我在尝试实现MXNet的教程，在这一页，在计算梯度下降时：
def SGD(params, lr):for param in params: param[:] = param - lr * param.grad我注意到当lr<1时，标量与数组的乘法
lr * param.grad变成了与param.grad相同大小的零矩阵
我不知道为什么会这样。有人能帮我理解一下吗？
非常感谢
回答：
我在本地PyCharm中运行了那个笔记本的代码，并附加了一个调试器来尝试重现你看到的情况。
def SGD(params, lr): for param in params: t = lr * param.grad param[:] = param - t当我在上述代码中设置断点并检查t时，调试器显示所有值为零。但当我运行代码时，一切正常。这很奇怪。所以，我修改了代码以打印每次迭代中每个像素的所有梯度之和。
def SGD(params, lr): for param in params: t = lr * param.grad print(nd.sum(param.grad, axis=-1)) param[:] = param - t我得到了这个输出。下面显示了一个迭代的输出：
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -6.23335197e-08 2.91620381e-08 2.32830644e-10 1.11467671e-08 -7.45058060e-09 2.09547579e-09 -3.11993062e-08 3.44589353e-08 -7.45058060e-09 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.73723835e-09 -1.45343968e-08 1.51213424e-08 -4.47034836e-08 -1.19209290e-07 -8.10250640e-08 8.66129994e-08 3.77185643e-08 8.47503543e-08 8.66129994e-08 8.70786607e-08 5.35510480e-09 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.47231149e-09 -4.99075004e-10 -4.99075004e-10 6.05359674e-09 -9.66247171e-09 1.83936208e-08 -7.50416707e-09 1.12872200e-07 9.31513569e-08 2.45261617e-07 -1.86264515e-07 -5.28991222e-07 -2.68220901e-07 -1.22934580e-07 0.00000000e+00 2.08616257e-07 1.89989805e-07 4.33064997e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -2.75546541e-09 2.39708999e-08 -2.04163371e-08 6.70552254e-08 6.14672899e-08 4.40049917e-08 8.94069672e-08 8.94069672e-08 2.98023224e-07 1.19209290e-07 -5.96046448e-08 -2.38418579e-07 -4.76837158e-07 -4.76837158e-07 -2.38418579e-07 1.78813934e-07 4.13507223e-07 2.83122063e-07 3.09199095e-07 1.34285074e-07 6.17840215e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -2.19943086e-09 1.58324838e-08 6.26314431e-08 4.47034836e-08 5.96046448e-08 2.38418579e-07 4.76837158e-07 2.38418579e-07 4.76837158e-07 5.66244125e-07 1.49011612e-07 -4.17232513e-07 -2.38418579e-07 -3.57627869e-07 0.00000000e+00 0.00000000e+00 1.22934580e-07 1.97440386e-07 2.73808837e-07 4.55183908e-08 1.16733858e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 7.33416528e-09 4.35829861e-08 4.36230039e-08 3.72456270e-08 1.67638063e-08 5.96046448e-08 1.49011612e-07 2.38418579e-07 -1.19209290e-07 -2.38418579e-07 -1.78813934e-07 2.38418579e-07 6.25848770e-07 3.57627869e-07 2.98023224e-08 -4.17232513e-07 -3.57627869e-07 -5.96046448e-08 -1.04308128e-07 1.19209290e-07 1.08033419e-07 -6.14672899e-08 -1.40164047e-07 -1.22236088e-07 -1.07334927e-07 0.00000000e+00 0.00000000e+00 0.00000000e+00 -2.17623892e-08 6.13508746e-08 7.71033228e-08 4.65661287e-09 0.00000000e+00 -5.96046448e-08 0.00000000e+00 2.38418579e-07 1.19209290e-07 3.57627869e-07 4.76837158e-07 1.19209290e-07 -2.38418579e-07 -1.78813934e-07 -4.76837158e-07 0.00000000e+00 1.78813934e-07 1.19209290e-07 5.06639481e-07 1.78813934e-07 -1.19209290e-07 -1.19209290e-07 -1.19209290e-07 0.00000000e+00 -1.34168658e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.71118256e-10 2.32830644e-09 2.53785402e-08 -2.83122063e-07 1.19209290e-07 1.19209290e-07 2.98023224e-07 0.00000000e+00 2.38418579e-07 3.20374966e-07 0.00000000e+00 3.57627869e-07 -2.38418579e-07 2.38418579e-07 -9.53674316e-07 -7.15255737e-07 1.19209290e-07 3.57627869e-07 4.17232513e-07 -7.45058060e-08 -5.96046448e-08 -1.19209290e-07 1.49011612e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -2.59024091e-09 -5.21540642e-08 -7.45058060e-09 -1.78813934e-07 5.96046448e-08 1.78813934e-07 1.19209290e-07 4.76837158e-07 2.98023224e-07 -5.96046448e-08 2.38418579e-07 3.57627869e-07 5.96046448e-08 -4.76837158e-07 1.78813934e-07 4.17232513e-07 -1.19209290e-07 2.98023224e-07 5.96046448e-08 2.04890966e-08 -1.30385160e-08 -4.83123586e-09 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 3.23634595e-08 -5.58793545e-08 -1.34110451e-07 -1.49011612e-08 -5.96046448e-08 1.49011612e-07 1.49011612e-08 1.78813934e-07 0.00000000e+00 0.00000000e+00 8.94069672e-08 5.96046448e-08 -1.19209290e-07 -3.57627869e-07 0.00000000e+00 4.76837158e-07 -2.38418579e-07 0.00000000e+00 1.02445483e-07 1.46217644e-07 -1.32713467e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -2.79396772e-08 -1.56462193e-07 -1.93715096e-07 1.82539225e-07 3.68803740e-07 4.32133675e-07 5.96046448e-08 -1.19209290e-07 0.00000000e+00 2.98023224e-07 -1.19209290e-07 -5.06639481e-07 1.19209290e-07 4.76837158e-07 8.94069672e-08 0.00000000e+00 0.00000000e+00 5.96046448e-08 8.61473382e-08 4.39467840e-08 4.85442797e-11 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -2.79396772e-08 -1.86264515e-07 -1.93715096e-07 2.98023224e-08 3.57627869e-07 3.57627869e-07 -2.38418579e-07 -1.19209290e-07 -2.38418579e-07 -1.78813934e-07 -1.78813934e-07 0.00000000e+00 -4.02331352e-07 -2.23517418e-08 2.08616257e-07 -2.98023224e-08 2.38418579e-07 2.08616257e-07 2.37952918e-07 7.72388375e-08 3.80787242e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -7.45058060e-08 -2.98023224e-08 1.11758709e-07 1.78813934e-07 4.76837158e-07 3.57627869e-07 -1.19209290e-07 0.00000000e+00 -3.57627869e-07 2.38418579e-07 -1.19209290e-07 5.96046448e-08 1.19209290e-07 1.49011612e-07 8.94069672e-08 1.78813934e-07 4.17232513e-07 4.47034836e-07 4.47034836e-07 8.42846930e-08 -3.70637281e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -1.04308128e-07 0.00000000e+00 3.35276127e-07 2.68220901e-07 7.74860382e-07 7.15255737e-07 2.38418579e-07 1.19209290e-07 -2.38418579e-07 -3.57627869e-07 -3.57627869e-07 -8.94069672e-08 3.87430191e-07 3.57627869e-07 3.57627869e-07 5.96046448e-08 2.98023224e-07 4.76837158e-07 5.36441803e-07 4.84287739e-08 4.59044713e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -9.77270531e-09 -1.19209290e-07 7.45058060e-09 0.00000000e+00 3.57627869e-07 2.38418579e-07 5.96046448e-07 3.57627869e-07 9.53674316e-07 4.76837158e-07 1.19209290e-07 -2.38418579e-07 1.19209290e-07 -2.98023224e-07 -1.19209290e-07 -5.96046448e-08 -3.57627869e-07 1.78813934e-07 4.47034836e-07 3.87430191e-07 1.08033419e-07 3.81596692e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -4.18361026e-08 -5.63450158e-08 -4.84287739e-08 5.96046448e-08 2.38418579e-07 4.76837158e-07 3.57627869e-07 5.96046448e-07 1.78813934e-07 -1.19209290e-07 -1.19209290e-07 -2.68220901e-07 0.00000000e+00 0.00000000e+00 2.38418579e-07 -5.96046448e-07 -3.87430191e-07 1.19209290e-07 3.57627869e-07 3.87430191e-07 1.24797225e-07 6.91925379e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -4.36812968e-08 -5.60366971e-08 -8.84756446e-08 1.19209290e-07 -1.19209290e-07 3.57627869e-07 5.96046448e-08 0.00000000e+00 0.00000000e+00 -5.96046448e-07 1.19209290e-07 -2.98023224e-08 1.78813934e-07 -2.38418579e-07 -2.38418579e-07 3.57627869e-07 -3.57627869e-07 2.38418579e-07 2.38418579e-07 3.42726707e-07 1.14087015e-07 2.49773286e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -1.28651578e-09 -1.01268569e-07 -1.50499545e-07 -4.47034836e-08 0.00000000e+00 5.96046448e-08 3.57627869e-07 1.19209290e-07 -3.57627869e-07 -3.57627869e-07 2.98023224e-07 3.12924385e-07 5.96046448e-08 0.00000000e+00 -3.57627869e-07 -4.76837158e-07 5.96046448e-08 2.98023224e-07 2.98023224e-07 1.78813934e-07 7.29633030e-08 1.60424936e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -7.72104300e-08 -3.55039447e-08 2.83529516e-07 2.63098627e-08 3.25031579e-07 7.45058060e-09 1.19209290e-07 -5.96046448e-08 -3.27825546e-07 6.70552254e-08 3.12924385e-07 4.17232513e-07 -3.57627869e-07 -4.76837158e-07 -1.19209290e-07 1.78813934e-07 2.98023224e-07 1.71363354e-07 6.27478585e-08 1.92048901e-08 -3.83870313e-09 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -6.35419752e-08 -5.20981303e-08 2.64481059e-08 6.12053555e-08 2.30735168e-07 -2.51457095e-08 -2.98023224e-07 -3.57627869e-07 1.78813934e-07 6.55651093e-07 2.38418579e-07 -5.96046448e-08 -3.57627869e-07 3.57627869e-07 1.19209290e-07 1.78813934e-07 1.49011612e-07 8.19563866e-08 4.22005542e-08 2.06365929e-08 2.06365929e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 2.09386428e-08 5.31318420e-08 -5.19480636e-09 -1.55114321e-07 7.33416528e-08 -2.98023224e-08 -2.98023224e-08 -2.98023224e-07 4.76837158e-07 2.38418579e-07 5.96046448e-08 3.57627869e-07 1.19209290e-07 0.00000000e+00 2.38418579e-07 1.04308128e-07 3.91155481e-08 4.49217623e-08 2.17316245e-08 2.06365929e-08 2.23254459e-09 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.64938871e-08 6.39862421e-08 5.16827185e-08 1.46355887e-07 -1.34110451e-07 -5.96046448e-08 2.38418579e-07 7.74860382e-07 9.53674316e-07 1.19209290e-07 0.00000000e+00 3.57627869e-07 2.38418579e-07 0.00000000e+00 1.19209290e-07 1.44354999e-08 2.26427801e-08 1.19798682e-09 -4.99121011e-09 -4.99121011e-09 -2.63172950e-09 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 3.20321369e-08 5.63813956e-08 8.19563866e-08 0.00000000e+00 -1.19209290e-07 1.19209290e-07 1.19209290e-07 1.19209290e-07 -1.19209290e-07 0.00000000e+00 0.00000000e+00 5.96046448e-08 -7.45058060e-09 4.09781933e-08 1.63381628e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -7.45058060e-08 -5.96046448e-08 -1.78813934e-07 5.96046448e-08 -2.88709998e-08 -3.35276127e-08 6.28060661e-08 2.98023224e-08 1.19209290e-07 5.96046448e-08 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 -1.19209290e-07 -1.19209290e-07 0.00000000e+00 -1.49011612e-08 -2.67755240e-08 -1.49157131e-09 7.06131686e-09 6.01576176e-08 -2.69501470e-08 1.49011612e-08 0.00000000e+00 0.00000000e+00 -7.45058060e-09 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]<NDArray 784 @cpu(0)>请注意，前几十个像素的权重梯度始终为零。由于调试器仅显示ndarray的前几个值，你看到的只是零值。
前几十个像素的权重梯度为零，很可能是由于这些像素不包含任何有助于分类图像中数字的信息。请注意，MNIST图像是居中的，边缘的像素值通常为0。
希望这对你有帮助。
