by_train = by_train.astype('int32')
bx_test = bx_test.astype('float32')
by_test = by_test.astype('int32')
# [DEBUG]
print(' : Load the backdoor dataset [{}]'.format(args.poisonp))
print(' Train : {} in [{}, {}]'.format(bx_train.shape, bx_train.min(),
bx_train.max()))
print(' Test : {} in [{}, {}]'.format(bx_test.shape, bx_test.min(),
bx_test.max()))
# blend the backdoor data, and compose into the tensorflow datasets
bd_x_train = np.concatenate((x_train, bx_train), axis=0)
bd_y_train = np.concatenate((y_train, by_train), axis=0)
bd_train_dataset = datasets.convert_to_tf_dataset(bd_x_train,
bd_y_train,
batch=batch_size,
shuffle=True)
bd_ctest_dataset = datasets.convert_to_tf_dataset(x_test,
y_test,
batch=batch_size)
bd_btest_dataset = datasets.convert_to_tf_dataset(bx_test,
by_test,
batch=batch_size)
print(' : Construct them into the TF datasets')
# # compute the baseline accuracy
baseline_acc = _validate(base_model, bd_ctest_dataset)
baseline_bacc = _validate(base_model, bd_btest_dataset)
print(
' : Baseline accuracies on clean [{:.4f}] / backdoor [{:.4f}]'.format(
baseline_acc, baseline_bacc))
y_poison = y_poison.astype('int32')
# enforce the poisons to be within [0, 1] range
x_poison = np.clip(x_poison, 0., 1.)
# [DEBUG]
print(' : Load the poison data from [{}]'.format(args.poisonp))
print(' Train : {} in [{}, {}]'.format(x_train.shape, x_train.min(),
x_train.max()))
print(' Test : {} in [{}, {}]'.format(x_test.shape, x_test.min(),
x_test.max()))
print(' Poison: {} in [{}, {}]'.format(x_poison.shape, x_poison.min(),
x_poison.max()))
# compose into the tensorflow datasets
clean_validset = datasets.convert_to_tf_dataset(x_test, y_test)
# load the baseline acc
baseline_acc = _validate(baseline_model, clean_validset)
print(' : Baseline model\'s accuracy is [{}]'.format(baseline_acc))
# --------------------------------------------------------------------------
# Substitute the numpy module used by JAX (when privacy)
# --------------------------------------------------------------------------
import jax.numpy as np
# --------------------------------------------------------------------------
# Set the location to store...
# --------------------------------------------------------------------------
# extract the setup
poison_task = args.poisonp.split('/')[3]
(x_train, y_train), (x_test, y_test), (x_poison, y_poison) = \
datasets.load_slab_poisons(args.poisonp)
else:
assert False, ('Error: unknown format file - {}'.format(args.poisonp))
# enforce the poisons to be within [0, 1] range
x_poison = np.clip(x_poison, 0., 1.)
# [DEBUG]
print (' : Load the poison data from [{}]'.format(args.poisonp))
print (' Train : {} in [{}, {}]'.format(x_train.shape, x_train.min(), x_train.max()))
print (' Test : {} in [{}, {}]'.format(x_test.shape, x_test.min(), x_test.max()))
print (' Poison: {} in [{}, {}]'.format(x_poison.shape, x_poison.min(), x_poison.max()))
# compose into the tensorflow datasets
clean_validset = datasets.convert_to_tf_dataset(x_test, y_test)
# to examine the training time accuracy on clean and poison samples
ctrain_examine = datasets.convert_to_tf_dataset(x_train, y_train)
ptrain_examine = datasets.convert_to_tf_dataset(x_poison, y_poison)
# load the baseline acc
baseline_acc = _validate(baseline_model, clean_validset)
print (' : Baseline model\'s accuracy is [{}]'.format(baseline_acc))
# --------------------------------------------------------------------------
# Set the location to store...
# --------------------------------------------------------------------------
# extract the setup
poison_task = args.poisonp.split('/')[3]