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, n_heads=1, d_model=1024, kernel_initializer=init.GlorotUniformInitializer()): super(ComputeAttentionOutput, self).__init__() self._n_heads = n_heads self._d_model = d_model self._kernel_initializer = kernel_initializer
def __init__(self, n_heads=1, d_head=64, kernel_initializer=init.GlorotUniformInitializer()): super(ComputeAttentionHeads, self).__init__() self._n_heads = n_heads self._d_head = d_head self._kernel_initializer = kernel_initializer
def __init__(self, d_feature, vocab_size, kernel_initializer=init.GlorotUniformInitializer()): super(Embedding, self).__init__() self._d_feature = d_feature # feature dimensionality self._vocab_size = vocab_size self._kernel_initializer = kernel_initializer
def __init__(self, feature_depth, vocab_size, kernel_initializer=init.GlorotUniformInitializer()): super(Embedding, self).__init__() self._feature_depth = feature_depth self._vocab_size = vocab_size self._kernel_initializer = kernel_initializer
def test_glorot_uniform(self): initializer = initializers.GlorotUniformInitializer() input_shape = (29, 5, 7, 20) init_value = initializer(input_shape, random.get_prng(0)) self.assertEqual(tuple(init_value.shape), input_shape)