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):
# 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)