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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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@ -1791,7 +1791,7 @@ namespace dlib
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}
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template <typename net_type>
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void disable_duplicative_bias (
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void disable_duplicative_biases (
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net_type& net
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)
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{
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@ -1810,7 +1810,7 @@ namespace dlib
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// ----------------------------------------------------------------------------------------
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template <typename net_type>
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void disable_duplicative_bias (
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void disable_duplicative_biases (
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const net_type& net
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);
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/*!
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@ -3918,7 +3918,7 @@ namespace
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relu<bn_con<conp<4 * growth_rate, 1, 1,
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relu<bn_con<tag1<SUBNET>>>>>>>>>;
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template <typename SUBNET> using dense_layer_32 = dense_layer<32, 8, SUBNET>;
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void test_disable_duplicative_bias()
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void test_disable_duplicative_biases()
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{
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using net_type = fc<10, relu<layer_norm<fc<15, relu<bn_fc<fc<20,
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relu<layer_norm<conp<32, 3, 1,
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@ -3934,7 +3934,7 @@ namespace
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DLIB_TEST(layer<21>(net).layer_details().bias_is_disabled() == false);
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DLIB_TEST(layer<24>(net).layer_details().bias_is_disabled() == false);
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DLIB_TEST(layer<31>(net).layer_details().bias_is_disabled() == false);
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disable_duplicative_bias(net);
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disable_duplicative_biases(net);
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DLIB_TEST(layer<0>(net).layer_details().bias_is_disabled() == false);
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DLIB_TEST(layer<3>(net).layer_details().bias_is_disabled() == true);
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DLIB_TEST(layer<6>(net).layer_details().bias_is_disabled() == true);
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@ -4130,7 +4130,7 @@ namespace
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test_loss_multimulticlass_log();
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test_loss_mmod();
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test_layers_scale_and_scale_prev();
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test_disable_duplicative_bias();
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test_disable_duplicative_biases();
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}
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void perform_test()
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@ -134,8 +134,8 @@ int main(int argc, char** argv) try
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// setup all leaky_relu_ layers in the discriminator to have alpha = 0.2
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visit_computational_layers(discriminator, [](leaky_relu_& l){ l = leaky_relu_(0.2); });
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// Remove the bias learning from all bn_ inputs in both networks
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disable_duplicative_bias(generator);
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disable_duplicative_bias(discriminator);
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disable_duplicative_biases(generator);
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disable_duplicative_biases(discriminator);
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// Forward random noise so that we see the tensor size at each layer
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discriminator(generate_image(generator, make_noise(rnd)));
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cout << "generator (" << count_parameters(generator) << " parameters)" << endl;
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