failout_survival_rate = failout_survival_setting[i]
# node failed
layer_name = node
layer = model.get_layer(name=layer_name)
layer_weights = layer.get_weights()
# make new weights for the connections
new_weights = layer_weights[0] * failout_survival_rate
# make new weights for biases
new_bias_weights = layer_weights[1] * failout_survival_rate
layer.set_weights([new_weights, new_bias_weights])
# runs all 3 failure configurations for all 3 models
if __name__ == "__main__":
accuracy = accuracy("Health")
calc_expected_accuracy = accuracy.calc_expected_accuracy
data, num_vars = init_data()
load_for_inference = False
failout_survival_settings = [
[0.95, 0.95, 0.95],
[0.9, 0.9, 0.9],
[0.7, 0.7, 0.7],
[0.5, 0.5, 0.5],
]
no_information_flow_map = make_no_information_flow_map(
"Health", default_skip_hyperconnection_config)
# file name with the experiments accuracy output
mux_adds_str = "mux_adds" if ResiliNetPlus else ""
output_name = "results/health_failout_rate" + mux_adds_str + ".txt"
output_list.append(model_name + str(skip_hyperconnection_configuration) +
"\n")
print(model_name + str(skip_hyperconnection_configuration))
output[model_name][str(reliability_setting)][str(
skip_hyperconnection_configuration)][iteration -
1] = calc_expected_accuracy(
model,
no_information_flow_map,
reliability_setting,
output_list,
data=data,
)
if __name__ == "__main__":
accuracy = accuracy("Camera")
calc_expected_accuracy = accuracy.calc_expected_accuracy
data = init_data()
skip_hyperconnection_configurations = [
# [f2,f3,f4,e1,e2,e3,e4]
[1, 1, 1, 1, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1, 1],
[1, 1, 1, 1, 0, 0, 1],
[1, 1, 1, 1, 1, 1, 1],
]
no_information_flow_map = {}
data,
train_datagen,
batch_size,
epochs,
progress_verbose,
checkpoint_verbose,
train_steps_per_epoch,
val_steps_per_epoch,
num_gpus,
)
return model
# hyperconnection weight experiment
if __name__ == "__main__":
accuracy = accuracy("ResNet")
calc_expected_accuracy = accuracy.calc_expected_accuracy
data = init_data()
model_name = "ResiliNet Hyperconnection Weight"
output, weight_schemes = make_output_dictionary_hyperconnection_weight(
model_name, reliability_settings, num_iterations)
considered_weight_schemes = [1, 2, 3, 4]
no_information_flow_map = make_no_information_flow_map(
"ResNet", default_skip_hyperconnection_config)
load_for_inference = False
train_steps_per_epoch = math.ceil(len(data.train) / batch_size)
val_steps_per_epoch = math.ceil(len(data.val) / batch_size)
model_file,
data,
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")
calc_expected_accuracy = accuracy.calc_expected_accuracy
data = init_data()
ResiliNet_no_information_flow_map = make_no_information_flow_map(
"CIFAR/Imagenet", default_skip_hyperconnection_config)
DFG_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")
train_steps_per_epoch = math.ceil(len(data.train) / batch_size)
val_steps_per_epoch = math.ceil(len(data.val) / batch_size)
output = make_output_dictionary_average_accuracy(reliability_settings,
num_iterations)
print(model_name)
output[model_name][str(reliability_setting)][
iteration - 1
] = calc_expected_accuracy(
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")
calc_expected_accuracy = accuracy.calc_expected_accuracy
train_generator, test_generator = init_data(num_gpus)
ResiliNet_no_information_flow_map = make_no_information_flow_map(
"CIFAR/Imagenet", default_skip_hyperconnection_config
)
DFG_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