Does parameters actually update in `ApplyUpdata` in caffe?
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ApplyUpdata in caffe shows below. And I get two questions:
Does parameters actually update in
ApplyUpdata?backwardonly calculate the gradient but nothing is done to parameters?What's the meaning of
this->iter_? TakingGANtraining as an example, how should I deal withthis->iter_if I want to train generator three time while discriminator only trained once, asApplyUpdatewill increasethis->iter_three times for generator but only once for discriminator.
THX!
void SGDSolver<Dtype>::ApplyUpdate()
Dtype rate = GetLearningRate();
if (this->param_.display() && this->iter_ % this->param_.display() == 0)
LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << this->iter_
<< ", lr = " << rate;
ClipGradients();
for (int param_id = 0; param_id < this->net_->learnable_params().size();
++param_id)
Normalize(param_id);
Regularize(param_id);
ComputeUpdateValue(param_id, rate);
this->net_->Update();
// Increment the internal iter_ counter -- its value should always indicate
// the number of times the weights have been updated.
++this->iter_;
caffe pycaffe
asked Nov 14 '18 at 9:25
inkzkinkzk
2261318
add a comment |
ApplyUpdata in caffe shows below. And I get two questions:
Does parameters actually update in
ApplyUpdata?backwardonly calculate the gradient but nothing is done to parameters?What's the meaning of
this->iter_? TakingGANtraining as an example, how should I deal withthis->iter_if I want to train generator three time while discriminator only trained once, asApplyUpdatewill increasethis->iter_three times for generator but only once for discriminator.
THX!
void SGDSolver<Dtype>::ApplyUpdate()
Dtype rate = GetLearningRate();
if (this->param_.display() && this->iter_ % this->param_.display() == 0)
LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << this->iter_
<< ", lr = " << rate;
ClipGradients();
for (int param_id = 0; param_id < this->net_->learnable_params().size();
++param_id)
Normalize(param_id);
Regularize(param_id);
ComputeUpdateValue(param_id, rate);
this->net_->Update();
// Increment the internal iter_ counter -- its value should always indicate
// the number of times the weights have been updated.
++this->iter_;
caffe pycaffe
asked Nov 14 '18 at 9:25
inkzkinkzk
2261318
add a comment |
ApplyUpdata in caffe shows below. And I get two questions:
Does parameters actually update in
ApplyUpdata?backwardonly calculate the gradient but nothing is done to parameters?What's the meaning of
this->iter_? TakingGANtraining as an example, how should I deal withthis->iter_if I want to train generator three time while discriminator only trained once, asApplyUpdatewill increasethis->iter_three times for generator but only once for discriminator.
THX!
void SGDSolver<Dtype>::ApplyUpdate()
Dtype rate = GetLearningRate();
if (this->param_.display() && this->iter_ % this->param_.display() == 0)
LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << this->iter_
<< ", lr = " << rate;
ClipGradients();
for (int param_id = 0; param_id < this->net_->learnable_params().size();
++param_id)
Normalize(param_id);
Regularize(param_id);
ComputeUpdateValue(param_id, rate);
this->net_->Update();
// Increment the internal iter_ counter -- its value should always indicate
// the number of times the weights have been updated.
++this->iter_;
caffe pycaffe
asked Nov 14 '18 at 9:25
inkzkinkzk
2261318
ApplyUpdata in caffe shows below. And I get two questions:
Does parameters actually update in
ApplyUpdata?backwardonly calculate the gradient but nothing is done to parameters?What's the meaning of
this->iter_? TakingGANtraining as an example, how should I deal withthis->iter_if I want to train generator three time while discriminator only trained once, asApplyUpdatewill increasethis->iter_three times for generator but only once for discriminator.
THX!
void SGDSolver<Dtype>::ApplyUpdate()
Dtype rate = GetLearningRate();
if (this->param_.display() && this->iter_ % this->param_.display() == 0)
LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << this->iter_
<< ", lr = " << rate;
ClipGradients();
for (int param_id = 0; param_id < this->net_->learnable_params().size();
++param_id)
Normalize(param_id);
Regularize(param_id);
ComputeUpdateValue(param_id, rate);
this->net_->Update();
// Increment the internal iter_ counter -- its value should always indicate
// the number of times the weights have been updated.
++this->iter_;
caffe pycaffe
caffe pycaffe
asked Nov 14 '18 at 9:25
inkzkinkzk
2261318
asked Nov 14 '18 at 9:25
inkzkinkzk
2261318
asked Nov 14 '18 at 9:25
inkzkinkzk
2261318
asked Nov 14 '18 at 9:25
inkzkinkzk
2261318
asked Nov 14 '18 at 9:25
inkzkinkzk
2261318
2261318
add a comment |
add a comment |
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