def test_clone_optimizer_in_different_graph(self):
with tf.Graph().as_default():
with self.session():
model = test_utils.get_small_sequential_mlp(3, 4)
optimizer = keras.optimizers.optimizer_v2.adam.Adam()
model.compile(
optimizer,
"mse",
metrics=["acc", metrics.categorical_accuracy],
)
model.fit(
x=np.array([[1.0, 2.0, 3.0, 4.0]]),
y=np.array([[1.0, 1.0, 1.0, 1.0]]),
epochs=1,
)
optimizer_config = optimizer.get_config()
with tf.Graph().as_default():
with self.session():
with self.assertRaisesRegex(
ValueError, "Cannot use the given session"
):
models.clone_and_build_model(model, compile_clone=True)
# The optimizer_config object allows the model to be cloned in a
# different graph.
models.clone_and_build_model(
model, compile_clone=True, optimizer_config=optimizer_config
)
def _clone_and_build_test_helper(self, model, model_type):
inp = np.random.random((10, 4))
out = np.random.random((10, 4))
is_subclassed = (model_type == 'subclass')
# With placeholder creation
new_model = models.clone_and_build_model(model,
compile_clone=True,
in_place_reset=is_subclassed)
self._assert_same_compile_params(new_model)
new_model.train_on_batch(inp, out)
new_model.evaluate(inp, out)
# Create new tensors for inputs.
input_a = keras.Input(shape=(4, ), name='a')
new_model = models.clone_and_build_model(model,
input_tensors=input_a,
compile_clone=True,
in_place_reset=is_subclassed)
self._assert_same_compile_params(new_model)
new_model.train_on_batch(inp, out)
new_model.evaluate(inp, out)
new_model = models.clone_and_build_model(model,
input_tensors=input_a,
target_tensors=None,
compile_clone=True,
in_place_reset=is_subclassed)
self._assert_same_compile_params(new_model)
new_model.train_on_batch(inp, out)
new_model.evaluate(inp, out)
def test_clone_and_build_non_compiled_model(self):
inp = np.random.random((10, 4))
out = np.random.random((10, 4))
model = _get_model()
with self.assertRaisesRegex(ValueError, "has not been compiled"):
models.clone_and_build_model(model, compile_clone=True)
is_subclassed = test_utils.get_model_type() == "subclass"
# With placeholder creation
new_model = models.clone_and_build_model(
model, compile_clone=False, in_place_reset=is_subclassed
)
with self.assertRaisesRegex(RuntimeError, "must compile"):
new_model.evaluate(inp, out)
with self.assertRaisesRegex(RuntimeError, "must compile"):
new_model.train_on_batch(inp, out)
new_model.compile(
test_utils.get_v2_optimizer("rmsprop"),
"mse",
run_eagerly=test_utils.should_run_eagerly(),
)
new_model.train_on_batch(inp, out)
# Create new tensors for inputs.
input_a = keras.Input(shape=(4,))
new_model = models.clone_and_build_model(
model,
input_tensors=input_a,
compile_clone=False,
in_place_reset=is_subclassed,
)
with self.assertRaisesRegex(RuntimeError, "must compile"):
new_model.evaluate(inp, out)
with self.assertRaisesRegex(RuntimeError, "must compile"):
new_model.train_on_batch(inp, out)
new_model.compile(
test_utils.get_v2_optimizer("rmsprop"),
"mse",
run_eagerly=test_utils.should_run_eagerly(),
)
new_model.train_on_batch(inp, out)
def assert_optimizer_iterations_increases(self, optimizer):
model = _get_model()
model.compile(
optimizer,
'mse',
metrics=['acc', metrics.categorical_accuracy],
run_eagerly=test_utils.should_run_eagerly())
global_step = keras.backend.variable(123, dtype=tf.int64)
clone_model = models.clone_and_build_model(
model, compile_clone=True, optimizer_iterations=global_step,
in_place_reset=(test_utils.get_model_type() == 'subclass'))
inp = np.random.random((10, 4))
out = np.random.random((10, 4))
clone_model.train_on_batch(inp, out)
self.assertEqual(backend.eval(global_step), 124)
