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Commit ad40ddd3 authored by Davis King's avatar Davis King
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Made test_layer() a little more robust.

parent cbce85ec
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dlib/dnn/core.h
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......@@ -1896,168 +1896,174 @@ namespace dlib
using namespace timpl;
// Do some setup
dlib::rand 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(subnetwork);
impl::call_layer_forward(l, subnetwork, output);
resizable_tensor input_grad;
input_grad.copy_size(output);
fill_with_gassuan_random_numbers(input_grad, rnd);
std::ostringstream sout;
// The f() we are computing gradients of is this thing. It's value at the current
// parameter and data values is:
//sout << "f(data,params): " << dot(output, input_grad) << std::endl;
// 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 = 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] = subnetwork.get_gradient_input_element(i);
// Now tell the layer to compute all the gradients. In the rest of this function
// we will just be checking that these gradients were computed correctly by
// comparing them to a central differences approximation.
resizable_tensor params_grad;
params_grad.copy_size(l.get_layer_params());
// But first, set the params grad to something crazy so that it's very obvious if
// it doesn't get fully assigned.
params_grad = std::numeric_limits<float>::infinity();
impl::call_layer_backward(l, output, input_grad, subnetwork, params_grad);
static_assert(impl::is_inplace_layer(l, subnetwork) == impl::has_inplace_backward(l, subnetwork),
"Layer not defined correctly. forward and backward methods must either both be in-place or both out-of-place. ");
// Make sure the outputs of forward() and backward() are the same when they are run
// in in-place mode.
if (impl::is_inplace_layer(l, subnetwork))
{
test_layer_subnet subnetwork2(rnd);
layer_details_type ll(l);
ll.setup(subnetwork2);
resizable_tensor ip_out;
impl::call_layer_forward(ll, subnetwork2, ip_out);
impl::call_layer_forward(ll, subnetwork2, subnetwork2.get_mutable_output());
const auto forward_error = max(abs(mat(ip_out) - mat(subnetwork2.get_output())));
if (forward_error > 0.00001)
{
using namespace std;
sout << "This layer is supposed to support in-place computations but the output of forward_inplace()\n";
sout << "changes when invoked in-place vs. out-of-place. The error was: " << forward_error << endl;
return layer_test_results(sout.str());
}
for (int iter = 0; iter < 5; ++iter)
{
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(subnetwork);
impl::call_layer_forward(l, subnetwork, output);
resizable_tensor input_grad;
input_grad.copy_size(output);
fill_with_gassuan_random_numbers(input_grad, rnd);
std::ostringstream sout;
// The f() we are computing gradients of is this thing. It's value at the current
// parameter and data values is:
//sout << "f(data,params): " << dot(output, input_grad) << std::endl;
// 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 = 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] = subnetwork.get_gradient_input_element(i);
// Now tell the layer to compute all the gradients. In the rest of this function
// we will just be checking that these gradients were computed correctly by
// comparing them to a central differences approximation.
resizable_tensor params_grad;
params_grad.copy_size(ll.get_layer_params());
params_grad.copy_size(l.get_layer_params());
// But first, set the params grad to something crazy so that it's very obvious if
// it doesn't get fully assigned.
params_grad = std::numeric_limits<float>::infinity();
impl::call_layer_backward(l, output, input_grad, subnetwork, params_grad);
resizable_tensor input_grad;
input_grad.copy_size(ip_out);
fill_with_gassuan_random_numbers(input_grad, rnd);
resizable_tensor params_grad1, params_grad2, data_grad1, data_grad2;
params_grad1 = params_grad;
params_grad2 = params_grad;
// Now call backward() and make sure it works as well.
subnetwork2.get_gradient_input() = 9999;
impl::call_layer_backward(ll, ip_out, input_grad, subnetwork2, params_grad1);
data_grad1 = subnetwork2.get_gradient_input();
subnetwork2.get_gradient_input() = mat(input_grad);
impl::call_layer_backward(ll, ip_out, subnetwork2.get_gradient_input(), subnetwork2, params_grad2);
data_grad2 = subnetwork2.get_gradient_input();
if (params_grad.size() != 0)
static_assert(impl::is_inplace_layer(l, subnetwork) == impl::has_inplace_backward(l, subnetwork),
"Layer not defined correctly. forward and backward methods must either both be in-place or both out-of-place. ");
// Make sure the outputs of forward() and backward() are the same when they are run
// in in-place mode.
if (impl::is_inplace_layer(l, subnetwork))
{
const auto backward_param_error = max(abs(mat(params_grad1) - mat(params_grad2)));
if (backward_param_error > 0.00001)
test_layer_subnet subnetwork2(rnd);
layer_details_type ll(l);
ll.setup(subnetwork2);
resizable_tensor ip_out;
impl::call_layer_forward(ll, subnetwork2, ip_out);
impl::call_layer_forward(ll, subnetwork2, subnetwork2.get_mutable_output());
const auto forward_error = max(abs(mat(ip_out) - mat(subnetwork2.get_output())));
if (forward_error > 0.00001)
{
using namespace std;
sout << "This layer is supposed to support in-place computations but the output of forward_inplace()\n";
sout << "changes when invoked in-place vs. out-of-place. The error was: " << forward_error << endl;
return layer_test_results(sout.str());
}
resizable_tensor params_grad;
params_grad.copy_size(ll.get_layer_params());
params_grad = std::numeric_limits<float>::infinity();
resizable_tensor input_grad;
input_grad.copy_size(ip_out);
fill_with_gassuan_random_numbers(input_grad, rnd);
resizable_tensor params_grad1, params_grad2, data_grad1, data_grad2;
params_grad1 = params_grad;
params_grad2 = params_grad;
// Now call backward() and make sure it works as well.
subnetwork2.get_gradient_input() = 9999;
impl::call_layer_backward(ll, ip_out, input_grad, subnetwork2, params_grad1);
data_grad1 = subnetwork2.get_gradient_input();
subnetwork2.get_gradient_input() = mat(input_grad);
impl::call_layer_backward(ll, ip_out, subnetwork2.get_gradient_input(), subnetwork2, params_grad2);
data_grad2 = subnetwork2.get_gradient_input();
if (params_grad.size() != 0)
{
const auto backward_param_error = max(abs(mat(params_grad1) - mat(params_grad2)));
if (backward_param_error > 0.00001)
{
using namespace std;
sout << "This layer is supposed to support in-place computations but the output of backward_inplace()\n";
sout << "changes when invoked in-place vs. out-of-place. The error was: " << backward_param_error << endl;
return layer_test_results(sout.str());
}
}
const auto backward_data_error = max(abs(mat(data_grad1) - mat(data_grad2)));
if (backward_data_error > 0.00001)
{
using namespace std;
sout << "This layer is supposed to support in-place computations but the output of backward_inplace()\n";
sout << "changes when invoked in-place vs. out-of-place. The error was: " << backward_param_error << endl;
sout << "changes when invoked in-place vs. out-of-place. The error was: " << backward_data_error << endl;
return layer_test_results(sout.str());
}
}
const auto backward_data_error = max(abs(mat(data_grad1) - mat(data_grad2)));
if (backward_data_error > 0.00001)
{
using namespace std;
sout << "This layer is supposed to support in-place computations but the output of backward_inplace()\n";
sout << "changes when invoked in-place vs. out-of-place. The error was: " << backward_data_error << endl;
return layer_test_results(sout.str());
}
}
// ==================================================================
// first validate the way the parameter gradients are computed
for (unsigned long i = 0; i < params_grad.size(); ++i)
{
layer_details_type l1(l);
float eps = l1.get_layer_params().host()[i]*base_eps;
if (eps == 0)
eps = base_eps;
const float oldval = l1.get_layer_params().host()[i];
l1.get_layer_params().host()[i] = oldval+eps;
impl::call_layer_forward(l1, subnetwork, out2);
l1.get_layer_params().host()[i] = oldval-eps;
impl::call_layer_forward(l1, subnetwork, out3);
l1.get_layer_params().host()[i] = oldval;
// 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 = params_grad.host()[i];
double relative_error = (reference_derivative - output_derivative)/(reference_derivative + 1e-100);
if (std::abs(relative_error) > 0.01)
// ==================================================================
// first validate the way the parameter gradients are computed
for (unsigned long i = 0; i < params_grad.size(); ++i)
{
using namespace std;
sout << "Gradient error in parameter #" << i <<". Relative error: "<< relative_error << endl;
sout << "expected derivative: " << reference_derivative << endl;
sout << "output derivative: " << output_derivative << endl;
return layer_test_results(sout.str());
}
layer_details_type l1(l);
float eps = l1.get_layer_params().host()[i]*base_eps;
if (eps == 0)
eps = base_eps;
const float oldval = l1.get_layer_params().host()[i];
l1.get_layer_params().host()[i] = oldval+eps;
impl::call_layer_forward(l1, subnetwork, out2);
l1.get_layer_params().host()[i] = oldval-eps;
impl::call_layer_forward(l1, subnetwork, out3);
l1.get_layer_params().host()[i] = oldval;
// 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 = params_grad.host()[i];
double relative_error = (reference_derivative - output_derivative)/(reference_derivative + 1e-100);
double absolute_error = (reference_derivative - output_derivative);
if (std::abs(relative_error) > 0.02 && std::abs(absolute_error) > 0.001)
{
using namespace std;
sout << "Gradient error in parameter #" << i <<". Relative error: "<< relative_error << endl;
sout << "expected derivative: " << reference_derivative << endl;
sout << "output derivative: " << output_derivative << endl;
return layer_test_results(sout.str());
}
}
}
// ==================================================================
// now validate the data gradients
for (unsigned long i = 0; i < num_data_inputs; ++i)
{
const float oldval = subnetwork.get_output_element(i);
float eps = oldval*base_eps;
if (eps == 0)
eps = base_eps;
subnetwork.get_output_element(i) = oldval+eps;
impl::call_layer_forward(l, subnetwork, out2);
subnetwork.get_output_element(i) = oldval-eps;
impl::call_layer_forward(l, subnetwork, out3);
subnetwork.get_output_element(i) = oldval;
// 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 = subnetwork.get_gradient_input_element(i);
if (!impl::is_inplace_layer(l,subnetwork))
output_derivative -= initial_gradient_input[i];
double relative_error = (reference_derivative - output_derivative)/(reference_derivative + 1e-100);
if (std::abs(relative_error) > 0.01)
// ==================================================================
// now validate the data gradients
for (unsigned long i = 0; i < num_data_inputs; ++i)
{
using namespace std;
sout << "Gradient error in data variable #" << i <<". Relative error: "<< relative_error << endl;
sout << "expected derivative: " << reference_derivative << endl;
sout << "output derivative: " << output_derivative << endl;
return layer_test_results(sout.str());
const float oldval = subnetwork.get_output_element(i);
float eps = oldval*base_eps;
if (eps == 0)
eps = base_eps;
subnetwork.get_output_element(i) = oldval+eps;
impl::call_layer_forward(l, subnetwork, out2);
subnetwork.get_output_element(i) = oldval-eps;
impl::call_layer_forward(l, subnetwork, out3);
subnetwork.get_output_element(i) = oldval;
// 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 = subnetwork.get_gradient_input_element(i);
if (!impl::is_inplace_layer(l,subnetwork))
output_derivative -= initial_gradient_input[i];
double relative_error = (reference_derivative - output_derivative)/(reference_derivative + 1e-100);
double absolute_error = (reference_derivative - output_derivative);
if (std::abs(relative_error) > 0.02 && std::abs(absolute_error) > 0.001)
{
using namespace std;
sout << "Gradient error in data variable #" << i <<". Relative error: "<< relative_error << endl;
sout << "expected derivative: " << reference_derivative << endl;
sout << "output derivative: " << output_derivative << endl;
return layer_test_results(sout.str());
}
}
}
} // end for (int iter = 0; iter < 5; ++iter)
return layer_test_results();
}
......
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