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Commit de365031 authored by Davis King's avatar Davis King
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renamed a variable

parent 463e1ce0
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Showing with 47 additions and 48 deletions
dlib/dnn/core.h
View file @ de365031
......@@ -126,17 +126,17 @@ namespace dlib
typedef T wrapped_type;
const static size_t num_layers = T::num_layers;
subnet_wrapper(T& l_) : l(l_),sub(l.subnet()) {}
subnet_wrapper(T& l_) : l(l_),subnetwork(l.subnet()) {}
const tensor& get_output() const { return l.get_output(); }
tensor& get_gradient_input() { return l.get_gradient_input(); }
const subnet_wrapper<typename T::subnet_type>& subnet() const { sub; }
subnet_wrapper<typename T::subnet_type>& subnet() { sub; }
const subnet_wrapper<typename T::subnet_type>& subnet() const { subnetwork; }
subnet_wrapper<typename T::subnet_type>& subnet() { subnetwork; }
private:
T& l;
subnet_wrapper<typename T::subnet_type> sub;
subnet_wrapper<typename T::subnet_type> subnetwork;
};
}
......@@ -773,7 +773,7 @@ namespace dlib
const add_loss_layer<T,U>& item
) :
loss(item.loss_details()),
sub(item.subnet())
subnetwork(item.subnet())
{}
template <typename ...T>
......@@ -782,7 +782,7 @@ namespace dlib
T&& ...args
) :
loss(layer_det),
sub(std::forward<T>(args)...)
subnetwork(std::forward<T>(args)...)
{
}
......@@ -792,7 +792,7 @@ namespace dlib
T&& ...args
) :
loss(std::move(layer_det)),
sub(std::forward<T>(args)...)
subnetwork(std::forward<T>(args)...)
{
}
......@@ -800,9 +800,8 @@ namespace dlib
add_loss_layer(
T ...args
) :
sub(std::move(args)...)
subnetwork(std::move(args)...)
{
// TODO, rename sub to subnetwork
}
template <typename input_iterator>
......@@ -812,7 +811,7 @@ namespace dlib
resizable_tensor& data
) const
{
sub.to_tensor(ibegin,iend,data);
subnetwork.to_tensor(ibegin,iend,data);
}
template <typename output_iterator>
......@@ -821,8 +820,8 @@ namespace dlib
output_iterator obegin
)
{
sub.forward(x);
const dimpl::subnet_wrapper<subnet_type> wsub(sub);
subnetwork.forward(x);
const dimpl::subnet_wrapper<subnet_type> wsub(subnetwork);
loss.to_label(wsub, obegin);
}
......@@ -849,8 +848,8 @@ namespace dlib
label_iterator lbegin
)
{
sub.forward(x);
dimpl::subnet_wrapper<subnet_type> wsub(sub);
subnetwork.forward(x);
dimpl::subnet_wrapper<subnet_type> wsub(subnetwork);
return loss.compute_loss(x, lbegin, wsub);
}
......@@ -869,8 +868,8 @@ namespace dlib
const tensor& x
)
{
sub.forward(x);
dimpl::subnet_wrapper<subnet_type> wsub(sub);
subnetwork.forward(x);
dimpl::subnet_wrapper<subnet_type> wsub(subnetwork);
return loss.compute_loss(x, wsub);
}
......@@ -891,10 +890,10 @@ namespace dlib
sstack<solver_type,num_layers>& solvers
)
{
sub.forward(x);
dimpl::subnet_wrapper<subnet_type> wsub(sub);
subnetwork.forward(x);
dimpl::subnet_wrapper<subnet_type> wsub(subnetwork);
double l = loss.compute_loss(x, lbegin, wsub);
sub.update(x, solvers);
subnetwork.update(x, solvers);
return l;
}
......@@ -916,10 +915,10 @@ namespace dlib
sstack<solver_type,num_layers>& solvers
)
{
sub.forward(x);
dimpl::subnet_wrapper<subnet_type> wsub(sub);
subnetwork.forward(x);
dimpl::subnet_wrapper<subnet_type> wsub(subnetwork);
double l = loss.compute_loss(x, wsub);
sub.update(x, solvers);
subnetwork.update(x, solvers);
return l;
}
......@@ -934,8 +933,8 @@ namespace dlib
return update(temp_tensor, solvers);
}
const subnet_type& subnet() const { return sub; }
subnet_type& subnet() { return sub; }
const subnet_type& subnet() const { return subnetwork; }
subnet_type& subnet() { return subnetwork; }
const loss_details_type& loss_details() const { return loss; }
loss_details_type& loss_details() { return loss; }
......@@ -943,13 +942,13 @@ namespace dlib
)
{
temp_tensor.clear();
sub.clear();
subnetwork.clear();
}
private:
loss_details_type loss;
subnet_type sub;
subnet_type subnetwork;
// These two objects don't logically contribute to the state of this object. They
// are here to prevent them from being reallocated over and over.
......@@ -1219,17 +1218,17 @@ namespace dlib
const tensor& get_output() const { return output; }
const test_layer_subnet& subnet() const { init_sub(); return *sub; }
const test_layer_subnet& subnet() const { init_sub(); return *subnetwork; }
tensor& get_gradient_input() { return gradient_input; }
test_layer_subnet& subnet() { init_sub(); return *sub; }
test_layer_subnet& subnet() { init_sub(); return *subnetwork; }
unsigned long count_outputs() const
{
if (sub)
return sub->count_outputs() + output.size();
if (subnetwork)
return subnetwork->count_outputs() + output.size();
else
return output.size();
}
......@@ -1256,12 +1255,12 @@ namespace dlib
// subnet()
void init_sub() const
{
if (!sub)
sub.reset(new test_layer_subnet(rnd));
if (!subnetwork)
subnetwork.reset(new test_layer_subnet(rnd));
}
dlib::rand& rnd;
mutable std::unique_ptr<test_layer_subnet> sub;
mutable std::unique_ptr<test_layer_subnet> subnetwork;
resizable_tensor output;
resizable_tensor gradient_input;
};
......@@ -1289,14 +1288,14 @@ namespace dlib
using namespace timpl;
// Do some setup
dlib::rand rnd;
test_layer_subnet sub(rnd);
test_layer_subnet subnetwork(rnd);
resizable_tensor output, out2, out3;
// Run setup() and forward() as well to make sure any calls to subnet() have
// happened before we start assuming we know how many data elements there are
// (since we do a lazy layer creation thing based on calls to subnet() inside
// test_layer_subnet).
l.setup(sub);
l.forward(sub, output);
l.setup(subnetwork);
l.forward(subnetwork, output);
resizable_tensor input_grad;
input_grad.copy_size(output);
......@@ -1307,14 +1306,14 @@ namespace dlib
// parameter and data values is:
std::cout << "f(data,params): " << dot(output, input_grad) << std::endl;
// We are going to save a copy of the sub.get_gradient_input() data before we do
// We are going to save a copy of the subnetwork.get_gradient_input() data before we do
// backpropagation since the backward() function is supposed to *add* to the
// gradients rather than overwrite them. We will use this saved data to check if
// that is the case.
const unsigned long num_data_inputs = sub.count_outputs();
const unsigned long num_data_inputs = subnetwork.count_outputs();
std::vector<float> initial_gradient_input(num_data_inputs);
for (unsigned long i = 0; i < num_data_inputs; ++i)
initial_gradient_input[i] = sub.get_gradient_input_element(i);
initial_gradient_input[i] = subnetwork.get_gradient_input_element(i);
// Now tell the layer to compute all the gradients. In the rest of this function
......@@ -1325,7 +1324,7 @@ namespace dlib
random_noise.copy_size(l.get_layer_params());
randomize_parameters(random_noise, 5, rnd);
params_grad = random_noise;
l.backward(input_grad, sub, params_grad);
l.backward(input_grad, subnetwork, params_grad);
running_stats<double> rs_param, rs_data;
......@@ -1340,9 +1339,9 @@ namespace dlib
eps = base_eps;
const float oldval = l1.get_layer_params().host()[i];
l1.get_layer_params().host()[i] = oldval+eps;
l1.forward(sub, out2);
l1.forward(subnetwork, out2);
l1.get_layer_params().host()[i] = oldval-eps;
l1.forward(sub, out3);
l1.forward(subnetwork, out3);
// Compute a reference derivative via a central differences approximation and
// compare it to the one output by the layer and make sure they match.
......@@ -1364,19 +1363,19 @@ namespace dlib
// now validate the data gradients
for (unsigned long i = 0; i < num_data_inputs; ++i)
{
const float oldval = sub.get_output_element(i);
const float oldval = subnetwork.get_output_element(i);
float eps = oldval*base_eps;
if (eps == 0)
eps = base_eps;
sub.get_output_element(i) = oldval+eps;
l.forward(sub, out2);
sub.get_output_element(i) = oldval-eps;
l.forward(sub, out3);
subnetwork.get_output_element(i) = oldval+eps;
l.forward(subnetwork, out2);
subnetwork.get_output_element(i) = oldval-eps;
l.forward(subnetwork, out3);
// Compute a reference derivative via a central differences approximation and
// compare it to the one output by the layer and make sure they match.
double reference_derivative = (dot(out2,input_grad)-dot(out3, input_grad))/(2*eps);
double output_derivative = sub.get_gradient_input_element(i)-initial_gradient_input[i];
double output_derivative = subnetwork.get_gradient_input_element(i)-initial_gradient_input[i];
double relative_error = (reference_derivative - output_derivative)/(reference_derivative + 1e-100);
if (std::abs(relative_error) > 0.01)
{
......
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