def __init__(self, units,
kernel_initializer=init.GlorotUniformInitializer(),
bias_initializer=init.RandomNormalInitializer(1e-6)):
super(Dense, self).__init__()
self._units = units
self._kernel_initializer = kernel_initializer
self._bias_initializer = bias_initializer
def __init__(self,
filters,
kernel_width=3,
kernel_initializer=None,
bias_initializer=init.RandomNormalInitializer(1e-6)):
super(CausalConv,
self).__init__(filters=filters,
kernel_size=(kernel_width, ),
strides=None,
padding='VALID',
dimension_numbers=('NWC', 'WIO', 'NWC'),
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer)
def __init__(self, filters, kernel_size, strides=None, padding='VALID',
dimension_numbers=('NHWC', 'HWIO', 'NHWC'),
kernel_initializer=None,
bias_initializer=init.RandomNormalInitializer(1e-6)):
super(Conv, self).__init__()
self._filters = filters
self._kernel_size = kernel_size
self._padding = padding
self._dimension_numbers = dimension_numbers
self._lhs_spec, self._rhs_spec, self._out_spec = dimension_numbers
self._one = (1,) * len(kernel_size)
self._strides = strides or self._one
self._bias_initializer = bias_initializer
rhs_spec = self._rhs_spec
self._kernel_initializer = kernel_initializer
if kernel_initializer is None:
self._kernel_initializer = init.GlorotNormalInitializer(
rhs_spec.index('O'), rhs_spec.index('I'))
def __init__(self, initializer=init.RandomNormalInitializer(0.01)):
super(ShiftRightLearned, self).__init__()
self._initializer = initializer
def test_random_normal(self):
initializer = initializers.RandomNormalInitializer()
input_shape = (29, 5, 7, 20)
init_value = initializer(input_shape, random.get_prng(0))
self.assertEqual(tuple(init_value.shape), input_shape)
