diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py
index 992f4abf15be8cb52dcac549ace723a8b1cf0b17..2d57bafde67ee7d5186f4503241978fc7a357778 100644
--- a/ANN_Data_Generator.py
+++ b/ANN_Data_Generator.py
@@ -31,7 +31,8 @@ class TrainingDataGenerator:
"""
def __init__(self, initial_conditions, left_bound=-1, right_bound=1,
- balance=0.5, stencil_length=3, directory='test_data'):
+ balance=0.5, stencil_length=3, directory='test_data',
+ add_reconstructions=True):
"""Initializes TrainingDataGenerator.
Parameters
@@ -49,11 +50,15 @@ class TrainingDataGenerator:
directory : str, optional
Path to directory in which training data is saved.
Default: 'test_data'.
+ add_reconstructions: bool, optional
+ Flag whether reconstructions of the middle cell are included.
+ Default: True.
"""
self._balance = balance
self._left_bound = left_bound
self._right_bound = right_bound
+ self._add_reconstructions = add_reconstructions
# Set stencil length
if stencil_length % 2 == 0:
@@ -174,7 +179,10 @@ class TrainingDataGenerator:
print('Samples to complete:', num_samples)
tic = time.perf_counter()
- input_data = np.zeros((num_samples, self._stencil_length+2))
+ num_datapoints = self._stencil_length
+ if self._add_reconstructions:
+ num_datapoints += 2
+ input_data = np.zeros((num_samples, num_datapoints))
num_init_cond = len(initial_conditions)
count = 0
for i in range(num_samples):
@@ -202,7 +210,8 @@ class TrainingDataGenerator:
right_bound=right_bound, quadrature='Gauss',
quadrature_config={'num_eval_points': polynomial_degree+1})
input_data[i] = dg_scheme.build_training_data(
- adjustment, self._stencil_length, initial_condition)
+ adjustment, self._stencil_length, self._add_reconstructions,
+ initial_condition)
count += 1
if count % 1000 == 0:
diff --git a/DG_Approximation.py b/DG_Approximation.py
index 014f90d507c8d1449b5009dcaba01d9c35d9040b..334892294e2879698ab0af082184514eab1289d4 100644
--- a/DG_Approximation.py
+++ b/DG_Approximation.py
@@ -8,6 +8,9 @@ TODO: Adapt TCD from Soraya
(Dropbox->...->TEST_troubled-cell-detector->Troubled_Cell_Detector)
TODO: Add verbose output
TODO: Improve file naming (e.g. use '.' instead of '__')
+TODO: Give option to leave out reconstructions for ANN data -> Done
+TODO: Force input_size for each ANN model to be stencil length
+TODO: Combine ANN workflows
Critical, but not urgent:
TODO: Use cfl_number for updating, not just time
@@ -384,6 +387,8 @@ class DGScheme:
Extent of adjustment of each evaluation point in x-direction.
stencil_length : int
Size of training data array.
+ add_reconstructions: bool
+ Flag whether reconstructions of the middle cell are included.
initial_condition : InitialCondition object, optional
Initial condition used for calculation.
Default: None (i.e. instance variable).
@@ -399,5 +404,6 @@ class DGScheme:
initial_condition = self._init_cond
projection = self._do_initial_projection(initial_condition, adjustment)
- return self._detector.calculate_cell_average_and_reconstructions(
- projection[:, 1:-1], stencil_length)
+ return self._detector.calculate_cell_average(projection[:, 1:-1],
+ stencil_length,
+ add_reconstructions)
diff --git a/Troubled_Cell_Detector.py b/Troubled_Cell_Detector.py
index 18f6cb73ec46098553c5b029cc21a20caca352f5..1537699384f8e330e35c799dd8a5edf613195071 100644
--- a/Troubled_Cell_Detector.py
+++ b/Troubled_Cell_Detector.py
@@ -132,13 +132,13 @@ class TroubledCellDetector:
"""
pass
- def calculate_cell_average_and_reconstructions(self, projection,
- stencil_length):
- """Calculates cell averages and reconstructions for a given projection.
+ def calculate_cell_average(self, projection, stencil_length,
+ add_reconstructions=True):
+ """Calculates cell averages for a given projection.
Calculate the cell averages of all cells in a projection.
- Reconstructions are only calculated for the middle cell and added
- left and right to it, respectively.
+ If desired, reconstructions are calculated for the middle cell
+ and added left and right to it, respectively.
Parameters
----------
@@ -146,29 +146,35 @@ class TroubledCellDetector:
Matrix of projection for each polynomial degree.
stencil_length : int
Size of data array.
+ add_reconstructions: bool, optional
+ Flag whether reconstructions of the middle cell are included.
+ Default: True.
Returns
-------
ndarray
- Matrix containing cell averages and reconstructions for initial
+ Matrix containing cell averages (and reconstructions) for initial
projection.
"""
cell_averages = calculate_approximate_solution(
projection, [0], 0, self._basis.get_basis_vector())
- left_reconstructions = calculate_approximate_solution(
- projection, [-1], self._polynomial_degree,
- self._basis.get_basis_vector())
- right_reconstructions = calculate_approximate_solution(
- projection, [1], self._polynomial_degree,
- self._basis.get_basis_vector())
- middle_idx = stencil_length//2
- return np.array(
- list(map(np.float64, zip(cell_averages[:, :middle_idx],
- left_reconstructions[:, middle_idx],
- cell_averages[:, middle_idx],
- right_reconstructions[:, middle_idx],
- cell_averages[:, middle_idx+1:]))))
+
+ if add_reconstructions:
+ left_reconstructions = calculate_approximate_solution(
+ projection, [-1], self._polynomial_degree,
+ self._basis.get_basis_vector())
+ right_reconstructions = calculate_approximate_solution(
+ projection, [1], self._polynomial_degree,
+ self._basis.get_basis_vector())
+ middle_idx = stencil_length//2
+ return np.array(
+ list(map(np.float64, zip(cell_averages[:, :middle_idx],
+ left_reconstructions[:, middle_idx],
+ cell_averages[:, middle_idx],
+ right_reconstructions[:, middle_idx],
+ cell_averages[:, middle_idx+1:]))))
+ return np.array(list(map(np.float64, cell_averages)))
def plot_results(self, projection, troubled_cell_history, time_history):
"""Plots results and troubled cells of a projection.
@@ -283,9 +289,13 @@ class ArtificialNeuralNetwork(TroubledCellDetector):
super()._reset(config)
self._stencil_len = config.pop('stencil_len', 3)
+ self._add_reconstructions = config.pop('add_reconstructions', True)
self._model = config.pop('model', 'ThreeLayerReLu')
+ num_datapoints = self._stencil_len
+ if self._add_reconstructions:
+ num_datapoints += 2
self._model_config = config.pop('model_config', {
- 'input_size': self._stencil_len+2, 'first_hidden_size': 8,
+ 'input_size': num_datapoints, 'first_hidden_size': 8,
'second_hidden_size': 4, 'output_size': 2,
'activation_function': 'Softmax', 'activation_config': {'dim': 1}})
model_state = config.pop('model_state', 'Snakemake-Test/trained '
@@ -322,9 +332,9 @@ class ArtificialNeuralNetwork(TroubledCellDetector):
# Calculate input data depending on stencil length
input_data = torch.from_numpy(
- np.vstack([self.calculate_cell_average_and_reconstructions(
+ np.vstack([self.calculate_cell_average(
projection[:, cell-num_ghost_cells:cell+num_ghost_cells+1],
- self._stencil_len)
+ self._stencil_len, self._add_reconstructions)
for cell in range(num_ghost_cells,
len(projection[0])-num_ghost_cells)]))
diff --git a/config.yaml b/config.yaml
index edeea1a3771f534960da20ebdf951fdd67048318..c260bc19457e4029a01046941d3ddbf86188acec 100644
--- a/config.yaml
+++ b/config.yaml
@@ -1,9 +1,9 @@
-data_dir: 'model-Mar01'
+data_dir: 'model-Soraya_Mar02'
random_seed: 1234
# Parameter for Approximation with Troubled Cell Detection
Approximation:
- plot_dir: 'fig-Mar01'
+ plot_dir: 'fig-Soraya_Mar02'
schemes:
Separation_Test:
@@ -21,6 +21,7 @@ Approximation:
detector_config:
fold_len: 16
whisker_len: 3
+ add_reconstructions: False
model_state: 'Adam.model.pt'
init_cond: 'Sine'
@@ -51,6 +52,7 @@ ANN_Data:
smooth_troubled_balance: 0.5
stencil_length: 3
+ add_reconstructions : False
# Initial Conditions for Training Data
functions:
@@ -85,7 +87,7 @@ ANN_Training:
threshold: 1.0e-5
batch_size: 500
model: ThreeLayerReLu
- model_config: {}
+ model_config: {input_size: 3}
loss_function: BCELoss
optimizer: Adam
SGD:
@@ -93,7 +95,7 @@ ANN_Training:
threshold: 1.0e-5
batch_size: 500
model: ThreeLayerReLu
- model_config: {}
+ model_config: {input_size: 3}
loss_function: BCELoss
optimizer: SGD
diff --git a/workflows/ANN_data.smk b/workflows/ANN_data.smk
index 9e29805a8ad5b12cf1106278120043e074759797..c0af4d03a5a3dbdb592250d694d8b193a284b3a5 100644
--- a/workflows/ANN_data.smk
+++ b/workflows/ANN_data.smk
@@ -22,6 +22,7 @@ rule generate_data:
balance = config['smooth_troubled_balance'],
stencil_length = config['stencil_length'],
sample_number = config['sample_number'],
+ reconstruction_flag = config['add_reconstructions'],
functions = expand('{FUNCTION}', FUNCTION=config['functions'])
log:
DIR+'/log/generate_data.log'
@@ -41,6 +42,7 @@ rule generate_data:
initial_conditions=initial_conditions,
left_bound=params.left_bound, right_bound=params.right_bound,
balance=params.balance,
- stencil_length=params.stencil_length, directory=DIR)
+ stencil_length=params.stencil_length, directory=DIR,
+ add_reconstructions=params.reconstruction_flag)
data = generator.build_training_data(
num_samples=params.sample_number)
\ No newline at end of file