def trainer(hyperp, run_options, file_paths):
#=== GPU Settings ===#
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = run_options.which_gpu
#=== Load Data ===#
obs_indices, parameter_train, state_obs_train,\
parameter_test, state_obs_test,\
data_input_shape_temp, parameter_dimension\
= load_thermal_fin_data(file_paths, run_options.num_data_train, run_options.num_data_test, run_options.parameter_dimensions)
output_dimensions_temp = len(obs_indices)
#=== Forward or Inverse Mapping Input and Output Dimensions ===#
if run_options.forward_mapping == 1:
data_input_shape = data_input_shape_temp
output_dimensions = output_dimensions_temp
if run_options.inverse_mapping == 1:
data_input_shape = np.array([output_dimensions_temp, 1])
output_dimensions = data_input_shape_temp[0]
#=== Construct Validation Set and Batches ===#
if run_options.forward_mapping == 1:
parameter_and_state_obs_train, parameter_and_state_obs_val, parameter_and_state_obs_test,\
run_options.num_data_train, num_data_val, run_options.num_data_test,\
num_batches_train, num_batches_val, num_batches_test\
= form_train_val_test_batches(parameter_train, state_obs_train, parameter_test, state_obs_test, hyperp.batch_size, run_options.random_seed)
if run_options.inverse_mapping == 1:
parameter_and_state_obs_train, parameter_and_state_obs_val, parameter_and_state_obs_test,\
run_options.num_data_train, num_data_val, run_options.num_data_test,\
num_batches_train, num_batches_val, num_batches_test\
= form_train_val_test_batches(state_obs_train, parameter_train, state_obs_test, parameter_test, hyperp.batch_size, run_options.random_seed)
#=== Neural network ===#
if run_options.use_L1 == 0:
kernel_regularizer = None
bias_regularizer = None
else:
kernel_regularizer = tf.keras.regularizers.l1(hyperp.regularization)
bias_regularizer = tf.keras.regularizers.l1(hyperp.regularization)
NN = FCLayerwise(hyperp, run_options, data_input_shape, output_dimensions,
kernel_regularizer, bias_regularizer)
#=== Training ===#
optimize(hyperp, run_options, file_paths, NN, data_loss_regression,
relative_error, parameter_and_state_obs_train,
parameter_and_state_obs_val, parameter_and_state_obs_test,
output_dimensions, num_batches_train)
def trainer(hyperp, run_options, file_paths):
#=== GPU Settings ===#
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = run_options.which_gpu
#=== Load Data ===#
data_train, labels_train,\
data_test, labels_test,\
data_input_shape, num_channels, label_dimensions\
= load_data(file_paths.NN_type, file_paths.dataset, run_options.random_seed)
#=== Construct Validation Set and Batches ===#
data_and_labels_train, data_and_labels_val, data_and_labels_test,\
num_data_train, num_data_val, num_data_test,\
num_batches_train, num_batches_val, num_batches_test\
= form_train_val_test_batches(data_train, labels_train, \
data_test, labels_test, \
hyperp.batch_size, run_options.random_seed)
#=== Neural network ===#
NN = CNNLayerwise(hyperp, run_options, data_input_shape, label_dimensions,
num_channels, None, None)
#=== Training ===#
optimize_ADMM(hyperp, run_options, file_paths, NN,
data_loss_classification, accuracy_classification,
data_and_labels_train, data_and_labels_val,
data_and_labels_test, label_dimensions, num_batches_train)
def trainer(hyperp, run_options, file_paths):
#=== GPU Settings ===#
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = run_options.which_gpu
#=== Load Data ===#
data_train, labels_train,\
data_test, labels_test,\
data_input_shape, num_channels, label_dimensions\
= load_data(file_paths.NN_type, file_paths.dataset, run_options.random_seed)
#=== Construct Validation Set and Batches ===#
data_and_labels_train, data_and_labels_val, data_and_labels_test,\
num_data_train, num_data_val, num_data_test,\
num_batches_train, num_batches_val, num_batches_test\
= form_train_val_test_batches(data_train, labels_train, \
data_test, labels_test, \
hyperp.batch_size, run_options.random_seed)
#=== Neural network ===#
if run_options.use_L1 == 0 and run_options.use_L2 == 0:
kernel_regularizer = None
bias_regularizer = None
if run_options.use_L1 == 1:
kernel_regularizer = tf.keras.regularizers.l1(hyperp.regularization)
bias_regularizer = tf.keras.regularizers.l1(hyperp.regularization)
if run_options.use_L2 == 1:
kernel_regularizer = tf.keras.regularizers.l2(hyperp.regularization)
bias_regularizer = tf.keras.regularizers.l2(hyperp.regularization)
NN = CNNLayerwise(hyperp, run_options, data_input_shape, label_dimensions, num_channels,
kernel_regularizer, bias_regularizer)
#=== Training ===#
optimize(hyperp, run_options, file_paths, NN, data_loss_classification, accuracy_classification, data_and_labels_train, data_and_labels_val, data_and_labels_test, label_dimensions, num_batches_train)
def trainer(hyperp, run_options, file_paths):
#=== GPU Settings ===# Must put this first! Because TF2 will automatically work on a GPU and it may clash with used ones if the visible device list is not yet specified
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
if run_options.use_distributed_training == 0:
os.environ["CUDA_VISIBLE_DEVICES"] = run_options.which_gpu
GLOBAL_BATCH_SIZE = hyperp.batch_size
if run_options.use_distributed_training == 1:
os.environ["CUDA_VISIBLE_DEVICES"] = run_options.dist_which_gpus
gpus = tf.config.experimental.list_physical_devices('GPU')
GLOBAL_BATCH_SIZE = hyperp.batch_size * len(
gpus
) # To avoid the core dump issue, have to do this instead of hyperp.batch_size * dist_strategy.num_replicas_in_sync
#=== Load Data ===#
obs_indices, parameter_train, state_obs_train,\
parameter_test, state_obs_test,\
data_input_shape, parameter_dimension\
= load_thermal_fin_data(file_paths, run_options.num_data_train, run_options.num_data_test, run_options.parameter_dimensions)
#=== Construct Validation Set and Batches ===#
parameter_and_state_obs_train, parameter_and_state_obs_val, parameter_and_state_obs_test,\
run_options.num_data_train, num_data_val, run_options.num_data_test,\
num_batches_train, num_batches_val, num_batches_test\
= form_train_val_test_batches(parameter_train, state_obs_train, parameter_test, state_obs_test, GLOBAL_BATCH_SIZE, run_options.random_seed)
#=== Data and Latent Dimensions of Autoencoder ===#
data_dimension = parameter_dimension
if hyperp.data_type == 'full':
latent_dimension = run_options.full_domain_dimensions
if hyperp.data_type == 'bnd':
latent_dimension = len(obs_indices)
#=== Non-distributed Training ===#
if run_options.use_distributed_training == 0:
#=== Neural Network ===#
NN = AutoencoderFwdInv(hyperp, data_dimension, latent_dimension)
#=== Training ===#
storage_array_loss_train, storage_array_loss_train_autoencoder, storage_array_loss_train_forward_problem, storage_array_loss_train_model_augmented,\
storage_array_loss_val, storage_array_loss_val_autoencoder, storage_array_loss_val_forward_problem, storage_array_loss_val_model_augmented,\
storage_array_loss_test, storage_array_loss_test_autoencoder, storage_array_loss_test_forward_problem, storage_array_loss_test_model_augmented,\
storage_array_relative_error_parameter_autoencoder, storage_array_relative_error_state_obs, storage_array_relative_error_parameter_inverse_problem\
= optimize(hyperp, run_options, file_paths, NN, obs_indices, loss_autoencoder, loss_encoder, loss_model_augmented, relative_error,\
parameter_and_state_obs_train, parameter_and_state_obs_val, parameter_and_state_obs_test,\
parameter_dimension, num_batches_train)
#=== Distributed Training ===#
if run_options.use_distributed_training == 1:
dist_strategy = tf.distribute.MirroredStrategy()
with dist_strategy.scope():
#=== Neural Network ===#
NN = AutoencoderFwdInv(hyperp, data_dimension, latent_dimension)
#=== Training ===#
storage_array_loss_train, storage_array_loss_train_autoencoder, storage_array_loss_train_forward_problem, storage_array_loss_train_model_augmented,\
storage_array_loss_val, storage_array_loss_val_autoencoder, storage_array_loss_val_forward_problem, storage_array_loss_val_model_augmented,\
storage_array_loss_test, storage_array_loss_test_autoencoder, storage_array_loss_test_forward_problem, storage_array_loss_test_model_augmented,\
storage_array_relative_error_parameter_autoencoder, storage_array_relative_error_state_obs, storage_array_relative_error_parameter_inverse_problem\
= optimize_distributed(dist_strategy, GLOBAL_BATCH_SIZE,
hyperp, run_options, file_paths, NN, obs_indices, loss_autoencoder, loss_model_augmented, relative_error,\
parameter_and_state_obs_train, parameter_and_state_obs_val, parameter_and_state_obs_test,\
parameter_dimension, num_batches_train)
#=== Saving Metrics ===#
metrics_dict = {}
metrics_dict['loss_train'] = storage_array_loss_train
metrics_dict[
'loss_train_autoencoder'] = storage_array_loss_train_autoencoder
metrics_dict[
'loss_train_forward_problem'] = storage_array_loss_train_forward_problem
metrics_dict[
'loss_train_model_augmented'] = storage_array_loss_train_model_augmented
metrics_dict['loss_val'] = storage_array_loss_val
metrics_dict['loss_val_autoencoder'] = storage_array_loss_val_autoencoder
metrics_dict[
'loss_val_forward_problem'] = storage_array_loss_val_forward_problem
metrics_dict[
'loss_val_model_augmented'] = storage_array_loss_val_model_augmented
metrics_dict[
'relative_error_parameter_autoencoder'] = storage_array_relative_error_parameter_autoencoder
metrics_dict[
'relative_error_state_obs'] = storage_array_relative_error_state_obs
metrics_dict[
'relative_error_parameter_inverse_problem'] = storage_array_relative_error_parameter_inverse_problem
df_metrics = pd.DataFrame(metrics_dict)
df_metrics.to_csv(file_paths.NN_savefile_name + "_metrics" + '.csv',
index=False)
def objective_functional(**hyperp_of_interest_objective_args_tuple):
#=== Assign Hyperparameters of Interest ===#
for key, val in hyperp_of_interest_objective_args_tuple.items():
setattr(hyperp, key, val)
hyperp.truncation_layer = int(np.ceil(hyperp.num_hidden_layers / 2))
#=== Update File Paths with New Hyperparameters ===#
file_paths = FilePaths(hyperp, run_options)
#=== Construct Validation Set and Batches ===#
if run_options.use_distributed_training == 0:
GLOBAL_BATCH_SIZE = hyperp.batch_size
if run_options.use_distributed_training == 1:
GLOBAL_BATCH_SIZE = hyperp.batch_size * len(
gpus
) # To avoid the core dump issue, have to do this instead of hyperp.batch_size * dist_strategy.num_replicas_in_sync
parameter_and_state_obs_train, parameter_and_state_obs_val, parameter_and_state_obs_test,\
run_options.num_data_train, num_data_val, run_options.num_data_test,\
num_batches_train, num_batches_val, num_batches_test\
= form_train_val_test_batches(parameter_train, state_obs_train, parameter_test, state_obs_test, GLOBAL_BATCH_SIZE, run_options.random_seed)
#=== Non-distributed Training ===#
if run_options.use_distributed_training == 0:
#=== Neural Network ===#
NN = AutoencoderFwdInv(hyperp, parameter_dimension,
run_options.full_domain_dimensions,
obs_indices)
#=== Training ===#
storage_array_loss_train, storage_array_loss_train_autoencoder, storage_array_loss_train_forward_problem,\
storage_array_loss_val, storage_array_loss_val_autoencoder, storage_array_loss_val_forward_problem,\
storage_array_loss_test, storage_array_loss_test_autoencoder, storage_array_loss_test_forward_problem,\
storage_array_relative_error_parameter_autoencoder, storage_array_relative_error_parameter_inverse_problem, storage_array_relative_error_state_obs\
= optimize(hyperp, run_options, file_paths, NN, loss_autoencoder, loss_forward_problem, relative_error,\
parameter_and_state_obs_train, parameter_and_state_obs_val, parameter_and_state_obs_test,\
parameter_dimension, num_batches_train)
#=== Distributed Training ===#
if run_options.use_distributed_training == 1:
dist_strategy = tf.distribute.MirroredStrategy()
GLOBAL_BATCH_SIZE = hyperp.batch_size * dist_strategy.num_replicas_in_sync
with dist_strategy.scope():
#=== Neural Network ===#
NN = AutoencoderFwdInv(hyperp, parameter_dimension,
run_options.full_domain_dimensions,
obs_indices)
#=== Training ===#
storage_array_loss_train, storage_array_loss_train_autoencoder, storage_array_loss_train_forward_problem,\
storage_array_loss_val, storage_array_loss_val_autoencoder, storage_array_loss_val_forward_problem,\
storage_array_loss_test, storage_array_loss_test_autoencoder, storage_array_loss_test_forward_problem,\
storage_array_relative_error_parameter_autoencoder, storage_array_relative_error_parameter_inverse_problem, storage_array_relative_error_state_obs\
= optimize_distributed(dist_strategy, GLOBAL_BATCH_SIZE, hyperp, run_options, file_paths, NN, loss_autoencoder, loss_forward_problem, relative_error,\
parameter_and_state_obs_train, parameter_and_state_obs_val, parameter_and_state_obs_test,\
parameter_dimension, num_batches_train)
#=== Saving Metrics ===#
metrics_dict = {}
metrics_dict['loss_train'] = storage_array_loss_train
metrics_dict[
'loss_train_autoencoder'] = storage_array_loss_train_autoencoder
metrics_dict[
'loss_train_forward_problem'] = storage_array_loss_train_forward_problem
metrics_dict['loss_val'] = storage_array_loss_val
metrics_dict[
'loss_val_autoencoder'] = storage_array_loss_val_autoencoder
metrics_dict[
'loss_val_forward_problem'] = storage_array_loss_val_forward_problem
metrics_dict[
'relative_error_parameter_autoencoder'] = storage_array_relative_error_parameter_autoencoder
metrics_dict[
'relative_error_parameter_inverse_problem'] = storage_array_relative_error_parameter_inverse_problem
metrics_dict[
'relative_error_state_obs'] = storage_array_relative_error_state_obs
df_metrics = pd.DataFrame(metrics_dict)
df_metrics.to_csv(file_paths.NN_savefile_name + "_metrics" + '.csv',
index=False)
return storage_array_loss_val[-1]
