cpuForward
====================

.. js:function:: ConvolutionalLayer.cpuForward() 

    CPUによる順伝播

ソース
^^^^^^

.. code-block:: js

            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++;
                            }
    
                        }
                    }
                }
            }