public static void CheckLayer(ILayer layer, int fanInWidth, int fanInHeight, int fanInDepth,
int batchSize, float epsilon, Random random)
{
var accuracyCondition = 1e-2;
layer.Initialize(fanInWidth, fanInHeight, fanInDepth, batchSize,
Initialization.GlorotUniform, random);
var fanIn = fanInWidth * fanInHeight * fanInDepth;
var fanOut = layer.Width * layer.Height * layer.Depth;
// Forward pass - set input activation in layer
var input = Matrix <float> .Build.Random(batchSize, fanIn, random.Next());
layer.Forward(input);
// Set delta to 1
var delta = Matrix <float> .Build.Dense(batchSize, fanOut, 1.0f);
// Backward pass to calculate gradients
layer.Backward(delta);
// get weights and gradients
var parametersAndGradients = new List <ParametersAndGradients>();
layer.AddParameresAndGradients(parametersAndGradients);
foreach (var parameterAndGradient in parametersAndGradients)
{
var gradients = parameterAndGradient.Gradients;
var parameters = parameterAndGradient.Parameters;
var output1 = Matrix <float> .Build.Dense(batchSize, fanOut);
var output2 = Matrix <float> .Build.Dense(batchSize, fanOut);
for (int i = 0; i < parameters.Length; i++)
{
output1.Clear();
output2.Clear();
var oldValue = parameters[i];
parameters[i] = oldValue + epsilon;
layer.Forward(input).CopyTo(output1);
parameters[i] = oldValue - epsilon;
layer.Forward(input).CopyTo(output2);
parameters[i] = oldValue;
output1.Subtract(output2, output1); // output1 = output1 - output2
var grad = output1.ToRowMajorArray().Select(f => f / (2.0f * epsilon));
var gradient = grad.Sum(); // approximated gradient
var actual = gradients[i];
Assert.AreEqual(gradient, actual, accuracyCondition);
}
}
}
