// Classify data using trained network parameters and count classification errors
public int Classify(ManagedArray test_input, ManagedArray test_output, int classes, int items, int batchsize, ManagedArray classification, bool pool = false)
{
var errors = 0;
var tempx = new ManagedArray(test_input.x, test_input.y, batchsize, false);
var tempy = new ManagedArray(batchsize, classes, false);
var tempclass = new ManagedArray(1, batchsize, false);
ManagedOps.Free(classification);
classification = new ManagedArray(1, items, false);
for (var i = 0; i < items; i += batchsize)
{
// generate batch
ManagedOps.Copy3D(tempx, test_input, 0, 0, i);
ManagedOps.Copy2D(tempy, test_output, i, 0);
// classify
FeedForward(tempx, pool);
// count classifcation errors
errors += Test(tempy, tempclass);
// save classification
ManagedOps.Copy2DOffset(classification, tempclass, i, 0);
}
ManagedOps.Free(tempx, tempy, tempclass);
return(errors);
}
// Combine two arrays column-wise
public static ManagedArray CBind(ManagedArray A, ManagedArray B)
{
if (A.y == B.y)
{
var resultx = A.x + B.x;
var resulty = A.y;
var result = new ManagedArray(resultx, resulty, false);
ManagedOps.Copy2DOffset(result, A, 0, 0);
ManagedOps.Copy2DOffset(result, B, A.x, 0);
return(result);
}
return(null);
}
// Compute Forward Transform on 3D Input
public void FeedForward(ManagedArray batch, bool pool = false)
{
var n = Layers.Count;
var last = n - 1;
var InputMaps = 1;
ManagedOps.Free(Layers[0].Activation);
Layers[0].Activation = new ManagedArray(batch, false);
ManagedOps.Copy4D3D(Layers[0].Activation, batch, 0);
for (var l = 1; l < n; l++)
{
var prev = l - 1;
if (Layers[l].Type == LayerTypes.Convolution)
{
var zx = Layers[prev].Activation.x - Layers[l].KernelSize + 1;
var zy = Layers[prev].Activation.y - Layers[l].KernelSize + 1;
var zz = batch.z;
ManagedOps.Free(Layers[l].Activation);
Layers[l].Activation = new ManagedArray(zx, zy, zz, Layers[l].OutputMaps, 1, false);
var Activation = new ManagedArray(Layers[prev].Activation.x, Layers[prev].Activation.y, batch.z, false);
var FeatureMap = new ManagedArray(Layers[l].KernelSize, Layers[l].KernelSize, false);
// create temp output map
var z = new ManagedArray(zx, zy, zz);
var ztemp = new ManagedArray(zx, zy, zz, false);
// !!below can probably be handled by insane matrix operations
for (var j = 0; j < Layers[l].OutputMaps; j++) // for each output map
{
ManagedOps.Set(z, 0.0);
for (var i = 0; i < InputMaps; i++)
{
// copy Layers
ManagedOps.Copy4D3D(Activation, Layers[prev].Activation, i);
ManagedOps.Copy4DIJ2D(FeatureMap, Layers[l].FeatureMap, i, j);
// convolve with corresponding kernel and add to temp output map
ManagedConvolution.Valid(Activation, FeatureMap, ztemp);
ManagedMatrix.Add(z, ztemp);
}
// add bias, pass through nonlinearity
ManagedMatrix.Add(z, Layers[l].Bias[j]);
var sigm = ManagedMatrix.Sigm(z);
ManagedOps.Copy3D4D(Layers[l].Activation, sigm, j);
ManagedOps.Free(sigm);
}
ManagedOps.Free(Activation, FeatureMap, z, ztemp);
InputMaps = Layers[l].OutputMaps;
}
else if (Layers[l].Type == LayerTypes.Subsampling)
{
// downsample
// generate downsampling kernel
var scale = (double)(Layers[l].Scale * Layers[l].Scale);
var FeatureMap = new ManagedArray(Layers[l].Scale, Layers[l].Scale, false);
ManagedOps.Set(FeatureMap, 1.0 / scale);
ManagedOps.Free(Layers[l].Activation);
Layers[l].Activation = new ManagedArray(Layers[prev].Activation.x / Layers[l].Scale, Layers[prev].Activation.y / Layers[l].Scale, batch.z, InputMaps, 1);
var Activation = new ManagedArray(Layers[prev].Activation.x, Layers[prev].Activation.y, batch.z, false);
var z = new ManagedArray(Layers[prev].Activation.x - Layers[l].Scale + 1, Layers[prev].Activation.y - Layers[l].Scale + 1, batch.z, false);
for (var j = 0; j < InputMaps; j++)
{
// copy Layers
ManagedOps.Copy4D3D(Activation, Layers[prev].Activation, j);
// Subsample
ManagedConvolution.Valid(Activation, FeatureMap, z);
if (pool)
{
ManagedOps.Pool3D4D(Layers[l].Activation, z, j, Layers[l].Scale);
}
else
{
ManagedOps.Copy3D4D(Layers[l].Activation, z, j, Layers[l].Scale);
}
}
ManagedOps.Free(Activation, FeatureMap, z);
}
}
var MapSize = Layers[last].Activation.x * Layers[last].Activation.y;
ManagedOps.Free(FeatureVector);
FeatureVector = new ManagedArray(batch.z, MapSize * Layers[last].Activation.i);
var temp1D = new ManagedArray(Layers[last].Activation.y, Layers[last].Activation.x, false);
var temp2D = new ManagedArray(Layers[last].Activation.x, Layers[last].Activation.y, false);
// concatenate all end layer feature maps into vector
for (var j = 0; j < Layers[last].Activation.i; j++)
{
for (var ii = 0; ii < batch.z; ii++)
{
// Use Row-major in flattening the feature map
ManagedOps.Copy4D2D(temp2D, Layers[last].Activation, ii, j);
ManagedMatrix.Transpose(temp1D, temp2D);
temp1D.Reshape(1, MapSize);
ManagedOps.Copy2DOffset(FeatureVector, temp1D, ii, j * MapSize);
}
}
var WeightsFeatureVector = new ManagedArray(FeatureVector.x, Weights.y, false);
ManagedMatrix.Multiply(WeightsFeatureVector, Weights, FeatureVector);
var repmat = new ManagedArray(batch.z, Bias.Length(), false);
ManagedMatrix.Expand(Bias, batch.z, 1, repmat);
ManagedMatrix.Add(WeightsFeatureVector, repmat);
// feedforward into output perceptrons
ManagedOps.Free(Output);
Output = ManagedMatrix.Sigm(WeightsFeatureVector);
ManagedOps.Free(WeightsFeatureVector, repmat, temp1D, temp2D);
}
