<html><!-- Created using the cpp_pretty_printer from the dlib C++ library. See http://dlib.net for updates. --><head><title>dlib C++ Library - trainer_abstract.h</title></head><body bgcolor='white'><pre> <font color='#009900'>// Copyright (C) 2015 Davis E. King (davis@dlib.net) </font><font color='#009900'>// License: Boost Software License See LICENSE.txt for the full license. </font><font color='#0000FF'>#undef</font> DLIB_DNn_TRAINER_ABSTRACT_H_ <font color='#0000FF'>#ifdef</font> DLIB_DNn_TRAINER_ABSTRACT_H_ <font color='#0000FF'>#include</font> "<a style='text-decoration:none' href='core_abstract.h.html'>core_abstract.h</a>" <font color='#0000FF'>#include</font> "<a style='text-decoration:none' href='solvers_abstract.h.html'>solvers_abstract.h</a>" <font color='#0000FF'>#include</font> <font color='#5555FF'><</font>vector<font color='#5555FF'>></font> <font color='#0000FF'>#include</font> <font color='#5555FF'><</font>chrono<font color='#5555FF'>></font> <font color='#0000FF'>namespace</font> dlib <b>{</b> <font color='#009900'>// ---------------------------------------------------------------------------------------- </font> <font color='#0000FF'>enum</font> <font color='#0000FF'>class</font> <b><a name='force_flush_to_disk'></a>force_flush_to_disk</b> <b>{</b> no <font color='#5555FF'>=</font> <font color='#979000'>0</font>, yes <font color='#5555FF'>=</font> <font color='#979000'>1</font> <b>}</b>; <font color='#009900'>// ---------------------------------------------------------------------------------------- </font> <font color='#0000FF'>template</font> <font color='#5555FF'><</font> <font color='#0000FF'>typename</font> net_type, <font color='#0000FF'>typename</font> solver_type <font color='#5555FF'>=</font> sgd <font color='#5555FF'>></font> <font color='#0000FF'>class</font> <b><a name='dnn_trainer'></a>dnn_trainer</b> <b>{</b> <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'>public</font>: <font color='#0000FF'>typedef</font> <font color='#0000FF'>typename</font> net_type::training_label_type training_label_type; <font color='#0000FF'>typedef</font> <font color='#0000FF'>typename</font> net_type::input_type input_type; <font color='#0000FF'>const</font> <font color='#0000FF'>static</font> <font color='#0000FF'><u>size_t</u></font> num_computational_layers <font color='#5555FF'>=</font> net_type::num_computational_layers; <font color='#0000FF'>using</font> threads <font color='#5555FF'>=</font> std::vector<font color='#5555FF'><</font>std::shared_ptr<font color='#5555FF'><</font>thread_pool<font color='#5555FF'>></font><font color='#5555FF'>></font>; <b><a name='dnn_trainer'></a>dnn_trainer</b><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font> <font color='#5555FF'>=</font> <font color='#0000FF'>delete</font>; <b><a name='dnn_trainer'></a>dnn_trainer</b><font face='Lucida Console'>(</font><font color='#0000FF'>const</font> dnn_trainer<font color='#5555FF'>&</font><font face='Lucida Console'>)</font> <font color='#5555FF'>=</font> <font color='#0000FF'>delete</font>; dnn_trainer<font color='#5555FF'>&</font> <b><a name='operator'></a>operator</b><font color='#5555FF'>=</font><font face='Lucida Console'>(</font><font color='#0000FF'>const</font> dnn_trainer<font color='#5555FF'>&</font><font face='Lucida Console'>)</font> <font color='#5555FF'>=</font> <font color='#0000FF'>delete</font>; <b><a name='dnn_trainer'></a>dnn_trainer</b><font face='Lucida Console'>(</font> net_type<font color='#5555FF'>&</font> net, <font color='#0000FF'>const</font> solver_type<font color='#5555FF'>&</font> solver <font color='#5555FF'>=</font> <font color='#BB00BB'>solver_type</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font>, <font color='#0000FF'>const</font> std::vector<font color='#5555FF'><</font><font color='#0000FF'><u>int</u></font><font color='#5555FF'>></font><font color='#5555FF'>&</font> cuda_extra_devices <font color='#5555FF'>=</font> <b>{</b><b>}</b>, std::shared_ptr<font color='#5555FF'><</font>threads<font color='#5555FF'>></font> thread_pools <font color='#5555FF'>=</font> std::shared_ptr<font color='#5555FF'><</font>threads<font color='#5555FF'>></font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font> <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. - if (thread_pools.get() != nullptr) then - Any new threads spun within the trainer will execute within the passed thread pools vector. This means that the same threads can be re-used across different dnn_trainer instances. Otherwise, the CUDA runtime may leak memory. This, however, is relevant only if your program is going to instantiate a large number of trainers, and generally stay up and running for a very long time. If not, then you need not worry about this. NB: Any particular thread pools vector should be passed to max one trainer instance at a time. NB: The mentioned leak isn't happening because dlib is or isn't doing something. Instead, it is a limitation of the CUDA runtime that dlib has no control over. !*/</font> net_type<font color='#5555FF'>&</font> <b><a name='get_net'></a>get_net</b> <font face='Lucida Console'>(</font> force_flush_to_disk force_flush <font color='#5555FF'>=</font> force_flush_to_disk::yes <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. - If force_flush is yes, then 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. !*/</font> <font color='#0000FF'>const</font> std::vector<font color='#5555FF'><</font>solver_type<font color='#5555FF'>></font><font color='#5555FF'>&</font> <b><a name='get_solvers'></a>get_solvers</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> <b><a name='get_mini_batch_size'></a>get_mini_batch_size</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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(). !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='set_mini_batch_size'></a>set_mini_batch_size</b> <font face='Lucida Console'>(</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> batch_size <font face='Lucida Console'>)</font>; <font color='#009900'>/*! requires - batch_size > 0 ensures - #get_mini_batch_size() == batch_size !*/</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> <b><a name='get_max_num_epochs'></a>get_max_num_epochs</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! ensures - train() will execute at most get_max_num_epochs() iterations over the training data before returning. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='set_max_num_epochs'></a>set_max_num_epochs</b> <font face='Lucida Console'>(</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> num <font face='Lucida Console'>)</font>; <font color='#009900'>/*! requires - num > 0 ensures - #get_max_num_epochs() == num !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='set_learning_rate'></a>set_learning_rate</b> <font face='Lucida Console'>(</font> <font color='#0000FF'><u>double</u></font> lr <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>double</u></font> <b><a name='get_learning_rate'></a>get_learning_rate</b><font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='set_min_learning_rate'></a>set_min_learning_rate</b> <font face='Lucida Console'>(</font> <font color='#0000FF'><u>double</u></font> lr <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>double</u></font> <b><a name='get_min_learning_rate'></a>get_min_learning_rate</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'>template</font> <font color='#5555FF'><</font><font color='#0000FF'>typename</font> EXP<font color='#5555FF'>></font> <font color='#0000FF'><u>void</u></font> <b><a name='set_learning_rate_schedule'></a>set_learning_rate_schedule</b> <font face='Lucida Console'>(</font> <font color='#0000FF'>const</font> matrix_exp<font color='#5555FF'><</font>EXP<font color='#5555FF'>></font><font color='#5555FF'>&</font> schedule <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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 !*/</font> <font color='#0000FF'>const</font> matrix<font color='#5555FF'><</font><font color='#0000FF'><u>double</u></font>,<font color='#979000'>0</font>,<font color='#979000'>1</font><font color='#5555FF'>></font><font color='#5555FF'>&</font> <b><a name='get_learning_rate_schedule'></a>get_learning_rate_schedule</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> <b><a name='get_steps_without_progress'></a>get_steps_without_progress</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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 !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='set_iterations_without_progress_threshold'></a>set_iterations_without_progress_threshold</b> <font face='Lucida Console'>(</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> thresh <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> <b><a name='get_iterations_without_progress_threshold'></a>get_iterations_without_progress_threshold</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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". !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='set_learning_rate_shrink_factor'></a>set_learning_rate_shrink_factor</b> <font face='Lucida Console'>(</font> <font color='#0000FF'><u>double</u></font> shrink <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>double</u></font> <b><a name='get_learning_rate_shrink_factor'></a>get_learning_rate_shrink_factor</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> <font color='#0000FF'><u>long</u></font> <b><a name='get_train_one_step_calls'></a>get_train_one_step_calls</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! ensures - returns the number of times train_one_step() has been called. !*/</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> <font color='#0000FF'><u>long</u></font> <b><a name='get_test_one_step_calls'></a>get_test_one_step_calls</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! ensures - returns the number of times test_one_step() has been called. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='be_verbose'></a>be_verbose</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font>; <font color='#009900'>/*! ensures - This object will print status messages to standard out so that a user can observe the progress of the algorithm. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='be_quiet'></a>be_quiet</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font>; <font color='#009900'>/*! ensures - This object will not print anything to standard out !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='set_synchronization_file'></a>set_synchronization_file</b> <font face='Lucida Console'>(</font> <font color='#0000FF'>const</font> std::string<font color='#5555FF'>&</font> filename, std::chrono::seconds time_between_syncs <font color='#5555FF'>=</font> std::chrono::<font color='#BB00BB'>minutes</font><font face='Lucida Console'>(</font><font color='#979000'>15</font><font face='Lucida Console'>)</font> <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'>const</font> std::string<font color='#5555FF'>&</font> <b><a name='get_synchronization_file'></a>get_synchronization_file</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='train'></a>train</b> <font face='Lucida Console'>(</font> <font color='#0000FF'>const</font> std::vector<font color='#5555FF'><</font>input_type<font color='#5555FF'>></font><font color='#5555FF'>&</font> data, <font color='#0000FF'>const</font> std::vector<font color='#5555FF'><</font>training_label_type<font color='#5555FF'>></font><font color='#5555FF'>&</font> labels <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='train'></a>train</b> <font face='Lucida Console'>(</font> <font color='#0000FF'>const</font> std::vector<font color='#5555FF'><</font>input_type<font color='#5555FF'>></font><font color='#5555FF'>&</font> data <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='train_one_step'></a>train_one_step</b> <font face='Lucida Console'>(</font> <font color='#0000FF'>const</font> std::vector<font color='#5555FF'><</font>input_type<font color='#5555FF'>></font><font color='#5555FF'>&</font> data, <font color='#0000FF'>const</font> std::vector<font color='#5555FF'><</font>training_label_type<font color='#5555FF'>></font><font color='#5555FF'>&</font> labels <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'>template</font> <font color='#5555FF'><</font> <font color='#0000FF'>typename</font> data_iterator, <font color='#0000FF'>typename</font> label_iterator <font color='#5555FF'>></font> <font color='#0000FF'><u>void</u></font> <b><a name='train_one_step'></a>train_one_step</b> <font face='Lucida Console'>(</font> data_iterator dbegin, data_iterator dend, label_iterator lbegin <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='train_one_step'></a>train_one_step</b> <font face='Lucida Console'>(</font> <font color='#0000FF'>const</font> std::vector<font color='#5555FF'><</font>input_type<font color='#5555FF'>></font><font color='#5555FF'>&</font> data <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'>template</font> <font color='#5555FF'><</font> <font color='#0000FF'>typename</font> data_iterator <font color='#5555FF'>></font> <font color='#0000FF'><u>void</u></font> <b><a name='train_one_step'></a>train_one_step</b> <font face='Lucida Console'>(</font> data_iterator dbegin, data_iterator dend <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>double</u></font> <b><a name='get_average_loss'></a>get_average_loss</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='clear_average_loss'></a>clear_average_loss</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#009900'>// ---------------------- </font> <font color='#0000FF'><u>double</u></font> <b><a name='get_average_test_loss'></a>get_average_test_loss</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='test_one_step'></a>test_one_step</b> <font face='Lucida Console'>(</font> <font color='#0000FF'>const</font> std::vector<font color='#5555FF'><</font>input_type<font color='#5555FF'>></font><font color='#5555FF'>&</font> data, <font color='#0000FF'>const</font> std::vector<font color='#5555FF'><</font>training_label_type<font color='#5555FF'>></font><font color='#5555FF'>&</font> labels <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'>template</font> <font color='#5555FF'><</font> <font color='#0000FF'>typename</font> data_iterator, <font color='#0000FF'>typename</font> label_iterator <font color='#5555FF'>></font> <font color='#0000FF'><u>void</u></font> <b><a name='test_one_step'></a>test_one_step</b> <font face='Lucida Console'>(</font> data_iterator dbegin, data_iterator dend, label_iterator lbegin <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='test_one_step'></a>test_one_step</b> <font face='Lucida Console'>(</font> <font color='#0000FF'>const</font> std::vector<font color='#5555FF'><</font>input_type<font color='#5555FF'>></font><font color='#5555FF'>&</font> data <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'>template</font> <font color='#5555FF'><</font> <font color='#0000FF'>typename</font> data_iterator <font color='#5555FF'>></font> <font color='#0000FF'><u>void</u></font> <b><a name='test_one_step'></a>test_one_step</b> <font face='Lucida Console'>(</font> data_iterator dbegin, data_iterator dend <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>void</u></font> <b><a name='set_test_iterations_without_progress_threshold'></a>set_test_iterations_without_progress_threshold</b> <font face='Lucida Console'>(</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> thresh <font face='Lucida Console'>)</font>; <font color='#009900'>/*! 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. !*/</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> <b><a name='get_test_iterations_without_progress_threshold'></a>get_test_iterations_without_progress_threshold</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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". !*/</font> <font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> <b><a name='get_test_steps_without_progress'></a>get_test_steps_without_progress</b> <font face='Lucida Console'>(</font> <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>; <font color='#009900'>/*! 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 !*/</font> <b>}</b>; <font color='#009900'>// ---------------------------------------------------------------------------------------- </font> <font color='#0000FF'>template</font> <font color='#5555FF'><</font> <font color='#0000FF'>typename</font> net_type, <font color='#0000FF'>typename</font> solver_type <font color='#5555FF'>></font> std::ostream<font color='#5555FF'>&</font> <b><a name='operator'></a>operator</b><font color='#5555FF'><</font><font color='#5555FF'><</font> <font face='Lucida Console'>(</font> std::ostream<font color='#5555FF'>&</font> out, dnn_trainer<font color='#5555FF'><</font>net_type,solver_type<font color='#5555FF'>></font><font color='#5555FF'>&</font> trainer <font face='Lucida Console'>)</font>; <font color='#009900'>/*! ensures - Prints a log of the current parameters of trainer to out. !*/</font> <font color='#009900'>// ---------------------------------------------------------------------------------------- </font> <b>}</b> <font color='#0000FF'>#endif</font> <font color='#009900'>// DLIB_DNn_TRAINER_ABSTRACT_H_ </font> </pre></body></html>