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* Copyright (C) 2018 The Android Open Source Project
* 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.
#include <vector>
#include "lang_id/common/embedding-network-params.h"
#include "lang_id/common/fel/feature-extractor.h"
namespace libtextclassifier3 {
namespace mobile {
// Classifier using a hand-coded feed-forward neural network.
// No gradient computation, just inference.
// Based on the more general nlp_saft::EmbeddingNetwork (without ::mobile).
// Classification works as follows:
// Discrete features -> Embeddings -> Concatenation -> Hidden+ -> Softmax
// In words: given some discrete features, this class extracts the embeddings
// for these features, concatenates them, passes them through one or more hidden
// layers (each layer uses Relu) and next through a softmax layer that computes
// an unnormalized score for each possible class. Note: there is always a
// softmax layer at the end.
class EmbeddingNetwork {
// Constructs an embedding network using the parameters from model.
// Note: model should stay alive for at least the lifetime of this
// EmbeddingNetwork object.
explicit EmbeddingNetwork(const EmbeddingNetworkParams *model);
virtual ~EmbeddingNetwork() {}
// Runs forward computation to fill scores with unnormalized output unit
// scores. This is useful for making predictions.
void ComputeFinalScores(const std::vector<FeatureVector> &features,
std::vector<float> *scores) const;
// Same as above, but allows specification of extra extra neural network
// inputs that will be appended to the embedding vector build from features.
void ComputeFinalScores(const std::vector<FeatureVector> &features,
const std::vector<float> &extra_inputs,
std::vector<float> *scores) const;
// Constructs the concatenated input embedding vector in place in output
// vector concat.
void ConcatEmbeddings(const std::vector<FeatureVector> &features,
std::vector<float> *concat) const;
// Pointer to the model object passed to the constructor. Not owned.
const EmbeddingNetworkParams *model_;
// Network parameters.
// One weight matrix for each embedding.
std::vector<EmbeddingNetworkParams::Matrix> embedding_matrices_;
// embedding_row_size_in_bytes_[i] is the size (in bytes) of a row from
// embedding_matrices_[i]. We precompute this in order to quickly find the
// beginning of the k-th row from an embedding matrix (which is stored in
// row-major order).
std::vector<int> embedding_row_size_in_bytes_;
// concat_offset_[i] is the input layer offset for i-th embedding space.
std::vector<int> concat_offset_;
// Size of the input ("concatenation") layer.
int concat_layer_size_ = 0;
// One weight matrix and one vector of bias weights for each layer of neurons.
// Last layer is the softmax layer, the previous ones are the hidden layers.
std::vector<EmbeddingNetworkParams::Matrix> layer_weights_;
std::vector<EmbeddingNetworkParams::Matrix> layer_bias_;
} // namespace mobile
} // namespace nlp_saft