tesseract  5.0.0
fullyconnected.h
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1 // File: fullyconnected.h
3 // Description: Simple feed-forward layer with various non-linearities.
4 // Author: Ray Smith
5 //
6 // (C) Copyright 2014, Google Inc.
7 // Licensed under the Apache License, Version 2.0 (the "License");
8 // you may not use this file except in compliance with the License.
9 // You may obtain a copy of the License at
10 // http://www.apache.org/licenses/LICENSE-2.0
11 // Unless required by applicable law or agreed to in writing, software
12 // distributed under the License is distributed on an "AS IS" BASIS,
13 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 // See the License for the specific language governing permissions and
15 // limitations under the License.
17 
18 #ifndef TESSERACT_LSTM_FULLYCONNECTED_H_
19 #define TESSERACT_LSTM_FULLYCONNECTED_H_
20 
21 #include "network.h"
22 #include "networkscratch.h"
23 #include "tesstypes.h"
24 
25 namespace tesseract {
26 
27 // C++ Implementation of the Softmax (output) class from lstm.py.
28 class FullyConnected : public Network {
29 public:
30  TESS_API
31  FullyConnected(const std::string &name, int ni, int no, NetworkType type);
32  ~FullyConnected() override = default;
33 
34  // Returns the shape output from the network given an input shape (which may
35  // be partially unknown ie zero).
36  StaticShape OutputShape(const StaticShape &input_shape) const override;
37 
38  std::string spec() const override {
39  std::string spec;
40  if (type_ == NT_TANH) {
41  spec += "Ft" + std::to_string(no_);
42  } else if (type_ == NT_LOGISTIC) {
43  spec += "Fs" + std::to_string(no_);
44  } else if (type_ == NT_RELU) {
45  spec += "Fr" + std::to_string(no_);
46  } else if (type_ == NT_LINEAR) {
47  spec += "Fl" + std::to_string(no_);
48  } else if (type_ == NT_POSCLIP) {
49  spec += "Fp" + std::to_string(no_);
50  } else if (type_ == NT_SYMCLIP) {
51  spec += "Fn" + std::to_string(no_);
52  } else if (type_ == NT_SOFTMAX) {
53  spec += "Fc" + std::to_string(no_);
54  } else {
55  spec += "Fm" + std::to_string(no_);
56  }
57  return spec;
58  }
59 
60  // Changes the type to the given type. Used to commute a softmax to a
61  // non-output type for adding on other networks.
63  type_ = type;
64  }
65 
66  // Suspends/Enables training by setting the training_ flag. Serialize and
67  // DeSerialize only operate on the run-time data if state is false.
68  void SetEnableTraining(TrainingState state) override;
69 
70  // Sets up the network for training. Initializes weights using weights of
71  // scale `range` picked according to the random number generator `randomizer`.
72  int InitWeights(float range, TRand *randomizer) override;
73  // Recursively searches the network for softmaxes with old_no outputs,
74  // and remaps their outputs according to code_map. See network.h for details.
75  int RemapOutputs(int old_no, const std::vector<int> &code_map) override;
76 
77  // Converts a float network to an int network.
78  void ConvertToInt() override;
79 
80  // Provides debug output on the weights.
81  void DebugWeights() override;
82 
83  // Writes to the given file. Returns false in case of error.
84  bool Serialize(TFile *fp) const override;
85  // Reads from the given file. Returns false in case of error.
86  bool DeSerialize(TFile *fp) override;
87 
88  // Runs forward propagation of activations on the input line.
89  // See Network for a detailed discussion of the arguments.
90  void Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose,
91  NetworkScratch *scratch, NetworkIO *output) override;
92  // Components of Forward so FullyConnected can be reused inside LSTM.
93  void SetupForward(const NetworkIO &input, const TransposedArray *input_transpose);
94  void ForwardTimeStep(int t, TFloat *output_line);
95  void ForwardTimeStep(const TFloat *d_input, int t, TFloat *output_line);
96  void ForwardTimeStep(const int8_t *i_input, int t, TFloat *output_line);
97 
98  // Runs backward propagation of errors on the deltas line.
99  // See Network for a detailed discussion of the arguments.
100  bool Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch,
101  NetworkIO *back_deltas) override;
102  // Components of Backward so FullyConnected can be reused inside LSTM.
103  void BackwardTimeStep(const NetworkIO &fwd_deltas, int t, TFloat *curr_errors,
104  TransposedArray *errors_t, TFloat *backprop);
105  void FinishBackward(const TransposedArray &errors_t);
106 
107  // Updates the weights using the given learning rate, momentum and adam_beta.
108  // num_samples is used in the adam computation iff use_adam_ is true.
109  void Update(float learning_rate, float momentum, float adam_beta, int num_samples) override;
110  // Sums the products of weight updates in *this and other, splitting into
111  // positive (same direction) in *same and negative (different direction) in
112  // *changed.
113  void CountAlternators(const Network &other, TFloat *same, TFloat *changed) const override;
114 
115 protected:
116  // Weight arrays of size [no, ni + 1].
118  // Transposed copy of input used during training of size [ni, width].
120  // Pointer to transposed input stored elsewhere. If not null, this is used
121  // in preference to calculating the transpose and storing it in source_t_.
123  // Activations from forward pass of size [width, no].
125  // Memory of the integer mode input to forward as softmax always outputs
126  // float, so the information is otherwise lost.
127  bool int_mode_;
128 };
129 
130 } // namespace tesseract.
131 
132 #endif // TESSERACT_LSTM_FULLYCONNECTED_H_
TrainingState
Definition: network.h:90
NetworkType
Definition: network.h:41
@ NT_LINEAR
Definition: network.h:65
@ NT_RELU
Definition: network.h:64
@ NT_SOFTMAX
Definition: network.h:66
@ NT_LOGISTIC
Definition: network.h:60
@ NT_SYMCLIP
Definition: network.h:62
@ NT_POSCLIP
Definition: network.h:61
@ NT_TANH
Definition: network.h:63
double TFloat
Definition: tesstypes.h:39
void ForwardTimeStep(int t, TFloat *output_line)
std::string spec() const override
bool DeSerialize(TFile *fp) override
void FinishBackward(const TransposedArray &errors_t)
void SetupForward(const NetworkIO &input, const TransposedArray *input_transpose)
bool Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch, NetworkIO *back_deltas) override
void SetEnableTraining(TrainingState state) override
const TransposedArray * external_source_
void Update(float learning_rate, float momentum, float adam_beta, int num_samples) override
int InitWeights(float range, TRand *randomizer) override
void Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose, NetworkScratch *scratch, NetworkIO *output) override
void CountAlternators(const Network &other, TFloat *same, TFloat *changed) const override
void BackwardTimeStep(const NetworkIO &fwd_deltas, int t, TFloat *curr_errors, TransposedArray *errors_t, TFloat *backprop)
void ChangeType(NetworkType type)
int RemapOutputs(int old_no, const std::vector< int > &code_map) override
TESS_API FullyConnected(const std::string &name, int ni, int no, NetworkType type)
~FullyConnected() override=default
StaticShape OutputShape(const StaticShape &input_shape) const override
bool Serialize(TFile *fp) const override
NetworkType type_
Definition: network.h:300
const std::string & name() const
Definition: network.h:140
NetworkType type() const
Definition: network.h:110
#define TESS_API
Definition: export.h:34