def __init__(self):
super().__init__(
source_inputter=inputters.WordEmbedder(embedding_size=512),
target_inputter=inputters.WordEmbedder(embedding_size=512),
encoder=encoders.RNNEncoder(
num_layers=2,
num_units=512,
residual_connections=False,
dropout=0.3,
cell_class=tf.keras.layers.LSTMCell,
),
decoder=decoders.AttentionalRNNDecoder(
num_layers=2,
num_units=512,
bridge_class=layers.CopyBridge,
attention_mechanism_class=tfa.seq2seq.LuongAttention,
attention_layer_activation=None,
cell_class=tf.keras.layers.LSTMCell,
dropout=0.3,
residual_connections=False,
),
)
def testAttentionalRNNDecoderFirstLayer(self): decoder = decoders.AttentionalRNNDecoder(2, 20, first_layer_attention=True) self._testDecoder(decoder)
def testAttentionalRNNDecoder(self): decoder = decoders.AttentionalRNNDecoder(2, 20) self._testDecoder(decoder)
def testAttentionalRNNDecoderTraining(self):
decoder = decoders.AttentionalRNNDecoder(2, 20)
self._testDecoderTraining(decoder, support_alignment_history=True)
def testAttentionalRNNDecoderWithDenseBridge(self):
decoder = decoders.AttentionalRNNDecoder(2, 36, bridge=bridge.DenseBridge())
encoder_cell = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.LSTMCell(5),
tf.nn.rnn_cell.LSTMCell(5)])
initial_state_fn = lambda batch_size, dtype: encoder_cell.zero_state(batch_size, dtype)
self._testDecoder(decoder, initial_state_fn=initial_state_fn)
