mirror of
https://github.com/tesseract-ocr/tesseract.git
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93348a83a3
They were replaced by Python3 scripts (part of the tesstrain repository). Signed-off-by: Stefan Weil <sw@weilnetz.de>
215 lines
9.5 KiB
C++
215 lines
9.5 KiB
C++
// (C) Copyright 2017, 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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// Generating the training data:
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// If the format of the lstmf (ImageData) file changes, the training data will
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// have to be regenerated as follows:
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//
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// Use --xsize 800 for text2image to be similar to original training data.
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//
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// tesstrain.py --fonts_dir /usr/share/fonts --lang eng \
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// --linedata_only --noextract_font_properties --langdata_dir ../langdata_lstm \
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// --tessdata_dir ../tessdata --output_dir ~/tesseract/test/testdata \
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// --fontlist "Arial" --maxpages 10
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//
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#include "lstm_test.h"
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namespace tesseract {
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// Tests that some simple networks can learn Arial and meet accuracy targets.
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TEST_F(LSTMTrainerTest, BasicTest) {
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// A Convolver sliding window classifier without LSTM.
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SetupTrainer(
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"[1,32,0,1 Ct5,5,16 Mp4,4 Ct1,1,16 Ct3,3,128 Mp4,1 Ct1,1,64 S2,1 "
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"Ct1,1,64O1c1]",
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"no-lstm", "eng/eng.unicharset", "eng.Arial.exp0.lstmf", false, false, 2e-4, false, "eng");
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double non_lstm_err = TrainIterations(kTrainerIterations * 4);
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EXPECT_LT(non_lstm_err, 98);
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LOG(INFO) << "********** Expected < 98 ************\n";
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// A basic single-layer, single direction LSTM.
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SetupTrainerEng("[1,1,0,32 Lfx100 O1c1]", "1D-lstm", false, false);
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double lstm_uni_err = TrainIterations(kTrainerIterations * 2);
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EXPECT_LT(lstm_uni_err, 86);
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LOG(INFO) << "********** Expected < 86 ************\n";
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// Beats the convolver. (Although it does have a lot more weights, it still
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// iterates faster.)
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EXPECT_LT(lstm_uni_err, non_lstm_err);
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}
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// Color learns almost as fast as normalized grey/2D.
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TEST_F(LSTMTrainerTest, ColorTest) {
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// A basic single-layer, single direction LSTM.
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SetupTrainerEng("[1,32,0,3 S4,2 L2xy16 Ct1,1,16 S8,1 Lbx100 O1c1]", "2D-color-lstm", true, true);
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double lstm_uni_err = TrainIterations(kTrainerIterations);
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EXPECT_LT(lstm_uni_err, 85);
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// EXPECT_GT(lstm_uni_err, 66);
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LOG(INFO) << "********** Expected < 85 ************\n";
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}
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TEST_F(LSTMTrainerTest, BidiTest) {
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// A basic single-layer, bi-di 1d LSTM.
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SetupTrainerEng("[1,1,0,32 Lbx100 O1c1]", "bidi-lstm", false, false);
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double lstm_bi_err = TrainIterations(kTrainerIterations);
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EXPECT_LT(lstm_bi_err, 75);
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LOG(INFO) << "********** Expected < 75 ************\n";
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// Int mode training is dead, so convert the trained network to int and check
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// that its error rate is close to the float version.
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TestIntMode(kTrainerIterations);
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}
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// Tests that a 2d-2-layer network learns correctly.
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// It takes a lot of iterations to get there.
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TEST_F(LSTMTrainerTest, Test2D) {
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// A 2-layer LSTM with a 2-D feature-extracting LSTM on the bottom.
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SetupTrainerEng("[1,32,0,1 S4,2 L2xy16 Ct1,1,16 S8,1 Lbx100 O1c1]", "2-D-2-layer-lstm", false,
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false);
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double lstm_2d_err = TrainIterations(kTrainerIterations * 3 / 2);
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EXPECT_LT(lstm_2d_err, 98);
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// EXPECT_GT(lstm_2d_err, 90);
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LOG(INFO) << "********** Expected < 98 ************\n";
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// Int mode training is dead, so convert the trained network to int and check
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// that its error rate is close to the float version.
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TestIntMode(kTrainerIterations);
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}
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// Tests that a 2d-2-layer network with Adam does *a lot* better than
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// without it.
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TEST_F(LSTMTrainerTest, TestAdam) {
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// A 2-layer LSTM with a 2-D feature-extracting LSTM on the bottom.
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SetupTrainerEng("[1,32,0,1 S4,2 L2xy16 Ct1,1,16 S8,1 Lbx100 O1c1]", "2-D-2-layer-lstm", false,
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true);
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double lstm_2d_err = TrainIterations(kTrainerIterations);
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EXPECT_LT(lstm_2d_err, 70);
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LOG(INFO) << "********** Expected < 70 ************\n";
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TestIntMode(kTrainerIterations);
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}
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// Trivial test of training speed on a fairly complex network.
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TEST_F(LSTMTrainerTest, SpeedTest) {
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SetupTrainerEng(
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"[1,30,0,1 Ct5,5,16 Mp2,2 L2xy24 Ct1,1,48 Mp5,1 Ct1,1,32 S3,1 Lbx64 "
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"O1c1]",
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"2-D-2-layer-lstm", false, true);
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TrainIterations(kTrainerIterations);
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LOG(INFO) << "********** *** ************\n";
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}
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// Tests that two identical networks trained the same get the same results.
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// Also tests that the same happens with a serialize/deserialize in the middle.
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TEST_F(LSTMTrainerTest, DeterminismTest) {
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SetupTrainerEng("[1,32,0,1 S4,2 L2xy16 Ct1,1,16 S8,1 Lbx100 O1c1]", "2-D-2-layer-lstm", false,
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false);
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double lstm_2d_err_a = TrainIterations(kTrainerIterations);
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double act_error_a = trainer_->ActivationError();
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double char_error_a = trainer_->CharError();
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std::vector<char> trainer_a_data;
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EXPECT_TRUE(trainer_->SaveTrainingDump(NO_BEST_TRAINER, *trainer_, &trainer_a_data));
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SetupTrainerEng("[1,32,0,1 S4,2 L2xy16 Ct1,1,16 S8,1 Lbx100 O1c1]", "2-D-2-layer-lstm", false,
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false);
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double lstm_2d_err_b = TrainIterations(kTrainerIterations);
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double act_error_b = trainer_->ActivationError();
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double char_error_b = trainer_->CharError();
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EXPECT_FLOAT_EQ(lstm_2d_err_a, lstm_2d_err_b);
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EXPECT_FLOAT_EQ(act_error_a, act_error_b);
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EXPECT_FLOAT_EQ(char_error_a, char_error_b);
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// Now train some more iterations.
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lstm_2d_err_b = TrainIterations(kTrainerIterations / 3);
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act_error_b = trainer_->ActivationError();
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char_error_b = trainer_->CharError();
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// Unpack into a new trainer and train that some more too.
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SetupTrainerEng("[1,32,0,1 S4,2 L2xy16 Ct1,1,16 S8,1 Lbx100 O1c1]", "2-D-2-layer-lstm", false,
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false);
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EXPECT_TRUE(trainer_->ReadTrainingDump(trainer_a_data, *trainer_));
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lstm_2d_err_a = TrainIterations(kTrainerIterations / 3);
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act_error_a = trainer_->ActivationError();
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char_error_a = trainer_->CharError();
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EXPECT_FLOAT_EQ(lstm_2d_err_a, lstm_2d_err_b);
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EXPECT_FLOAT_EQ(act_error_a, act_error_b);
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EXPECT_FLOAT_EQ(char_error_a, char_error_b);
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LOG(INFO) << "********** *** ************\n";
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}
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// The baseline network against which to test the built-in softmax.
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TEST_F(LSTMTrainerTest, SoftmaxBaselineTest) {
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// A basic single-layer, single direction LSTM.
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SetupTrainerEng("[1,1,0,32 Lfx96 O1c1]", "1D-lstm", false, true);
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double lstm_uni_err = TrainIterations(kTrainerIterations * 2);
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EXPECT_LT(lstm_uni_err, 60);
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// EXPECT_GT(lstm_uni_err, 48);
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LOG(INFO) << "********** Expected < 60 ************\n";
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// Check that it works in int mode too.
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TestIntMode(kTrainerIterations);
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// If we run TestIntMode again, it tests that int_mode networks can
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// serialize and deserialize correctly.
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double delta = TestIntMode(kTrainerIterations);
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// The two tests (both of int mode this time) should be almost identical.
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LOG(INFO) << "Delta in Int mode error rates = " << delta << "\n";
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EXPECT_LT(delta, 0.01);
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}
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// Tests that the built-in softmax does better than the external one,
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// which has an error rate slightly less than 55%, as tested by
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// SoftmaxBaselineTest.
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TEST_F(LSTMTrainerTest, SoftmaxTest) {
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// LSTM with a built-in softmax can beat the external softmax.
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SetupTrainerEng("[1,1,0,32 LS96]", "Lstm-+-softmax", false, true);
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double lstm_sm_err = TrainIterations(kTrainerIterations * 2);
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EXPECT_LT(lstm_sm_err, 49.0);
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LOG(INFO) << "********** Expected < 49 ************\n";
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// Check that it works in int mode too.
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TestIntMode(kTrainerIterations);
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}
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// Tests that the built-in encoded softmax does better than the external one.
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// It takes a lot of iterations to get there.
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TEST_F(LSTMTrainerTest, EncodedSoftmaxTest) {
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// LSTM with a built-in encoded softmax can beat the external softmax.
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SetupTrainerEng("[1,1,0,32 LE96]", "Lstm-+-softmax", false, true);
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double lstm_sm_err = TrainIterations(kTrainerIterations * 2);
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EXPECT_LT(lstm_sm_err, 62.0);
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LOG(INFO) << "********** Expected < 62 ************\n";
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// Check that it works in int mode too.
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TestIntMode(kTrainerIterations);
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}
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// Tests that layer access methods work correctly.
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TEST_F(LSTMTrainerTest, TestLayerAccess) {
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// A 2-layer LSTM with a Squashed feature-extracting LSTM on the bottom.
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SetupTrainerEng("[1,32,0,1 Ct5,5,16 Mp2,2 Lfys32 Lbx128 O1c1]", "SQU-lstm", false, false);
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// Number of layers.
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const size_t kNumLayers = 8;
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// Expected layer names.
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const char *kLayerIds[kNumLayers] = {":0", ":1:0", ":1:1", ":2", ":3:0", ":4:0", ":4:1:0", ":5"};
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const char *kLayerNames[kNumLayers] = {"Input", "Convolve", "ConvNL", "Maxpool",
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"Lfys32", "Lbx128LTR", "Lbx128", "Output"};
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// Expected number of weights.
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const int kNumWeights[kNumLayers] = {0,
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0,
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16 * (25 + 1),
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0,
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32 * (4 * (32 + 16 + 1)),
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128 * (4 * (128 + 32 + 1)),
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128 * (4 * (128 + 32 + 1)),
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112 * (2 * 128 + 1)};
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auto layers = trainer_->EnumerateLayers();
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EXPECT_EQ(kNumLayers, layers.size());
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for (unsigned i = 0; i < kNumLayers && i < layers.size(); ++i) {
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EXPECT_STREQ(kLayerIds[i], layers[i].c_str());
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EXPECT_STREQ(kLayerNames[i], trainer_->GetLayer(layers[i])->name().c_str());
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EXPECT_EQ(kNumWeights[i], trainer_->GetLayer(layers[i])->num_weights());
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}
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}
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} // namespace tesseract.
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