def get_model(self, trainable=None):
trainable = self.trainable if trainable is None else trainable
inputs = Input(shape=(self.max_len, ))
emb = get_embedding_layer(self.data.tokenizer,
max_len=self.max_len,
embedding_dim=self.embed_size,
use_pretrained=self.use_pretrained,
trainable=trainable,
use_new_vector=self.use_new_vector)(inputs)
x = Bidirectional(
CuDNNLSTM(cfg.LSTM_hidden_size, return_sequences=True))(emb)
x = Bidirectional(
CuDNNLSTM(cfg.LSTM_hidden_size, return_sequences=True))(x)
con = concatenate([x, emb], axis=2)
concat_x = []
for filter_size in cfg.TEXT_CNN_filters:
x = con
x = self._conv_relu_maxpool(x, filter_size)
concat_x.append(x)
x = concatenate(concat_x, axis=1)
x = Dropout(0.5)(x)
x = Dense(5, activation='relu')(x)
x = Dense(1, activation='linear')(x)
model = Model(inputs=inputs, outputs=x)
model.compile(loss='mse', optimizer=self.optimizer)
return model
def get_model(self, trainable=None):
inputs = Input(shape=(self.max_len, ))
emb = get_embedding_layer(self.data.tokenizer,
max_len=self.max_len,
embedding_dim=self.embed_size,
use_pretrained=self.use_pretrained,
trainable=trainable,
use_new_vector=self.use_new_vector)(inputs)
x = Conv1D(128, self.filter_window, activation='relu')(emb)
x = GlobalMaxPool1D()(x)
x = Dense(1, activation='linear')(x)
model = Model(inputs=inputs, outputs=x)
model.compile(loss='mse', optimizer=self.optimizer)
return model
def get_model(self, trainable=None):
trainable = self.trainable if trainable is None else trainable
inputs = Input(shape=(self.max_len,))
emb = get_embedding_layer(self.data.tokenizer, max_len=self.max_len, embedding_dim=self.embed_size,
use_pretrained=self.use_pretrained, trainable=trainable,
use_new_vector=self.use_new_vector)(inputs)
emb = SpatialDropout1D(0.5)(emb)
x = Bidirectional(CuDNNGRU(cfg.LSTM_hidden_size, return_sequences=True))(emb)
x = Attention(self.max_len)(x)
x = Dropout(0.5)(x)
x = Dense(5, activation='relu')(x)
x = Dense(1, activation='linear')(x)
model = Model(inputs=inputs, outputs=x)
model.compile(loss='mse', optimizer=self.optimizer)
return model
def get_model(self, trainable=None):
inputs = Input(shape=(self.max_len, ))
emb = get_embedding_layer(self.data.tokenizer,
max_len=self.max_len,
embedding_dim=self.embed_size,
use_pretrained=self.use_pretrained,
trainable=trainable,
use_new_vector=self.use_new_vector)(inputs)
x = Bidirectional(CuDNNGRU(128, return_sequences=True))(emb)
x = self.attention_3d_block(x)
x = GlobalMaxPool1D()(x)
x = Dense(128, activation='relu')(x)
x = Dense(1, activation='linear')(x)
model = Model(inputs=inputs, outputs=x)
model.compile(loss='mse', optimizer=self.optimizer)
return model
def get_model(self, trainable=None):
trainable = self.trainable if trainable is None else trainable
inputs = Input(shape=(self.max_len, ))
emb = get_embedding_layer(self.data.tokenizer,
max_len=self.max_len,
embedding_dim=self.embed_size,
use_pretrained=self.use_pretrained,
trainable=trainable,
use_new_vector=self.use_new_vector)(inputs)
concat_x = []
for filter_size in self.filters:
x = emb
x = self._conv_relu_maxpool(x, filter_size)
concat_x.append(x)
x = concatenate(concat_x, axis=1)
x = Dropout(0.5)(x)
x = Dense(5, activation='relu')(x)
x = Dense(1, activation='linear')(x)
model = Model(inputs=inputs, outputs=x)
model.compile(loss='mse', optimizer=self.optimizer)
return model
