Source code for deeppavlov.models.ranking.keras_siamese_model

# 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from abc import abstractmethod
from logging import getLogger
from typing import List

import numpy as np
from tensorflow.keras import losses
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

from deeppavlov.core.models.keras_model import KerasModel
from deeppavlov.models.ranking.siamese_model import SiameseModel

log = getLogger(__name__)


[docs]class KerasSiameseModel(SiameseModel, KerasModel): """The class implementing base functionality for siamese neural networks in keras. Args: learning_rate: Learning rate. use_matrix: Whether to use a trainable matrix with token (word) embeddings. emb_matrix: An embeddings matrix to initialize an embeddings layer of a model. Only used if ``use_matrix`` is set to ``True``. max_sequence_length: A maximum length of text sequences in tokens. Longer sequences will be truncated and shorter ones will be padded. dynamic_batch: Whether to use dynamic batching. If ``True``, the maximum length of a sequence for a batch will be equal to the maximum of all sequences lengths from this batch, but not higher than ``max_sequence_length``. attention: Whether any attention mechanism is used in the siamese network. *args: Other parameters. **kwargs: Other parameters. """ def __init__(self, learning_rate: float = 1e-3, use_matrix: bool = True, emb_matrix: np.ndarray = None, max_sequence_length: int = None, dynamic_batch: bool = False, attention: bool = False, *args, **kwargs) -> None: super(KerasSiameseModel, self).__init__(*args, **kwargs) self.learning_rate = learning_rate self.attention = attention self.use_matrix = use_matrix self.emb_matrix = emb_matrix if dynamic_batch: self.max_sequence_length = None else: self.max_sequence_length = max_sequence_length self.model = self.create_model() self.compile() if self.load_path.exists(): self.load() else: self.load_initial_emb_matrix() if not self.attention: self.context_model = self.create_context_model() self.response_model = self.create_response_model() def compile(self) -> None: optimizer = Adam(lr=self.learning_rate) loss = losses.binary_crossentropy self.model.compile(loss=loss, optimizer=optimizer) def load(self) -> None: log.info("[initializing `{}` from saved]".format(self.__class__.__name__)) self.model.load_weights(str(self.load_path)) def save(self) -> None: log.info("[saving `{}`]".format(self.__class__.__name__)) self.model.save_weights(str(self.save_path)) def load_initial_emb_matrix(self) -> None: log.info("[initializing new `{}`]".format(self.__class__.__name__)) if self.use_matrix: self.model.get_layer(name="embedding").set_weights([self.emb_matrix]) @abstractmethod def create_model(self) -> Model: pass def create_context_model(self) -> Model: m = Model(self.model.inputs[:-1], self.model.get_layer("sentence_embedding").get_output_at(0)) return m def create_response_model(self) -> Model: m = Model(self.model.inputs[-1], self.model.get_layer("sentence_embedding").get_output_at(1)) return m def _train_on_batch(self, batch: List[np.ndarray], y: List[int]) -> float: loss = self.model.train_on_batch(batch, np.asarray(y)) return loss def _predict_on_batch(self, batch: List[np.ndarray]) -> np.ndarray: y_pred = self.model.predict_on_batch(batch) return y_pred def _predict_context_on_batch(self, batch: List[np.ndarray]) -> np.ndarray: return self.context_model.predict_on_batch(batch) def _predict_response_on_batch(self, batch: List[np.ndarray]) -> np.ndarray: return self.response_model.predict_on_batch(batch)