cpuDeltaX¶
-
ConvolutionalLayer.
cpuDeltaX
()¶ CPUによるδxの計算
ソース¶
cpuDeltaX() {
var prev_layer = this.prevLayer;
var num_rows_cols = this.numRows * this.numCols;
var prev_y_idx = 0;
// バッチ内のデータに対し
for (var batch_idx = 0; batch_idx < miniBatchSize; batch_idx++) {
// 入力のチャネルに対し
for(var prev_channel_idx = 0; prev_channel_idx < prev_layer.numChannels; prev_channel_idx++){
// 入力の行に対し
for (var r3 = 0; r3 < prev_layer.numRows; r3++) {
// 入力の列に対し
for (var c3 = 0; c3 < prev_layer.numCols; c3++) {
var sum = 0.0;
// 出力のチャネルに対し
for(var channel_idx = 0; channel_idx < this.numChannels; channel_idx++){
var delta_z_base = batch_idx * this.unitSize + channel_idx * num_rows_cols;
var weight_base = (channel_idx * prev_layer.numChannels + prev_channel_idx) * this.filterSize * this.filterSize;
// フィルターの行に対し
for (var r2 = 0; r2 < this.filterSize; r2++) {
// 出力の行
var r1 = r3 - r2;
if(0 <= r1 && r1 < this.numRows){
// フィルターの列に対し
for (var c2 = 0; c2 < this.filterSize; c2++) {
// 出力の列
var c1 = c3 - c2;
if(0 <= c1 && c1 < this.numCols){
var delta_z_idx = delta_z_base + r1 * this.numCols + c1;
var weight_idx = weight_base + r2 * this.filterSize + c2;
sum += this.deltaZ.dt[delta_z_idx] * this.weight.dt[weight_idx];
}
}
}
}
}
this.deltaX.dt[prev_y_idx] = sum;
prev_y_idx++;
}
}
}
}
Assert(prev_y_idx == miniBatchSize * prev_layer.unitSize);
}