Rename function to disable_duplicative_biases (#2246)

* Rename function to disable_duplicative_biases

* rename also the functions in the tests... oops
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Adrià Arrufat 2020-11-25 12:07:04 +09:00 committed by GitHub
parent b6bf8aefee
commit a7627cbd07
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4 changed files with 7 additions and 7 deletions

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@ -1791,7 +1791,7 @@ namespace dlib
}
template <typename net_type>
void disable_duplicative_bias (
void disable_duplicative_biases (
net_type& net
)
{

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@ -1810,7 +1810,7 @@ namespace dlib
// ----------------------------------------------------------------------------------------
template <typename net_type>
void disable_duplicative_bias (
void disable_duplicative_biases (
const net_type& net
);
/*!

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@ -3918,7 +3918,7 @@ namespace
relu<bn_con<conp<4 * growth_rate, 1, 1,
relu<bn_con<tag1<SUBNET>>>>>>>>>;
template <typename SUBNET> using dense_layer_32 = dense_layer<32, 8, SUBNET>;
void test_disable_duplicative_bias()
void test_disable_duplicative_biases()
{
using net_type = fc<10, relu<layer_norm<fc<15, relu<bn_fc<fc<20,
relu<layer_norm<conp<32, 3, 1,
@ -3934,7 +3934,7 @@ namespace
DLIB_TEST(layer<21>(net).layer_details().bias_is_disabled() == false);
DLIB_TEST(layer<24>(net).layer_details().bias_is_disabled() == false);
DLIB_TEST(layer<31>(net).layer_details().bias_is_disabled() == false);
disable_duplicative_bias(net);
disable_duplicative_biases(net);
DLIB_TEST(layer<0>(net).layer_details().bias_is_disabled() == false);
DLIB_TEST(layer<3>(net).layer_details().bias_is_disabled() == true);
DLIB_TEST(layer<6>(net).layer_details().bias_is_disabled() == true);
@ -4130,7 +4130,7 @@ namespace
test_loss_multimulticlass_log();
test_loss_mmod();
test_layers_scale_and_scale_prev();
test_disable_duplicative_bias();
test_disable_duplicative_biases();
}
void perform_test()

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@ -134,8 +134,8 @@ int main(int argc, char** argv) try
// setup all leaky_relu_ layers in the discriminator to have alpha = 0.2
visit_computational_layers(discriminator, [](leaky_relu_& l){ l = leaky_relu_(0.2); });
// Remove the bias learning from all bn_ inputs in both networks
disable_duplicative_bias(generator);
disable_duplicative_bias(discriminator);
disable_duplicative_biases(generator);
disable_duplicative_biases(discriminator);
// Forward random noise so that we see the tensor size at each layer
discriminator(generate_image(generator, make_noise(rnd)));
cout << "generator (" << count_parameters(generator) << " parameters)" << endl;