官术网_书友最值得收藏!

Training a neural network

Training a neural network basically means calibrating all of the weights in a neural network by repeating two key steps: forward-propagation and back-propagation.

In forward-propagation, we apply a set of weights to the input data, pass it through the hidden layer, perform the nonlinear activation on the hidden layer output, and then connect the hidden layer to the output layer by multiplying the hidden layer node values with another set of weights. For the first forward-propagation, the values of the weights are initialized randomly.

In back-propagation, we try to decrease the error by measuring the margin of error of output and then adjust weight accordingly. Neural networks repeat both forward- and back-propagation to predict an output until the weights are calibrated.

主站蜘蛛池模板: 连城县| 会昌县| 左权县| 莆田市| 喀喇沁旗| 古丈县| 万载县| 本溪市| 柘城县| 镇赉县| 新河县| 布拖县| 鹤峰县| 普兰店市| 石景山区| 梁平县| 稷山县| 涪陵区| 饶阳县| 雷州市| 修水县| 潜山县| 高雄市| 江西省| 华亭县| 华宁县| 固阳县| 澳门| 五台县| 安宁市| 榆社县| 洪泽县| 罗山县| 铁力市| 灌云县| 长丰县| 故城县| 水城县| 微山县| 苏尼特左旗| 三明市|