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lstm.h
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lstm.h
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///////////////////////////////////////////////////////////////////////
// File: lstm.h
// Description: Long-term-short-term-memory Recurrent neural network.
// Author: Ray Smith
// Created: Wed May 01 17:33:06 PST 2013
//
// (C) Copyright 2013, 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_LSTM_H_
#define TESSERACT_LSTM_LSTM_H_
#include "network.h"
#include "fullyconnected.h"
namespace tesseract {
// C++ Implementation of the LSTM class from lstm.py.
class LSTM : public Network {
public:
// Enum for the different weights in LSTM, to reduce some of the I/O and
// setup code to loops. The elements of the enum correspond to elements of an
// array of WeightMatrix or a corresponding array of NetworkIO.
enum WeightType {
CI, // Cell Inputs.
GI, // Gate at the input.
GF1, // Forget gate at the memory (1-d or looking back 1 timestep).
GO, // Gate at the output.
GFS, // Forget gate at the memory, looking back in the other dimension.
WT_COUNT // Number of WeightTypes.
};
// Constructor for NT_LSTM (regular 1 or 2-d LSTM), NT_LSTM_SOFTMAX (LSTM with
// additional softmax layer included and fed back into the input at the next
// timestep), or NT_LSTM_SOFTMAX_ENCODED (as LSTM_SOFTMAX, but the feedback
// is binary encoded instead of categorical) only.
// 2-d and bidi softmax LSTMs are not rejected, but are impossible to build
// in the conventional way because the output feedback both forwards and
// backwards in time does become impossible.
LSTM(const STRING& name, int num_inputs, int num_states, int num_outputs,
bool two_dimensional, NetworkType type);
virtual ~LSTM();
// Returns the shape output from the network given an input shape (which may
// be partially unknown ie zero).
virtual StaticShape OutputShape(const StaticShape& input_shape) const;
virtual STRING spec() const {
STRING spec;
if (type_ == NT_LSTM)
spec.add_str_int("Lfx", ns_);
else if (type_ == NT_LSTM_SUMMARY)
spec.add_str_int("Lfxs", ns_);
else if (type_ == NT_LSTM_SOFTMAX)
spec.add_str_int("LS", ns_);
else if (type_ == NT_LSTM_SOFTMAX_ENCODED)
spec.add_str_int("LE", ns_);
if (softmax_ != NULL) spec += softmax_->spec();
return spec;
}
// 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 up the network for training. Initializes weights using weights of
// scale `range` picked according to the random number generator `randomizer`.
virtual int InitWeights(float range, TRand* randomizer);
// Converts a float network to an int network.
virtual void ConvertToInt();
// Provides debug output on the weights.
virtual void DebugWeights();
// 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.
// If swap is true, assumes a big/little-endian swap is needed.
virtual bool DeSerialize(bool swap, TFile* fp);
// Runs forward propagation of activations on the input line.
// See Network for a detailed discussion of the arguments.
virtual void Forward(bool debug, const NetworkIO& input,
const TransposedArray* input_transpose,
NetworkScratch* scratch, NetworkIO* output);
// Runs backward propagation of errors on the deltas line.
// See Network for a detailed discussion of the arguments.
virtual bool Backward(bool debug, const NetworkIO& fwd_deltas,
NetworkScratch* scratch,
NetworkIO* back_deltas);
// 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;
// Prints the weights for debug purposes.
void PrintW();
// Prints the weight deltas for debug purposes.
void PrintDW();
// Returns true of this is a 2-d lstm.
bool Is2D() const {
return is_2d_;
}
private:
// Resizes forward data to cope with an input image of the given width.
void ResizeForward(const NetworkIO& input);
private:
// Size of padded input to weight matrices = ni_ + no_ for 1-D operation
// and ni_ + 2 * no_ for 2-D operation. Note that there is a phantom 1 input
// for the bias that makes the weight matrices of size [na + 1][no].
inT32 na_;
// Number of internal states. Equal to no_ except for a softmax LSTM.
// ns_ is NOT serialized, but is calculated from gate_weights_.
inT32 ns_;
// Number of additional feedback states. The softmax types feed back
// additional output information on top of the ns_ internal states.
// In the case of a binary-coded (EMBEDDED) softmax, nf_ < no_.
inT32 nf_;
// Flag indicating 2-D operation.
bool is_2d_;
// Gate weight arrays of size [na + 1, no].
WeightMatrix gate_weights_[WT_COUNT];
// Used only if this is a softmax LSTM.
FullyConnected* softmax_;
// Input padded with previous output of size [width, na].
NetworkIO source_;
// Internal state used during forward operation, of size [width, ns].
NetworkIO state_;
// State of the 2-d maxpool, generated during forward, used during backward.
GENERIC_2D_ARRAY<inT8> which_fg_;
// Internal state saved from forward, but used only during backward.
NetworkIO node_values_[WT_COUNT];
// Preserved input stride_map used for Backward when NT_LSTM_SQUASHED.
StrideMap input_map_;
int input_width_;
};
} // namespace tesseract.
#endif // TESSERACT_LSTM_LSTM_H_