation = self._activation.get(activation_type)
+ kernel_initializer = self._initializer.get(activation_type, "glorot_uniform")
+ layer_opts["kernel_initializer"] = kernel_initializer
+ follow_up_layer = activation
+ if self.bn is True:
+ another_layer = keras.layers.BatchNormalization
+ if activation_type in ["relu", "linear", "prelu", "leakyrelu"]:
+ follow_up_layer = (another_layer, follow_up_layer)
+ else:
+ follow_up_layer = (follow_up_layer, another_layer)
+ regularizer_type = layer_opts.pop("kernel_regularizer", None)
+ if regularizer_type is not None:
+ layer_opts["kernel_regularizer"] = self._set_regularizer(regularizer_type, **self.kwargs)
+ return layer, layer_opts, follow_up_layer
+
+
class CNN(AbstractModelClass): # pragma: no cover
_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"),
- "prelu": partial(keras.layers.PReLU, alpha_initializer=keras.initializers.constant(value=0.25))}
+ "prelu": partial(keras.layers.PReLU, alpha_initializer=keras.initializers.constant(value=0.25)),
+ "leakyrelu": partial(keras.layers.LeakyReLU)}
_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()}
@@ -25,9 +168,67 @@ class CNN(AbstractModelClass): # pragma: no cover
_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}
+ _pooling = {"max": keras.layers.MaxPooling2D, "average": keras.layers.AveragePooling2D,
+ "mean": keras.layers.AveragePooling2D}
+
+ """
+ Define CNN model as in the following examples:
+
+ * use same kernel for all layers and use in total 3 conv layers, no dropout or pooling is applied
+
+ ```python
+ model=CNN,
+ kernel_size=5,
+ n_layer=3,
+ dense_layer_configuration=[128, 64],
+ ```
+
+ * specify the kernel sizes, make sure len of kernel size parameter matches number of layers
+
+ ```python
+ model=CNN,
+ kernel_size=[3, 7, 11],
+ n_layer=3,
+ dense_layer_configuration=[128, 64],
+ ```
+
+ * use different number of filters in each layer (can be combined either with fixed or individual kernel sizes),
+ make sure that lengths match. Using layer_configuration always overwrites any value given to n_layers parameter.
+
+ ```python
+ model=CNN,
+ kernel_size=[3, 7, 11],
+ layer_configuration=[24, 48, 48],
+ ```
+
+ * now specify individual kernel sizes and number of filters for each layer
+
+ ```python
+ model=CNN,
+ layer_configuration=[(16, 3), (32, 7), (64, 11)],
+ dense_layer_configuration=[128, 64],
+ ```
+
+ * add also some dropout and pooling every 2nd layer, dropout is applied after the conv layer, pooling before. Note
+ that pooling will not used in the init layer whereas dropout is already applied there.
+
+ ```python
+ model=CNN,
+ dropout_freq=2,
+ dropout=0.3,
+ pooling_type="max",
+ pooling_freq=2,
+ pooling_size=3,
+ layer_configuration=[(16, 3), (32, 7), (64, 11)],
+ dense_layer_configuration=[128, 64],
+ ```
+ """
def __init__(self, input_shape: list, output_shape: list, activation="relu", activation_output="linear",
- optimizer="adam", regularizer=None, kernel_size=1, dropout=None, **kwargs):
+ optimizer="adam", regularizer=None, kernel_size=7, dropout=None, dropout_freq=None, pooling_freq=None,
+ pooling_type="max",
+ n_layer=1, n_filter=10, layer_configuration=None, pooling_size=None,
+ dense_layer_configuration=None, **kwargs):
assert len(input_shape) == 1
assert len(output_shape) == 1
@@ -42,12 +243,31 @@ class CNN(AbstractModelClass): # pragma: no cover
self.kernel_regularizer = self._set_regularizer(regularizer, **kwargs)
self.kernel_size = kernel_size
self.optimizer = self._set_optimizer(optimizer, **kwargs)
+ self.layer_configuration = (n_layer, n_filter, self.kernel_size) if layer_configuration is None else layer_configuration
+ self.dense_layer_configuration = dense_layer_configuration or []
+ self.pooling = self._set_pooling(pooling_type)
+ self.pooling_size = pooling_size
self.dropout, self.dropout_rate = self._set_dropout(activation, dropout)
+ self.dropout_freq = self._set_layer_freq(dropout_freq)
+ self.pooling_freq = self._set_layer_freq(pooling_freq)
# 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=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_pooling(self, pooling):
+ try:
+ return self._pooling.get(pooling.lower())
+ except KeyError:
+ raise AttributeError(f"Given pooling {pooling} is not supported in this model class.")
+
+ def _set_layer_freq(self, param):
+ param = 0 if param is None else param
+ assert 0 <= param
+ assert isinstance(param, int)
+ return param
def _set_activation(self, activation):
try:
@@ -91,6 +311,67 @@ class CNN(AbstractModelClass): # pragma: no cover
assert 0 <= dropout_rate < 1
return self._dropout.get(activation, keras.layers.Dropout), dropout_rate
+ def set_model(self):
+ """
+ Build the model.
+ """
+ if isinstance(self.layer_configuration, tuple) is True:
+ n_layer, n_hidden, kernel_size = self.layer_configuration
+ if isinstance(kernel_size, list):
+ assert len(kernel_size) == n_layer # use individual filter sizes for each layer
+ conf = [(n_hidden, kernel_size[i]) for i in range(n_layer)]
+ else:
+ assert isinstance(kernel_size, int) # use same filter size for all layers
+ conf = [(n_hidden, kernel_size) for _ in range(n_layer)]
+ else:
+ assert isinstance(self.layer_configuration, list) is True
+ if not isinstance(self.layer_configuration[0], tuple):
+ if isinstance(self.kernel_size, list):
+ assert len(self.kernel_size) == len(self.layer_configuration) # use individual filter sizes for each layer
+ conf = [(n_filter, self.kernel_size[i]) for i, n_filter in enumerate(self.layer_configuration)]
+ else:
+ assert isinstance(self.kernel_size, int) # use same filter size for all layers
+ conf = [(n_filter, self.kernel_size) for n_filter in self.layer_configuration]
+ else:
+ assert len(self.layer_configuration[0]) == 2
+ conf = self.layer_configuration
+
+ x_input = keras.layers.Input(shape=self._input_shape)
+ x_in = x_input
+ for layer, (n_filter, kernel_size) in enumerate(conf):
+ if self.pooling_size is not None and self.pooling_freq > 0 and layer % self.pooling_freq == 0 and layer > 0:
+ x_in = self.pooling((self.pooling_size, 1), strides=(1, 1), padding='valid')(x_in)
+ x_in = keras.layers.Conv2D(filters=n_filter, kernel_size=(kernel_size, 1),
+ kernel_initializer=self.kernel_initializer,
+ kernel_regularizer=self.kernel_regularizer)(x_in)
+ x_in = self.activation()(x_in)
+ if self.dropout is not None and self.dropout_freq > 0 and layer % self.dropout_freq == 0:
+ x_in = self.dropout(self.dropout_rate)(x_in)
+
+ x_in = keras.layers.Flatten()(x_in)
+ 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])
+ print(self.model.summary())
+
+ def set_compile_options(self):
+ # self.compile_options = {"loss": [custom_loss([keras.losses.mean_squared_error, var_loss])],
+ # "metrics": ["mse", "mae", var_loss]}
+ self.compile_options = {"loss": [keras.losses.mean_squared_error],
+ "metrics": ["mse", "mae", var_loss]}
+
+
+class CNN_16_32_64(CNN):
+
def set_model(self):
"""
Build the model.
@@ -123,7 +404,3 @@ class CNN(AbstractModelClass): # pragma: no cover
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])
-
- def set_compile_options(self):
- self.compile_options = {"loss": [custom_loss([keras.losses.mean_squared_error, var_loss])],
- "metrics": ["mse", "mae", var_loss]}
diff --git a/mlair/run_modules/experiment_setup.py b/mlair/run_modules/experiment_setup.py
index aca5f583bff181281aee9104df6750da61ad4f93..df797ffc23370bf4f45bb2b4f76e5f71e9bd030f 100644
--- a/mlair/run_modules/experiment_setup.py
+++ b/mlair/run_modules/experiment_setup.py
@@ -389,7 +389,7 @@ class ExperimentSetup(RunEnvironment):
self._set_param("neighbors", ["DEBW030"]) # TODO: just for testing
# set competitors
- if model_display_name is not None and model_display_name in competitors:
+ if model_display_name is not None and competitors is not None and model_display_name in competitors:
raise IndexError(f"Given model_display_name {model_display_name} is also present in the competitors "
f"variable {competitors}. To assure a proper workflow it is required to have unique names "
f"for each model and competitor. Please use a different model display name or competitor.")
diff --git a/mlair/run_modules/model_setup.py b/mlair/run_modules/model_setup.py
index 98263eb732d8067fba0950c7a4882fb3ef020995..4e9f8fa4439e9885a6c16c2b2eccfee2c97fd936 100644
--- a/mlair/run_modules/model_setup.py
+++ b/mlair/run_modules/model_setup.py
@@ -172,6 +172,7 @@ class ModelSetup(RunEnvironment):
def report_model(self):
# report model settings
+ _f = self._clean_name
model_settings = self.model.get_settings()
model_settings.update(self.model.compile_options)
model_settings.update(self.model.optimizer.get_config())
@@ -180,9 +181,12 @@ class ModelSetup(RunEnvironment):
if v is None:
continue
if isinstance(v, list):
- v = ",".join(self._clean_name(str(u)) for u in v)
+ if isinstance(v[0], dict):
+ v = ["{" + vi + "}" for vi in [",".join(f"{_f(str(uk))}:{_f(str(uv))}" for uk, uv in d.items()) for d in v]]
+ else:
+ v = ",".join(_f(str(u)) for u in v)
if "<" in str(v):
- v = self._clean_name(str(v))
+ v = _f(str(v))
df.loc[k] = str(v)
df.loc["count params"] = str(self.model.count_params())
df.sort_index(inplace=True)
@@ -202,5 +206,8 @@ class ModelSetup(RunEnvironment):
@staticmethod
def _clean_name(orig_name: str):
mod_name = re.sub(r'^{0}'.format(re.escape("<")), '', orig_name).replace("'", "").split(" ")
- mod_name = mod_name[1] if any(map(lambda x: x in mod_name[0], ["class", "function", "method"])) else mod_name[0]
- return mod_name[:-1] if mod_name[-1] == ">" else mod_name
+ mod_name = mod_name[1] if any(map(lambda x: x in mod_name[0], ["class", "function", "method"])) else mod_name
+ mod_name = mod_name[0].split(".")[-1] if any(
+ map(lambda x: x in mod_name[0], ["tensorflow", "keras"])) else mod_name
+ mod_name = mod_name[:-1] if mod_name[-1] == ">" else "".join(mod_name)
+ return mod_name.split(".")[-1] if any(map(lambda x: x in mod_name, ["tensorflow", "keras"])) else mod_name
diff --git a/test/test_run_modules/test_model_setup.py b/test/test_run_modules/test_model_setup.py
index 7cefd0e58f5b9b0787bafddffe1ad07e4851a068..60b37207ceefc4088b33fa002dac9db7c6c35399 100644
--- a/test/test_run_modules/test_model_setup.py
+++ b/test/test_run_modules/test_model_setup.py
@@ -126,6 +126,14 @@ class TestModelSetup:
def test_init(self):
pass
+ def test_clean_name(self, setup):
+ in_str = "<tensorflow.python.keras.initializers.initializers_v2.HeNormal object at 0x7fecfa0da9b0>"
+ assert setup._clean_name(in_str) == "HeNormal"
+ in_str = "<class 'tensorflow.python.keras.layers.convolutional.Conv2D'>"
+ assert setup._clean_name(in_str) == "Conv2D"
+ in_str = "default"
+ assert setup._clean_name(in_str) == "default"
+
class DummyData:
@@ -141,4 +149,4 @@ class DummyData:
def get_Y(self, upsampling=False, as_numpy=True):
Y1 = np.random.randint(0, 10, size=(self.number_of_samples, 5)) # samples, window
Y2 = np.random.randint(21, 30, size=(self.number_of_samples, 3)) # samples, window
- return [Y1, Y2]
\ No newline at end of file
+ return [Y1, Y2]