// Copyright (C) 2015 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_DNn_TRAINER_ABSTRACT_H_
#ifdef DLIB_DNn_TRAINER_ABSTRACT_H_
#include "core_abstract.h"
#include "solvers_abstract.h"
#include <vector>
#include <chrono>
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename net_type,
typename solver_type = sgd
>
class dnn_trainer
{
/*!
REQUIREMENTS ON net_type
- net_type is an add_loss_layer object.
REQUIREMENTS ON solver_type
- solver_type is an implementation of the EXAMPLE_SOLVER interface defined
in solvers_abstract.h
WHAT THIS OBJECT REPRESENTS
This object is a tool training a deep neural network. To use it you supply
a neural network type and a solver, then you call train() with your
training data and it will output a new network instance that has hopefully
learned something useful from your training data.
If you are compiling with CUDA then this object will use the GPU that is
currently selected (i.e. the one indicated by cudaGetDevice()) when
dnn_trainer is constructed. It will continue to use that device even if
you later change it by a call to cudaSetDevice().
EXCEPTIONS
If an exception is thrown by any part of the neural network during training
then the exception will be propagated out of the trainer to the user.
Moreover, the trainer instance will be unusable and should be destroyed.
!*/
public:
typedef typename net_type::training_label_type training_label_type;
typedef typename net_type::input_type input_type;
const static size_t num_computational_layers = net_type::num_computational_layers;
dnn_trainer() = delete;
dnn_trainer(const dnn_trainer&) = delete;
dnn_trainer& operator=(const dnn_trainer&) = delete;
dnn_trainer(
net_type& net,
const solver_type& solver = solver_type(),
const std::vector<int>& cuda_extra_devices = {}
);
/*!
requires
- for all valid i:
- 0 <= cuda_extra_devices[i] < dlib::cuda::get_num_devices()
ensures
- &#get_net() == &net
(i.e. The dnn_trainer holds a reference to net, it does not copy it.
Therefore, you must ensure net has a lifetime at least as long as the
dnn_trainer).
- #get_solvers() == a set of solvers that are all initialized with the
provided solver instance.
- #get_max_num_epochs() == 10000
- #get_mini_batch_size() == 128
- #get_learning_rate() == 1e-2
- #get_min_learning_rate() == 1e-5
- #get_iterations_without_progress_threshold() == 2000
- #get_test_iterations_without_progress_threshold() == 500
- #get_learning_rate_shrink_factor() == 0.1
- #get_learning_rate_schedule().size() == 0
- #get_train_one_step_calls() == 0
- #get_test_one_step_calls() == 0
- #get_synchronization_file() == ""
- if (cuda_extra_devices.size() > 0) then
- This object will use multiple graphics cards to run the learning
algorithms. In particular, it will always use whatever device is
currently selected on the calling thread (the device indicated by
cudaGetDevice()). In addition, you can ask to use additional
devices, which you do by putting their device numbers into
cuda_extra_devices.
!*/
net_type& get_net (
);
/*!
ensures
- returns the neural network object used by this trainer. This is the
network that is optimized when you call train() or train_one_step().
Recall that the dnn_trainer doesn't contain the net_type object but
simply holds a reference to an external network which was provided to the
dnn_trainer's constructor.
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
- This function will sync the trainer state to disk if the current state
hasn't already been synced to disk since the last network modification.
!*/
const std::vector<solver_type>& get_solvers (
) const;
/*!
ensures
- returns the solvers used to optimize each layer of the neural network
get_net(). In particular, the first layer's solver is
get_solvers()[0], the second layer's solver is
get_solvers()[1], and so on.
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
!*/
unsigned long get_mini_batch_size (
) const;
/*!
ensures
- During training, we call the network's update() routine over and over
with training data. The number of training samples we give to each call
to update is the "mini-batch size", which is defined by
get_mini_batch_size().
!*/
void set_mini_batch_size (
unsigned long batch_size
);
/*!
requires
- batch_size > 0
ensures
- #get_mini_batch_size() == batch_size
!*/
unsigned long get_max_num_epochs (
) const;
/*!
ensures
- train() will execute at most get_max_num_epochs() iterations over the
training data before returning.
!*/
void set_max_num_epochs (
unsigned long num
);
/*!
requires
- num > 0
ensures
- #get_max_num_epochs() == num
!*/
void set_learning_rate (
double lr
);
/*!
requires
- lr > 0
ensures
- #get_learning_rate() == lr
- #get_learning_rate_schedule().size() == 0
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
!*/
double get_learning_rate(
) const;
/*!
ensures
- During each training step, a solver tells us how to modify the parameters
of each layer in the network. It does this by outputting a step vector
that, when added to the parameters, will hopefully result in improved
network performance. The learning rate is one of the inputs to the
solver and influences the size of this step vector. This function
returns the current learning rate, that is, the learning rate that will
be used during the next training step.
!*/
void set_min_learning_rate (
double lr
);
/*!
requires
- lr > 0
ensures
- #get_min_learning_rate() == lr
- #get_learning_rate_schedule().size() == 0
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
!*/
double get_min_learning_rate (
) const;
/*!
ensures
- During training via this->train(), this object will test if progress is
still being made and if it isn't then it will reduce get_learning_rate()
by setting it to get_learning_rate()*get_learning_rate_shrink_factor().
However, it will not reduce it below get_min_learning_rate(). Once this
minimum learning rate is crossed the training will terminate.
- get_min_learning_rate() doesn't apply if you are using train_one_step().
You can keep calling train_one_step() as many times as you want and the
learning rate will drop infinitely close to 0 if you run long enough.
!*/
template <typename EXP>
void set_learning_rate_schedule (
const matrix_exp<EXP>& schedule
);
/*!
requires
- schedule.size() > 0
- min(schedule) > 0
ensures
- #get_learning_rate_schedule() == reshape_to_column_vector(schedule)
- #get_learning_rate() == schedule(0,0)
- #get_min_learning_rate() == min(schedule)
- #set_learning_rate_shrink_factor() == 1
!*/
const matrix<double,0,1>& get_learning_rate_schedule (
) const;
/*!
ensures
- if (this function returns a non-empty matrix) then
- This trainer will use an explicit learning rate schedule defined by
the learning rate values in get_learning_rate_schedule(). For
example, if get_learning_rate_schedule() returned {0.1, 0.09, 0.08,
0.07, 0.06} then the first training mini-batch would use a learning
rate of 0.1, then the next training mini-batch uses 0.09, and then
0.8, and so on until the end of the schedule is reached.
If you continue to run training after the end of the schedule has
been reached then the learning rate will be fixed to 0.99 times the
final value. So in our example, eventually the learning rate would
be fixed to 0.99*0.06. This allows you to test if we have reached the
end of the schedule by checking if get_learning_rate() >= 0.06.
!*/
unsigned long get_steps_without_progress (
) const;
/*!
ensures
- if (get_learning_rate_shrink_factor() != 1) then
- returns an estimate of how many mini-batches have executed without us
observing a statistically significant decrease in the training error.
- else
- returns 0
!*/
void set_iterations_without_progress_threshold (
unsigned long thresh
);
/*!
ensures
- #get_iterations_without_progress_threshold() == thresh
- #get_learning_rate_schedule().size() == 0
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
!*/
unsigned long get_iterations_without_progress_threshold (
) const;
/*!
ensures
- This object monitors the progress of training and estimates if the
training error is being reduced. It does this by looking at the previous
get_iterations_without_progress_threshold() mini-batch results and
applying the statistical test defined by the running_gradient object to
see if the training error is getting smaller. If it isn't being reduced
then get_learning_rate() is made smaller by a factor of get_learning_rate_shrink_factor().
Therefore, get_iterations_without_progress_threshold() should always be
set to something sensibly large so that this test can be done with
reasonably high confidence. Think of this test as saying "if the loss
hasn't decreased for the previous get_iterations_without_progress_threshold()
then shrink the learning rate".
!*/
void set_learning_rate_shrink_factor (
double shrink
);
/*!
requires
- 0 < shrink && shrink <= 1
ensures
- #get_learning_rate_shrink_factor() == shrink
- #get_learning_rate_schedule().size() == 0
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
!*/
double get_learning_rate_shrink_factor (
) const;
/*!
ensures
- Whenever the training routine thinks it isn't making progress anymore it
will reduce get_learning_rate() by multiplying it by get_learning_rate_shrink_factor().
- You can disable the automatic learning rate reduction by setting
get_learning_rate_shrink_factor() to 1.
!*/
unsigned long long get_train_one_step_calls (
) const;
/*!
ensures
- returns the number of times train_one_step() has been called.
!*/
unsigned long long get_test_one_step_calls (
) const;
/*!
ensures
- returns the number of times test_one_step() has been called.
!*/
void be_verbose (
);
/*!
ensures
- This object will print status messages to standard out so that a
user can observe the progress of the algorithm.
!*/
void be_quiet (
);
/*!
ensures
- This object will not print anything to standard out
!*/
void set_synchronization_file (
const std::string& filename,
std::chrono::seconds time_between_syncs = std::chrono::minutes(15)
);
/*!
ensures
- #get_synchronization_file() == filename
- While training is running, either via train() or repeated calls to
train_one_step(), this object will save its entire state, including the
state of get_net(), to disk in the file named filename every
time_between_syncs seconds.
- If the filename file already exists then the state of this trainer will
be loaded from that file by this call to set_synchronization_file().
This allows you to resume a training session which was previously
interrupted.
- It should be noted that when saving, the trainer will alternate between
saving to a file called filename and another file called filename+"_".
We do this because it's possible that your computer might crash (not
because of dlib, just in general) before the data is safely saved to
disk. This way, you will always have a backup file if the write to disk
gets corrupted or is incomplete. Moreover, when loading, we will always
load from the newest of the two possible files.
!*/
const std::string& get_synchronization_file (
);
/*!
ensures
- Returns the name of the file the dnn_trainer will periodically save it's
state to. If the return value is "" then synchronization is disabled.
!*/
void train (
const std::vector<input_type>& data,
const std::vector<training_label_type>& labels
);
/*!
requires
- data.size() == labels.size()
- data.size() > 0
- net_type uses a supervised loss.
i.e. net_type::training_label_type != no_label_type.
ensures
- Trains a supervised neural network based on the given training data.
The goal of training is to find the network parameters that minimize
get_net().compute_loss(data.begin(), data.end(), labels.begin()).
- The optimizer will run until get_learning_rate() < get_min_learning_rate()
or get_max_num_epochs() training epochs have been executed.
- Each layer in the network will be optimized by its corresponding solver
in get_solvers().
- Each call to train DOES NOT reinitialize the state of get_net() or
get_solvers(). That is, the existing state of the solvers and network is
the starting point for the optimization each time train() is called. In
particular, if you use the set_synchronization_file() method you can
resume an interrupted train() call by simply calling train() again and it
will pick up from the last synchronization point.
- You can obtain the average loss value during the final training epoch by
calling get_average_loss().
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
!*/
void train (
const std::vector<input_type>& data
);
/*!
requires
- data.size() > 0
- net_type uses an unsupervised loss.
i.e. net_type::training_label_type == no_label_type.
ensures
- Trains an unsupervised neural network based on the given training data.
The goal of training is to find the network parameters that minimize
get_net().compute_loss(data.begin(), data.end()).
- The optimizer will run until get_learning_rate() < get_min_learning_rate()
or get_max_num_epochs() training epochs have been executed.
- Each layer in the network will be optimized by its corresponding solver
in get_solvers().
- Each call to train DOES NOT reinitialize the state of get_net() or
get_solvers(). That is, the existing state of the solvers and network is
the starting point for the optimization each time train() is called. In
particular, if you use the set_synchronization_file() method you can
resume an interrupted train() call by simply calling train() again and it
will pick up from the last synchronization point.
- You can obtain the average loss value during the final training epoch by
calling get_average_loss().
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
!*/
void train_one_step (
const std::vector<input_type>& data,
const std::vector<training_label_type>& labels
);
/*!
requires
- data.size() == labels.size()
- data.size() > 0
- net_type uses a supervised loss.
i.e. net_type::training_label_type != no_label_type.
ensures
- Performs one stochastic gradient update step based on the mini-batch of
data and labels supplied to this function. In particular, calling
train_one_step() in a loop is equivalent to calling the train() method
defined above. However, train_one_step() allows you to stream data from
disk into the training process while train() requires you to first load
all the training data into RAM. Otherwise, these training methods are
equivalent.
- You can observe the current average loss value by calling get_average_loss().
- The network training will happen in another thread. Therefore, after
calling this function you should call get_net() before you touch the net
object from the calling thread to ensure no other threads are still
accessing the network.
- #get_train_one_step_calls() == get_train_one_step_calls() + 1.
!*/
template <
typename data_iterator,
typename label_iterator
>
void train_one_step (
data_iterator dbegin,
data_iterator dend,
label_iterator lbegin
);
/*!
requires
- std::advance(lbegin, std::distance(dbegin, dend) - 1) is dereferencable
- std::distance(dbegin, dend) > 0
- net_type uses a supervised loss.
i.e. net_type::training_label_type != no_label_type.
ensures
- Performs one stochastic gradient update step based on the mini-batch of
data and labels supplied to this function. In particular, calling
train_one_step() in a loop is equivalent to calling the train() method
defined above. However, train_one_step() allows you to stream data from
disk into the training process while train() requires you to first load
all the training data into RAM. Otherwise, these training methods are
equivalent.
- You can observe the current average loss value by calling get_average_loss().
- The network training will happen in another thread. Therefore, after
calling this function you should call get_net() before you touch the net
object from the calling thread to ensure no other threads are still
accessing the network.
- #get_train_one_step_calls() == get_train_one_step_calls() + 1.
!*/
void train_one_step (
const std::vector<input_type>& data
);
/*!
requires
- data.size() > 0
- net_type uses an unsupervised loss.
i.e. net_type::training_label_type == no_label_type.
ensures
- Performs one stochastic gradient update step based on the mini-batch of
data supplied to this function. In particular, calling train_one_step()
in a loop is equivalent to calling the train() method defined above.
However, train_one_step() allows you to stream data from disk into the
training process while train() requires you to first load all the
training data into RAM. Otherwise, these training methods are
equivalent.
- You can observe the current average loss value by calling get_average_loss().
- The network training will happen in another thread. Therefore, after
calling this function you should call get_net() before you touch the net
object from the calling thread to ensure no other threads are still
accessing the network.
- #get_train_one_step_calls() == get_train_one_step_calls() + 1.
!*/
template <
typename data_iterator
>
void train_one_step (
data_iterator dbegin,
data_iterator dend
);
/*!
requires
- std::distance(dbegin, dend) > 0
- net_type uses an unsupervised loss.
i.e. net_type::training_label_type == no_label_type.
ensures
- Performs one stochastic gradient update step based on the mini-batch of
data supplied to this function. In particular, calling train_one_step()
in a loop is equivalent to calling the train() method defined above.
However, train_one_step() allows you to stream data from disk into the
training process while train() requires you to first load all the
training data into RAM. Otherwise, these training methods are
equivalent.
- You can observe the current average loss value by calling get_average_loss().
- The network training will happen in another thread. Therefore, after
calling this function you should call get_net() before you touch the net
object from the calling thread to ensure no other threads are still
accessing the network.
- #get_train_one_step_calls() == get_train_one_step_calls() + 1.
!*/
double get_average_loss (
) const;
/*!
ensures
- returns the average loss value observed during previous calls to
train_one_step() or train(). That is, the average output of
net_type::update() during the previous mini-batch updates.
- Note that, if be_verbose() has been called, then this object will
automatically call clear_average_loss() periodically when it logs the
loss to the console.
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
!*/
void clear_average_loss (
);
/*!
ensures
- #get_average_loss() == 0
- get_average_loss() uses a dlib::running_stats object to keep a running
average of the loss values seen during the previous mini-batch updates
applied during training. Calling clear_average_loss() resets the
running_stats object so it forgets about all previous loss values
observed.
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
!*/
// ----------------------
double get_average_test_loss (
) const;
/*!
ensures
- returns the average loss value observed during previous calls to
test_one_step().
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
!*/
void test_one_step (
const std::vector<input_type>& data,
const std::vector<training_label_type>& labels
);
/*!
requires
- data.size() == labels.size()
- data.size() > 0
- net_type uses a supervised loss.
i.e. net_type::training_label_type != no_label_type.
ensures
- Runs the given data through the network and computes and records the loss.
- This call does not modify network parameters. The point of
test_one_step() is two fold, to allow you to observe the accuracy of the
network on hold out data during training, and to allow the trainer to
automatically adjust the learning rate when the test loss stops
improving. It should be noted that you are not required to use
test_one_step() at all, but if you want to do this kind of thing it is
available.
- You can observe the current average loss value by calling get_average_test_loss().
- The computation will happen in another thread. Therefore, after calling
this function you should call get_net() before you touch the net object
from the calling thread to ensure no other threads are still accessing
the network.
- #get_test_one_step_calls() == get_test_one_step_calls() + 1.
!*/
template <
typename data_iterator,
typename label_iterator
>
void test_one_step (
data_iterator dbegin,
data_iterator dend,
label_iterator lbegin
);
/*!
requires
- std::advance(lbegin, std::distance(dbegin, dend) - 1) is dereferencable
- std::distance(dbegin, dend) > 0
- net_type uses a supervised loss.
i.e. net_type::training_label_type != no_label_type.
ensures
- Runs the given data through the network and computes and records the loss.
- This call does not modify network parameters. The point of
test_one_step() is two fold, to allow you to observe the accuracy of the
network on hold out data during training, and to allow the trainer to
automatically adjust the learning rate when the test loss stops
improving. It should be noted that you are not required to use
test_one_step() at all, but if you want to do this kind of thing it is
available.
- You can observe the current average loss value by calling get_average_test_loss().
- The computation will happen in another thread. Therefore, after calling
this function you should call get_net() before you touch the net object
from the calling thread to ensure no other threads are still accessing
the network.
- #get_test_one_step_calls() == get_test_one_step_calls() + 1.
!*/
void test_one_step (
const std::vector<input_type>& data
);
/*!
requires
- data.size() > 0
- net_type uses an unsupervised loss.
i.e. net_type::training_label_type == no_label_type.
ensures
- Runs the given data through the network and computes and records the loss.
- This call does not modify network parameters. The point of
test_one_step() is two fold, to allow you to observe the accuracy of the
network on hold out data during training, and to allow the trainer to
automatically adjust the learning rate when the test loss stops
improving. It should be noted that you are not required to use
test_one_step() at all, but if you want to do this kind of thing it is
available.
- You can observe the current average loss value by calling get_average_test_loss().
- The computation will happen in another thread. Therefore, after calling
this function you should call get_net() before you touch the net object
from the calling thread to ensure no other threads are still accessing
the network.
- #get_test_one_step_calls() == get_test_one_step_calls() + 1.
!*/
template <
typename data_iterator
>
void test_one_step (
data_iterator dbegin,
data_iterator dend
);
/*!
requires
- std::distance(dbegin, dend) > 0
- net_type uses an unsupervised loss.
i.e. net_type::training_label_type == no_label_type.
ensures
- Runs the given data through the network and computes and records the loss.
- This call does not modify network parameters. The point of
test_one_step() is two fold, to allow you to observe the accuracy of the
network on hold out data during training, and to allow the trainer to
automatically adjust the learning rate when the test loss stops
improving. It should be noted that you are not required to use
test_one_step() at all, but if you want to do this kind of thing it is
available.
- You can observe the current average loss value by calling get_average_test_loss().
- The computation will happen in another thread. Therefore, after calling
this function you should call get_net() before you touch the net object
from the calling thread to ensure no other threads are still accessing
the network.
- #get_test_one_step_calls() == get_test_one_step_calls() + 1.
!*/
void set_test_iterations_without_progress_threshold (
unsigned long thresh
);
/*!
ensures
- #get_test_iterations_without_progress_threshold() == thresh
- #get_learning_rate_schedule().size() == 0
- This function blocks until all threads inside the dnn_trainer have
stopped touching the net.
!*/
unsigned long get_test_iterations_without_progress_threshold (
) const;
/*!
ensures
- This object monitors the progress of training and estimates if the
testing error is being reduced. It does this by looking at the previous
get_test_iterations_without_progress_threshold() mini-batch results from
test_one_step() and applying the statistical test defined by the
running_gradient object to see if the testing error is getting smaller.
If it isn't being reduced then get_learning_rate() is made smaller by a
factor of get_learning_rate_shrink_factor().
Therefore, get_test_iterations_without_progress_threshold() should always be
set to something sensibly large so that this test can be done with
reasonably high confidence. Think of this test as saying "if the testing loss
hasn't decreased for the previous get_test_iterations_without_progress_threshold()
calls to test_one_step() then shrink the learning rate".
!*/
unsigned long get_test_steps_without_progress (
) const;
/*!
ensures
- if (get_learning_rate_shrink_factor() != 1) then
- returns an estimate of how many mini-batches have executed without us
observing a statistically significant decrease in the testing error
(i.e. the error on the data given to the trainer via test_one_step()
calls).
- else
- returns 0
!*/
};
// ----------------------------------------------------------------------------------------
template <
typename net_type,
typename solver_type
>
std::ostream& operator<< (
std::ostream& out,
dnn_trainer<net_type,solver_type>& trainer
);
/*!
ensures
- Prints a log of the current parameters of trainer to out.
!*/
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_DNn_TRAINER_ABSTRACT_H_