def test_depthwise_conv2d_set_weights(self):
input_shape = (1, 10, 10, 3)
input_data = np.random.normal(size=input_shape)
with tf.Session():
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.DepthwiseConv2D(kernel_size=(2, 2),
batch_input_shape=input_shape))
expected = model.predict(input_data)
k_weights = model.get_weights()
k_config = model.get_config()
with tfe.protocol.SecureNN():
x = tfe.define_private_input(
"inputter",
lambda: tf.convert_to_tensor(input_data))
tfe_model = tfe.keras.models.model_from_config(k_config)
tfe_model.set_weights(k_weights)
y = tfe_model(x)
with KE.get_session() as sess:
actual = sess.run(y.reveal())
np.testing.assert_allclose(actual, expected, rtol=1e-2, atol=1e-2)
KE.clear_session()
def test_from_config(self):
input_shape = (1, 3)
input_data = np.random.normal(size=input_shape)
expected, k_weights, k_config = _model_predict_keras(
input_data, input_shape)
with tfe.protocol.SecureNN():
x = tfe.define_private_input(
"inputter", lambda: tf.convert_to_tensor(input_data))
tfe_model = Sequential.from_config(k_config)
tfe_model.set_weights(k_weights)
y = tfe_model(x)
with KE.get_session() as sess:
actual = sess.run(y.reveal())
np.testing.assert_allclose(actual, expected, rtol=1e-2, atol=1e-3)
KE.clear_session()
def test_conv_model(self):
num_classes = 10
input_shape = (1, 28, 28, 1)
input_data = np.random.normal(size=input_shape)
with tf.Session():
model = tf.keras.models.Sequential()
model.add(
tf.keras.layers.Conv2D(2, (3, 3),
batch_input_shape=input_shape))
model.add(tf.keras.layers.ReLU())
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.AveragePooling2D((2, 2)))
model.add(tf.keras.layers.Conv2D(2, (3, 3)))
model.add(tf.keras.layers.ReLU())
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.AveragePooling2D((2, 2)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(num_classes, name="logit"))
expected = model.predict(input_data)
k_weights = model.get_weights()
k_config = model.get_config()
with tfe.protocol.SecureNN():
x = tfe.define_private_input(
"inputter", lambda: tf.convert_to_tensor(input_data))
tfe_model = tfe.keras.models.model_from_config(k_config)
tfe_model.set_weights(k_weights)
y = tfe_model(x)
with KE.get_session() as sess:
actual = sess.run(y.reveal())
np.testing.assert_allclose(actual, expected, rtol=1e-2, atol=1e-2)
KE.clear_session()
def test_clone_model(self):
input_shape = (1, 3)
input_data = np.random.normal(size=input_shape)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(2, batch_input_shape=input_shape))
model.add(tf.keras.layers.Dense(3))
expected = model.predict(input_data)
with tfe.protocol.SecureNN():
x = tfe.define_private_input(
"inputter", lambda: tf.convert_to_tensor(input_data))
tfe_model = tfe.keras.models.clone_model(model)
with KE.get_session() as sess:
y = tfe_model(x)
actual = sess.run(y.reveal())
np.testing.assert_allclose(actual, expected, rtol=1e-2, atol=1e-3)
KE.clear_session()