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 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)