def __init__(self):
super(HasMapping, self).__init__()
self.layer_dict = tf.__internal__.tracking.wrap(dict(output=core.Dense(7)))
self.layer_dict["norm"] = tf.__internal__.tracking.wrap([])
self.layer_dict["dense"] = tf.__internal__.tracking.wrap([])
self.layer_dict["dense"].extend(
[core.Dense(5),
core.Dense(6, kernel_regularizer=tf.reduce_sum)])
self.layer_dict["norm"].append(
batch_normalization_v1.BatchNormalization())
self.layer_dict["norm"].append(
batch_normalization_v1.BatchNormalization())
def my_func():
layer = batch_normalization_v1.BatchNormalization()
x = tf.ones((10, 1))
y = layer(x, training=True)
# Updates should be tracked in a `wrap_function`.
self.assertLen(layer.updates, 2)
return y
def test_v1_fused_attribute(self):
norm = batch_normalization_v1.BatchNormalization()
inp = keras.layers.Input((4, 4, 4))
norm(inp)
self.assertEqual(norm.fused, True)
norm = batch_normalization_v1.BatchNormalization(fused=False)
self.assertEqual(norm.fused, False)
inp = keras.layers.Input(shape=(4, 4, 4))
norm(inp)
self.assertEqual(norm.fused, False)
norm = batch_normalization_v1.BatchNormalization(virtual_batch_size=2)
self.assertEqual(norm.fused, True)
inp = keras.layers.Input(shape=(2, 2, 2))
norm(inp)
self.assertEqual(norm.fused, False)
def __init__(self):
super().__init__()
self.layer_list = (
core.Dense(3),
core.Dense(4),
core.Dense(5, kernel_regularizer=tf.reduce_sum),
)
self.layers_with_updates = (
batch_normalization_v1.BatchNormalization(), )
def __init__(self):
super(HasList, self).__init__()
self.layer_list = tf.__internal__.tracking.wrap([core.Dense(3)])
self.layer_list.append(core.Dense(4))
self.layer_list.extend(
[core.Dense(5),
core.Dense(6, kernel_regularizer=tf.reduce_sum)])
self.layer_list += [
core.Dense(7, bias_regularizer=tf.reduce_sum),
core.Dense(8)
]
self.layer_list += (tf.__internal__.tracking.wrap([core.Dense(9)]) +
tf.__internal__.tracking.wrap([core.Dense(10)]))
self.layer_list.extend(
tf.__internal__.tracking.wrap(
list([core.Dense(11)]) + [core.Dense(12)]))
self.layers_with_updates = tf.__internal__.tracking.wrap(
[batch_normalization_v1.BatchNormalization()])
