def __init__(
self,
in_channel=1,
rnn_hidden_size=200,
decoder_size=128,
is_test=False,
):
super(Encoder, self).__init__()
self.rnn_hidden_size = rnn_hidden_size
self.backbone = CNN(in_ch=in_channel, is_test=is_test)
para_attr = fluid.ParamAttr(
initializer=fluid.initializer.Normal(0.0, 0.02))
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Normal(
0.0, 0.02),
learning_rate=2.0)
self.gru_fwd = RNN(cell=GRUCell(input_size=128 * 6,
hidden_size=rnn_hidden_size,
param_attr=para_attr,
bias_attr=bias_attr,
candidate_activation='relu'),
is_reverse=False,
time_major=False)
self.gru_bwd = RNN(cell=GRUCell(input_size=128 * 6,
hidden_size=rnn_hidden_size,
param_attr=para_attr,
bias_attr=bias_attr,
candidate_activation='relu'),
is_reverse=True,
time_major=False)
self.encoded_proj_fc = fluid.dygraph.Linear(rnn_hidden_size * 2,
decoder_size,
bias_attr=False)
def __init__(self,
vocab_size,
embed_dim,
hidden_size,
num_layers,
dropout_prob=0.,
init_scale=0.1):
super(Encoder, self).__init__()
self.embedder = Embedding(
size=[vocab_size, embed_dim],
param_attr=ParamAttr(initializer=UniformInitializer(
low=-init_scale, high=init_scale)))
self.stack_lstm = RNN(EncoderCell(num_layers, embed_dim, hidden_size,
dropout_prob, init_scale),
is_reverse=False,
time_major=False)
def __init__(self,
vocab_size,
embed_dim,
hidden_size,
num_layers,
dropout_prob=0.,
init_scale=0.1):
super(Decoder, self).__init__()
self.embedder = Embedding(
size=[vocab_size, embed_dim],
param_attr=ParamAttr(initializer=UniformInitializer(
low=-init_scale, high=init_scale)))
self.lstm_attention = RNN(DecoderCell(
num_layers, embed_dim, hidden_size, dropout_prob, init_scale),
is_reverse=False,
time_major=False)
self.output_layer = Linear(
hidden_size,
vocab_size,
param_attr=ParamAttr(initializer=UniformInitializer(
low=-init_scale, high=init_scale)),
bias_attr=False)
def __init__(self, num_classes, emb_dim, encoder_size, decoder_size):
super(Decoder, self).__init__()
self.decoder_attention = RNN(DecoderCell(encoder_size, decoder_size))
self.fc = fluid.dygraph.Linear(decoder_size,
num_classes + 2,
act='softmax')