def test_frn_serialization(self):
layer0 = frn.FRN(reg_epsilon=1.0e-4)
config0 = layer0.get_config()
layer1 = layer0.__class__(**config0)
config1 = layer1.get_config()
self.assertEqual(config0,
config1,
msg='Serialization does not capture all state.')
def resnet_layer(inputs,
filters,
kernel_size=3,
strides=1,
activation=None,
pfac=None,
use_frn=False,
use_bias=True):
"""2D Convolution-Batch Normalization-Activation stack builder.
Args:
inputs: tf.Tensor.
filters: Number of filters for Conv2D.
kernel_size: Kernel dimensions for Conv2D.
strides: Stride dimensinons for Conv2D.
activation: tf.keras.activations.Activation.
pfac: prior.PriorFactory object.
use_frn: if True, use Filter Response Normalization (FRN) layer
use_bias: if True, use biases in Conv layers.
Returns:
tf.Tensor.
"""
x = inputs
logging.info('Applying conv layer.')
x = pfac(tf.keras.layers.Conv2D(
filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
kernel_initializer='he_normal',
use_bias=use_bias))(x)
if use_frn:
x = pfac(frn.FRN())(x)
else:
x = tf.keras.layers.BatchNormalization()(x)
if activation is not None:
x = tf.keras.layers.Activation(activation)(x)
return x