def _prepare_model(self):
base_model = self.embedding.model
blstm_layer = Bidirectional(
LSTM(**self.hyper_parameters['lstm_layer']))(base_model.output)
dense_layer = Dense(128, activation='tanh')(blstm_layer)
crf = CRF(len(self.label2idx), sparse_target=False)
crf_layer = crf(dense_layer)
self.model = Model(base_model.inputs, crf_layer)
def build_model(self):
base_model = self.embedding.model
blstm_layer = Bidirectional(LSTM(**self.hyper_parameters['lstm_layer']))(base_model.output)
dense_layer = Dense(128, activation='tanh')(blstm_layer)
crf = CRF(len(self.label2idx), sparse_target=False)
crf_layer = crf(dense_layer)
model = Model(base_model.inputs, crf_layer)
model.compile(loss=crf_loss,
optimizer='adam',
metrics=[crf_accuracy])
self.model = model
self.model.summary()
def _prepare_model(self):
base_model = self.embedding.model
emb = base_model.output
emb = SpatialDropout1D(0.3)(emb)
emb = TimestepDropout(0.1)(emb)
blstm_out = Bidirectional(CuDNNLSTM(**self.hyper_parameters['lstm_layer']))(emb)
blstm_out = BatchNormalization()(blstm_out)
dense_ = Dense(512, activation='relu')(blstm_out)
dense_ = SpatialDropout1D(0.5)(dense_)
dense_ = TimestepDropout(0.2)(dense_)
crf_out = CRF(len(self.label2idx), sparse_target=False)(dense_)
self.model = Model(base_model.inputs, crf_out)
def create_custom_objects(model_info):
custom_objects = {}
loss = model_info.get('loss')
if loss and loss['name'] == 'weighted_categorical_crossentropy':
loss_f = helper.weighted_categorical_crossentropy(
np.array(loss['weights']))
custom_objects['loss'] = loss_f
if loss and loss['name'] == 'crf':
custom_objects['CRF'] = CRF
custom_objects['crf_loss'] = crf_loss
custom_objects['crf_viterbi_accuracy'] = CRF(128).accuracy
return custom_objects
def create_custom_objects(model_info):
custom_objects = {}
loss = model_info.get('loss')
if loss and loss['name'] == 'weighted_categorical_crossentropy':
loss_f = helper.weighted_categorical_crossentropy(
np.array(loss['weights']))
custom_objects['loss'] = loss_f
if loss and loss['name'] == 'crf':
custom_objects['CRF'] = CRF
custom_objects['crf_loss'] = crf_loss
custom_objects['crf_viterbi_accuracy'] = CRF(128).accuracy
embedding = model_info.get('embedding')
if embedding and embedding['type'] == 'bert':
custom_objects['NonMaskingLayer'] = helper.NonMaskingLayer
custom_objects.update(get_bert_custom_objects())
return custom_objects
def _prepare_model(self):
base_model = self.embedding.model
emb = base_model.output
emb = SpatialDropout1D(0.3)(emb)
crf_out = CRF(len(self.label2idx), sparse_target=False)(emb)
self.model = Model(base_model.inputs, crf_out)