tesseract/src/lstm/fullyconnected.h

///////////////////////////////////////////////////////////////////////
// File:        fullyconnected.h
// Description: Simple feed-forward layer with various non-linearities.
// Author:      Ray Smith
//
// (C) Copyright 2014, Google Inc.
// 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
// 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
// limitations under the License.
///////////////////////////////////////////////////////////////////////

#ifndef TESSERACT_LSTM_FULLYCONNECTED_H_
#define TESSERACT_LSTM_FULLYCONNECTED_H_

#include "network.h"
#include "networkscratch.h"

namespace tesseract {

// C++ Implementation of the Softmax (output) class from lstm.py.
class FullyConnected : public Network {
public:
  TESS_API
  FullyConnected(const std::string &name, int ni, int no, NetworkType type);
  ~FullyConnected() override = default;

  // Returns the shape output from the network given an input shape (which may
  // be partially unknown ie zero).
  StaticShape OutputShape(const StaticShape &input_shape) const override;

  std::string spec() const override {
    std::string spec;
    if (type_ == NT_TANH) {
      spec += "Ft" + std::to_string(no_);
    } else if (type_ == NT_LOGISTIC) {
      spec += "Fs" + std::to_string(no_);
    } else if (type_ == NT_RELU) {
      spec += "Fr" + std::to_string(no_);
    } else if (type_ == NT_LINEAR) {
      spec += "Fl" + std::to_string(no_);
    } else if (type_ == NT_POSCLIP) {
      spec += "Fp" + std::to_string(no_);
    } else if (type_ == NT_SYMCLIP) {
      spec += "Fn" + std::to_string(no_);
    } else if (type_ == NT_SOFTMAX) {
      spec += "Fc" + std::to_string(no_);
    } else {
      spec += "Fm" + std::to_string(no_);
    }
    return spec;
  }

  // Changes the type to the given type. Used to commute a softmax to a
  // non-output type for adding on other networks.
  void ChangeType(NetworkType type) {
    type_ = type;
  }

  // Suspends/Enables training by setting the training_ flag. Serialize and
  // DeSerialize only operate on the run-time data if state is false.
  void SetEnableTraining(TrainingState state) override;

  // Sets up the network for training. Initializes weights using weights of
  // scale `range` picked according to the random number generator `randomizer`.
  int InitWeights(float range, TRand *randomizer) override;
  // Recursively searches the network for softmaxes with old_no outputs,
  // and remaps their outputs according to code_map. See network.h for details.
  int RemapOutputs(int old_no, const std::vector<int> &code_map) override;

  // Converts a float network to an int network.
  void ConvertToInt() override;

  // Provides debug output on the weights.
  void DebugWeights() override;

  // Writes to the given file. Returns false in case of error.
  bool Serialize(TFile *fp) const override;
  // Reads from the given file. Returns false in case of error.
  bool DeSerialize(TFile *fp) override;

  // Runs forward propagation of activations on the input line.
  // See Network for a detailed discussion of the arguments.
  void Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose,
               NetworkScratch *scratch, NetworkIO *output) override;
  // Components of Forward so FullyConnected can be reused inside LSTM.
  void SetupForward(const NetworkIO &input, const TransposedArray *input_transpose);
  void ForwardTimeStep(int t, double *output_line);
  void ForwardTimeStep(const double *d_input, int t, double *output_line);
  void ForwardTimeStep(const int8_t *i_input, int t, double *output_line);

  // Runs backward propagation of errors on the deltas line.
  // See Network for a detailed discussion of the arguments.
  bool Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch,
                NetworkIO *back_deltas) override;
  // Components of Backward so FullyConnected can be reused inside LSTM.
  void BackwardTimeStep(const NetworkIO &fwd_deltas, int t, double *curr_errors,
                        TransposedArray *errors_t, double *backprop);
  void FinishBackward(const TransposedArray &errors_t);

  // Updates the weights using the given learning rate, momentum and adam_beta.
  // num_samples is used in the adam computation iff use_adam_ is true.
  void Update(float learning_rate, float momentum, float adam_beta, int num_samples) override;
  // Sums the products of weight updates in *this and other, splitting into
  // positive (same direction) in *same and negative (different direction) in
  // *changed.
  void CountAlternators(const Network &other, double *same, double *changed) const override;

protected:
  // Weight arrays of size [no, ni + 1].
  WeightMatrix weights_;
  // Transposed copy of input used during training of size [ni, width].
  TransposedArray source_t_;
  // Pointer to transposed input stored elsewhere. If not null, this is used
  // in preference to calculating the transpose and storing it in source_t_.
  const TransposedArray *external_source_;
  // Activations from forward pass of size [width, no].
  NetworkIO acts_;
  // Memory of the integer mode input to forward as softmax always outputs
  // float, so the information is otherwise lost.
  bool int_mode_;
};

} // namespace tesseract.

#endif // TESSERACT_LSTM_FULLYCONNECTED_H_