def __init__(self, methodname):
"""Initialize the test class."""
super().__init__(methodname)
self.ntrain = 100
self.nclass = 5
self.ndim = 5
# Generate random data.
np.random.seed(1234)
self.data = np.random.rand(self.ntrain, self.ndim) * 10
self.labels = np.random.randint(self.nclass, size=self.ntrain)
self.labels = (np.arange(self.nclass) == self.labels[:, None]).astype(
'f') # make one-hot
# Set model, optimizer and loss.
self.model = DNNet(layer_sizes=[self.ndim, self.nclass],
activation=objax.functional.softmax)
self.model_vars = self.model.vars()
def loss_function(x, y):
logit = self.model(x)
loss = ((y - logit)**2).mean(1).mean(0)
return loss, {'loss': loss}
self.loss = loss_function
def test_typical_training_loop(self):
# Define model and optimizer
model = DNNet((32, 10), objax.functional.leaky_relu)
opt = objax.optimizer.Momentum(model.vars(), nesterov=True)
# Predict op
predict_op = lambda x: objax.functional.softmax(model(x, training=False))
self.assertDictEqual(objax.util.find_used_variables(predict_op),
model.vars(scope='model.'))
# Loss function
def loss(x, label):
logit = model(x, training=True)
xe_loss = objax.functional.loss.cross_entropy_logits_sparse(logit, label).mean()
return xe_loss
self.assertDictEqual(objax.util.find_used_variables(loss),
model.vars(scope='model.'))
# Gradients and loss function
loss_gv = objax.GradValues(loss, objax.util.find_used_variables(loss))
def train_op(x, y, learning_rate):
grads, loss = loss_gv(x, y)
opt(learning_rate, grads)
return loss
self.assertDictEqual(objax.util.find_used_variables(train_op),
{**model.vars(scope='loss_gv.model.'), **opt.vars(scope='opt.')})
class TestPrivateGradValues(unittest.TestCase):
def __init__(self, methodname):
"""Initialize the test class."""
super().__init__(methodname)
self.ntrain = 100
self.nclass = 5
self.ndim = 5
# Generate random data.
np.random.seed(1234)
self.data = np.random.rand(self.ntrain, self.ndim) * 10
self.labels = np.random.randint(self.nclass, size=self.ntrain)
self.labels = (np.arange(self.nclass) == self.labels[:, None]).astype(
'f') # make one-hot
# Set model, optimizer and loss.
self.model = DNNet(layer_sizes=[self.ndim, self.nclass],
activation=objax.functional.softmax)
self.model_vars = self.model.vars()
def loss_function(x, y):
logit = self.model(x)
loss = ((y - logit)**2).mean(1).mean(0)
return loss, {'loss': loss}
self.loss = loss_function
def test_private_gradvalues_compare_nonpriv(self):
"""Test if PrivateGradValues without clipping / noise is the same as non-private GradValues."""
l2_norm_clip = 1e10
noise_multiplier = 0
for use_norm_accumulation in [True, False]:
for microbatch in [1, 10, self.ntrain]:
gv_priv = objax.Jit(
objax.privacy.dpsgd.PrivateGradValues(
self.loss,
self.model_vars,
noise_multiplier,
l2_norm_clip,
microbatch,
batch_axis=(0, 0),
use_norm_accumulation=use_norm_accumulation))
gv = objax.GradValues(self.loss, self.model_vars)
g_priv, v_priv = gv_priv(self.data, self.labels)
g, v = gv(self.data, self.labels)
# Check the shape of the gradient.
self.assertEqual(g_priv[0].shape, tuple([self.nclass]))
self.assertEqual(g_priv[1].shape,
tuple([self.ndim, self.nclass]))
# Check if the private gradient is similar to the non-private gradient.
np.testing.assert_allclose(g[0], g_priv[0], atol=1e-7)
np.testing.assert_allclose(g[1], g_priv[1], atol=1e-7)
np.testing.assert_allclose(v_priv[0],
self.loss(self.data,
self.labels)[0],
atol=1e-7)
def test_private_gradvalues_clipping(self):
"""Test if the gradient norm is within l2_norm_clip."""
noise_multiplier = 0
acceptable_float_error = 1e-8
for use_norm_accumulation in [True, False]:
for microbatch in [1, 10, self.ntrain]:
for l2_norm_clip in [0, 1e-2, 1e-1, 1.0]:
gv_priv = objax.Jit(
objax.privacy.dpsgd.PrivateGradValues(
self.loss,
self.model_vars,
noise_multiplier,
l2_norm_clip,
microbatch,
batch_axis=(0, 0),
use_norm_accumulation=use_norm_accumulation))
g_priv, v_priv = gv_priv(self.data, self.labels)
# Get the actual squared norm of the gradient.
g_normsquared = sum([np.sum(g**2) for g in g_priv])
self.assertLessEqual(
g_normsquared,
l2_norm_clip**2 + acceptable_float_error)
np.testing.assert_allclose(v_priv[0],
self.loss(
self.data, self.labels)[0],
atol=1e-7)
def test_private_gradvalues_noise(self):
"""Test if the noise std is around expected."""
runs = 100
alpha = 0.0001
for use_norm_accumulation in [True, False]:
for microbatch in [1, 10, self.ntrain]:
for noise_multiplier in [0.1, 10.0]:
for l2_norm_clip in [0.01, 0.1]:
gv_priv = objax.Jit(
objax.privacy.dpsgd.PrivateGradValues(
self.loss,
self.model_vars,
noise_multiplier,
l2_norm_clip,
microbatch,
batch_axis=(0, 0),
use_norm_accumulation=use_norm_accumulation))
# Repeat the run and collect all gradients.
g_privs = []
for i in range(runs):
g_priv, v_priv = gv_priv(self.data, self.labels)
g_privs.append(
np.concatenate(
[g_n.reshape(-1) for g_n in g_priv]))
np.testing.assert_allclose(v_priv[0],
self.loss(
self.data,
self.labels)[0],
atol=1e-7)
g_privs = np.array(g_privs)
# Compute empirical std and expected std.
std_empirical = np.std(g_privs, axis=0, ddof=1)
std_theoretical = l2_norm_clip * noise_multiplier / (
self.ntrain // microbatch)
# Conduct chi-square test for correct expected standard
# deviation.
chi2_value = (
runs - 1) * std_empirical**2 / std_theoretical**2
chi2_cdf = chi2.cdf(chi2_value, runs - 1)
self.assertTrue(
np.all(alpha <= chi2_cdf)
and np.all(chi2_cdf <= 1.0 - alpha))
# Conduct chi-square test for incorrect expected standard
# deviations: expect failure.
chi2_value = (runs - 1) * std_empirical**2 / (
1.25 * std_theoretical)**2
chi2_cdf = chi2.cdf(chi2_value, runs - 1)
self.assertFalse(
np.all(alpha <= chi2_cdf)
and np.all(chi2_cdf <= 1.0 - alpha))
chi2_value = (runs - 1) * std_empirical**2 / (
0.75 * std_theoretical)**2
chi2_cdf = chi2.cdf(chi2_value, runs - 1)
self.assertFalse(
np.all(alpha <= chi2_cdf)
and np.all(chi2_cdf <= 1.0 - alpha))
flat_test_images = np.reshape(
data['test']['image'].transpose(0, 3, 1, 2) / 127.5 - 1,
(test_size, image_size))
test = EasyDict(image=flat_test_images, label=data['test']['label'])
train = EasyDict(image=flat_train_images, label=data['train']['label'])
del data
# Settings
lr = 0.0002
batch = 64
num_train_epochs = 40
dnn_layer_sizes = image_size, 128, 10
logdir = f'experiments/classify/img/mnist/filters{dnn_layer_sizes}'
# Model
model = DNNet(dnn_layer_sizes, leaky_relu)
model_ema = objax.optimizer.ExponentialMovingAverageModule(model,
momentum=0.999)
opt = objax.optimizer.Adam(model.vars())
@objax.Function.with_vars(model.vars())
def loss(x, label):
logit = model(x)
return objax.functional.loss.cross_entropy_logits(logit, label).mean()
gv = objax.GradValues(loss, model.vars())
@objax.Function.with_vars(model.vars() + gv.vars() + opt.vars() +