cpuDeltaX2

ConvolutionalLayer.cpuDeltaX2()

CPUによるδxの計算

ソース

cpuDeltaX2() {
    var prev_layer = this.prevLayer;
    var delta_x = new Float32Array(miniBatchSize * prev_layer.unitSize);

    var prev_y_dt = prev_layer.y_.dt;
    var z_dt = this.z_.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];

                                delta_x[prev_y_idx] += this.deltaZ.dt[output_idx] * this.weight.dt[weight_idx]
                                weight_idx++;
                            }
                        }
                        prev_y_base += prev_layer.numRows * prev_layer.numCols;
                    }

                    output_idx++;
                }
            }
        }
    }

    return delta_x;
}