Source code for deeppavlov.models.ranking.mpm_siamese_network

# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from keras.layers import Input, LSTM, Lambda, Dense, Dropout
from keras.models import Model
from keras.layers.wrappers import Bidirectional
from keras.initializers import glorot_uniform, Orthogonal
from keras import backend as K

from deeppavlov.core.common.log import get_logger
from deeppavlov.core.common.registry import register
from deeppavlov.models.ranking.bilstm_siamese_network import BiLSTMSiameseNetwork
from deeppavlov.core.layers.keras_layers import FullMatchingLayer, MaxpoolingMatchingLayer
from deeppavlov.core.layers.keras_layers import AttentiveMatchingLayer, MaxattentiveMatchingLayer

log = get_logger(__name__)

[docs]@register('mpm_nn') class MPMSiameseNetwork(BiLSTMSiameseNetwork): """The class implementing a siamese neural network with bilateral multi-Perspective matching. The network architecture is based on Args: dense_dim: Dimensionality of the dense layer. perspective_num: Number of perspectives in multi-perspective matching layers. aggregation dim: Dimensionality of the hidden state in the second BiLSTM layer. inpdrop_val: Float between 0 and 1. A dropout value for the linear transformation of the inputs. recdrop_val: Float between 0 and 1. A dropout value for the linear transformation of the recurrent state. ldrop_val: A dropout value of the dropout layer before the second BiLSTM layer. dropout_val: A dropout value of the dropout layer after the second BiLSTM layer. """ def __init__(self, dense_dim: int = 50, perspective_num: int = 20, aggregation_dim: int = 200, recdrop_val: float = 0.0, inpdrop_val: float = 0.0, ldrop_val: float = 0.0, dropout_val: float = 0.0, *args, **kwargs) -> None: self.dense_dim = dense_dim self.perspective_num = perspective_num self.aggregation_dim = aggregation_dim self.ldrop_val = ldrop_val self.recdrop_val = recdrop_val self.inpdrop_val = inpdrop_val self.dropout_val = dropout_val self.seed = kwargs.get("triplet_loss") self.triplet_mode = kwargs.get("triplet_loss") super(MPMSiameseNetwork, self).__init__(*args, **kwargs) def create_lstm_layer_1(self): ker_in = glorot_uniform(seed=self.seed) rec_in = Orthogonal(seed=self.seed) bioutp = Bidirectional(LSTM(self.hidden_dim, input_shape=(self.max_sequence_length, self.embedding_dim,), kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, recurrent_dropout=self.recdrop_val, dropout=self.inpdrop_val, kernel_initializer=ker_in, recurrent_initializer=rec_in, return_sequences=True), merge_mode=None) return bioutp def create_lstm_layer_2(self): ker_in = glorot_uniform(seed=self.seed) rec_in = Orthogonal(seed=self.seed) bioutp = Bidirectional(LSTM(self.aggregation_dim, input_shape=(self.max_sequence_length, 8*self.perspective_num,), kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, recurrent_dropout=self.recdrop_val, dropout=self.inpdrop_val, kernel_initializer=ker_in, recurrent_initializer=rec_in, return_sequences=False), merge_mode='concat', name="sentence_embedding") return bioutp def create_model(self) -> Model: if self.use_matrix: context = Input(shape=(self.max_sequence_length,)) response = Input(shape=(self.max_sequence_length,)) emb_layer = self.embedding_layer() emb_c = emb_layer(context) emb_r = emb_layer(response) else: context = Input(shape=(self.max_sequence_length, self.embedding_dim,)) response = Input(shape=(self.max_sequence_length, self.embedding_dim,)) emb_c = context emb_r = response lstm_layer = self.create_lstm_layer_1() lstm_a = lstm_layer(emb_c) lstm_b = lstm_layer(emb_r) f_layer_f = FullMatchingLayer(self.perspective_num) f_layer_b = FullMatchingLayer(self.perspective_num) f_a_forw = f_layer_f([lstm_a[0], lstm_b[0]])[0] f_a_back = f_layer_b([Lambda(lambda x: K.reverse(x, 1))(lstm_a[1]), Lambda(lambda x: K.reverse(x, 1))(lstm_b[1])])[0] f_a_back = Lambda(lambda x: K.reverse(x, 1))(f_a_back) f_b_forw = f_layer_f([lstm_b[0], lstm_a[0]])[0] f_b_back = f_layer_b([Lambda(lambda x: K.reverse(x, 1))(lstm_b[1]), Lambda(lambda x: K.reverse(x, 1))(lstm_a[1])])[0] f_b_back = Lambda(lambda x: K.reverse(x, 1))(f_b_back) mp_layer_f = MaxpoolingMatchingLayer(self.perspective_num) mp_layer_b = MaxpoolingMatchingLayer(self.perspective_num) mp_a_forw = mp_layer_f([lstm_a[0], lstm_b[0]])[0] mp_a_back = mp_layer_b([lstm_a[1], lstm_b[1]])[0] mp_b_forw = mp_layer_f([lstm_b[0], lstm_a[0]])[0] mp_b_back = mp_layer_b([lstm_b[1], lstm_a[1]])[0] at_layer_f = AttentiveMatchingLayer(self.perspective_num) at_layer_b = AttentiveMatchingLayer(self.perspective_num) at_a_forw = at_layer_f([lstm_a[0], lstm_b[0]])[0] at_a_back = at_layer_b([lstm_a[1], lstm_b[1]])[0] at_b_forw = at_layer_f([lstm_b[0], lstm_a[0]])[0] at_b_back = at_layer_b([lstm_b[1], lstm_a[1]])[0] ma_layer_f = MaxattentiveMatchingLayer(self.perspective_num) ma_layer_b = MaxattentiveMatchingLayer(self.perspective_num) ma_a_forw = ma_layer_f([lstm_a[0], lstm_b[0]])[0] ma_a_back = ma_layer_b([lstm_a[1], lstm_b[1]])[0] ma_b_forw = ma_layer_f([lstm_b[0], lstm_a[0]])[0] ma_b_back = ma_layer_b([lstm_b[1], lstm_a[1]])[0] concat_a = Lambda(lambda x: K.concatenate(x, axis=-1))([f_a_forw, f_a_back, mp_a_forw, mp_a_back, at_a_forw, at_a_back, ma_a_forw, ma_a_back]) concat_b = Lambda(lambda x: K.concatenate(x, axis=-1))([f_b_forw, f_b_back, mp_b_forw, mp_b_back, at_b_forw, at_b_back, ma_b_forw, ma_b_back]) concat_a = Dropout(self.ldrop_val)(concat_a) concat_b = Dropout(self.ldrop_val)(concat_b) lstm_layer_agg = self.create_lstm_layer_2() agg_a = lstm_layer_agg(concat_a) agg_b = lstm_layer_agg(concat_b) agg_a = Dropout(self.dropout_val)(agg_a) agg_b = Dropout(self.dropout_val)(agg_b) reduced = Lambda(lambda x: K.concatenate(x, axis=-1))([agg_a, agg_b]) if self.triplet_mode: dist = Lambda(self._pairwise_distances)([agg_a, agg_b]) else: ker_in = glorot_uniform(seed=self.seed) dense = Dense(self.dense_dim, kernel_initializer=ker_in)(reduced) dist = Dense(1, activation='sigmoid', name="score_model")(dense) model = Model([context, response], dist) return model