tesseract/lstm/plumbing.h

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///////////////////////////////////////////////////////////////////////
// File: plumbing.h
// Description: Base class for networks that organize other networks
// eg series or parallel.
// Author: Ray Smith
// Created: Mon May 12 08:11:36 PST 2014
//
// (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_PLUMBING_H_
#define TESSERACT_LSTM_PLUMBING_H_
#include "genericvector.h"
#include "matrix.h"
#include "network.h"
namespace tesseract {
// Holds a collection of other networks and forwards calls to each of them.
class Plumbing : public Network {
public:
// ni_ and no_ will be set by AddToStack.
explicit Plumbing(const STRING& name);
virtual ~Plumbing();
// Returns the required shape input to the network.
virtual StaticShape InputShape() const { return stack_[0]->InputShape(); }
virtual STRING spec() const {
return "Sub-classes of Plumbing must implement spec()!";
}
// Returns true if the given type is derived from Plumbing, and thus contains
// multiple sub-networks that can have their own learning rate.
virtual bool IsPlumbingType() const { return true; }
// Suspends/Enables training by setting the training_ flag. Serialize and
// DeSerialize only operate on the run-time data if state is false.
virtual void SetEnableTraining(TrainingState state);
// Sets flags that control the action of the network. See NetworkFlags enum
// for bit values.
virtual void SetNetworkFlags(uinT32 flags);
// Sets up the network for training. Initializes weights using weights of
// scale `range` picked according to the random number generator `randomizer`.
// Note that randomizer is a borrowed pointer that should outlive the network
// and should not be deleted by any of the networks.
// Returns the number of weights initialized.
virtual int InitWeights(float range, TRand* randomizer);
// Converts a float network to an int network.
virtual void ConvertToInt();
// Provides a pointer to a TRand for any networks that care to use it.
// Note that randomizer is a borrowed pointer that should outlive the network
// and should not be deleted by any of the networks.
virtual void SetRandomizer(TRand* randomizer);
// Adds the given network to the stack.
virtual void AddToStack(Network* network);
// Sets needs_to_backprop_ to needs_backprop and returns true if
// needs_backprop || any weights in this network so the next layer forward
// can be told to produce backprop for this layer if needed.
virtual bool SetupNeedsBackprop(bool needs_backprop);
// Returns an integer reduction factor that the network applies to the
// time sequence. Assumes that any 2-d is already eliminated. Used for
// scaling bounding boxes of truth data.
// WARNING: if GlobalMinimax is used to vary the scale, this will return
// the last used scale factor. Call it before any forward, and it will return
// the minimum scale factor of the paths through the GlobalMinimax.
virtual int XScaleFactor() const;
// Provides the (minimum) x scale factor to the network (of interest only to
// input units) so they can determine how to scale bounding boxes.
virtual void CacheXScaleFactor(int factor);
// Provides debug output on the weights.
virtual void DebugWeights();
// Returns the current stack.
const PointerVector<Network>& stack() const {
return stack_;
}
// Returns a set of strings representing the layer-ids of all layers below.
void EnumerateLayers(const STRING* prefix,
GenericVector<STRING>* layers) const;
// Returns a pointer to the network layer corresponding to the given id.
Network* GetLayer(const char* id) const;
// Returns the learning rate for a specific layer of the stack.
float LayerLearningRate(const char* id) const {
const float* lr_ptr = LayerLearningRatePtr(id);
ASSERT_HOST(lr_ptr != NULL);
return *lr_ptr;
}
// Scales the learning rate for a specific layer of the stack.
void ScaleLayerLearningRate(const char* id, double factor) {
float* lr_ptr = LayerLearningRatePtr(id);
ASSERT_HOST(lr_ptr != NULL);
*lr_ptr *= factor;
}
// Returns a pointer to the learning rate for the given layer id.
float* LayerLearningRatePtr(const char* id) const;
// Writes to the given file. Returns false in case of error.
virtual bool Serialize(TFile* fp) const;
// Reads from the given file. Returns false in case of error.
virtual bool DeSerialize(TFile* fp);
// Updates the weights using the given learning rate and momentum.
// num_samples is the quotient to be used in the adagrad computation iff
// use_ada_grad_ is true.
virtual void Update(float learning_rate, float momentum, int num_samples);
// Sums the products of weight updates in *this and other, splitting into
// positive (same direction) in *same and negative (different direction) in
// *changed.
virtual void CountAlternators(const Network& other, double* same,
double* changed) const;
protected:
// The networks.
PointerVector<Network> stack_;
// Layer-specific learning rate iff network_flags_ & NF_LAYER_SPECIFIC_LR.
// One element for each element of stack_.
GenericVector<float> learning_rates_;
};
} // namespace tesseract.
#endif // TESSERACT_LSTM_PLUMBING_H_