/*
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* Copyright (C) 2018 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef NLP_SAFT_COMPONENTS_COMMON_MOBILE_EMBEDDING_NETWORK_H_
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#define NLP_SAFT_COMPONENTS_COMMON_MOBILE_EMBEDDING_NETWORK_H_
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#include <vector>
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#include "lang_id/common/embedding-network-params.h"
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#include "lang_id/common/fel/feature-extractor.h"
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namespace libtextclassifier3 {
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namespace mobile {
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// Classifier using a hand-coded feed-forward neural network.
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//
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// No gradient computation, just inference.
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//
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// Based on the more general nlp_saft::EmbeddingNetwork (without ::mobile).
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//
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// Classification works as follows:
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//
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// Discrete features -> Embeddings -> Concatenation -> Hidden+ -> Softmax
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//
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// In words: given some discrete features, this class extracts the embeddings
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// for these features, concatenates them, passes them through one or more hidden
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// layers (each layer uses Relu) and next through a softmax layer that computes
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// an unnormalized score for each possible class. Note: there is always a
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// softmax layer at the end.
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class EmbeddingNetwork {
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public:
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// Constructs an embedding network using the parameters from model.
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//
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// Note: model should stay alive for at least the lifetime of this
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// EmbeddingNetwork object.
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explicit EmbeddingNetwork(const EmbeddingNetworkParams *model);
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virtual ~EmbeddingNetwork() {}
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// Runs forward computation to fill scores with unnormalized output unit
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// scores. This is useful for making predictions.
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void ComputeFinalScores(const std::vector<FeatureVector> &features,
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std::vector<float> *scores) const;
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// Same as above, but allows specification of extra extra neural network
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// inputs that will be appended to the embedding vector build from features.
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void ComputeFinalScores(const std::vector<FeatureVector> &features,
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const std::vector<float> &extra_inputs,
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std::vector<float> *scores) const;
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private:
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// Constructs the concatenated input embedding vector in place in output
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// vector concat.
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void ConcatEmbeddings(const std::vector<FeatureVector> &features,
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std::vector<float> *concat) const;
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// Pointer to the model object passed to the constructor. Not owned.
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const EmbeddingNetworkParams *model_;
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// Network parameters.
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// One weight matrix for each embedding.
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std::vector<EmbeddingNetworkParams::Matrix> embedding_matrices_;
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// embedding_row_size_in_bytes_[i] is the size (in bytes) of a row from
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// embedding_matrices_[i]. We precompute this in order to quickly find the
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// beginning of the k-th row from an embedding matrix (which is stored in
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// row-major order).
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std::vector<int> embedding_row_size_in_bytes_;
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// concat_offset_[i] is the input layer offset for i-th embedding space.
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std::vector<int> concat_offset_;
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// Size of the input ("concatenation") layer.
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int concat_layer_size_ = 0;
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// One weight matrix and one vector of bias weights for each layer of neurons.
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// Last layer is the softmax layer, the previous ones are the hidden layers.
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std::vector<EmbeddingNetworkParams::Matrix> layer_weights_;
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std::vector<EmbeddingNetworkParams::Matrix> layer_bias_;
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};
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} // namespace mobile
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} // namespace nlp_saft
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#endif // NLP_SAFT_COMPONENTS_COMMON_MOBILE_EMBEDDING_NETWORK_H_
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