def test_activity_regularization():
layer = layers.ActivityRegularization(l1=0.01, l2=0.01)
# test in functional API
x = layers.Input(shape=(3,))
z = layers.Dense(2)(x)
y = layer(z)
model = Model(x, y)
model.compile('rmsprop', 'mse')
model.predict(np.random.random((2, 3)))
# test serialization
model_config = model.get_config()
model = Model.from_config(model_config)
model.compile('rmsprop', 'mse')
def test_activity_regularization(self): x = Normal(loc=tf.zeros([100, 10, 5]), scale=tf.ones([100, 10, 5])) y = layers.ActivityRegularization(l1=0.1)(x.value())
def __init__(self):
super().__init__()
self.loss_layer = layers.ActivityRegularization(l2=0.001)
self.add_weight(shape=(1, ), regularizer="l2")
def __init__(self): super(LayerWithSharedNestedLossLayer, self).__init__() self.loss_layer = layers.ActivityRegularization(l2=0.001) self.add_weight(shape=(1,), regularizer='l2')
def test_activity_regularization(self):
x = Normal(mu=tf.zeros([100, 10, 5]), sigma=tf.ones([100, 10, 5]))
y = layers.ActivityRegularization(l1=0.1)(x)
