output_list.append(model_name + "\n")
print(model_name)
output[model_name][str(reliability_setting)][
iteration - 1] = calculateExpectedAccuracy(
model,
no_information_flow_map,
reliability_setting,
output_list,
training_labels=training_labels,
test_data=test_data,
test_labels=test_labels)
# runs all 3 failure configurations for all 3 models
if __name__ == "__main__":
accuracy = accuracy("Health")
calculateExpectedAccuracy = accuracy.calculateExpectedAccuracy
use_GCP = False
training_data, val_data, test_data, training_labels, val_labels, test_labels = init_data(
use_GCP)
ResiliNet_no_information_flow_map = make_no_information_flow_map(
"Health", default_skip_hyperconnection_config)
deepFogGuard_no_information_flow_map = make_no_information_flow_map(
"Health", default_skip_hyperconnection_config)
Vanilla_no_information_flow_map = make_no_information_flow_map("Health")
num_iterations, num_vars, num_classes, reliability_settings, num_train_epochs, hidden_units, batch_size = init_common_experiment_params(
training_data)
load_for_inference = False
input_shape=input_shape,
alpha=alpha,
reliability_setting=reliability_setting,
skip_hyperconnection_config=skip_hyperconnection_configuration,
strides=strides,
num_gpus=num_gpus)
get_model_weights_CNN_cifar(
model, parallel_model, model_name, load_for_inference, model_file,
training_data, training_labels, val_data, val_labels, train_datagen,
batch_size, epochs, progress_verbose, checkpoint_verbose,
train_steps_per_epoch, val_steps_per_epoch, num_gpus)
return model
if __name__ == "__main__":
accuracy = accuracy("CIFAR")
calculateExpectedAccuracy = accuracy.calculateExpectedAccuracy
training_data, test_data, training_labels, test_labels, val_data, val_labels = init_data(
)
num_iterations, classes, reliability_settings, train_datagen, batch_size, epochs, progress_verbose, checkpoint_verbose, use_GCP, alpha, input_shape, strides, num_gpus = init_common_experiment_params(
)
skip_hyperconnection_configurations = [
# [e1,IoT]
[0, 0],
[1, 0],
[0, 1],
[1, 1],
]
model_name = "ResiliNet Hyperconnection Weight Sensitivity"
default_reliability_setting = [1.0, 1.0, 1.0]
hidden_units,
reliability_setting=reliability_setting,
hyperconnection_weights_scheme=weight_scheme)
model_file = "models/" + str(iteration) + "_" + str(
reliability_setting) + "_" + str(
weight_scheme) + 'camera_hyperconnection_ResiliNet.h5'
get_model_weights_MLP_camera(model, model_name, load_for_inference,
model_file, training_data, training_labels,
val_data, val_labels, num_train_epochs,
batch_size, verbose)
return model
# runs all 3 failure configurations for all 3 models
if __name__ == "__main__":
accuracy = accuracy("Camera")
calculateExpectedAccuracy = accuracy.calculateExpectedAccuracy
use_GCP = False
training_data, val_data, test_data, training_labels, val_labels, test_labels = init_data(
use_GCP)
reliability_settings, input_shape, num_classes, hidden_units, batch_size, num_train_epochs, num_iterations = init_common_experiment_params(
)
no_information_flow_map = make_no_information_flow_map(
"Camera", default_skip_hyperconnection_config)
load_for_inference = False
# file name with the experiments accuracy output
output_name = "results/camera_hyperconnection_weight.txt"
verbose = 2
model_name = "ResiliNet Hyperconnection Weight"
num_test_examples):
output_list.append(model_name + "\n")
print(model_name)
output[model_name][str(reliability_setting)][
iteration - 1] = calculateExpectedAccuracy(
model,
no_information_flow_map,
reliability_setting,
output_list,
test_generator=test_generator,
num_test_examples=num_test_examples)
# runs all 3 failure configurations for all 3 models
if __name__ == "__main__":
accuracy = accuracy("Imagenet")
calculateExpectedAccuracy = accuracy.calculateExpectedAccuracy
use_GCP = False
num_iterations, num_train_examples, num_test_examples, reliability_settings, input_shape, num_classes, alpha, epochs, num_gpus, strides, num_workers = init_common_experiment_params(
)
train_generator, test_generator = init_data(use_GCP, num_gpus)
ResiliNet_no_information_flow_map = make_no_information_flow_map(
"CIFAR/Imagenet", default_skip_hyperconnection_config)
deepFogGuard_no_information_flow_map = make_no_information_flow_map(
"CIFAR/Imagenet", default_skip_hyperconnection_config)
Vanilla_no_information_flow_map = make_no_information_flow_map(
"CIFAR/Imagenet")
load_for_inference = False
continue_training = False # loads a pre-trained model and improves it with more training
