def test_checkpoint_sam(self):
model = keras.Sequential([
keras.Input([2, 2]),
keras.layers.Dense(4),
keras.layers.Dense(1),
])
sam_model_1 = sharpness_aware_minimization.SharpnessAwareMinimization(model)
sam_model_2 = sharpness_aware_minimization.SharpnessAwareMinimization(model)
data = tf.random.uniform([1, 2, 2])
label = data[:, 0] > 0.5
sam_model_1.compile(
optimizer=adam.Adam(),
loss=keras.losses.BinaryCrossentropy(from_logits=True),
)
sam_model_1.fit(data, label)
checkpoint = tf.train.Checkpoint(sam_model_1)
checkpoint2 = tf.train.Checkpoint(sam_model_2)
temp_dir = self.get_temp_dir()
save_path = checkpoint.save(temp_dir)
checkpoint2.restore(save_path)
self.assertAllClose(sam_model_1(data), sam_model_2(data))
def test_save_sam(self):
model = keras.Sequential(
[
keras.Input([2, 2]),
keras.layers.Dense(4),
keras.layers.Dense(1),
]
)
sam_model = sharpness_aware_minimization.SharpnessAwareMinimization(
model
)
data = tf.random.uniform([1, 2, 2])
label = data[:, 0] > 0.5
sam_model.compile(
optimizer=adam.Adam(),
loss=keras.losses.BinaryCrossentropy(from_logits=True),
)
sam_model.fit(data, label)
path = os.path.join(self.get_temp_dir(), "model")
sam_model.save(path)
loaded_sam_model = keras.models.load_model(path)
loaded_sam_model.load_weights(path)
self.assertAllClose(sam_model(data), loaded_sam_model(data))
def test_sam_model_call(self):
model = keras.Sequential([
keras.Input([2, 2]),
keras.layers.Dense(4),
])
sam_model = sharpness_aware_minimization.SharpnessAwareMinimization(model)
data = tf.random.uniform([2, 2])
self.assertAllClose(model(data), sam_model(data))
def test_sam_model_fit(self, strategy):
with strategy.scope():
model = keras.Sequential([
keras.Input([2, 2]),
keras.layers.Dense(4),
keras.layers.Dense(1),
])
sam_model = sharpness_aware_minimization.SharpnessAwareMinimization(model)
data = tf.random.uniform([2, 2])
label = data[:, 0] > 0.5
sam_model.compile(
optimizer=adam.Adam(),
loss=keras.losses.BinaryCrossentropy(from_logits=True),
)
sam_model.fit(data, label, steps_per_epoch=1)
