diff --git a/04d_TrainModels_ANN.py b/04d_TrainModels_ANN.py
index 2a1339fc1e74e225d84e601e615aaafc0fdb97f3..092e34767d2b5edeeff859b679594744c2e4edad 100644
--- a/04d_TrainModels_ANN.py
+++ b/04d_TrainModels_ANN.py
@@ -13,9 +13,9 @@ models = {
'ANN': ANN(),
}
-EPOCHS = 1_000
+EPOCHS = 10_000
BATCH_SIZE = 32
-LEARNING_RATE = 1e-2
+LEARNING_RATE = 1e-1
PLOT_LEARNING = True
diff --git a/99_VisReconstructMethods.ipynb b/99_VisReconstructMethods.ipynb
index ad43610bbd1b1613e872bff7910638c16d287f3a..e7f67ce09a591391a0daaf67cde2e448beacff56 100644
--- a/99_VisReconstructMethods.ipynb
+++ b/99_VisReconstructMethods.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
@@ -31,7 +31,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -69,17 +69,15 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Model: \t\t\tANN\n",
- "Singular value: \t21\n",
- "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n",
- "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n"
+ "Model: \t\t\tradialBasisMod_multiquadric\n",
+ "Singular value: \t21\n"
]
}
],
@@ -225,7 +223,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
@@ -239,7 +237,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
@@ -265,7 +263,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
@@ -284,16 +282,16 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([[3.44496305]])"
+ "array([[0.14369799]])"
]
},
- "execution_count": 12,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/Results/ANN/Error_POD_Nodes_L_1.jpg b/Results/ANN/Error_POD_Nodes_L_1.jpg
index 05e8a0dc3961f6c223c086b183e5c8b47ccee569..d4efffea8c882d27e925de21b175d1f02b9810d9 100644
Binary files a/Results/ANN/Error_POD_Nodes_L_1.jpg and b/Results/ANN/Error_POD_Nodes_L_1.jpg differ
diff --git a/Results/ANN/Error_POD_Nodes_L_2.jpg b/Results/ANN/Error_POD_Nodes_L_2.jpg
index 2a315d6950e17e99986ab245c8f92bc7da01da9f..789aca5db00c9a09264ce1fac96820731be4d494 100644
Binary files a/Results/ANN/Error_POD_Nodes_L_2.jpg and b/Results/ANN/Error_POD_Nodes_L_2.jpg differ
diff --git a/Results/ANN/Error_POD_Nodes_L_infty.jpg b/Results/ANN/Error_POD_Nodes_L_infty.jpg
index b6ee79f69e98c2ed040f22af83be0d83ca614cc0..831097bb285704cf890b7601f7c44420c1b89ea5 100644
Binary files a/Results/ANN/Error_POD_Nodes_L_infty.jpg and b/Results/ANN/Error_POD_Nodes_L_infty.jpg differ
diff --git a/Results/ANN/Error_POD_Nodes_semilogy_L_1.jpg b/Results/ANN/Error_POD_Nodes_semilogy_L_1.jpg
index ac3b09b047d4f9459a17e25358e08929ec0954a3..1526093175c06716b857d582073e55c04c0f1251 100644
Binary files a/Results/ANN/Error_POD_Nodes_semilogy_L_1.jpg and b/Results/ANN/Error_POD_Nodes_semilogy_L_1.jpg differ
diff --git a/Results/ANN/Error_POD_Nodes_semilogy_L_2.jpg b/Results/ANN/Error_POD_Nodes_semilogy_L_2.jpg
index d15ea62d7687ec550383f73f622e4707d8862c6c..f6c308e782149fb941027361278e2becd756c6de 100644
Binary files a/Results/ANN/Error_POD_Nodes_semilogy_L_2.jpg and b/Results/ANN/Error_POD_Nodes_semilogy_L_2.jpg differ
diff --git a/Results/ANN/Error_POD_Nodes_semilogy_L_infty.jpg b/Results/ANN/Error_POD_Nodes_semilogy_L_infty.jpg
index 43b4051323c5df9bbda7c81a14a1d839b229aebb..bfeb634e8efb25d445dafc679040216c9da38136 100644
Binary files a/Results/ANN/Error_POD_Nodes_semilogy_L_infty.jpg and b/Results/ANN/Error_POD_Nodes_semilogy_L_infty.jpg differ
diff --git a/Results/ANN/ErrorsSVD.xlsx b/Results/ANN/ErrorsSVD.xlsx
index 1563dc91a1076cd465c40846bb1f3fa1ab338afb..684730667f20bc10cde2d197baa0d85f37503a14 100644
Binary files a/Results/ANN/ErrorsSVD.xlsx and b/Results/ANN/ErrorsSVD.xlsx differ
diff --git a/Results/ANN/OptimalSetup.npz b/Results/ANN/OptimalSetup.npz
index 0db1b1a0156b147002ec1b8970a1cb604d77b902..78c07f9f298270b290f3b17331fc930f45a03d03 100644
Binary files a/Results/ANN/OptimalSetup.npz and b/Results/ANN/OptimalSetup.npz differ
diff --git a/Results/radialBasisMod_gaussian/ErrorsSVD.xlsx b/Results/radialBasisMod_gaussian/ErrorsSVD.xlsx
index ab5ba54b67e4c5a9d8d909a9966b87c0b9aebd46..e71ecd418b5852e754d618479003bdcacd859b05 100644
Binary files a/Results/radialBasisMod_gaussian/ErrorsSVD.xlsx and b/Results/radialBasisMod_gaussian/ErrorsSVD.xlsx differ
diff --git a/Results/radialBasisMod_multiquadric/ErrorsSVD.xlsx b/Results/radialBasisMod_multiquadric/ErrorsSVD.xlsx
index 31ddfe6b52f343762dc4e72c4edbf9500ea32710..511ec2ec27cc835e190dbd50065810edda7203cd 100644
Binary files a/Results/radialBasisMod_multiquadric/ErrorsSVD.xlsx and b/Results/radialBasisMod_multiquadric/ErrorsSVD.xlsx differ
diff --git a/models/ANN.py b/models/ANN.py
index 46db4d33d1149b4a8bc7384864474d17591254e9..b6b49614816917d423d0cc4d53cb8d087ba963e3 100644
--- a/models/ANN.py
+++ b/models/ANN.py
@@ -2,56 +2,69 @@ from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
+from kerastuner import HyperModel, Hyperband
import matplotlib.pyplot as plt
from models.RegressionModels import RegressionModels
class ANN(RegressionModels):
def train(self, X_r_velocity, X_r_pressure, lr=1e-1, epochs=10_000, batch_size=32, plot_learning=True):
- # model_velocity, model_pressure = LinearRegression(), LinearRegression()
- model_velocity = Sequential(
- [
- Dense(256, input_dim=self.param_matrix_train.shape[1], activation='relu'),
- Dense(128, activation='relu'),
- Dense(X_r_velocity.T.shape[1])
- ]
- )
- model_pressure = Sequential(
- [
- Dense(256, input_dim=self.param_matrix_train.shape[1], activation='relu'),
- Dense(128, activation='relu'),
- Dense(X_r_pressure.T.shape[1])
- ]
- )
+ callbacks = [
+ EarlyStopping(monitor='mse', patience=500),
+ ReduceLROnPlateau(monitor='mse', factor=0.1, patience=200)
+ ]
- optimizer_velocity = Adam(learning_rate=lr)
- optimizer_pressure = Adam(learning_rate=lr)
- model_velocity.compile(
- optimizer=optimizer_velocity,
- loss='mean_squared_error',
- metrics = ['mae', 'mse'],
+ max_trials_tuner = 25
+ epochs_tuner = 100
+ tuner_velocity = Hyperband(
+ ANNHyperModel(
+ param_matrix_train=self.param_matrix_train,
+ X_r=X_r_velocity
+ ),
+ objective='val_mse',
+ max_epochs=max_trials_tuner,
+ directory='models/trained_models/ANN',
+ project_name='ANN_Hyperband_velocity'
)
- model_pressure.compile(
- optimizer=optimizer_pressure,
- loss='mean_squared_error',
- metrics = ['mae', 'mse'],
+
+ tuner_velocity.search(
+ self.param_matrix_train, X_r_velocity.T,
+ validation_split=0.2,
+ epochs=epochs_tuner, batch_size=batch_size,
+ callbacks=callbacks
)
+ best_hp_velocity = tuner_velocity.get_best_hyperparameters(num_trials=1)[0]
+ best_model_velocity = tuner_velocity.hypermodel.build(best_hp_velocity)
- callbacks = [
- EarlyStopping(monitor='mse', patience=250),
- ReduceLROnPlateau(monitor='mse', factor=0.1, patience=100)
- ]
-
- print(f'X_r_velocity.shape: {X_r_velocity.shape}')
- print(f'X_r_pressure.shape: {X_r_pressure.shape}')
- print(f'self.param_matrix_train.shape: {self.param_matrix_train.shape}')
- history_velocity = model_velocity.fit(
+ history_velocity = best_model_velocity.fit(
self.param_matrix_train, X_r_velocity.T,
validation_split=0.2,
epochs=epochs, batch_size=batch_size,
callbacks=callbacks
)
- history_pressure = model_pressure.fit(
+
+ tuner_pressure = Hyperband(
+ ANNHyperModel(
+ param_matrix_train=self.param_matrix_train,
+ X_r=X_r_pressure
+ ),
+ objective='val_mse',
+ max_epochs=max_trials_tuner,
+ directory='models/trained_models/ANN',
+ project_name='ANN_Hyperband_pressure'
+ )
+
+ tuner_pressure.search(
+ self.param_matrix_train, X_r_pressure.T,
+ validation_split=0.2,
+ epochs=epochs_tuner, batch_size=batch_size,
+ callbacks=callbacks
+ )
+
+ best_hp_pressure = tuner_pressure.get_best_hyperparameters(num_trials=1)[0]
+ best_model_pressure = tuner_pressure.hypermodel.build(best_hp_pressure)
+
+ history_pressure = best_model_pressure.fit(
self.param_matrix_train, X_r_pressure.T,
validation_split=0.2,
epochs=epochs, batch_size=batch_size,
@@ -97,4 +110,39 @@ class ANN(RegressionModels):
fig.savefig('models/trained_models/ANN/learning_curve_pressure.png')
fig.show()
- return model_velocity, model_pressure
+ return best_model_velocity, best_model_pressure
+
+
+class ANNHyperModel(HyperModel):
+ def __init__(self, param_matrix_train, X_r):
+ self.param_matrix_train = param_matrix_train
+ self.X_r = X_r
+
+ def build(self, hp):
+ model = Sequential()
+
+ units_values = [8, 16, 32, 64, 128, 256]
+ activation_values = ['relu', 'tanh', 'sigmoid']
+ # Add input layer
+ num_units_init = hp.Choice('num_units_init', values=units_values)
+ activation_init = hp.Choice('activation_init', values=activation_values)
+ model.add(Dense(num_units_init, input_dim=self.param_matrix_train.shape[1], activation=activation_init))
+
+ num_units_1 = hp.Choice('num_units_1', values=units_values)
+ activation_1 = hp.Choice('activation_1', values=activation_values)
+ model.add(Dense(num_units_1, activation=activation_1))
+ num_units_2 = hp.Choice('num_units_2', values=units_values)
+ activation_2 = hp.Choice('activation_2', values=activation_values)
+ model.add(Dense(num_units_2, activation=activation_2))
+
+ # Add output layer
+ model.add(Dense(self.X_r.T.shape[1]))
+
+ # Compile model
+ model.compile(
+ optimizer=Adam(learning_rate=1e-3), # Also change this to e.g. adam, sgd, rmsprop, adagrad?
+ loss='mean_squared_error',
+ metrics = ['mae', 'mse'],
+ )
+
+ return model
\ No newline at end of file
diff --git a/models/__pycache__/ANN.cpython-312.pyc b/models/__pycache__/ANN.cpython-312.pyc
index dc4c97172bb9cb0b36876e86b00943f7cfadfee9..45a3ef305cdd13851dcee9f0b872f94e63dc1fe1 100644
Binary files a/models/__pycache__/ANN.cpython-312.pyc and b/models/__pycache__/ANN.cpython-312.pyc differ
diff --git a/models/trained_models/ANN/ANN_Hyperband_pressure/oracle.json b/models/trained_models/ANN/ANN_Hyperband_pressure/oracle.json
new file mode 100644
index 0000000000000000000000000000000000000000..47d4ccd117a1c4c797ebba9eeebe441bb2b73380
--- /dev/null
+++ b/models/trained_models/ANN/ANN_Hyperband_pressure/oracle.json
@@ -0,0 +1 @@
+{"ongoing_trials": {}, "hyperparameters": {"space": [{"class_name": "Choice", "config": {"name": "num_layer", "default": 1, "conditions": [], "values": [1, 2, 3], "ordered": true}}, {"class_name": "Choice", "config": {"name": "num_units", "default": 32, "conditions": [], "values": [32, 64, 128, 256], "ordered": true}}, {"class_name": "Choice", "config": {"name": "activation", "default": "relu", "conditions": [], "values": ["relu", "tanh", "sigmoid"], "ordered": false}}], "values": {"num_layer": 1, "num_units": 32, "activation": "relu"}}, "start_order": ["0000", "0001", "0002", "0003", "0004", "0005", "0006", "0007", "0008", "0009", "0010", "0011", "0012", "0013", "0014", "0015", "0016", "0017", "0018", "0019", "0020", "0021", "0022", "0023", "0024", "0025", "0026", "0027", "0028", "0029"], "end_order": ["0000", "0001", "0002", "0003", "0004", "0005", "0006", "0007", "0008", "0009", "0010", "0011", "0012", "0013", "0014", "0015", "0016", "0017", "0018", "0019", "0020", "0021", "0022", "0023", "0024", "0025", "0026", "0027", "0028", "0029"], "run_times": {"0000": 1, "0001": 1, "0002": 1, "0003": 1, "0004": 1, "0005": 1, "0006": 1, "0007": 1, "0008": 1, "0009": 1, "0010": 1, "0011": 1, "0012": 1, "0013": 1, "0014": 1, "0015": 1, "0016": 1, "0017": 1, "0018": 1, "0019": 1, "0020": 1, "0021": 1, "0022": 1, "0023": 1, "0024": 1, "0025": 1, "0026": 1, "0027": 1, "0028": 1, "0029": 1}, "retry_queue": [], "seed": 5675, "seed_state": 5774, "tried_so_far": ["f842c3f59783f7e189d09da1dbc56678", "e6d2d35a72ea049d5f2512b04fd470ac", "af86d6fbeb41ca75c4cd6d021b18c926", "8d02b060e818213a3b0353c475a66f8f", "ea19b4694d25a0329c5720354ca6f32e", "e5789cb9c9aebd07b287c8e20c1910e8", "a0a60d7049dd736e9e3ff2916584db22", "d758eb3704b93311fdadce37d682d9cb", "14261e175ba92dd3346e60a4737e4ef5", "5a76bb08ec1dc3ea7279760d70b7c0f0", "ee0d3846ae44615012fc3aa4ac34869b", "71d4f156637029c941f4341beffdb23b", "3cbb486684781e303d9fd97d9aab0285", "2346984cb09ee8c03b837bdbe59fdb74", "de03a128d1e3bbd66dcbbd591aad0ffd", "4f6b1e99a3ab8706ea8419d8491e52ce", "a7937e18ed55e7cf70d2d80fa0f8740c", "dd380883cb0c514d986812d6677623d8", "63e095019005555764605bc67a059a9c", "8d118acaf5826bcbd266c30fd94b8ca7", "1b030794a747ed5f4ef6912f2635a701", "3c59680d2304f3ebf1834b92f9115364", "c47bbcba08e23c4041a8cb4ef74c4f42", "82aa2ca3aeb8f17f53acf52e2002b83f", "f4ef11809c64cb8e0e4d823f4f336514", "06edff67c4160e7b77e0792d4b145f72", "bc461bea21f67c1e20b9d89065c595f9", "46f2f73c43f06501ce7994340653093f", "e86535599b9a44102247caf10dbc9aab", "231d07abdedb7e36fe3f1b8404de4646"], "id_to_hash": {"0000": "d758eb3704b93311fdadce37d682d9cb", "0001": "ee0d3846ae44615012fc3aa4ac34869b", "0002": "e86535599b9a44102247caf10dbc9aab", "0003": "82aa2ca3aeb8f17f53acf52e2002b83f", "0004": "46f2f73c43f06501ce7994340653093f", "0005": "63e095019005555764605bc67a059a9c", "0006": "a7937e18ed55e7cf70d2d80fa0f8740c", "0007": "06edff67c4160e7b77e0792d4b145f72", "0008": "e6d2d35a72ea049d5f2512b04fd470ac", "0009": "3c59680d2304f3ebf1834b92f9115364", "0010": "af86d6fbeb41ca75c4cd6d021b18c926", "0011": "f4ef11809c64cb8e0e4d823f4f336514", "0012": "2346984cb09ee8c03b837bdbe59fdb74", "0013": "a0a60d7049dd736e9e3ff2916584db22", "0014": "bc461bea21f67c1e20b9d89065c595f9", "0015": "14261e175ba92dd3346e60a4737e4ef5", "0016": "c47bbcba08e23c4041a8cb4ef74c4f42", "0017": "8d02b060e818213a3b0353c475a66f8f", "0018": "ea19b4694d25a0329c5720354ca6f32e", "0019": "1b030794a747ed5f4ef6912f2635a701", "0020": "f842c3f59783f7e189d09da1dbc56678", "0021": "3cbb486684781e303d9fd97d9aab0285", "0022": "dd380883cb0c514d986812d6677623d8", "0023": "8d118acaf5826bcbd266c30fd94b8ca7", "0024": "de03a128d1e3bbd66dcbbd591aad0ffd", "0025": "5a76bb08ec1dc3ea7279760d70b7c0f0", "0026": "4f6b1e99a3ab8706ea8419d8491e52ce", "0027": "71d4f156637029c941f4341beffdb23b", "0028": "231d07abdedb7e36fe3f1b8404de4646", "0029": "e5789cb9c9aebd07b287c8e20c1910e8"}, "display": {"search_start": "2024-12-04T18:01:21.527047", "trial_start": {"0000": "2024-12-04T18:01:21.527047", "0001": "2024-12-04T18:01:22.166957", "0002": "2024-12-04T18:01:22.858516", "0003": "2024-12-04T18:01:23.608528", "0004": "2024-12-04T18:01:24.310025", "0005": "2024-12-04T18:01:25.078415", "0006": "2024-12-04T18:01:25.791142", "0007": "2024-12-04T18:01:26.497854", "0008": "2024-12-04T18:01:27.288021", "0009": "2024-12-04T18:01:28.026095", "0010": "2024-12-04T18:01:28.745275", "0011": "2024-12-04T18:01:29.472566", "0012": "2024-12-04T18:01:30.266953", "0013": "2024-12-04T18:01:31.159840", "0014": "2024-12-04T18:01:31.948535", "0015": "2024-12-04T18:01:32.783376", "0016": "2024-12-04T18:01:33.630794", "0017": "2024-12-04T18:01:34.841108", "0018": "2024-12-04T18:01:35.909593", "0019": "2024-12-04T18:01:36.761844", "0020": "2024-12-04T18:01:37.636919", "0021": "2024-12-04T18:01:38.644052", "0022": "2024-12-04T18:01:39.620818", "0023": "2024-12-04T18:01:40.575275", "0024": "2024-12-04T18:01:41.564696", "0025": "2024-12-04T18:01:42.765930", "0026": "2024-12-04T18:01:43.904328", "0027": "2024-12-04T18:01:45.224878", "0028": "2024-12-04T18:01:46.605081", "0029": "2024-12-04T18:01:48.001582"}, "trial_number": {"0000": 1, "0001": 2, "0002": 3, "0003": 4, "0004": 5, "0005": 6, "0006": 7, "0007": 8, "0008": 9, "0009": 10, "0010": 11, "0011": 12, "0012": 13, "0013": 14, "0014": 15, "0015": 16, "0016": 17, "0017": 18, "0018": 19, "0019": 20, "0020": 21, "0021": 22, "0022": 23, "0023": 24, "0024": 25, "0025": 26, "0026": 27, "0027": 28, "0028": 29, "0029": 30}}, "hyperband_iterations": 1, "max_epochs": 25, "min_epochs": 1, "factor": 3, "brackets": [{"bracket_num": 0, "rounds": [[{"past_id": null, "id": "0026"}, {"past_id": null, "id": "0027"}, {"past_id": null, "id": "0028"}, {"past_id": null, "id": "0029"}]]}], "current_bracket": 0, "current_iteration": 0}
\ No newline at end of file
diff --git a/models/trained_models/ANN/ANN_Hyperband_pressure/trial_0000/build_config.json b/models/trained_models/ANN/ANN_Hyperband_pressure/trial_0000/build_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..2df7e172f0f5f6f0c51a87764b99a3adb712b486
--- /dev/null
+++ b/models/trained_models/ANN/ANN_Hyperband_pressure/trial_0000/build_config.json
@@ -0,0 +1 @@
+{"input_shape": [null, 3]}
\ No newline at end of file
diff --git a/models/trained_models/ANN/ANN_Hyperband_pressure/trial_0000/checkpoint.weights.h5 b/models/trained_models/ANN/ANN_Hyperband_pressure/trial_0000/checkpoint.weights.h5
new file mode 100644
index 0000000000000000000000000000000000000000..fd8a245a8c20a788c7b54f7d2b0aaa523478a9d8
Binary files /dev/null and b/models/trained_models/ANN/ANN_Hyperband_pressure/trial_0000/checkpoint.weights.h5 differ
diff --git a/models/trained_models/ANN/ANN_Hyperband_pressure/trial_0000/trial.json b/models/trained_models/ANN/ANN_Hyperband_pressure/trial_0000/trial.json
new file mode 100644
index 0000000000000000000000000000000000000000..bea666421a1920cdaa9c0f672bae1a44b4ae6e17
--- /dev/null
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diff --git a/temp_ANN/predict_velocity_field_0.xml b/temp_ANN/predict_velocity_field_0.xml
deleted file mode 100644
index 7432c83c354b8b1778e807e2089db36747c38837..0000000000000000000000000000000000000000
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\ No newline at end of file