cpuForward¶
-
ConvolutionalLayer.
cpuForward
()¶ CPUによる順伝播
ソース¶
cpuForward() {
var prev_layer = this.prevLayer;
var prev_y_dt = prev_layer.y_.dt;
var z_dt = this.z_.dt;
var y_dt = this.y_.dt;
// 出力先
var output_idx = 0;
// バッチ内のデータに対し
for (var batch_idx = 0; batch_idx < miniBatchSize; batch_idx++) {
// すべての特徴マップに対し
for (var channel_idx = 0; channel_idx < this.numChannels; channel_idx++) {
// 出力の行に対し
for (var r1 = 0; r1 < this.numRows; r1++) {
// 出力の列に対し
for (var c1 = 0; c1 < this.numCols; c1++) {
var sum = 0.0;
var weight_idx = channel_idx * prev_layer.numChannels * this.filterSize * this.filterSize;
var prev_y_base = batch_idx * prev_layer.numChannels * prev_layer.numRows * prev_layer.numCols;
// 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++){
// フィルターの行に対し
for (var r2 = 0; r2 < this.filterSize; r2++) {
// フィルターの列に対し
for (var c2 = 0; c2 < this.filterSize; c2++) {
var prev_y_idx = prev_y_base + (r1 + r2) * prev_layer.numCols + (c1 + c2);
sum += prev_y_dt[prev_y_idx] * this.weight.dt[weight_idx];
weight_idx++;
}
}
prev_y_base += prev_layer.numRows * prev_layer.numCols;
}
var z_val = sum + this.bias.dt[channel_idx];
z_dt[output_idx] = z_val;
// 活性化関数
switch(this.activationFunction){
case ActivationFunction.none:
y_dt[output_idx] = z_val;
break;
case ActivationFunction.sigmoid:
y_dt[output_idx] = sigmoid(z_val);
break;
case ActivationFunction.ReLU:
y_dt[output_idx] = (0.0 < z_val ? z_val : 0.0);
break;
}
output_idx++;
}
}
}
}
}