From 68f1b79741d597665642b55b0437b06063dc9d07 Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Tue, 22 Sep 2020 14:59:36 +0200
Subject: [PATCH] parameters are renamed, docs updated
---
README.md | 32 +++----
docs/_source/customise.rst | 20 ++---
docs/_source/get-started.rst | 12 +--
mlair/model_modules/model_class.py | 86 +++++++++----------
mlair/run_modules/model_setup.py | 8 +-
test/test_data_handler/test_iterator.py | 4 +-
.../test_advanced_paddings.py | 4 +-
test/test_model_modules/test_flatten_tail.py | 4 +-
test/test_model_modules/test_model_class.py | 12 +--
test/test_run_modules/test_model_setup.py | 2 +-
10 files changed, 92 insertions(+), 92 deletions(-)
diff --git a/README.md b/README.md
index c33aab4b..8e424adc 100644
--- a/README.md
+++ b/README.md
@@ -47,7 +47,7 @@ mlair.run()
The logging output will show you many informations. Additional information (including debug messages) are collected
inside the experiment path in the logging folder.
```log
-INFO: mlair started
+INFO: DefaultWorkflow started
INFO: ExperimentSetup started
INFO: Experiment path is: /home/<usr>/mlair/testrun_network
...
@@ -55,7 +55,7 @@ INFO: load data for DEBW001 from JOIN
...
INFO: Training started
...
-INFO: mlair finished after 00:00:12 (hh:mm:ss)
+INFO: DefaultWorkflow finished after 00:00:12 (hh:mm:ss)
```
## Example 2
@@ -77,7 +77,7 @@ mlair.run(stations=stations,
```
The output looks similar, but we can see, that the new stations are loaded.
```log
-INFO: mlair started
+INFO: DefaultWorkflow started
INFO: ExperimentSetup started
...
INFO: load data for DEBW030 from JOIN
@@ -85,7 +85,7 @@ INFO: load data for DEBW037 from JOIN
...
INFO: Training started
...
-INFO: mlair finished after 00:00:24 (hh:mm:ss)
+INFO: DefaultWorkflow finished after 00:00:24 (hh:mm:ss)
```
## Example 3
@@ -111,11 +111,11 @@ mlair.run(stations=stations,
```
We can see from the terminal that no training was performed. Analysis is now made on the new stations.
```log
-INFO: mlair started
+INFO: DefaultWorkflow started
...
INFO: No training has started, because trainable parameter was false.
...
-INFO: mlair finished after 00:00:06 (hh:mm:ss)
+INFO: DefaultWorkflow finished after 00:00:06 (hh:mm:ss)
```
@@ -137,7 +137,7 @@ DefaultWorkflow.run()
```
The output of running this default workflow will be structured like the following.
```log
-INFO: mlair started
+INFO: DefaultWorkflow started
INFO: ExperimentSetup started
...
INFO: ExperimentSetup finished after 00:00:01 (hh:mm:ss)
@@ -153,7 +153,7 @@ INFO: Training finished after 00:02:15 (hh:mm:ss)
INFO: PostProcessing started
...
INFO: PostProcessing finished after 00:01:37 (hh:mm:ss)
-INFO: mlair finished after 00:04:05 (hh:mm:ss)
+INFO: DefaultWorkflow finished after 00:04:05 (hh:mm:ss)
```
# Customised Run Module and Workflow
@@ -199,7 +199,7 @@ CustomWorkflow.run()
The output will look like:
```log
-INFO: mlair started
+INFO: Workflow started
...
INFO: ExperimentSetup finished after 00:00:12 (hh:mm:ss)
INFO: CustomStage started
@@ -207,7 +207,7 @@ INFO: Just running a custom stage.
INFO: test_string = Hello World
INFO: epochs = 128
INFO: CustomStage finished after 00:00:01 (hh:mm:ss)
-INFO: mlair finished after 00:00:13 (hh:mm:ss)
+INFO: Workflow finished after 00:00:13 (hh:mm:ss)
```
# Custom Model
@@ -226,9 +226,9 @@ import keras
class MyCustomisedModel(AbstractModelClass):
- def __init__(self, shape_inputs: list, shape_outputs: list):
+ def __init__(self, input_shape: list, output_shape: list):
- super().__init__(shape_inputs[0], shape_outputs[0])
+ super().__init__(input_shape[0], output_shape[0])
# settings
self.dropout_rate = 0.1
@@ -250,21 +250,21 @@ class MyCustomisedModel(AbstractModelClass):
loss has been added for demonstration only, because we use a build-in loss function. Nonetheless, we always encourage
you to add the loss as custom object, to prevent potential errors when loading an already created model instead of
training a new one.
-* Now build your model inside `set_model()` by using the instance attributes `self.shape_inputs` and
- `self.shape_outputs` and storing the model as `self.model`.
+* Now build your model inside `set_model()` by using the instance attributes `self._input_shape` and
+ `self._output_shape` and storing the model as `self.model`.
```python
class MyCustomisedModel(AbstractModelClass):
def set_model(self):
- x_input = keras.layers.Input(shape=self.shape_inputs)
+ x_input = keras.layers.Input(shape=self._input_shape)
x_in = keras.layers.Conv2D(32, (1, 1), padding='same', name='{}_Conv_1x1'.format("major"))(x_input)
x_in = self.activation(name='{}_conv_act'.format("major"))(x_in)
x_in = keras.layers.Flatten(name='{}'.format("major"))(x_in)
x_in = keras.layers.Dropout(self.dropout_rate, name='{}_Dropout_1'.format("major"))(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.shape_outputs, name='{}_Dense'.format("major"))(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])
```
diff --git a/docs/_source/customise.rst b/docs/_source/customise.rst
index 4c9ee538..3d3873cb 100644
--- a/docs/_source/customise.rst
+++ b/docs/_source/customise.rst
@@ -27,7 +27,7 @@ The output of running this default workflow will be structured like the followin
.. code-block::
- INFO: mlair started
+ INFO: DefaultWorkflow started
INFO: ExperimentSetup started
...
INFO: ExperimentSetup finished after 00:00:01 (hh:mm:ss)
@@ -43,7 +43,7 @@ The output of running this default workflow will be structured like the followin
INFO: PostProcessing started
...
INFO: PostProcessing finished after 00:01:37 (hh:mm:ss)
- INFO: mlair finished after 00:04:05 (hh:mm:ss)
+ INFO: DefaultWorkflow finished after 00:04:05 (hh:mm:ss)
Custom Model
------------
@@ -65,9 +65,9 @@ How to create a customised model?
class MyCustomisedModel(AbstractModelClass):
- def __init__(self, shape_inputs: list, shape_outputs: list):
+ def __init__(self, input_shape: list, output_shape: list):
- super().__init__(shape_inputs[0], shape_outputs[0])
+ super().__init__(input_shape[0], output_shape[0])
# settings
self.dropout_rate = 0.1
@@ -88,22 +88,22 @@ How to create a customised model?
loss has been added for demonstration only, because we use a build-in loss function. Nonetheless, we always encourage
you to add the loss as custom object, to prevent potential errors when loading an already created model instead of
training a new one.
-* Now build your model inside :py:`set_model()` by using the instance attributes :py:`self.shape_inputs` and
- :py:`self.shape_outputs` and storing the model as :py:`self.model`.
+* Now build your model inside :py:`set_model()` by using the instance attributes :py:`self._input_shape` and
+ :py:`self._output_shape` and storing the model as :py:`self.model`.
.. code-block:: python
class MyCustomisedModel(AbstractModelClass):
def set_model(self):
- x_input = keras.layers.Input(shape=self.shape_inputs)
+ x_input = keras.layers.Input(shape=self._input_shape)
x_in = keras.layers.Conv2D(32, (1, 1), padding='same', name='{}_Conv_1x1'.format("major"))(x_input)
x_in = self.activation(name='{}_conv_act'.format("major"))(x_in)
x_in = keras.layers.Flatten(name='{}'.format("major"))(x_in)
x_in = keras.layers.Dropout(self.dropout_rate, name='{}_Dropout_1'.format("major"))(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.shape_outputs, name='{}_Dense'.format("major"))(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])
@@ -342,7 +342,7 @@ The output will look like:
.. code-block::
- INFO: mlair started
+ INFO: Workflow started
...
INFO: ExperimentSetup finished after 00:00:12 (hh:mm:ss)
INFO: CustomStage started
@@ -350,4 +350,4 @@ The output will look like:
INFO: test_string = Hello World
INFO: epochs = 128
INFO: CustomStage finished after 00:00:01 (hh:mm:ss)
- INFO: mlair finished after 00:00:13 (hh:mm:ss)
\ No newline at end of file
+ INFO: Workflow finished after 00:00:13 (hh:mm:ss)
\ No newline at end of file
diff --git a/docs/_source/get-started.rst b/docs/_source/get-started.rst
index 2e8838fd..7c909b77 100644
--- a/docs/_source/get-started.rst
+++ b/docs/_source/get-started.rst
@@ -60,7 +60,7 @@ inside the experiment path in the logging folder.
.. code-block::
- INFO: mlair started
+ INFO: DefaultWorkflow started
INFO: ExperimentSetup started
INFO: Experiment path is: /home/<usr>/mlair/testrun_network
...
@@ -68,7 +68,7 @@ inside the experiment path in the logging folder.
...
INFO: Training started
...
- INFO: mlair finished after 00:00:12 (hh:mm:ss)
+ INFO: DefaultWorkflow finished after 00:00:12 (hh:mm:ss)
Example 2
@@ -94,7 +94,7 @@ The output looks similar, but we can see, that the new stations are loaded.
.. code-block::
- INFO: mlair started
+ INFO: DefaultWorkflow started
INFO: ExperimentSetup started
...
INFO: load data for DEBW030 from JOIN
@@ -102,7 +102,7 @@ The output looks similar, but we can see, that the new stations are loaded.
...
INFO: Training started
...
- INFO: mlair finished after 00:00:24 (hh:mm:ss)
+ INFO: DefaultWorkflow finished after 00:00:24 (hh:mm:ss)
Example 3
~~~~~~~~~
@@ -132,9 +132,9 @@ We can see from the terminal that no training was performed. Analysis is now mad
.. code-block::
- INFO: mlair started
+ INFO: DefaultWorkflow started
...
INFO: No training has started, because trainable parameter was false.
...
- INFO: mlair finished after 00:00:06 (hh:mm:ss)
+ INFO: DefaultWorkflow finished after 00:00:06 (hh:mm:ss)
diff --git a/mlair/model_modules/model_class.py b/mlair/model_modules/model_class.py
index 0e69d220..c9cc13bd 100644
--- a/mlair/model_modules/model_class.py
+++ b/mlair/model_modules/model_class.py
@@ -23,9 +23,9 @@ How to create a customised model?
class MyCustomisedModel(AbstractModelClass):
- def __init__(self, shape_inputs: list, shape_outputs: list):
+ def __init__(self, input_shape: list, output_shape: list):
- super().__init__(shape_inputs[0], shape_outputs[0])
+ super().__init__(input_shape[0], output_shape[0])
# settings
self.dropout_rate = 0.1
@@ -49,14 +49,14 @@ How to create a customised model?
class MyCustomisedModel(AbstractModelClass):
def set_model(self):
- x_input = keras.layers.Input(shape=self.shape_inputs)
+ x_input = keras.layers.Input(shape=self._input_shape)
x_in = keras.layers.Conv2D(32, (1, 1), padding='same', name='{}_Conv_1x1'.format("major"))(x_input)
x_in = self.activation(name='{}_conv_act'.format("major"))(x_in)
x_in = keras.layers.Flatten(name='{}'.format("major"))(x_in)
x_in = keras.layers.Dropout(self.dropout_rate, name='{}_Dropout_1'.format("major"))(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.shape_outputs, name='{}_Dense'.format("major"))(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])
@@ -139,7 +139,7 @@ class AbstractModelClass(ABC):
the corresponding loss function.
"""
- def __init__(self, shape_inputs, shape_outputs) -> None:
+ def __init__(self, input_shape, output_shape) -> None:
"""Predefine internal attributes for model and loss."""
self.__model = None
self.model_name = self.__class__.__name__
@@ -154,8 +154,8 @@ class AbstractModelClass(ABC):
}
self.__compile_options = self.__allowed_compile_options
self.__compile_options_is_set = False
- self.shape_inputs = shape_inputs
- self.shape_outputs = self.__extract_from_tuple(shape_outputs)
+ self._input_shape = input_shape
+ self._output_shape = self.__extract_from_tuple(output_shape)
def __getattr__(self, name: str) -> Any:
"""
@@ -355,17 +355,17 @@ class MyLittleModel(AbstractModelClass):
on the window_lead_time parameter.
"""
- def __init__(self, shape_inputs: list, shape_outputs: list):
+ def __init__(self, input_shape: list, output_shape: list):
"""
Sets model and loss depending on the given arguments.
- :param shape_inputs: list of input shapes (expect len=1 with shape=(window_hist, station, variables))
- :param shape_outputs: list of output shapes (expect len=1 with shape=(window_forecast))
+ :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(shape_inputs) == 1
- assert len(shape_outputs) == 1
- super().__init__(shape_inputs[0], shape_outputs[0])
+ assert len(input_shape) == 1
+ assert len(output_shape) == 1
+ super().__init__(input_shape[0], output_shape[0])
# settings
self.dropout_rate = 0.1
@@ -381,7 +381,7 @@ class MyLittleModel(AbstractModelClass):
"""
Build the model.
"""
- x_input = keras.layers.Input(shape=self.shape_inputs)
+ 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)
@@ -389,7 +389,7 @@ class MyLittleModel(AbstractModelClass):
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.shape_outputs, name='{}_Dense'.format("major"))(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])
@@ -410,17 +410,17 @@ class MyBranchedModel(AbstractModelClass):
Dense layer.
"""
- def __init__(self, shape_inputs: list, shape_outputs: list):
+ def __init__(self, input_shape: list, output_shape: list):
"""
Sets model and loss depending on the given arguments.
- :param shape_inputs: list of input shapes (expect len=1 with shape=(window_hist, station, variables))
- :param shape_outputs: list of output shapes (expect len=1 with shape=(window_forecast))
+ :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(shape_inputs) == 1
- assert len(shape_outputs) == 1
- super().__init__(shape_inputs[0], shape_outputs[0])
+ assert len(input_shape) == 1
+ assert len(output_shape) == 1
+ super().__init__(input_shape[0], output_shape[0])
# settings
self.dropout_rate = 0.1
@@ -438,22 +438,22 @@ class MyBranchedModel(AbstractModelClass):
"""
# add 1 to window_size to include current time step t0
- x_input = keras.layers.Input(shape=self.shape_inputs)
+ x_input = keras.layers.Input(shape=self._input_shape)
x_in = keras.layers.Conv2D(32, (1, 1), padding='same', name='{}_Conv_1x1'.format("major"))(x_input)
x_in = self.activation(name='{}_conv_act'.format("major"))(x_in)
x_in = keras.layers.Flatten(name='{}'.format("major"))(x_in)
x_in = keras.layers.Dropout(self.dropout_rate, name='{}_Dropout_1'.format("major"))(x_in)
x_in = keras.layers.Dense(64, name='{}_Dense_64'.format("major"))(x_in)
x_in = self.activation()(x_in)
- out_minor_1 = keras.layers.Dense(self.shape_outputs, name='{}_Dense'.format("minor_1"))(x_in)
+ out_minor_1 = keras.layers.Dense(self._output_shape, name='{}_Dense'.format("minor_1"))(x_in)
out_minor_1 = self.activation(name="minor_1")(out_minor_1)
x_in = keras.layers.Dense(32, name='{}_Dense_32'.format("major"))(x_in)
x_in = self.activation()(x_in)
- out_minor_2 = keras.layers.Dense(self.shape_outputs, name='{}_Dense'.format("minor_2"))(x_in)
+ out_minor_2 = keras.layers.Dense(self._output_shape, name='{}_Dense'.format("minor_2"))(x_in)
out_minor_2 = self.activation(name="minor_2")(out_minor_2)
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.shape_outputs, name='{}_Dense'.format("major"))(x_in)
+ x_in = keras.layers.Dense(self._output_shape, name='{}_Dense'.format("major"))(x_in)
out_main = self.activation(name="main")(x_in)
self.model = keras.Model(inputs=x_input, outputs=[out_minor_1, out_minor_2, out_main])
@@ -468,17 +468,17 @@ class MyBranchedModel(AbstractModelClass):
class MyTowerModel(AbstractModelClass):
- def __init__(self, shape_inputs: list, shape_outputs: list):
+ def __init__(self, input_shape: list, output_shape: list):
"""
Sets model and loss depending on the given arguments.
- :param shape_inputs: list of input shapes (expect len=1 with shape=(window_hist, station, variables))
- :param shape_outputs: list of output shapes (expect len=1 with shape=(window_forecast))
+ :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(shape_inputs) == 1
- assert len(shape_outputs) == 1
- super().__init__(shape_inputs[0], shape_outputs[0])
+ assert len(input_shape) == 1
+ assert len(output_shape) == 1
+ super().__init__(input_shape[0], output_shape[0])
# settings
self.dropout_rate = 1e-2
@@ -529,7 +529,7 @@ class MyTowerModel(AbstractModelClass):
##########################################
inception_model = InceptionModelBase()
- X_input = keras.layers.Input(shape=self.shape_inputs)
+ X_input = keras.layers.Input(shape=self._input_shape)
X_in = inception_model.inception_block(X_input, conv_settings_dict1, pool_settings_dict1,
regularizer=self.regularizer,
@@ -551,7 +551,7 @@ class MyTowerModel(AbstractModelClass):
# out_main = flatten_tail(X_in, 'Main', activation=activation, bound_weight=True, dropout_rate=self.dropout_rate,
# reduction_filter=64, inner_neurons=64, output_neurons=self.window_lead_time)
- out_main = flatten_tail(X_in, inner_neurons=64, activation=activation, output_neurons=self.shape_outputs,
+ out_main = flatten_tail(X_in, inner_neurons=64, activation=activation, output_neurons=self._output_shape,
output_activation='linear', reduction_filter=64,
name='Main', bound_weight=True, dropout_rate=self.dropout_rate,
kernel_regularizer=self.regularizer
@@ -566,17 +566,17 @@ class MyTowerModel(AbstractModelClass):
class MyPaperModel(AbstractModelClass):
- def __init__(self, shape_inputs: list, shape_outputs: list):
+ def __init__(self, input_shape: list, output_shape: list):
"""
Sets model and loss depending on the given arguments.
- :param shape_inputs: list of input shapes (expect len=1 with shape=(window_hist, station, variables))
- :param shape_outputs: list of output shapes (expect len=1 with shape=(window_forecast))
+ :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(shape_inputs) == 1
- assert len(shape_outputs) == 1
- super().__init__(shape_inputs[0], shape_outputs[0])
+ assert len(input_shape) == 1
+ assert len(output_shape) == 1
+ super().__init__(input_shape[0], output_shape[0])
# settings
self.dropout_rate = .3
@@ -643,7 +643,7 @@ class MyPaperModel(AbstractModelClass):
##########################################
inception_model = InceptionModelBase()
- X_input = keras.layers.Input(shape=self.shape_inputs)
+ X_input = keras.layers.Input(shape=self._input_shape)
pad_size = PadUtils.get_padding_for_same(first_kernel)
# X_in = adv_pad.SymmetricPadding2D(padding=pad_size)(X_input)
@@ -661,7 +661,7 @@ class MyPaperModel(AbstractModelClass):
padding=self.padding)
# out_minor1 = flatten_tail(X_in, 'minor_1', False, self.dropout_rate, self.window_lead_time,
# self.activation, 32, 64)
- out_minor1 = flatten_tail(X_in, inner_neurons=64, activation=activation, output_neurons=self.shape_outputs,
+ out_minor1 = flatten_tail(X_in, inner_neurons=64, activation=activation, output_neurons=self._output_shape,
output_activation='linear', reduction_filter=32,
name='minor_1', bound_weight=False, dropout_rate=self.dropout_rate,
kernel_regularizer=self.regularizer
@@ -679,8 +679,8 @@ class MyPaperModel(AbstractModelClass):
# batch_normalisation=True)
#############################################
- out_main = flatten_tail(X_in, inner_neurons=64 * 2, activation=activation, output_neurons=self.shape_outputs,
- output_activation='linear', reduction_filter=64 * 2,
+ out_main = flatten_tail(X_in, inner_neurons=64 * 2, activation=activation, output_neurons=self._output_shape,
+ output_activation='linear', reduction_filter=64 * 2,
name='Main', bound_weight=False, dropout_rate=self.dropout_rate,
kernel_regularizer=self.regularizer
)
diff --git a/mlair/run_modules/model_setup.py b/mlair/run_modules/model_setup.py
index e4bff5a6..c6af13b0 100644
--- a/mlair/run_modules/model_setup.py
+++ b/mlair/run_modules/model_setup.py
@@ -95,13 +95,13 @@ class ModelSetup(RunEnvironment):
def _set_shapes(self):
"""Set input and output shapes from train collection."""
shape = list(map(lambda x: x.shape[1:], self.data_store.get("data_collection", "train")[0].get_X()))
- self.data_store.set("shape_inputs", shape, self.scope)
+ self.data_store.set("input_shape", shape, self.scope)
shape = list(map(lambda y: y.shape[1:], self.data_store.get("data_collection", "train")[0].get_Y()))
- self.data_store.set("shape_outputs", shape, self.scope)
+ self.data_store.set("output_shape", shape, self.scope)
def compile_model(self):
"""
- Compiles the keras model. Compile options are mandetory and have to be set by implementing set_compile() method
+ Compiles the keras model. Compile options are mandatory and have to be set by implementing set_compile() method
in child class of AbstractModelClass.
"""
compile_options = self.model.compile_options
@@ -135,7 +135,7 @@ class ModelSetup(RunEnvironment):
def build_model(self):
"""Build model using input and output shapes from data store."""
- args_list = ["shape_inputs", "shape_outputs"]
+ args_list = ["input_shape", "output_shape"]
args = self.data_store.create_args_dict(args_list, self.scope)
model = self.data_store.get("model_class")
self.model = model(**args)
diff --git a/test/test_data_handler/test_iterator.py b/test/test_data_handler/test_iterator.py
index ff81fc7b..ec224c06 100644
--- a/test/test_data_handler/test_iterator.py
+++ b/test/test_data_handler/test_iterator.py
@@ -213,12 +213,12 @@ class TestKerasIterator:
def test_get_model_rank_single_output_branch(self):
iterator = object.__new__(KerasIterator)
- iterator.model = MyLittleModel(shape_inputs=[(14, 1, 2)], shape_outputs=[(3,)])
+ iterator.model = MyLittleModel(input_shape=[(14, 1, 2)], output_shape=[(3,)])
assert iterator._get_model_rank() == 1
def test_get_model_rank_multiple_output_branch(self):
iterator = object.__new__(KerasIterator)
- iterator.model = MyBranchedModel(shape_inputs=[(14, 1, 2)], shape_outputs=[(3,)])
+ iterator.model = MyBranchedModel(input_shape=[(14, 1, 2)], output_shape=[(3,)])
assert iterator._get_model_rank() == 3
def test_get_model_rank_error(self):
diff --git a/test/test_model_modules/test_advanced_paddings.py b/test/test_model_modules/test_advanced_paddings.py
index 8ca81c42..6035e8d7 100644
--- a/test/test_model_modules/test_advanced_paddings.py
+++ b/test/test_model_modules/test_advanced_paddings.py
@@ -472,7 +472,7 @@ class TestPadding2D:
else:
layer_name = pad_type
pd_ap = pd(padding=(1, 2), name=f"{layer_name}_layer")(input_x)
- assert pd_ap._keras_history[0].input_shape == (None, 32, 32, 3)
- assert pd_ap._keras_history[0].output_shape == (None, 34, 36, 3)
+ assert pd_ap._keras_history[0]._input_shape == (None, 32, 32, 3)
+ assert pd_ap._keras_history[0]._output_shape == (None, 34, 36, 3)
assert pd_ap._keras_history[0].padding == ((1, 1), (2, 2))
assert pd_ap._keras_history[0].name == f"{layer_name}_layer"
diff --git a/test/test_model_modules/test_flatten_tail.py b/test/test_model_modules/test_flatten_tail.py
index 623d51c0..1ef0bdd9 100644
--- a/test/test_model_modules/test_flatten_tail.py
+++ b/test/test_model_modules/test_flatten_tail.py
@@ -67,7 +67,7 @@ class TestFlattenTail:
flatten = self.step_in(inner_dense)
assert flatten.name == 'Main_tail'
input_layer = self.step_in(flatten)
- assert input_layer.input_shape == (None, 7, 1, 2)
+ assert input_layer._input_shape == (None, 7, 1, 2)
def test_flatten_tail_all_settings(self, model_input):
tail = flatten_tail(input_x=model_input, inner_neurons=64, activation=keras.layers.advanced_activations.ELU,
@@ -115,5 +115,5 @@ class TestFlattenTail:
assert isinstance(reduc_conv.kernel_regularizer, keras.regularizers.L1L2)
input_layer = self.step_in(reduc_conv)
- assert input_layer.input_shape == (None, 7, 1, 2)
+ assert input_layer._input_shape == (None, 7, 1, 2)
diff --git a/test/test_model_modules/test_model_class.py b/test/test_model_modules/test_model_class.py
index 3e77fd17..0ad5d123 100644
--- a/test/test_model_modules/test_model_class.py
+++ b/test/test_model_modules/test_model_class.py
@@ -12,7 +12,7 @@ class Paddings:
class AbstractModelSubClass(AbstractModelClass):
def __init__(self):
- super().__init__(shape_inputs=(12, 1, 2), shape_outputs=3)
+ super().__init__(input_shape=(12, 1, 2), output_shape=3)
self.test_attr = "testAttr"
@@ -20,7 +20,7 @@ class TestAbstractModelClass:
@pytest.fixture
def amc(self):
- return AbstractModelClass(shape_inputs=(14, 1, 2), shape_outputs=(3,))
+ return AbstractModelClass(input_shape=(14, 1, 2), output_shape=(3,))
@pytest.fixture
def amsc(self):
@@ -31,8 +31,8 @@ class TestAbstractModelClass:
# assert amc.loss is None
assert amc.model_name == "AbstractModelClass"
assert amc.custom_objects == {}
- assert amc.shape_inputs == (14, 1, 2)
- assert amc.shape_outputs == 3
+ assert amc._input_shape == (14, 1, 2)
+ assert amc._output_shape == 3
def test_model_property(self, amc):
amc.model = keras.Model()
@@ -204,7 +204,7 @@ class TestMyPaperModel:
@pytest.fixture
def mpm(self):
- return MyPaperModel(shape_inputs=[(7, 1, 9)], shape_outputs=[(4,)])
+ return MyPaperModel(input_shape=[(7, 1, 9)], output_shape=[(4,)])
def test_init(self, mpm):
# check if loss number of loss functions fit to model outputs
@@ -217,7 +217,7 @@ class TestMyPaperModel:
def test_set_model(self, mpm):
assert isinstance(mpm.model, keras.Model)
- assert mpm.model.layers[0].output_shape == (None, 7, 1, 9)
+ assert mpm.model.layers[0]._output_shape == (None, 7, 1, 9)
# check output dimensions
if isinstance(mpm.model.output_shape, tuple):
assert mpm.model.output_shape == (None, 4)
diff --git a/test/test_run_modules/test_model_setup.py b/test/test_run_modules/test_model_setup.py
index 1b3e43b2..d2989a93 100644
--- a/test/test_run_modules/test_model_setup.py
+++ b/test/test_run_modules/test_model_setup.py
@@ -64,7 +64,7 @@ class TestModelSetup:
@pytest.fixture
def setup_with_model(self, setup):
- setup.model = AbstractModelClass(shape_inputs=(12, 1), shape_outputs=2)
+ setup.model = AbstractModelClass(input_shape=(12, 1), output_shape=2)
setup.model.test_param = "42"
yield setup
RunEnvironment().__del__()
--
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