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104 lines
3.1 KiB
C++
104 lines
3.1 KiB
C++
// Copyright 2008 Google Inc.
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// All Rights Reserved.
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// Author: ahmadab@google.com (Ahmad Abdulkader)
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//
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// neuron.cpp: The implementation of a class for an object
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// that represents a single neuron in a neural network
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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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#include "neuron.h"
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#include "input_file_buffer.h"
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namespace tesseract {
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// Instantiate all supported templates
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template bool Neuron::ReadBinary(InputFileBuffer *input_buffer);
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// default and only constructor
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Neuron::Neuron() {
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Init();
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}
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// virtual destructor
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Neuron::~Neuron() {
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}
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// Initializer
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void Neuron::Init() {
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id_ = -1;
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frwd_dirty_ = false;
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fan_in_.clear();
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fan_in_weights_.clear();
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activation_ = 0.0f;
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output_ = 0.0f;
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bias_ = 0.0f;
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node_type_ = Unknown;
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}
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// Computes the activation and output of the neuron if not fresh
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// by pulling the outputs of all fan-in neurons
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void Neuron::FeedForward() {
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if (!frwd_dirty_ ) {
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return;
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}
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// nothing to do for input nodes: just pass the input to the o/p
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// otherwise, pull the output of all fan-in neurons
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if (node_type_ != Input) {
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int fan_in_cnt = fan_in_.size();
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// sum out the activation
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activation_ = -bias_;
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for (int in = 0; in < fan_in_cnt; in++) {
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if (fan_in_[in]->frwd_dirty_) {
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fan_in_[in]->FeedForward();
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}
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activation_ += ((*(fan_in_weights_[in])) * fan_in_[in]->output_);
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}
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// sigmoid it
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output_ = Sigmoid(activation_);
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}
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frwd_dirty_ = false;
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}
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// set the type of the neuron
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void Neuron::set_node_type(NeuronTypes Type) {
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node_type_ = Type;
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}
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// Adds new connections *to* this neuron *From*
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// a target neuron using specfied params
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// Note that what is actually copied in this function are pointers to the
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// specified Neurons and weights and not the actualt values. This is by
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// design to centralize the alloction of neurons and weights and so
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// increase the locality of reference and improve cache-hits resulting
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// in a faster net. This technique resulted in a 2X-10X speedup
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// (depending on network size and processor)
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void Neuron::AddFromConnection(Neuron *neurons,
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float *wts_offset,
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int from_cnt) {
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for (int in = 0; in < from_cnt; in++) {
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fan_in_.push_back(neurons + in);
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fan_in_weights_.push_back(wts_offset + in);
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}
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}
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// fast computation of sigmoid function using a lookup table
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// defined in sigmoid_table.cpp
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float Neuron::Sigmoid(float activation) {
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if (activation <= -10.0f) {
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return 0.0f;
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} else if (activation >= 10.0f) {
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return 1.0f;
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} else {
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return kSigmoidTable[static_cast<int>(100 * (activation + 10.0))];
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}
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}
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}
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