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184 lines
8.0 KiB
C
184 lines
8.0 KiB
C
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///////////////////////////////////////////////////////////////////////
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// File: weightmatrix.h
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// Description: Hides distinction between float/int implementations.
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// Author: Ray Smith
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// Created: Tue Jun 17 09:05:39 PST 2014
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//
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// (C) Copyright 2014, Google Inc.
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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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// http://www.apache.org/licenses/LICENSE-2.0
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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 TESSERACT_LSTM_WEIGHTMATRIX_H_
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#define TESSERACT_LSTM_WEIGHTMATRIX_H_
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#include "genericvector.h"
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#include "matrix.h"
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#include "tprintf.h"
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namespace tesseract {
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// Convenience instantiation of GENERIC_2D_ARRAY<double> with additional
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// operations to write a strided vector, so the transposed form of the input
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// is memory-contiguous.
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class TransposedArray : public GENERIC_2D_ARRAY<double> {
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public:
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// Copies the whole input transposed, converted to double, into *this.
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void Transpose(const GENERIC_2D_ARRAY<double>& input);
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// Writes a vector of data representing a timestep (gradients or sources).
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// The data is assumed to be of size1 in size (the strided dimension).
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void WriteStrided(int t, const float* data) {
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int size1 = dim1();
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for (int i = 0; i < size1; ++i) put(i, t, data[i]);
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}
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void WriteStrided(int t, const double* data) {
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int size1 = dim1();
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for (int i = 0; i < size1; ++i) put(i, t, data[i]);
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}
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// Prints the first and last num elements of the un-transposed array.
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void PrintUnTransposed(int num) {
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int num_features = dim1();
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int width = dim2();
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for (int y = 0; y < num_features; ++y) {
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for (int t = 0; t < width; ++t) {
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if (num == 0 || t < num || t + num >= width) {
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tprintf(" %g", (*this)(y, t));
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}
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}
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tprintf("\n");
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}
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}
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}; // class TransposedArray
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// Generic weight matrix for network layers. Can store the matrix as either
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// an array of floats or inT8. Provides functions to compute the forward and
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// backward steps with the matrix and updates to the weights.
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class WeightMatrix {
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public:
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WeightMatrix() : int_mode_(false), use_ada_grad_(false) {}
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// Sets up the network for training. Initializes weights using weights of
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// scale `range` picked according to the random number generator `randomizer`.
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// Note the order is outputs, inputs, as this is the order of indices to
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// the matrix, so the adjacent elements are multiplied by the input during
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// a forward operation.
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int InitWeightsFloat(int no, int ni, bool ada_grad, float weight_range,
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TRand* randomizer);
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// Converts a float network to an int network. Each set of input weights that
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// corresponds to a single output weight is converted independently:
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// Compute the max absolute value of the weight set.
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// Scale so the max absolute value becomes MAX_INT8.
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// Round to integer.
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// Store a multiplicative scale factor (as a float) that will reproduce
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// the original value, subject to rounding errors.
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void ConvertToInt();
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// Accessors.
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bool is_int_mode() const {
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return int_mode_;
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}
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int NumOutputs() const { return int_mode_ ? wi_.dim1() : wf_.dim1(); }
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// Provides one set of weights. Only used by peep weight maxpool.
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const double* GetWeights(int index) const { return wf_[index]; }
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// Provides access to the deltas (dw_).
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double GetDW(int i, int j) const { return dw_(i, j); }
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// Allocates any needed memory for running Backward, and zeroes the deltas,
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// thus eliminating any existing momentum.
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void InitBackward();
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// Writes to the given file. Returns false in case of error.
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bool Serialize(bool training, TFile* fp) const;
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// Reads from the given file. Returns false in case of error.
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// If swap is true, assumes a big/little-endian swap is needed.
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bool DeSerialize(bool training, bool swap, TFile* fp);
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// As DeSerialize, but reads an old (float) format WeightMatrix for
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// backward compatability.
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bool DeSerializeOld(bool training, bool swap, TFile* fp);
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// Computes matrix.vector v = Wu.
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// u is of size W.dim2() - 1 and the output v is of size W.dim1().
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// u is imagined to have an extra element at the end with value 1, to
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// implement the bias, but it doesn't actually have it.
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// Asserts that the call matches what we have.
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void MatrixDotVector(const double* u, double* v) const;
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void MatrixDotVector(const inT8* u, double* v) const;
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// MatrixDotVector for peep weights, MultiplyAccumulate adds the
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// component-wise products of *this[0] and v to inout.
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void MultiplyAccumulate(const double* v, double* inout);
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// Computes vector.matrix v = uW.
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// u is of size W.dim1() and the output v is of size W.dim2() - 1.
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// The last result is discarded, as v is assumed to have an imaginary
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// last value of 1, as with MatrixDotVector.
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void VectorDotMatrix(const double* u, double* v) const;
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// Fills dw_[i][j] with the dot product u[i][] . v[j][], using elements
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// from u and v, starting with u[i][offset] and v[j][offset].
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// Note that (matching MatrixDotVector) v[last][] is missing, presumed 1.0.
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// Runs parallel if requested. Note that inputs must be transposed.
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void SumOuterTransposed(const TransposedArray& u, const TransposedArray& v,
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bool parallel);
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// Updates the weights using the given learning rate and momentum.
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// num_samples is the quotient to be used in the adagrad computation iff
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// use_ada_grad_ is true.
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void Update(double learning_rate, double momentum, int num_samples);
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// Adds the dw_ in other to the dw_ is *this.
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void AddDeltas(const WeightMatrix& other);
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// Sums the products of weight updates in *this and other, splitting into
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// positive (same direction) in *same and negative (different direction) in
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// *changed.
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void CountAlternators(const WeightMatrix& other, double* same,
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double* changed) const;
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void Debug2D(const char* msg);
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// Computes and returns the dot product of the two n-vectors u and v.
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static double DotProduct(const double* u, const double* v, int n);
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// Utility function converts an array of float to the corresponding array
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// of double.
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static void FloatToDouble(const GENERIC_2D_ARRAY<float>& wf,
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GENERIC_2D_ARRAY<double>* wd);
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private:
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// Computes matrix.vector v = Wu.
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// u is of size starts.back()+extents.back() and the output v is of size
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// starts.size().
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// The weight matrix w, is of size starts.size()xMAX(extents)+add_bias_fwd.
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// If add_bias_fwd, an extra element at the end of w[i] is the bias weight
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// and is added to v[i].
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static void MatrixDotVectorInternal(const GENERIC_2D_ARRAY<double>& w,
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bool add_bias_fwd, bool skip_bias_back,
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const double* u, double* v);
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private:
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// Choice between float and 8 bit int implementations.
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GENERIC_2D_ARRAY<double> wf_;
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GENERIC_2D_ARRAY<inT8> wi_;
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// Transposed copy of wf_, used only for Backward, and set with each Update.
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TransposedArray wf_t_;
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// Which of wf_ and wi_ are we actually using.
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bool int_mode_;
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// True if we are running adagrad in this weight matrix.
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bool use_ada_grad_;
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// If we are using wi_, then scales_ is a factor to restore the row product
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// with a vector to the correct range.
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GenericVector<double> scales_;
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// Weight deltas. dw_ is the new delta, and updates_ the momentum-decaying
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// amount to be added to wf_/wi_.
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GENERIC_2D_ARRAY<double> dw_;
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GENERIC_2D_ARRAY<double> updates_;
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// Iff use_ada_grad_, the sum of squares of dw_. The number of samples is
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// given to Update(). Serialized iff use_ada_grad_.
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GENERIC_2D_ARRAY<double> dw_sq_sum_;
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};
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} // namespace tesseract.
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#endif // TESSERACT_LSTM_WEIGHTMATRIX_H_
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