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nnet-modify-learning-rates.cc
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nnet-modify-learning-rates.cc
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// nnet2bin/nnet-modify-learning-rates.cc
// Copyright 2013 Guoguo Chen
// See ../../COPYING for clarification regarding multiple authors
//
// 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
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "hmm/transition-model.h"
#include "nnet2/train-nnet.h"
#include "nnet2/am-nnet.h"
namespace kaldi {
namespace nnet2 {
void SetMaxChange(BaseFloat max_change, Nnet *nnet) {
for (int32 c = 0; c < nnet->NumComponents(); c++) {
Component *component = &(nnet->GetComponent(c));
AffineComponentPreconditioned *ac =
dynamic_cast<AffineComponentPreconditioned*>(component);
if (ac != NULL)
ac->SetMaxChange(max_change);
}
}
}
}
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet2;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
const char *usage =
"This program modifies the learning rates so as to equalize the\n"
"relative changes in parameters for each layer, while keeping their\n"
"geometric mean the same (or changing it to a value specified using\n"
"the --average-learning-rate option).\n"
"\n"
"Usage: nnet-modify-learning-rates [options] <prev-model> \\\n"
" <cur-model> <modified-cur-model>\n"
"e.g.: nnet-modify-learning-rates --average-learning-rate=0.0002 \\\n"
" 5.mdl 6.mdl 6.mdl\n";
bool binary_write = true;
bool retroactive = false;
BaseFloat average_learning_rate = 0.0;
BaseFloat first_layer_factor = 1.0;
BaseFloat last_layer_factor = 1.0;
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Register("average-learning-rate", &average_learning_rate,
"If supplied, change learning rate geometric mean to the given "
"value.");
po.Register("first-layer-factor", &first_layer_factor, "Factor that "
"reduces the target relative learning rate for first layer.");
po.Register("last-layer-factor", &last_layer_factor, "Factor that "
"reduces the target relative learning rate for last layer.");
po.Register("retroactive", &retroactive, "If true, scale the parameter "
"differences as well.");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
KALDI_ASSERT(average_learning_rate >= 0);
std::string prev_nnet_rxfilename = po.GetArg(1),
cur_nnet_rxfilename = po.GetArg(2),
modified_cur_nnet_rxfilename = po.GetOptArg(3);
TransitionModel trans_model;
AmNnet am_prev_nnet, am_cur_nnet;
{
bool binary_read;
Input ki(prev_nnet_rxfilename, &binary_read);
trans_model.Read(ki.Stream(), binary_read);
am_prev_nnet.Read(ki.Stream(), binary_read);
}
{
bool binary_read;
Input ki(cur_nnet_rxfilename, &binary_read);
trans_model.Read(ki.Stream(), binary_read);
am_cur_nnet.Read(ki.Stream(), binary_read);
}
if (am_prev_nnet.GetNnet().GetParameterDim() !=
am_cur_nnet.GetNnet().GetParameterDim()) {
KALDI_WARN << "Parameter-dim mismatch, cannot equalize the relative "
<< "changes in parameters for each layer.";
exit(0);
}
int32 ret = 0;
// Gets relative parameter differences.
int32 num_updatable = am_prev_nnet.GetNnet().NumUpdatableComponents();
Vector<BaseFloat> relative_diff(num_updatable);
{
Nnet diff_nnet(am_prev_nnet.GetNnet());
diff_nnet.AddNnet(-1.0, am_cur_nnet.GetNnet());
diff_nnet.ComponentDotProducts(diff_nnet, &relative_diff);
relative_diff.ApplyPow(0.5);
Vector<BaseFloat> baseline_prod(num_updatable);
am_prev_nnet.GetNnet().ComponentDotProducts(am_prev_nnet.GetNnet(),
&baseline_prod);
baseline_prod.ApplyPow(0.5);
relative_diff.DivElements(baseline_prod);
KALDI_LOG << "Relative parameter differences per layer are "
<< relative_diff;
// If relative parameter difference for a certain is zero, set it to the
// mean of the rest values.
int32 num_zero = 0;
for (int32 i = 0; i < num_updatable; i++) {
if (relative_diff(i) == 0.0) {
num_zero++;
}
}
if (num_zero > 0) {
BaseFloat average_diff = relative_diff.Sum()
/ static_cast<BaseFloat>(num_updatable - num_zero);
for (int32 i = 0; i < num_updatable; i++) {
if (relative_diff(i) == 0.0) {
relative_diff(i) = average_diff;
}
}
KALDI_LOG << "Zeros detected in the relative parameter difference "
<< "vector, updating the vector to " << relative_diff;
}
}
// Gets learning rates for previous neural net.
Vector<BaseFloat> prev_nnet_learning_rates(num_updatable),
cur_nnet_learning_rates(num_updatable);
am_prev_nnet.GetNnet().GetLearningRates(&prev_nnet_learning_rates);
am_cur_nnet.GetNnet().GetLearningRates(&cur_nnet_learning_rates);
KALDI_LOG << "Learning rates for previous model per layer are "
<< prev_nnet_learning_rates;
KALDI_LOG << "Learning rates for current model per layer are "
<< cur_nnet_learning_rates;
// Gets target geometric mean.
BaseFloat target_geometric_mean = 0.0;
if (average_learning_rate == 0.0) {
target_geometric_mean = Exp(cur_nnet_learning_rates.SumLog()
/ static_cast<BaseFloat>(num_updatable));
} else {
target_geometric_mean = average_learning_rate;
}
KALDI_ASSERT(target_geometric_mean > 0.0);
// Works out the new learning rates. We start from the previous model;
// this ensures that if this program is run twice, we get consistent
// results even if it's overwritten the current model.
Vector<BaseFloat> nnet_learning_rates(prev_nnet_learning_rates);
nnet_learning_rates.DivElements(relative_diff);
KALDI_ASSERT(last_layer_factor > 0.0);
nnet_learning_rates(num_updatable - 1) *= last_layer_factor;
KALDI_ASSERT(first_layer_factor > 0.0);
nnet_learning_rates(0) *= first_layer_factor;
BaseFloat cur_geometric_mean = Exp(nnet_learning_rates.SumLog()
/ static_cast<BaseFloat>(num_updatable));
nnet_learning_rates.Scale(target_geometric_mean / cur_geometric_mean);
KALDI_LOG << "New learning rates for current model per layer are "
<< nnet_learning_rates;
// Changes the parameter differences if --retroactivate is set to true.
if (retroactive) {
Vector<BaseFloat> scale_factors(nnet_learning_rates);
scale_factors.DivElements(prev_nnet_learning_rates);
am_cur_nnet.GetNnet().AddNnet(-1.0, am_prev_nnet.GetNnet());
am_cur_nnet.GetNnet().ScaleComponents(scale_factors);
am_cur_nnet.GetNnet().AddNnet(1.0, am_prev_nnet.GetNnet());
KALDI_LOG << "Scale parameter difference retroactively. Scaling factors "
<< "are " << scale_factors;
}
// Sets learning rates and writes updated model.
am_cur_nnet.GetNnet().SetLearningRates(nnet_learning_rates);
SetMaxChange(0.0, &(am_cur_nnet.GetNnet()));
Output ko(modified_cur_nnet_rxfilename, binary_write);
trans_model.Write(ko.Stream(), binary_write);
am_cur_nnet.Write(ko.Stream(), binary_write);
return ret;
} catch(const std::exception &e) {
std::cerr << e.what() << '\n';
return -1;
}
}