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Made the subnetwork objects passed to layers non-copyable. Also filled out 

more of the layers_abstract.h file.
This commit is contained in:
Davis King 2015-09-28 08:36:32 -04:00
parent f8b1a3de2e
commit f858ada0be
3 changed files with 63 additions and 12 deletions
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9
dlib/dnn/core.h
View File

@ -107,6 +107,9 @@ namespace dlib
!*/
public:
sub_net_wrapper(const sub_net_wrapper&) = delete;
sub_net_wrapper& operator=(const sub_net_wrapper&) = delete;
sub_net_wrapper(T& l_) {}
// Nothing here because in this case T is one of the input layer types
// that doesn't have anything in it.
@ -117,6 +120,9 @@ namespace dlib
{
public:
sub_net_wrapper(const sub_net_wrapper&) = delete;
sub_net_wrapper& operator=(const sub_net_wrapper&) = delete;
typedef T wrapped_type;
const static size_t num_layers = T::num_layers;
@ -501,6 +507,9 @@ namespace dlib
sub_net_wrapper(const tensor& x_, resizable_tensor& grad_final_ignored_) :
x(x_), grad_final_ignored(grad_final_ignored_) {}
sub_net_wrapper(const sub_net_wrapper&) = delete;
sub_net_wrapper& operator=(const sub_net_wrapper&) = delete;
const tensor& get_output() const { return x; }
tensor& get_gradient_input()
{

12
dlib/dnn/input_abstract.h
View File

@ -23,8 +23,8 @@ namespace dlib
Note that there is no dlib::EXAMPLE_INPUT_LAYER type. It is shown here
purely to document the interface that an input layer object must implement.
If you are using some kind of image or matrix object as your input_type
then you can use the provided dlib::input layer type defined below.
Otherwise, you need to define your own custom input layer.
then you can use the provided dlib::input layer defined below. Otherwise,
you need to define your own custom input layer.
!*/
public:
@ -33,8 +33,8 @@ namespace dlib
/*!
ensures
- Default constructs this object. This function is not required to do
anything in particular but it is required that layer objects be default
constructable.
anything in particular but it must exist, that is, it is required that
layer objects be default constructable.
!*/
EXAMPLE_INPUT_LAYER(
@ -70,10 +70,10 @@ namespace dlib
ensures
- Converts the iterator range into a tensor and stores it into #data.
- #data.num_samples() == distance(ibegin,iend)*sample_expansion_factor.
- Normally you would have #data.num_samples() == distance(ibegin,iend) but
Normally you would have #data.num_samples() == distance(ibegin,iend) but
you can also expand the output by some integer factor so long as the loss
you use can deal with it correctly.
- The data in the ith sample in #data corresponds to
- The data in the ith sample of #data corresponds to the input_type object
*(ibegin+i/sample_expansion_factor).
!*/
};

54
dlib/dnn/layers_abstract.h
View File

@ -16,26 +16,68 @@ namespace dlib
{
/*!
WHAT THIS OBJECT REPRESENTS
This object represents a deep neural network. In particular, it is
the simplified interface through which layer objects interact with their
subnetworks. A layer's two important tasks are to (1) take outputs from its
subnetwork and forward propagate them though itself and (2) to backwards
propagate an error gradient through itself and onto its subnetwork.
The idea of a subnetwork is illustrated in the following diagram:
By "Sub net" we mean the part of the network closer to the input. Whenever
you get a SUB_NET it will always have computed its outputs and they will be
available in get_output().
+---------------------------------------------------------+
| loss <-- layer1 <-- layer2 <-- ... <-- layern <-- input |
+---------------------------------------------------------+
^ ^
\__ subnetwork for layer1 __/
Therefore, by "subnetwork" we mean the part of the network closer to the
input.
!*/
public:
// You aren't allowed to copy subnetworks from inside a layer.
SUB_NET(const SUB_NET&) = delete;
SUB_NET& operator=(const SUB_NET&) = delete;
const tensor& get_output(
) const;
/*!
ensures
- returns the output of this subnetwork. This is the data that the next
layer in the network will take as input.
- have_same_dimensions(#get_gradient_input(), get_output()) == true
!*/
tensor& get_gradient_input(
);
/*!
ensures
- returns the error gradient for this subnetwork. That is, this is the
error gradient that this network will use to update itself. Therefore,
when performing back propagation, layers that sit on top of this
subnetwork write their back propagated error gradients into
get_gradient_input(). Or to put it another way, during back propagation,
layers take the contents of their get_gradient_input() and back propagate
it through themselves and store the results into their subnetwork's
get_gradient_input().
!*/
const NEXT_SUB_NET& sub_net(
) const;
/*!
ensures
- returns the subnetwork of *this network. With respect to the diagram
above, if *this was layer1 then sub_net() would return the network that
begins with layer2.
!*/
NEXT_SUB_NET& sub_net(
);
/*!
ensures
- returns the subnetwork of *this network. With respect to the diagram
above, if *this was layer1 then sub_net() would return the network that
begins with layer2.
!*/
};
// ----------------------------------------------------------------------------------------
@ -64,8 +106,8 @@ namespace dlib
/*!
ensures
- Default constructs this object. This function is not required to do
anything in particular but it is required that layer objects be default
constructable.
anything in particular but it must exist, that is, it is required that
layer objects be default constructable.
!*/
EXAMPLE_LAYER_(
@ -107,7 +149,7 @@ namespace dlib
- SUB_NET implements the SUB_NET interface defined at the top of this file.
- setup() has been called.
ensures
- Runs the output of the sub-network through this layer and stores the
- Runs the output of the subnetwork through this layer and stores the
output into #output. In particular, forward() can use any of the outputs
in sub (e.g. sub.get_output(), sub.sub_net().get_output(), etc.) to
compute whatever it wants.