deepFogGuard_no_information_flow_map,
reliability_setting, output_list, training_labels,
test_data, test_labels)
calc_accuracy(iteration, "Vanilla", Vanilla,
Vanilla_no_information_flow_map, reliability_setting,
output_list, training_labels, test_data, test_labels)
# clear session so that model will recycled back into memory
K.clear_session()
gc.collect()
del deepFogGuard
del ResiliNet
del Vanilla
# calculate average accuracies from all expected accuracies
for reliability_setting in reliability_settings:
ResiliNet_acc = average(output["ResiliNet"][str(reliability_setting)])
deepFogGuard_acc = average(
output["deepFogGuard"][str(reliability_setting)])
Vanilla_acc = average(output["Vanilla"][str(reliability_setting)])
output_list.append(
str(reliability_setting) + " ResiliNet Accuracy: " +
str(ResiliNet_acc) + '\n')
output_list.append(
str(reliability_setting) + " deepFogGuard Accuracy: " +
str(deepFogGuard_acc) + '\n')
output_list.append(
str(reliability_setting) + " Vanilla Accuracy: " +
str(Vanilla_acc) + '\n')
print(str(reliability_setting), "ResiliNet Accuracy:", ResiliNet_acc)
hyperconnection_weight,
no_information_flow_map,
reliability_setting,
output_list,
training_labels=training_labels,
test_data=test_data,
test_labels=test_labels)
# clear session so that model will recycled back into memory
K.clear_session()
gc.collect()
del hyperconnection_weight
# calculate average accuracies
for reliability_setting in reliability_settings:
for weight_scheme in weight_schemes:
hyperconnection_weight_acc = average(
output[model_name][weight_scheme][str(reliability_setting)])
output_list.append(
str(reliability_setting) + str(weight_scheme) + " " +
model_name + ": " + str(hyperconnection_weight_acc) + '\n')
print(str(reliability_setting), weight_scheme, model_name, ":",
hyperconnection_weight_acc)
hyperconnection_weight_std = np.std(
output[model_name][weight_scheme][str(reliability_setting)],
ddof=1)
output_list.append(
str(reliability_setting) + str(weight_scheme) + " " +
model_name + " std: " + str(hyperconnection_weight_std) + '\n')
print(str(reliability_setting), weight_scheme, model_name, "std:",
hyperconnection_weight_std)
write_n_upload(output_name, output_list, use_GCP)
model,
no_information_flow_map[tuple(
skip_hyperconnection_configuration)],
reliability_setting,
output_list,
training_labels=training_labels,
test_data=test_data,
test_labels=test_labels)
# clear session so that model will recycled back into memory
K.clear_session()
gc.collect()
del model
for reliability_setting in reliability_settings:
for skip_hyperconnection_configuration in skip_hyperconnection_configurations:
output_list.append(str(reliability_setting) + '\n')
acc = average(output[model_name][str(reliability_setting)][str(
skip_hyperconnection_configuration)])
std = np.std(output[model_name][str(reliability_setting)][str(
skip_hyperconnection_configuration)],
ddof=1)
output_list.append(
str(reliability_setting) +
str(skip_hyperconnection_configuration) + str(acc) + '\n')
output_list.append(
str(reliability_setting) +
str(skip_hyperconnection_configuration) + str(std) + '\n')
print(str(reliability_setting), acc)
print(str(reliability_setting), std)
write_n_upload(output_name, output_list, use_GCP)
print(output)
no_information_flow_map,
reliability_setting,
output_list,
training_labels=training_labels,
test_data=test_data,
test_labels=test_labels)
# clear session so that model will recycled back into memory
K.clear_session()
gc.collect()
del model
for reliability_setting in reliability_settings:
for weight_scheme in weight_schemes:
output_list.append(
str(reliability_setting) + str(weight_scheme) + '\n')
acc = average(
output[model_name][weight_scheme][str(reliability_setting)])
output_list.append(
str(reliability_setting) + str(weight_scheme) + str(acc) +
'\n')
print(str(reliability_setting), weight_scheme, acc)
std = np.std(
output[model_name][weight_scheme][str(reliability_setting)],
ddof=1)
output_list.append(
str(reliability_setting) + str(weight_scheme) + str(std) +
'\n')
print(str(reliability_setting), weight_scheme, std)
write_n_upload(output_name, output_list, use_GCP)
print(output)
1] = calculateExpectedAccuracy(
ResiliNet_failout_rate_fixed,
no_information_flow_map,
reliability_setting,
output_list,
training_labels=training_labels,
test_data=test_data,
test_labels=test_labels)
# clear session so that model will recycled back into memory
K.clear_session()
gc.collect()
del ResiliNet_failout_rate_fixed
# calculate average accuracies for variable failout rate
for reliability_setting in reliability_settings:
ResiliNet_failout_rate_acc = average(
output["Variable Failout 1x"][str(reliability_setting)])
output_list.append(
str(reliability_setting) + " Variable Failout 1x: " +
str(ResiliNet_failout_rate_acc) + '\n')
print(reliability_setting, "Variable Failout 1x:",
ResiliNet_failout_rate_acc)
ResiliNet_failout_rate_std = np.std(
output["Variable Failout 1x"][str(reliability_setting)], ddof=1)
output_list.append(
str(reliability_setting) + " Variable Failout 1x std: " +
str(ResiliNet_failout_rate_std) + '\n')
print(str(reliability_setting), " Variable Failout 1x std:",
ResiliNet_failout_rate_std)
# calculate average accuracies for fixed failout rate
for failout_survival_setting in failout_survival_settings:
calc_accuracy(iteration, "ResiliNet", ResiliNet,
ResiliNet_no_information_flow_map,
reliability_setting, output_list, test_generator,
num_test_examples)
# calc_accuracy(iteration, "deepFogGuard", deepFogGuard, deepFogGuard_no_information_flow_map, reliability_setting, output_list,test_generator, num_test_examples)
# calc_accuracy(iteration, "Vanilla", Vanilla, Vanilla_no_information_flow_map, reliability_setting, output_list,test_generator, num_test_examples)
# clear session so that model will recycled back into memory
K.clear_session()
gc.collect()
# del deepFogGuard
del ResiliNet
# del Vanilla
# calculate average accuracies from all expected accuracies
for reliability_setting in reliability_settings:
ResiliNet_acc = average(output["ResiliNet"][str(reliability_setting)])
# deepFogGuard_acc = average(output["deepFogGuard"][str(reliability_setting)])
# Vanilla_acc = average(output["Vanilla"][str(reliability_setting)])
ResiliNet_std = np.std(output["ResiliNet"][str(reliability_setting)],
ddof=1)
# deepFogGuard_std = np.std(output["deepFogGuard"][str(reliability_setting)],ddof = 1)
# Vanilla_std = np.std(output["Vanilla"][str(reliability_setting)],ddof = 1)
output_list.append(
str(reliability_setting) + " ResiliNet Accuracy: " +
str(ResiliNet_acc) + '\n')
# output_list.append(str(reliability_setting) + " deepFogGuard Accuracy: " + str(deepFogGuard_acc) + '\n')
# output_list.append(str(reliability_setting) + " Vanilla Accuracy: " + str(Vanilla_acc) + '\n')
output_list.append(