netGradientCheck
================================

.. js:function:: NeuralNetwork.netGradientCheck(batch_Y, exp_work, costs)

    勾配の計算のチェック

    :param float[] batch_Y: 正解の出力
    :param float[] exp_work: 作業用データ
    :param float[] costs: ミニバッチ内のコストの配列

ソース
^^^^^^

.. code-block:: js

            netGradientCheck(batch_Y, exp_work, costs){
                inGradientCheck = true;
    
                var last_layer = this.layers[this.layers.length - 1];
                var last_delta_y = new Float32Array(last_layer.deltaY.dt);
    
                for (var batch_idx = 0; batch_idx < miniBatchSize; batch_idx++){
                    last_layer.deltaY.dt = new Float32Array(last_delta_y.length);
    
                    for(var i = 0; i < last_layer.unitSize; i++){
                        var k = batch_idx * last_layer.unitSize + i;
                        last_layer.deltaY.dt[k] = last_delta_y[k];
                    }
    
                    for (var i = this.layers.length - 1; 1 <= i; i--) {
                        this.layers[i].backpropagation();
                    }
    
                    for(var layer_idx = 0; layer_idx < this.layers.length; layer_idx++){
    
                        console.log("勾配確認 %s", this.layers[layer_idx].constructor.name);
                        this.layers[layer_idx].gradientCheck(batch_Y, exp_work, costs[batch_idx], batch_idx, layer_idx);
                    }
                }
    
                inGradientCheck = false;
            }