def test_lambda_skip_state_variable_from_initializer(self):
# Force the initializers to use the tf.random.Generator, which will
# contain the state variable.
kernel_initializer = initializers.RandomNormalV2()
kernel_initializer._random_generator._rng_type = (
kernel_initializer._random_generator.RNG_STATEFUL)
dense = keras.layers.Dense(1,
use_bias=False,
kernel_initializer=kernel_initializer)
def lambda_fn(x):
return dense(x + 1) # Dense layer is built on first call
# While it is generally not advised to mix Variables with Lambda layers,
# if the variables are explicitly set as attributes then they are still
# tracked. This is consistent with the base Layer behavior.
layer = keras.layers.Lambda(lambda_fn)
layer.dense = dense
model = test_utils.get_model_from_layers([layer], input_shape=(10, ))
model.compile(
keras.optimizers.optimizer_v2.gradient_descent.SGD(0.1),
"mae",
run_eagerly=test_utils.should_run_eagerly(),
)
x, y = np.ones((10, 10), "float32"), 2 * np.ones((10, 10), "float32")
model.fit(x, y, batch_size=2, epochs=2, validation_data=(x, y))
self.assertLen(model.trainable_weights, 1)
def test_partition(self):
with self.cached_session():
partition_enabled_initializers = [
initializers.ZerosV2(),
initializers.OnesV2(),
initializers.RandomUniformV2(),
initializers.RandomNormalV2(),
initializers.TruncatedNormalV2(),
initializers.LecunUniformV2(),
initializers.GlorotUniformV2(),
initializers.HeUniformV2()
]
for initializer in partition_enabled_initializers:
got = initializer(
shape=(4, 2), partition_shape=(2, 2), partition_offset=(0, 0))
self.assertEqual(got.shape, (2, 2))
partition_forbidden_initializers = [
initializers.OrthogonalV2(),
initializers.IdentityV2()
]
for initializer in partition_forbidden_initializers:
with self.assertRaisesRegex(
ValueError,
"initializer doesn't support partition-related arguments"):
initializer(
shape=(4, 2), partition_shape=(2, 2), partition_offset=(0, 0))
def test_normal(self):
tensor_shape = (8, 12, 99)
with self.cached_session():
self._runner(
initializers.RandomNormalV2(mean=0, stddev=1, seed=153),
tensor_shape,
)