def build_model(self, inputs, outputs):
outputs_spati = L.SpatialDropout1D(self.dropout_spatial)(outputs)
conv_pools = []
for filter in self.filters_size:
x = L.Conv1D(
filters=self.filters_num,
kernel_size=filter,
padding="valid",
kernel_initializer="normal",
activation="relu",
)(outputs_spati)
capsule = Capsule_bojone(num_capsule=self.num_capsule,
dim_capsule=self.dim_capsule,
routings=self.routings,
kernel_size=(filter, 1),
share_weights=True)(x)
conv_pools.append(capsule)
capsule = L.Concatenate(axis=-1)(conv_pools)
x = L.Flatten()(capsule)
x = L.Dropout(self.dropout)(x)
# dense-mid
x = L.Dense(units=min(max(self.label, 64), self.embed_size),
activation=self.activate_mid)(x)
x = L.Dropout(self.dropout)(x)
# dense-end, 最后一层, dense到label
self.outputs = L.Dense(units=self.label,
activation=self.activate_end)(x)
self.model = M.Model(inputs=inputs, outputs=self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
"""
build_model.
Args:
inputs: tensor, input of model
outputs: tensor, output of model
Returns:
None
"""
embed_char = outputs[0]
embed_word = outputs[1]
if self.wclstm_embed_type == "ATTNENTION":
x_word = L.TimeDistributed(
SelfAttention(K.int_shape(embed_word)[-1]))(embed_word)
x_word_shape = K.int_shape(x_word)
x_word = L.Reshape(target_shape=(x_word_shape[:2],
x_word_shape[2] *
x_word_shape[3]))
x_word = L.Dense(self.embed_size,
activation=self.activate_mid)(x_word)
# elif self.wclstm_embed_type == "SHORT":
else:
x_word = L.Lambda(lambda x: x[:, :, 0, :])(embed_word)
outputs_concat = L.Concatenate(axis=-1)([embed_char, x_word])
# LSTM or GRU
if self.rnn_type == "LSTM":
rnn_cell = L.LSTM
elif self.rnn_type == "CuDNNLSTM":
rnn_cell = L.CuDNNLSTM
elif self.rnn_type == "CuDNNGRU":
rnn_cell = L.CuDNNGRU
else:
rnn_cell = L.GRU
# Bi-LSTM-CRF
for nrl in range(self.num_rnn_layers):
x = L.Bidirectional(
rnn_cell(
units=self.rnn_unit * (nrl + 1),
return_sequences=True,
activation=self.activate_mid,
))(outputs_concat)
outputs = L.Dropout(self.dropout)(x)
if self.use_crf:
x = L.Dense(units=self.label,
activation=self.activate_end)(outputs)
self.CRF = ConditionalRandomField(self.crf_lr_multiplier,
name="crf_bert4keras")
self.outputs = self.CRF(x)
self.trans = K.eval(self.CRF.trans).tolist()
self.loss = self.CRF.dense_loss if self.use_onehot else self.CRF.sparse_loss
self.metrics = [
self.CRF.dense_accuracy
if self.use_onehot else self.CRF.sparse_accuracy
]
else:
self.outputs = L.TimeDistributed(
L.Dense(units=self.label,
activation=self.activate_end))(outputs)
self.model = M.Model(inputs, self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
embedding_reshape = L.Reshape(
(self.length_max, self.embed_size, 1))(outputs)
# 提取n-gram特征和最大池化， 一般不用平均池化
conv_pools = []
for filter in self.filters_size:
conv = L.Conv2D(
filters=self.filters_num,
kernel_size=(filter, self.embed_size),
padding='valid',
kernel_initializer='normal',
activation='tanh',
)(embedding_reshape)
pooled = L.MaxPool2D(
pool_size=(self.length_max - filter + 1, 1),
strides=(1, 1),
padding='valid',
)(conv)
conv_pools.append(pooled)
# 拼接
x = L.Concatenate(axis=-1)(conv_pools)
x = L.Dropout(self.dropout)(x)
x = L.Flatten()(x)
self.outputs = L.Dense(units=self.label,
activation=self.activate_end)(x)
self.model = M.Model(inputs=inputs, outputs=self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
x = None
if self.rnn_type == "LSTM":
rnn_cell = L.LSTM
elif self.rnn_type == "CuDNNLSTM":
rnn_cell = L.CuDNNLSTM
elif self.rnn_type == "CuDNNGRU":
rnn_cell = L.CuDNNGRU
else:
rnn_cell = L.GRU
# Bi-RNN(LSTM/GRU)
for _ in range(self.rnn_layer_repeat):
x = L.Bidirectional(
rnn_cell(units=self.rnn_unit,
return_sequences=True,
activation=self.activate_mid))(outputs)
x = L.Dropout(self.dropout)(x)
# dense-mid
x = L.Flatten()(x)
x = L.Dense(units=min(max(self.label, 128), self.embed_size),
activation=self.activate_mid)(x)
x = L.Dropout(self.dropout)(x)
# dense-end
self.outputs = L.Dense(units=self.label,
activation=self.activate_end)(x)
self.model = M.Model(inputs=inputs, outputs=self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
# rnn type, RNN的类型
if self.rnn_type == "LSTM":
layer_cell = L.LSTM
else:
layer_cell = L.GRU
# backword, 反向
x_backwords = layer_cell(
units=self.rnn_unit,
return_sequences=True,
kernel_regularizer=keras.regularizers.l2(0.32 * 0.1),
recurrent_regularizer=keras.regularizers.l2(0.32),
go_backwards=True)(outputs)
x_backwords_reverse = L.Lambda(lambda x: K.reverse(x, axes=1))(
x_backwords)
# fordword, 前向
x_fordwords = layer_cell(
units=self.rnn_unit,
return_sequences=True,
kernel_regularizer=keras.regularizers.l2(0.32 * 0.1),
recurrent_regularizer=keras.regularizers.l2(0.32),
go_backwards=False)(outputs)
# concatenate, 拼接
x_feb = L.Concatenate(axis=2)(
[x_fordwords, outputs, x_backwords_reverse])
# dropout, 随机失活
x_feb = L.Dropout(self.dropout)(x_feb)
# Concatenate, 拼接后的embedding_size
dim_2 = K.int_shape(x_feb)[2]
x_feb_reshape = L.Reshape((self.length_max, dim_2, 1))(x_feb)
# n-gram, conv, maxpool, 使用n-gram进行卷积和池化
conv_pools = []
for filter in self.filters_size:
conv = L.Conv2D(
filters=self.filters_num,
kernel_size=(filter, dim_2),
padding='valid',
kernel_initializer='normal',
activation='relu',
)(x_feb_reshape)
pooled = L.MaxPooling2D(
pool_size=(self.length_max - filter + 1, 1),
strides=(1, 1),
padding='valid',
)(conv)
conv_pools.append(pooled)
# concatenate, 拼接TextCNN
x = L.Concatenate()(conv_pools)
x = L.Dropout(self.dropout)(x)
# dense-mid, 中间全连接到中间的隐藏元
x = L.Flatten()(x)
x = L.Dense(units=min(max(self.label, 64), self.embed_size),
activation=self.activate_mid)(x)
x = L.Dropout(self.dropout)(x)
# dense-end, 最后一层, dense到label
self.outputs = L.Dense(units=self.label,
activation=self.activate_end)(x)
self.model = M.Model(inputs=inputs, outputs=self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
x_m = L.GlobalMaxPooling1D()(outputs)
x_g = L.GlobalAveragePooling1D()(outputs)
x = L.Concatenate()([x_g, x_m])
x = L.Dense(min(max(self.label, 128), self.embed_size), activation=self.activate_mid)(x)
x = L.Dropout(self.dropout)(x)
self.outputs = L.Dense(units=self.label, activation=self.activate_end)(x)
self.model = M.Model(inputs=inputs, outputs=self.outputs)
self.model.summary(132)
def viterbi_decode(self, nodes: np.array, trans: np.array) -> np.array:
"""
viterbi decode of CRF, 维特比解码, Viterbi算法求最优路径
code from url: https://github.com/bojone/bert4keras
author : bojone
Args:
nodes: np.array, shape=[seq_len, num_labels], output of model predict
trans: np.array, shape=[num_labels, num_labels], state transition matrix
Returns:
res: np.array, label of sequence
"""
labels = np.arange(len(self.l2i)).reshape((1, -1))
scores = nodes[0].reshape((-1, 1))
scores[1:] -= np.inf # 第一个标签必然是0
paths = labels
for l in range(1, len(nodes)):
M = scores + trans + nodes[l].reshape((1, -1))
idxs = M.argmax(0)
scores = M.max(0).reshape((-1, 1))
path_idxs = paths[:, idxs]
paths = np.concatenate([path_idxs, labels], 0)
return paths[:, scores[0].argmax()]
def build_model(self, inputs, outputs):
x = L.Dense(units=self.label, activation=self.activate_mid)(outputs)
self.CRF = ConditionalRandomField(self.crf_lr_multiplier,
name="crf_bert4keras")
self.outputs = self.CRF(x)
self.model = M.Model(inputs, self.outputs)
self.model.summary(132)
self.trans = K.eval(self.CRF.trans).tolist()
self.loss = self.CRF.dense_loss if self.use_onehot else self.CRF.sparse_loss
self.metrics = [
self.CRF.dense_accuracy
if self.use_onehot else self.CRF.sparse_accuracy
]
def build_model(self, inputs, outputs):
if self.embed_type in ["xlnet"]:
# x = L.Lambda(lambda x: x[:, -2:-1, :])(outputs) # xlnet获取CLS
x = L.Lambda(lambda x: x[:, -1], name="Token-CLS")(outputs)
else:
# x = L.Lambda(lambda x: x[:, 0:1, :])(outputs) # bert-like获取CLS
x = L.Lambda(lambda x: x[:, 0], name="Token-CLS")(outputs)
# x = L.Flatten()(x)
# 最后就是softmax
self.outputs = L.Dense(
self.label,
activation=self.activate_end,
kernel_initializer=keras.initializers.TruncatedNormal(
stddev=0.02))(x)
self.model = M.Model(inputs, self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
x = L.SpatialDropout1D(self.dropout_spatial)(outputs)
x = SelfAttention(K.int_shape(outputs)[-1])(x)
x_max = L.GlobalMaxPooling1D()(x)
x_avg = L.GlobalAveragePooling1D()(x)
x = L.Concatenate()([x_max, x_avg])
x = L.Dropout(self.dropout)(x)
x = L.Flatten()(x)
# dense-mid
x = L.Dense(units=min(max(self.label, 64), self.embed_size),
activation=self.activate_mid)(x)
x = L.Dropout(self.dropout)(x)
# dense-end, 最后一层, dense到label
self.outputs = L.Dense(units=self.label,
activation=self.activate_end)(x)
self.model = M.Model(inputs=inputs, outputs=self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
# rnn type, RNN的类型
if self.rnn_unit == "LSTM":
layer_cell = L.LSTM
elif self.rnn_unit == "CuDNNLSTM":
layer_cell = L.CuDNNLSTM
elif self.rnn_unit == "CuDNNGRU":
layer_cell = L.CuDNNGRU
else:
layer_cell = L.GRU
# embedding遮挡
embedding_output_spatial = L.SpatialDropout1D(
self.dropout_spatial)(outputs)
# CNN
convs = []
for kernel_size in self.filters_size:
conv = L.Conv1D(
self.filters_num,
kernel_size=kernel_size,
strides=1,
padding='SAME',
kernel_regularizer=keras.regularizers.l2(self.l2),
bias_regularizer=keras.regularizers.l2(self.l2),
)(embedding_output_spatial)
convs.append(conv)
x = L.Concatenate(axis=1)(convs)
# Bi-LSTM, 论文中使用的是LSTM
x = L.Bidirectional(
layer_cell(units=self.rnn_unit,
return_sequences=True,
activation='relu',
kernel_regularizer=keras.regularizers.l2(self.l2),
recurrent_regularizer=keras.regularizers.l2(
self.l2)))(x)
x = L.Dropout(self.dropout)(x)
x = L.Flatten()(x)
# dense-mid
x = L.Dense(units=min(max(self.label, 64), self.embed_size),
activation=self.activate_mid)(x)
x = L.Dropout(self.dropout)(x)
# dense-end, 最后一层, dense到label
self.outputs = L.Dense(units=self.label,
activation=self.activate_end)(x)
self.model = M.Model(inputs=inputs, outputs=self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
# rnn type, RNN的类型
if self.rnn_unit == "LSTM":
layer_cell = L.LSTM
elif self.rnn_unit == "CuDNNLSTM":
layer_cell = L.CuDNNLSTM
elif self.rnn_unit == "CuDNNGRU":
layer_cell = L.CuDNNGRU
else:
layer_cell = L.GRU
x = L.Activation(self.activate_mid)(outputs)
# embedding遮挡
x = L.SpatialDropout1D(self.dropout_spatial)(x)
lstm_0_output = L.Bidirectional(layer_cell(
units=self.rnn_unit,
return_sequences=True,
activation='relu',
kernel_regularizer=keras.regularizers.l2(self.l2),
recurrent_regularizer=keras.regularizers.l2(self.l2)),
name="bi_lstm_0")(x)
lstm_1_output = L.Bidirectional(layer_cell(
units=self.rnn_unit,
return_sequences=True,
activation='relu',
kernel_regularizer=keras.regularizers.l2(self.l2),
recurrent_regularizer=keras.regularizers.l2(self.l2)),
name="bi_lstm_1")(lstm_0_output)
x = L.Concatenate()([lstm_1_output, lstm_0_output, x])
x = AttentionWeightedAverage(name='attlayer',
return_attention=False)(x)
x = L.Dropout(self.dropout)(x)
x = L.Flatten()(x)
# dense-mid
x = L.Dense(units=min(max(self.label, 64), self.embed_size),
activation=self.activate_mid)(x)
x = L.Dropout(self.dropout)(x)
# dense-end, 最后一层, dense到label
self.outputs = L.Dense(units=self.label,
activation=self.activate_end)(x)
self.model = M.Model(inputs=inputs, outputs=self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
# rnn type, RNN的类型
pools = []
for i in range(len(self.filters_size)):
# 第一个, 宽卷积, 动态k-max池化
conv_1 = wide_convolution(
name="wide_convolution_{}".format(i),
filter_num=self.filters_num,
filter_size=self.filters_size[i][0])(outputs)
top_k_1 = select_k(self.length_max, len(self.filters_size[i]),
1) # 求取k
dynamic_k_max_pooled_1 = dynamic_k_max_pooling(
top_k=top_k_1)(conv_1)
# 第二个, 宽卷积, 动态k-max池化
conv_2 = wide_convolution(
name="wide_convolution_{}_{}".format(i, i),
filter_num=self.filters_num,
filter_size=self.filters_size[i][1])(dynamic_k_max_pooled_1)
top_k_2 = select_k(self.length_max, len(self.filters_size[i]), 2)
dynamic_k_max_pooled_2 = dynamic_k_max_pooling(
top_k=top_k_2)(conv_2)
# 第三层, 宽卷积, Fold层, 动态k-max池化
conv_3 = wide_convolution(
name="wide_convolution_{}_{}_{}".format(i, i, i),
filter_num=self.filters_num,
filter_size=self.filters_size[i][2])(dynamic_k_max_pooled_2)
fold_conv_3 = prem_fold()(conv_3)
top_k_3 = select_k(self.length_max, len(self.filters_size[i]),
3) # 求取k
dynamic_k_max_pooled_3 = dynamic_k_max_pooling(
top_k=top_k_3)(fold_conv_3)
pools.append(dynamic_k_max_pooled_3)
pools_concat = L.Concatenate(axis=1)(pools)
pools_concat_dropout = L.Dropout(self.dropout)(pools_concat)
x = L.Flatten()(pools_concat_dropout)
# dense-end, 最后一层, dense到label
self.outputs = L.Dense(units=self.label,
activation=self.activate_end)(x)
self.model = M.Model(inputs=inputs, outputs=self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
"""
build_model.
Args:
inputs: tensor, input of model
outputs: tensor, output of model
Returns:
None
"""
# CuDNNGRU or GRU
x = None
if self.rnn_type.upper() == "CUDNNGRU":
rnn_cell = L.CuDNNGRU
else:
rnn_cell = L.GRU
# Bi-GRU
for nrl in range(self.num_rnn_layers):
x = L.Bidirectional(
rnn_cell(
units=self.rnn_unit,
return_sequences=True,
activation=self.activate_mid,
))(outputs)
x = L.Dropout(self.dropout)(x)
if self.use_crf:
x = L.Dense(units=self.label, activation=self.activate_end)(x)
self.CRF = ConditionalRandomField(self.crf_lr_multiplier,
name="crf_bert4keras")
self.outputs = self.CRF(x)
self.trans = K.eval(self.CRF.trans).tolist()
self.loss = self.CRF.dense_loss if self.use_onehot else self.CRF.sparse_loss
self.metrics = [
self.CRF.dense_accuracy
if self.use_onehot else self.CRF.sparse_accuracy
]
else:
self.outputs = L.TimeDistributed(
L.Dense(units=self.label, activation=self.activate_end))(x)
self.model = M.Model(inputs, self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
x = None
# cnn + pool
for char_cnn_size in self.char_cnn_layers:
x = L.Convolution1D(
filters=char_cnn_size[0],
kernel_size=char_cnn_size[1],
)(outputs)
x = L.ThresholdedReLU(self.threshold)(x)
if char_cnn_size[2] != -1:
x = L.MaxPooling1D(pool_size=char_cnn_size[2], strides=1)(x)
x = L.Flatten()(x)
# full-connect 2
for full in self.full_connect_layers:
x = L.Dense(units=full, )(x)
x = L.ThresholdedReLU(self.threshold)(x)
x = L.Dropout(self.dropout)(x)
# dense label
self.outputs = L.Dense(units=self.label,
activation=self.activate_end)(x)
self.model = M.Model(inputs=inputs, outputs=self.outputs)
self.model.summary(132)
def build_model(self, inputs, outputs):
"""
build_model.
Args:
inputs: tensor, input of model
outputs: tensor, output of model
Returns:
None
"""
# LSTM or GRU
if self.rnn_type == "LSTM":
rnn_cell = L.LSTM
elif self.rnn_type == "CuDNNLSTM":
rnn_cell = L.CuDNNLSTM
elif self.rnn_type == "CuDNNGRU":
rnn_cell = L.CuDNNGRU
else:
rnn_cell = L.GRU
# Bi-LSTM-LAN
for nrl in range(self.num_rnn_layers):
x = L.Bidirectional(rnn_cell(units=self.rnn_unit*(nrl+1),
return_sequences=True,
activation=self.activate_mid,
))(outputs)
x_att = SelfAttention(K.int_shape(x)[-1])(x)
outputs = L.Concatenate()([x, x_att])
outputs = L.Dropout(self.dropout)(outputs)
if self.use_crf:
x = L.Dense(units=self.label, activation=self.activate_end)(outputs)
self.CRF = ConditionalRandomField(self.crf_lr_multiplier, name="crf_bert4keras")
self.outputs = self.CRF(x)
self.trans = K.eval(self.CRF.trans).tolist()
self.loss = self.CRF.dense_loss if self.use_onehot else self.CRF.sparse_loss
self.metrics = [self.CRF.dense_accuracy if self.use_onehot else self.CRF.sparse_accuracy]
else:
self.outputs = L.TimeDistributed(L.Dense(units=self.label, activation=self.activate_end))(outputs)
self.model = M.Model(inputs, self.outputs)
self.model.summary(132)
def load_model(self):
"""
load model of keras of h5 which include graph-node and custom_objects
"""
self.model = M.load_model(self.path_model_h5, compile=False)
def build_model(self, inputs, outputs):
"""
build_model.
Args:
inputs: tensor, input of model
outputs: tensor, output of model
Returns:
None
"""
# CNN, 提取n-gram特征和最大池化, DGCNN膨胀卷积(IDCNN)
conv_pools = []
for i in range(len(self.filters_size)):
conv = L.Conv1D(
name="conv-{0}-{1}".format(i, self.filters_size[i]),
dilation_rate=self.atrous_rates[0],
kernel_size=self.filters_size[i],
activation=self.activate_mid,
filters=self.filters_num,
padding="SAME",
)(outputs)
for j in range(len(self.atrous_rates) - 1):
conv = L.Conv1D(
name="conv-{0}-{1}-{2}".format(i, self.filters_size[i], j),
dilation_rate=self.atrous_rates[j],
kernel_size=self.filters_size[i],
activation=self.activate_mid,
filters=self.filters_num,
padding="SAME",
)(conv)
conv = L.Dropout(
name="dropout-{0}-{1}-{2}".format(i, self.filters_size[i],
j),
rate=self.dropout,
)(conv)
conv_pools.append(conv)
# 拼接
x = L.Concatenate(axis=-1)(conv_pools)
x = L.Dropout(self.dropout)(x)
# CRF or Dense
if self.use_crf:
x = L.Dense(units=self.label, activation=self.activate_end)(x)
self.CRF = ConditionalRandomField(self.crf_lr_multiplier,
name="crf_bert4keras")
self.outputs = self.CRF(x)
self.trans = K.eval(self.CRF.trans).tolist()
self.loss = self.CRF.dense_loss if self.use_onehot else self.CRF.sparse_loss
self.metrics = [
self.CRF.dense_accuracy
if self.use_onehot else self.CRF.sparse_accuracy
]
else:
x = L.Bidirectional(
L.GRU(
activation=self.activate_mid,
return_sequences=True,
units=self.rnn_unit,
name="bi-gru",
))(x)
self.outputs = L.TimeDistributed(
L.Dense(
activation=self.activate_end,
name="dense-output",
units=self.label,
))(x)
self.model = M.Model(inputs, self.outputs)
self.model.summary(132)
logger.info("训练/验证语料读取完成")
# 数据预处理类初始化
preprocess_xy = ListPrerocessXY(embed,
train_data,
path_dir=path_model_dir,
length_max=length_max)
x = L.Lambda(lambda x: x[:, 0], name="Token-CLS")(embed.model.output)
# 最后就是softmax
outputs = L.Dense(
len(preprocess_xy.l2i),
activation="softmax",
kernel_initializer=keras.initializers.TruncatedNormal(stddev=0.02))(x)
model = M.Model(embed.model.input, outputs)
model.summary(132)
model.compile(optimizer=O.Adam(lr=1e-5),
loss="categorical_crossentropy",
metrics=["accuracy"])
len_train_data = len(train_data)
lg_train = ListGenerator(train_data,
preprocess_xy,
batch_size=batch_size,
len_data=len_train_data)
lg_dev = None
# monitor是早停和保存模型的依据, "loss", "acc", "val_loss", "val_acc"等
monitor = "val_loss"
if dev_data:
def build_model(self, inputs, outputs):
"""
build_model.
Args:
inputs: tensor, input of model
outputs: tensor, output of model
Returns:
None
"""
# LSTM or GRU
if self.rnn_type == "LSTM":
rnn_cell = L.LSTM
elif self.rnn_type == "CuDNNLSTM":
rnn_cell = L.CuDNNLSTM
elif self.rnn_type == "CuDNNGRU":
rnn_cell = L.CuDNNGRU
else:
rnn_cell = L.GRU
# CNN-LSTM, 提取n-gram特征和最大池化， 一般不用平均池化
conv_pools = []
for i in range(len(self.filters_size)):
conv = L.Conv1D(
name="conv-{0}-{1}".format(i, self.filters_size[i]),
kernel_size=self.filters_size[i],
activation=self.activate_mid,
filters=self.filters_num,
padding='same',
)(outputs)
conv_rnn = L.Bidirectional(
rnn_cell(
name="bi-lstm-{0}-{1}".format(i, self.filters_size[i]),
activation=self.activate_mid,
return_sequences=True,
units=self.rnn_unit,
))(conv)
x_dropout = L.Dropout(rate=self.dropout,
name="dropout-{0}-{1}".format(
i, self.filters_size[i]))(conv_rnn)
conv_pools.append(x_dropout)
# 拼接
x = L.Concatenate(axis=-1)(conv_pools)
x = L.Dropout(self.dropout)(x)
# CRF or Dense
if self.use_crf:
x = L.Dense(units=self.label, activation=self.activate_end)(x)
self.CRF = ConditionalRandomField(self.crf_lr_multiplier,
name="crf_bert4keras")
self.outputs = self.CRF(x)
self.trans = K.eval(self.CRF.trans).tolist()
self.loss = self.CRF.dense_loss if self.use_onehot else self.CRF.sparse_loss
self.metrics = [
self.CRF.dense_accuracy
if self.use_onehot else self.CRF.sparse_accuracy
]
else:
self.outputs = L.TimeDistributed(
L.Dense(units=self.label,
activation=self.activate_end,
name="dense-output"))(x)
self.model = M.Model(inputs, self.outputs)
self.model.summary(132)
