def test_positive_parameterizer(self): shape = (1, 2, 3, 4) var = self._test_parameterizer( parameterizers.NonnegativeParameterizer(minimum=.1), tf.initializers.random_uniform(), shape) self.assertEqual(var.shape, shape) self.assertTrue(np.all(var >= .1))
from __future__ import print_function # Dependency imports from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.layers import base from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow_compression.python.layers import parameterizers _default_beta_param = parameterizers.NonnegativeParameterizer(minimum=1e-6) _default_gamma_param = parameterizers.NonnegativeParameterizer() class GDN(base.Layer): """Generalized divisive normalization layer. Based on the papers: > "Density modeling of images using a generalized normalization > transformation"<br /> > J. Ballé, V. Laparra, E.P. Simoncelli<br /> > https://arxiv.org/abs/1511.06281 > "End-to-end optimized image compression"<br /> > J. Ballé, V. Laparra, E.P. Simoncelli<br />