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Commit ae4677bd authored by Davis King's avatar Davis King
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Moved dnn_trainer into its own file.

parent 37278e99
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dlib/dnn.h
View file @ ae4677bd
...@@ -9,6 +9,7 @@ ...@@ -9,6 +9,7 @@
#include "dnn/loss.h" #include "dnn/loss.h"
#include "dnn/core.h" #include "dnn/core.h"
#include "dnn/solvers.h" #include "dnn/solvers.h"
#include "dnn/trainer.h"
#endif // DLIB_DNn_ #endif // DLIB_DNn_
......
dlib/dnn/core.h
View file @ ae4677bd
...@@ -5,12 +5,12 @@ ...@@ -5,12 +5,12 @@
#include "core_abstract.h" #include "core_abstract.h"
#include "tensor.h" #include "tensor.h"
#include "solvers.h"
#include <iterator> #include <iterator>
#include <memory> #include <memory>
#include <type_traits> #include <type_traits>
#include "../statistics.h" #include "../statistics.h"
#include "../rand.h" #include "../rand.h"
#include "../algs.h"
#include <utility> #include <utility>
...@@ -1402,216 +1402,6 @@ namespace dlib ...@@ -1402,216 +1402,6 @@ namespace dlib
} }
} }
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
template <
typename net_type,
typename solver_type = sgd
>
class dnn_trainer
{
public:
static_assert(is_loss_layer_type<net_type>::value,
"The last layer in a network must be a loss layer.");
typedef typename net_type::label_type label_type;
typedef typename net_type::input_type input_type;
dnn_trainer(
)
{
init();
}
explicit dnn_trainer(const net_type& net_) : net(net_)
{
init();
}
dnn_trainer(
const net_type& net_,
const solver_type& solver_
) : net(net_), solvers(solver_)
{
init();
}
const net_type& get_net (
) const { return net; }
void set_net (
const net_type& net_
)
{
return net = net_;
}
void set_solver (
const solver_type& solver_
)
{
solvers = solver_;
}
unsigned long get_mini_batch_size (
) const { return mini_batch_size; }
void set_mini_batch_size (
unsigned long batch_size
)
{
DLIB_CASSERT(batch_size > 0,"");
mini_batch_size = batch_size;
}
unsigned long get_num_epochs (
) const { return num_epochs; }
void set_num_epochs (
unsigned long num
) const
{
DLIB_CASSERT(num > 0,"");
num_epochs = num;
}
const sstack<solver_type,net_type::num_layers>& get_solvers (
) const { return solvers; }
sstack<solver_type,net_type::num_layers>& get_solvers (
) { return solvers; }
const net_type& train (
const std::vector<input_type>& data,
const std::vector<label_type>& labels
)
{
DLIB_CASSERT(data.size() == labels.size() && data.size() > 0, "");
resizable_tensor t1, t2;
for (unsigned long epoch_iteration = 0; epoch_iteration < num_epochs; ++epoch_iteration)
{
unsigned long j = 0;
// Load two tensors worth of data at once so we can overlap the computation
// and data transfer between the host and the device.
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t1);
j += mini_batch_size;
}
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t2);
j += mini_batch_size;
}
unsigned long i = 0;
while (i < data.size())
{
net.update(t1, labels.begin()+i, solvers);
i += mini_batch_size;
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t1);
j += mini_batch_size;
}
if (i < data.size())
{
net.update(t2, labels.begin()+i, solvers);
i += mini_batch_size;
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t2);
j += mini_batch_size;
}
}
}
}
return net;
}
const net_type& train (
const std::vector<input_type>& data
)
{
DLIB_CASSERT(data.size() > 0, "");
const bool has_unsupervised_loss = std::is_same<no_label_type, label_type>::value;
static_assert(has_unsupervised_loss,
"You can only call this version of train() when using an unsupervised loss.");
resizable_tensor t1, t2;
for (unsigned long epoch_iteration = 0; epoch_iteration < num_epochs; ++epoch_iteration)
{
unsigned long j = 0;
// Load two tensors worth of data at once so we can overlap the computation
// and data transfer between the host and the device.
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t1);
j += mini_batch_size;
}
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t2);
j += mini_batch_size;
}
unsigned long i = 0;
while (i < data.size())
{
net.update(t1, solvers);
i += mini_batch_size;
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t1);
j += mini_batch_size;
}
if (i < data.size())
{
net.update(t2, solvers);
i += mini_batch_size;
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t2);
j += mini_batch_size;
}
}
}
}
return net;
}
private:
void init()
{
num_epochs = 300;
mini_batch_size = 11;
}
unsigned long num_epochs;
unsigned long mini_batch_size;
net_type net;
sstack<solver_type,net_type::num_layers> solvers;
};
// TODO, make dnn_trainer serializable.
// ---------------------------------------------------------------------------------------- // ----------------------------------------------------------------------------------------
} }
......
dlib/dnn/core_abstract.h
View file @ ae4677bd
...@@ -4,7 +4,6 @@ ...@@ -4,7 +4,6 @@
#ifdef DLIB_DNn_CORE_ABSTRACT_H_ #ifdef DLIB_DNn_CORE_ABSTRACT_H_
#include "tensor_abstract.h" #include "tensor_abstract.h"
#include "solvers_abstract.h"
#include <memory> #include <memory>
#include <type_traits> #include <type_traits>
#include "../rand.h" #include "../rand.h"
...@@ -919,101 +918,9 @@ namespace dlib ...@@ -919,101 +918,9 @@ namespace dlib
gradients and compares them to the outputs of the layer. gradients and compares them to the outputs of the layer.
!*/ !*/
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
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
!*/
public:
typedef typename net_type::label_type label_type;
typedef typename net_type::input_type input_type;
dnn_trainer(
);
explicit dnn_trainer(
const net_type& net
);
dnn_trainer(
const net_type& net,
const solver_type& solver
);
const net_type& get_net (
) const;
void set_net (
const net_type& net
);
void set_solver (
const solver_type& solver_
);
const sstack<solver_type,net_type::num_layers>& get_solvers (
) const;
sstack<solver_type,net_type::num_layers>& get_solvers (
);
unsigned long get_mini_batch_size (
) const;
void set_mini_batch_size (
unsigned long batch_size
);
unsigned long get_num_epochs (
) const;
void set_num_epochs (
unsigned long num
) const;
const net_type& train (
const std::vector<input_type>& data,
const std::vector<label_type>& labels
);
/*!
requires
- data.size() == labels.size()
- TODO: the net has a supervised loss layer.
!*/
const net_type& train (
const std::vector<input_type>& data
);
/*!
requires
- TODO: the net has an unsupervised loss layer.
ensures
- trains an auto-encoder
!*/
};
// ---------------------------------------------------------------------------------------- // ----------------------------------------------------------------------------------------
} }
#endif // DLIB_DNn_CORE_ABSTRACT_H_ DLIB_DNn_CORE_H_ #endif // DLIB_DNn_CORE_ABSTRACT_H_
dlib/dnn/trainer.h 0 → 100644
View file @ ae4677bd
// Copyright (C) 2015 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_DNn_TRAINER_H_
#define DLIB_DNn_TRAINER_H_
#include "trainer_abstract.h"
#include "core.h"
#include "solvers.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename net_type,
typename solver_type = sgd
>
class dnn_trainer
{
public:
static_assert(is_loss_layer_type<net_type>::value,
"The last layer in a network must be a loss layer.");
typedef typename net_type::label_type label_type;
typedef typename net_type::input_type input_type;
dnn_trainer(
)
{
init();
}
explicit dnn_trainer(const net_type& net_) : net(net_)
{
init();
}
dnn_trainer(
const net_type& net_,
const solver_type& solver_
) : net(net_), solvers(solver_)
{
init();
}
const net_type& get_net (
) const { return net; }
void set_net (
const net_type& net_
)
{
return net = net_;
}
void set_solver (
const solver_type& solver_
)
{
solvers = solver_;
}
unsigned long get_mini_batch_size (
) const { return mini_batch_size; }
void set_mini_batch_size (
unsigned long batch_size
)
{
DLIB_CASSERT(batch_size > 0,"");
mini_batch_size = batch_size;
}
unsigned long get_num_epochs (
) const { return num_epochs; }
void set_num_epochs (
unsigned long num
) const
{
DLIB_CASSERT(num > 0,"");
num_epochs = num;
}
const sstack<solver_type,net_type::num_layers>& get_solvers (
) const { return solvers; }
sstack<solver_type,net_type::num_layers>& get_solvers (
) { return solvers; }
const net_type& train (
const std::vector<input_type>& data,
const std::vector<label_type>& labels
)
{
DLIB_CASSERT(data.size() == labels.size() && data.size() > 0, "");
resizable_tensor t1, t2;
for (unsigned long epoch_iteration = 0; epoch_iteration < num_epochs; ++epoch_iteration)
{
unsigned long j = 0;
// Load two tensors worth of data at once so we can overlap the computation
// and data transfer between the host and the device.
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t1);
j += mini_batch_size;
}
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t2);
j += mini_batch_size;
}
unsigned long i = 0;
while (i < data.size())
{
net.update(t1, labels.begin()+i, solvers);
i += mini_batch_size;
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t1);
j += mini_batch_size;
}
if (i < data.size())
{
net.update(t2, labels.begin()+i, solvers);
i += mini_batch_size;
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t2);
j += mini_batch_size;
}
}
}
}
return net;
}
const net_type& train (
const std::vector<input_type>& data
)
{
DLIB_CASSERT(data.size() > 0, "");
const bool has_unsupervised_loss = std::is_same<no_label_type, label_type>::value;
static_assert(has_unsupervised_loss,
"You can only call this version of train() when using an unsupervised loss.");
resizable_tensor t1, t2;
for (unsigned long epoch_iteration = 0; epoch_iteration < num_epochs; ++epoch_iteration)
{
unsigned long j = 0;
// Load two tensors worth of data at once so we can overlap the computation
// and data transfer between the host and the device.
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t1);
j += mini_batch_size;
}
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t2);
j += mini_batch_size;
}
unsigned long i = 0;
while (i < data.size())
{
net.update(t1, solvers);
i += mini_batch_size;
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t1);
j += mini_batch_size;
}
if (i < data.size())
{
net.update(t2, solvers);
i += mini_batch_size;
if (j < data.size())
{
net.to_tensor(data.begin()+j, data.begin()+std::min(j+mini_batch_size,data.size()), t2);
j += mini_batch_size;
}
}
}
}
return net;
}
private:
void init()
{
num_epochs = 300;
mini_batch_size = 11;
}
unsigned long num_epochs;
unsigned long mini_batch_size;
net_type net;
sstack<solver_type,net_type::num_layers> solvers;
};
// TODO, make dnn_trainer serializable.
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_DNn_TRAINER_H_
dlib/dnn/trainer_abstract.h 0 → 100644
View file @ ae4677bd
// 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>
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
!*/
public:
typedef typename net_type::label_type label_type;
typedef typename net_type::input_type input_type;
dnn_trainer(
);
explicit dnn_trainer(
const net_type& net
);
dnn_trainer(
const net_type& net,
const solver_type& solver
);
const net_type& get_net (
) const;
void set_net (
const net_type& net
);
void set_solver (
const solver_type& solver_
);
const sstack<solver_type,net_type::num_layers>& get_solvers (
) const;
sstack<solver_type,net_type::num_layers>& get_solvers (
);
unsigned long get_mini_batch_size (
) const;
void set_mini_batch_size (
unsigned long batch_size
);
unsigned long get_num_epochs (
) const;
void set_num_epochs (
unsigned long num
) const;
const net_type& train (
const std::vector<input_type>& data,
const std::vector<label_type>& labels
);
/*!
requires
- data.size() == labels.size()
- TODO: the net has a supervised loss layer.
!*/
const net_type& train (
const std::vector<input_type>& data
);
/*!
requires
- TODO: the net has an unsupervised loss layer.
ensures
- trains an auto-encoder
!*/
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_DNn_TRAINER_ABSTRACT_H_
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