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dlib
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adapt to dlib indentation style

This commit is contained in:
Dennis Francis 2016-11-26 09:07:36 +05:30
parent af76e82633
commit d4da6c53b6
3 changed files with 55 additions and 60 deletions
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108
dlib/dnn/loss.h
View File

@ -1305,70 +1305,70 @@ namespace dlib
typename SUB_TYPE,
typename label_iterator
>
void to_label (
const tensor& input_tensor,
const SUB_TYPE& sub,
label_iterator iter
) const
void to_label (
const tensor& input_tensor,
const SUB_TYPE& sub,
label_iterator iter
) const
{
DLIB_CASSERT(sub.sample_expansion_factor() == 1);
const tensor& output_tensor = sub.get_output();
DLIB_CASSERT(output_tensor.nr() == 1 &&
output_tensor.nc() == 1 &&
output_tensor.k() == 1);
DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
const float* out_data = output_tensor.host();
for (long i = 0; i < output_tensor.num_samples(); ++i)
{
DLIB_CASSERT(sub.sample_expansion_factor() == 1);
const tensor& output_tensor = sub.get_output();
DLIB_CASSERT(output_tensor.nr() == 1 &&
output_tensor.nc() == 1 &&
output_tensor.k() == 1);
DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
const float* out_data = output_tensor.host();
for (long i = 0; i < output_tensor.num_samples(); ++i)
{
*iter++ = out_data[i];
}
*iter++ = out_data[i];
}
}
template <
typename const_label_iterator,
typename SUBNET
>
double compute_loss_value_and_gradient (
const tensor& input_tensor,
const_label_iterator truth,
SUBNET& sub
) const
double compute_loss_value_and_gradient (
const tensor& input_tensor,
const_label_iterator truth,
SUBNET& sub
) const
{
const tensor& output_tensor = sub.get_output();
tensor& grad = sub.get_gradient_input();
DLIB_CASSERT(sub.sample_expansion_factor() == 1);
DLIB_CASSERT(input_tensor.num_samples() != 0);
DLIB_CASSERT(input_tensor.num_samples()%sub.sample_expansion_factor() == 0);
DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples());
DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
DLIB_CASSERT(output_tensor.nr() == 1 &&
output_tensor.nc() == 1 &&
output_tensor.k() == 1);
DLIB_CASSERT(grad.nr() == 1 &&
grad.nc() == 1 &&
grad.k() == 1);
// The loss we output is the average loss over the mini-batch.
const double scale = 1.0/output_tensor.num_samples();
double loss = 0;
float* g = grad.host_write_only();
const float* out_data = output_tensor.host();
for (long i = 0; i < output_tensor.num_samples(); ++i)
{
const tensor& output_tensor = sub.get_output();
tensor& grad = sub.get_gradient_input();
const float y = *truth++;
const float temp1 = y - out_data[i];
const float temp2 = scale*temp1;
loss += 0.5*temp2*temp1;
g[i] = -temp2;
DLIB_CASSERT(sub.sample_expansion_factor() == 1);
DLIB_CASSERT(input_tensor.num_samples() != 0);
DLIB_CASSERT(input_tensor.num_samples()%sub.sample_expansion_factor() == 0);
DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples());
DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
DLIB_CASSERT(output_tensor.nr() == 1 &&
output_tensor.nc() == 1 &&
output_tensor.k() == 1);
DLIB_CASSERT(grad.nr() == 1 &&
grad.nc() == 1 &&
grad.k() == 1);
// The loss we output is the average loss over the mini-batch.
const double scale = 1.0/output_tensor.num_samples();
double loss = 0;
float* g = grad.host_write_only();
const float* out_data = output_tensor.host();
for (long i = 0; i < output_tensor.num_samples(); ++i)
{
const float y = *truth++;
const float temp1 = y - out_data[i];
const float temp2 = scale*temp1;
loss += 0.5*temp2*temp1;
g[i] = -temp2;
}
return loss;
}
return loss;
}
friend void serialize(const loss_mean_squared_& , std::ostream& out)
{
@ -1397,7 +1397,7 @@ namespace dlib
};
template <typename SUBNET>
using loss_mean_squared = add_loss_layer<loss_mean_squared_, SUBNET>;
using loss_mean_squared = add_loss_layer<loss_mean_squared_, SUBNET>;
// ----------------------------------------------------------------------------------------

1
dlib/dnn/loss_abstract.h
View File

@ -584,7 +584,6 @@ namespace dlib
template <typename SUBNET>
using loss_mean_squared = add_loss_layer<loss_mean_squared_, SUBNET>;
}
#endif // DLIB_DNn_LOSS_ABSTRACT_H_

6
dlib/test/dnn.cpp
View File

@ -1758,11 +1758,7 @@ namespace
y[ii] = (true_intercept + true_slope*static_cast<float>(val) + distribution(generator));
}
using net_type = loss_mean_squared<
fc<
1, input<matrix<double>>
>
>;
using net_type = loss_mean_squared<fc<1, input<matrix<double>>>>;
net_type net;
layer<1>(net).layer_details().set_bias_learning_rate_multiplier(300);
sgd defsolver;