def __init__(self): super().__init__( inputter=inputters.MixedInputter( [ inputters.WordEmbedder(embedding_size=100), inputters.CharConvEmbedder( embedding_size=30, num_outputs=30, kernel_size=3, stride=1, dropout=0.5, ), ], dropout=0.5, ), encoder=encoders.RNNEncoder( num_layers=1, num_units=400, bidirectional=True, dropout=0.5, residual_connections=False, cell_class=tf.keras.layers.LSTMCell, ), crf_decoding=True, )
def testBidirectionalRNNEncoder(self, cell_class): encoder = encoders.RNNEncoder(3, 20, bidirectional=True, cell_class=cell_class) inputs = tf.random.uniform([4, 5, 10]) lengths = tf.constant([4, 3, 5, 2]) outputs, states, _ = encoder(inputs, sequence_length=lengths, training=True) self.assertListEqual(outputs.shape.as_list(), [4, 5, 40]) self.assertEqual(len(states), 3)
def __init__(self): super(LuongAttention, self).__init__( source_inputter=inputters.WordEmbedder(embedding_size=512), target_inputter=inputters.WordEmbedder(embedding_size=512), encoder=encoders.RNNEncoder(num_layers=4, num_units=1000, dropout=0.2, residual_connections=False, cell_class=tf.keras.layers.LSTMCell), decoder=decoders.AttentionalRNNDecoder( num_layers=4, num_units=1000, bridge_class=layers.CopyBridge, attention_mechanism_class=tfa.seq2seq.LuongAttention, cell_class=tf.keras.layers.LSTMCell, dropout=0.2, residual_connections=False))
def __init__(self): # pylint: disable=bad-continuation super(LstmCnnCrfTagger, self).__init__( inputter=inputters.MixedInputter([ inputters.WordEmbedder(embedding_size=100), inputters.CharConvEmbedder(embedding_size=30, num_outputs=30, kernel_size=3, stride=1, dropout=0.5) ], dropout=0.5), encoder=encoders.RNNEncoder(num_layers=1, num_units=400, bidirectional=True, dropout=0.5, residual_connections=False, cell_class=tf.keras.layers.LSTMCell), crf_decoding=True)
def __init__(self): super(NMTMediumV1, self).__init__( source_inputter=inputters.WordEmbedder(embedding_size=512), target_inputter=inputters.WordEmbedder(embedding_size=512), encoder=encoders.RNNEncoder(num_layers=4, num_units=256, bidirectional=True, residual_connections=False, dropout=0.3, reducer=layers.ConcatReducer(), cell_class=tf.keras.layers.LSTMCell), decoder=decoders.AttentionalRNNDecoder( num_layers=4, 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))