Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/lstm/weightmatrix.h @ 46:7ee69f120f19 default tip

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>>>>> tag v1.26.5+1 for changeset b74429b0f5c4
author Franz Glasner <fzglas.hg@dom66.de>
date Sat, 11 Oct 2025 17:17:30 +0200
parents b50eed0cc0ef
children
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
rev   line source
2
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1 ///////////////////////////////////////////////////////////////////////
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2 // File: weightmatrix.h
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3 // Description: Hides distinction between float/int implementations.
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4 // Author: Ray Smith
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5 //
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6 // (C) Copyright 2014, Google Inc.
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7 // Licensed under the Apache License, Version 2.0 (the "License");
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8 // you may not use this file except in compliance with the License.
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9 // You may obtain a copy of the License at
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10 // http://www.apache.org/licenses/LICENSE-2.0
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11 // Unless required by applicable law or agreed to in writing, software
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12 // distributed under the License is distributed on an "AS IS" BASIS,
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13 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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14 // See the License for the specific language governing permissions and
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15 // limitations under the License.
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16 ///////////////////////////////////////////////////////////////////////
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17
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18 #ifndef TESSERACT_LSTM_WEIGHTMATRIX_H_
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19 #define TESSERACT_LSTM_WEIGHTMATRIX_H_
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20
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21 #include <memory>
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22 #include <vector>
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23 #include "intsimdmatrix.h"
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24 #include "matrix.h"
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25 #include "tesstypes.h"
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26 #include "tprintf.h"
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28 namespace tesseract {
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29
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30 // Convenience instantiation of GENERIC_2D_ARRAY<TFloat> with additional
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31 // operations to write a strided vector, so the transposed form of the input
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32 // is memory-contiguous.
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33 class TransposedArray : public GENERIC_2D_ARRAY<TFloat> {
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34 public:
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35 // Copies the whole input transposed, converted to TFloat, into *this.
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36 void Transpose(const GENERIC_2D_ARRAY<TFloat> &input);
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37 // Writes a vector of data representing a timestep (gradients or sources).
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38 // The data is assumed to be of size1 in size (the strided dimension).
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39 ~TransposedArray() override;
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40 void WriteStrided(int t, const float *data) {
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41 int size1 = dim1();
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42 for (int i = 0; i < size1; ++i) {
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43 put(i, t, data[i]);
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44 }
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45 }
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46 void WriteStrided(int t, const double *data) {
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47 int size1 = dim1();
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48 for (int i = 0; i < size1; ++i) {
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49 put(i, t, data[i]);
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50 }
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51 }
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52 // Prints the first and last num elements of the un-transposed array.
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53 void PrintUnTransposed(int num) {
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54 int num_features = dim1();
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55 int width = dim2();
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56 for (int y = 0; y < num_features; ++y) {
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57 for (int t = 0; t < width; ++t) {
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58 if (num == 0 || t < num || t + num >= width) {
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59 tprintf(" %g", static_cast<double>((*this)(y, t)));
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60 }
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61 }
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62 tprintf("\n");
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63 }
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64 }
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65 }; // class TransposedArray
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66
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67 // Generic weight matrix for network layers. Can store the matrix as either
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68 // an array of floats or int8_t. Provides functions to compute the forward and
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69 // backward steps with the matrix and updates to the weights.
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70 class WeightMatrix {
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71 public:
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72 WeightMatrix() : int_mode_(false), use_adam_(false) {}
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73 // Sets up the network for training. Initializes weights using weights of
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74 // scale `range` picked according to the random number generator `randomizer`.
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75 // Note the order is outputs, inputs, as this is the order of indices to
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76 // the matrix, so the adjacent elements are multiplied by the input during
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77 // a forward operation.
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78 int InitWeightsFloat(int no, int ni, bool use_adam, float weight_range, TRand *randomizer);
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79 // Changes the number of outputs to the size of the given code_map, copying
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80 // the old weight matrix entries for each output from code_map[output] where
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81 // non-negative, and uses the mean (over all outputs) of the existing weights
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82 // for all outputs with negative code_map entries. Returns the new number of
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83 // weights.
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84 int RemapOutputs(const std::vector<int> &code_map);
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85
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86 // Converts a float network to an int network. Each set of input weights that
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87 // corresponds to a single output weight is converted independently:
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88 // Compute the max absolute value of the weight set.
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89 // Scale so the max absolute value becomes INT8_MAX.
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90 // Round to integer.
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91 // Store a multiplicative scale factor (as a float) that will reproduce
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92 // the original value, subject to rounding errors.
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93 void ConvertToInt();
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94 // Returns the size rounded up to an internal factor used by the SIMD
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95 // implementation for its input.
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96 int RoundInputs(int size) const {
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97 if (!int_mode_ || !IntSimdMatrix::intSimdMatrix) {
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98 return size;
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99 }
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100 return IntSimdMatrix::intSimdMatrix->RoundInputs(size);
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101 }
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102
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103 // Accessors.
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104 bool is_int_mode() const {
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105 return int_mode_;
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106 }
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107 int NumOutputs() const {
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108 return int_mode_ ? wi_.dim1() : wf_.dim1();
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109 }
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110 // Provides one set of weights. Only used by peep weight maxpool.
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111 const TFloat *GetWeights(int index) const {
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112 return wf_[index];
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113 }
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114 // Provides access to the deltas (dw_).
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115 TFloat GetDW(int i, int j) const {
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116 return dw_(i, j);
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117 }
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118
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119 // Allocates any needed memory for running Backward, and zeroes the deltas,
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120 // thus eliminating any existing momentum.
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121 void InitBackward();
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122
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123 // Writes to the given file. Returns false in case of error.
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124 bool Serialize(bool training, TFile *fp) const;
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125 // Reads from the given file. Returns false in case of error.
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126 bool DeSerialize(bool training, TFile *fp);
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127 // As DeSerialize, but reads an old (float) format WeightMatrix for
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128 // backward compatibility.
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129 bool DeSerializeOld(bool training, TFile *fp);
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130
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131 // Computes matrix.vector v = Wu.
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132 // u is of size W.dim2() - 1 and the output v is of size W.dim1().
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133 // u is imagined to have an extra element at the end with value 1, to
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134 // implement the bias, but it doesn't actually have it.
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135 // Asserts that the call matches what we have.
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136 void MatrixDotVector(const TFloat *u, TFloat *v) const;
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137 void MatrixDotVector(const int8_t *u, TFloat *v) const;
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138 // MatrixDotVector for peep weights, MultiplyAccumulate adds the
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139 // component-wise products of *this[0] and v to inout.
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140 void MultiplyAccumulate(const TFloat *v, TFloat *inout);
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141 // Computes vector.matrix v = uW.
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142 // u is of size W.dim1() and the output v is of size W.dim2() - 1.
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143 // The last result is discarded, as v is assumed to have an imaginary
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144 // last value of 1, as with MatrixDotVector.
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145 void VectorDotMatrix(const TFloat *u, TFloat *v) const;
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146 // Fills dw_[i][j] with the dot product u[i][] . v[j][], using elements
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147 // from u and v, starting with u[i][offset] and v[j][offset].
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148 // Note that (matching MatrixDotVector) v[last][] is missing, presumed 1.0.
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149 // Runs parallel if requested. Note that inputs must be transposed.
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150 void SumOuterTransposed(const TransposedArray &u, const TransposedArray &v, bool parallel);
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151 // Updates the weights using the given learning rate, momentum and adam_beta.
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152 // num_samples is used in the Adam correction factor.
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153 void Update(float learning_rate, float momentum, float adam_beta, int num_samples);
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154 // Adds the dw_ in other to the dw_ is *this.
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155 void AddDeltas(const WeightMatrix &other);
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156 // Sums the products of weight updates in *this and other, splitting into
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157 // positive (same direction) in *same and negative (different direction) in
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158 // *changed.
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159 void CountAlternators(const WeightMatrix &other, TFloat *same, TFloat *changed) const;
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160
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161 void Debug2D(const char *msg);
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162
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163 private:
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164 // Choice between float and 8 bit int implementations.
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165 GENERIC_2D_ARRAY<TFloat> wf_;
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166 GENERIC_2D_ARRAY<int8_t> wi_;
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167 // Transposed copy of wf_, used only for Backward, and set with each Update.
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168 TransposedArray wf_t_;
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169 // Which of wf_ and wi_ are we actually using.
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170 bool int_mode_;
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171 // True if we are running adam in this weight matrix.
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172 bool use_adam_;
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173 // If we are using wi_, then scales_ is a factor to restore the row product
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174 // with a vector to the correct range.
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175 std::vector<TFloat> scales_;
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176 // Weight deltas. dw_ is the new delta, and updates_ the momentum-decaying
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177 // amount to be added to wf_/wi_.
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178 GENERIC_2D_ARRAY<TFloat> dw_;
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179 GENERIC_2D_ARRAY<TFloat> updates_;
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180 // Iff use_adam_, the sum of squares of dw_. The number of samples is
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181 // given to Update(). Serialized iff use_adam_.
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182 GENERIC_2D_ARRAY<TFloat> dw_sq_sum_;
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183 // The weights matrix reorganized in whatever way suits this instance.
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184 std::vector<int8_t> shaped_w_;
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185 };
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186
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187 } // namespace tesseract.
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188
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189 #endif // TESSERACT_LSTM_WEIGHTMATRIX_H_