2016-11-08 07:38:07 +08:00
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///////////////////////////////////////////////////////////////////////
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// File: plumbing.h
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// Description: Base class for networks that organize other networks
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// eg series or parallel.
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// Author: Ray Smith
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// Created: Mon May 12 08:11:36 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_PLUMBING_H_
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#define TESSERACT_LSTM_PLUMBING_H_
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#include "genericvector.h"
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#include "matrix.h"
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#include "network.h"
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namespace tesseract {
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// Holds a collection of other networks and forwards calls to each of them.
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class Plumbing : public Network {
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public:
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// ni_ and no_ will be set by AddToStack.
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explicit Plumbing(const STRING& name);
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virtual ~Plumbing();
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// Returns the required shape input to the network.
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virtual StaticShape InputShape() const { return stack_[0]->InputShape(); }
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virtual STRING spec() const {
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return "Sub-classes of Plumbing must implement spec()!";
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}
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// Returns true if the given type is derived from Plumbing, and thus contains
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// multiple sub-networks that can have their own learning rate.
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virtual bool IsPlumbingType() const { return true; }
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// Suspends/Enables training by setting the training_ flag. Serialize and
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// DeSerialize only operate on the run-time data if state is false.
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2016-12-01 07:51:17 +08:00
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virtual void SetEnableTraining(TrainingState state);
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2016-11-08 07:38:07 +08:00
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// Sets flags that control the action of the network. See NetworkFlags enum
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// for bit values.
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virtual void SetNetworkFlags(uinT32 flags);
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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 that randomizer is a borrowed pointer that should outlive the network
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// and should not be deleted by any of the networks.
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// Returns the number of weights initialized.
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virtual int InitWeights(float range, TRand* randomizer);
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// Converts a float network to an int network.
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virtual void ConvertToInt();
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// Provides a pointer to a TRand for any networks that care to use it.
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// Note that randomizer is a borrowed pointer that should outlive the network
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// and should not be deleted by any of the networks.
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virtual void SetRandomizer(TRand* randomizer);
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// Adds the given network to the stack.
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virtual void AddToStack(Network* network);
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// Sets needs_to_backprop_ to needs_backprop and returns true if
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// needs_backprop || any weights in this network so the next layer forward
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// can be told to produce backprop for this layer if needed.
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virtual bool SetupNeedsBackprop(bool needs_backprop);
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// Returns an integer reduction factor that the network applies to the
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// time sequence. Assumes that any 2-d is already eliminated. Used for
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// scaling bounding boxes of truth data.
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// WARNING: if GlobalMinimax is used to vary the scale, this will return
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// the last used scale factor. Call it before any forward, and it will return
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// the minimum scale factor of the paths through the GlobalMinimax.
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virtual int XScaleFactor() const;
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// Provides the (minimum) x scale factor to the network (of interest only to
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// input units) so they can determine how to scale bounding boxes.
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virtual void CacheXScaleFactor(int factor);
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// Provides debug output on the weights.
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virtual void DebugWeights();
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// Returns the current stack.
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const PointerVector<Network>& stack() const {
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return stack_;
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}
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// Returns a set of strings representing the layer-ids of all layers below.
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void EnumerateLayers(const STRING* prefix,
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GenericVector<STRING>* layers) const;
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// Returns a pointer to the network layer corresponding to the given id.
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Network* GetLayer(const char* id) const;
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// Returns the learning rate for a specific layer of the stack.
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float LayerLearningRate(const char* id) const {
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const float* lr_ptr = LayerLearningRatePtr(id);
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ASSERT_HOST(lr_ptr != NULL);
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return *lr_ptr;
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}
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// Scales the learning rate for a specific layer of the stack.
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void ScaleLayerLearningRate(const char* id, double factor) {
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float* lr_ptr = LayerLearningRatePtr(id);
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ASSERT_HOST(lr_ptr != NULL);
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*lr_ptr *= factor;
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}
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// Returns a pointer to the learning rate for the given layer id.
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float* LayerLearningRatePtr(const char* id) const;
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// Writes to the given file. Returns false in case of error.
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virtual bool Serialize(TFile* fp) const;
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// Reads from the given file. Returns false in case of error.
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2017-05-04 07:09:44 +08:00
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virtual bool DeSerialize(TFile* fp);
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2016-11-08 07:38:07 +08:00
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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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virtual void Update(float learning_rate, float momentum, int num_samples);
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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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virtual void CountAlternators(const Network& other, double* same,
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double* changed) const;
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protected:
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// The networks.
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PointerVector<Network> stack_;
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// Layer-specific learning rate iff network_flags_ & NF_LAYER_SPECIFIC_LR.
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// One element for each element of stack_.
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GenericVector<float> learning_rates_;
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};
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} // namespace tesseract.
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#endif // TESSERACT_LSTM_PLUMBING_H_
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