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95 lines
3.9 KiB
C++
95 lines
3.9 KiB
C++
///////////////////////////////////////////////////////////////////////
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// File: series.h
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// Description: Runs networks in series on the same input.
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// Author: Ray Smith
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// Created: Thu May 02 08:20:06 PST 2013
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//
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// (C) Copyright 2013, 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_SERIES_H_
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#define TESSERACT_LSTM_SERIES_H_
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#include "plumbing.h"
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namespace tesseract {
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// Runs two or more networks in series (layers) on the same input.
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class Series : public Plumbing {
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public:
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// ni_ and no_ will be set by AddToStack.
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explicit Series(const STRING& name);
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virtual ~Series();
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// Returns the shape output from the network given an input shape (which may
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// be partially unknown ie zero).
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virtual StaticShape OutputShape(const StaticShape& input_shape) const;
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virtual STRING spec() const {
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STRING spec("[");
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for (int i = 0; i < stack_.size(); ++i)
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spec += stack_[i]->spec();
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spec += "]";
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return spec;
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}
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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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// Returns the number of weights initialized.
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virtual int InitWeights(float range, TRand* randomizer);
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// Recursively searches the network for softmaxes with old_no outputs,
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// and remaps their outputs according to code_map. See network.h for details.
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int RemapOutputs(int old_no, const std::vector<int>& code_map) override;
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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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// Runs forward propagation of activations on the input line.
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// See Network for a detailed discussion of the arguments.
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virtual void Forward(bool debug, const NetworkIO& input,
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const TransposedArray* input_transpose,
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NetworkScratch* scratch, NetworkIO* output);
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// Runs backward propagation of errors on the deltas line.
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// See Network for a detailed discussion of the arguments.
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virtual bool Backward(bool debug, const NetworkIO& fwd_deltas,
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NetworkScratch* scratch,
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NetworkIO* back_deltas);
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// Splits the series after the given index, returning the two parts and
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// deletes itself. The first part, up to network with index last_start, goes
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// into start, and the rest goes into end.
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void SplitAt(int last_start, Series** start, Series** end);
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// Appends the elements of the src series to this, removing from src and
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// deleting it.
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void AppendSeries(Network* src);
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};
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} // namespace tesseract.
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#endif // TESSERACT_LSTM_SERIES_H_
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