PyTorch梯度下降反向传播实例分析

本文小编为大家详细介绍"PyTorch梯度下降反向传播实例分析"，内容详细，步骤清晰，细节处理妥当，希望这篇"PyTorch梯度下降反向传播实例分析"文章能帮助大家解决疑惑，下面跟着小编的思路慢慢深入，一起来学习新知识吧。

前言：

反向传播的目的是计算成本函数C对网络中任意w或b的偏导数。一旦我们有了这些偏导数，我们将通过一些常数 α的乘积和该数量相对于成本函数的偏导数来更新网络中的权重和偏差。这是流行的梯度下降算法。而偏导数给出了最大上升的方向。因此，关于反向传播算法，我们继续查看下文。

我们向相反的方向迈出了一小步——最大下降的方向，也就是将我们带到成本函数的局部最小值的方向

如题：

意思是利用这个二次模型来预测数据，减小损失函数(MSE)的值。

代码如下：

import torchimport matplotlib.pyplot as pltimport osos.environ["KMP_DUPLICATE_LIB_OK"]  =  "TRUE"# 数据集x_data = [1.0,2.0,3.0]y_data = [2.0,4.0,6.0]# 权重参数初始值均为1w = torch.tensor([1.0,1.0,1.0])w.requires_grad = True    # 需要计算梯度# 前向传播def forward(x):    return w[0]*(x**2)+w[1]*x+w[2]# 计算损失def loss(x,y):    y_pred = forward(x)    return (y_pred-y) ** 2# 训练模块print('predict (before tranining) ',4, forward(4).item())epoch_list = []w_list = []loss_list = []for epoch in range(1000):    for x,y in zip(x_data,y_data):        l = loss(x,y)        l.backward()        # 后向传播        print('\tgrad: ',x,y,w.grad.data)        w.data = w.data - 0.01 * w.grad.data        # 梯度下降                w.grad.data.zero_()    # 梯度清零操作            print('progress: ',epoch,l.item())    epoch_list.append(epoch)    w_list.append(w.data)    loss_list.append(l.item())print('predict (after tranining) ',4, forward(4).item())# 绘图plt.plot(epoch_list,loss_list,'b')plt.xlabel('Epoch')plt.ylabel('Loss')plt.grid()plt.show()

结果如下：

predict (before tranining)  4 21.0    grad:  1.0 2.0 tensor([2., 2., 2.])    grad:  2.0 4.0 tensor([22.8800, 11.4400,  5.7200])    grad:  3.0 6.0 tensor([77.0472, 25.6824,  8.5608])progress:  0 18.321826934814453    grad:  1.0 2.0 tensor([-1.1466, -1.1466, -1.1466])    grad:  2.0 4.0 tensor([-15.5367,  -7.7683,  -3.8842])    grad:  3.0 6.0 tensor([-30.4322, -10.1441,  -3.3814])progress:  1 2.858394145965576    grad:  1.0 2.0 tensor([0.3451, 0.3451, 0.3451])    grad:  2.0 4.0 tensor([2.4273, 1.2137, 0.6068])    grad:  3.0 6.0 tensor([19.4499,  6.4833,  2.1611])progress:  2 1.1675907373428345    grad:  1.0 2.0 tensor([-0.3224, -0.3224, -0.3224])    grad:  2.0 4.0 tensor([-5.8458, -2.9229, -1.4614])    grad:  3.0 6.0 tensor([-3.8829, -1.2943, -0.4314])progress:  3 0.04653334245085716    grad:  1.0 2.0 tensor([0.0137, 0.0137, 0.0137])    grad:  2.0 4.0 tensor([-1.9141, -0.9570, -0.4785])    grad:  3.0 6.0 tensor([6.8557, 2.2852, 0.7617])progress:  4 0.14506366848945618    grad:  1.0 2.0 tensor([-0.1182, -0.1182, -0.1182])    grad:  2.0 4.0 tensor([-3.6644, -1.8322, -0.9161])    grad:  3.0 6.0 tensor([1.7455, 0.5818, 0.1939])progress:  5 0.009403289295732975    grad:  1.0 2.0 tensor([-0.0333, -0.0333, -0.0333])    grad:  2.0 4.0 tensor([-2.7739, -1.3869, -0.6935])    grad:  3.0 6.0 tensor([4.0140, 1.3380, 0.4460])progress:  6 0.04972923547029495    grad:  1.0 2.0 tensor([-0.0501, -0.0501, -0.0501])    grad:  2.0 4.0 tensor([-3.1150, -1.5575, -0.7788])    grad:  3.0 6.0 tensor([2.8534, 0.9511, 0.3170])progress:  7 0.025129113346338272    grad:  1.0 2.0 tensor([-0.0205, -0.0205, -0.0205])    grad:  2.0 4.0 tensor([-2.8858, -1.4429, -0.7215])    grad:  3.0 6.0 tensor([3.2924, 1.0975, 0.3658])progress:  8 0.03345605731010437    grad:  1.0 2.0 tensor([-0.0134, -0.0134, -0.0134])    grad:  2.0 4.0 tensor([-2.9247, -1.4623, -0.7312])    grad:  3.0 6.0 tensor([2.9909, 0.9970, 0.3323])progress:  9 0.027609655633568764    grad:  1.0 2.0 tensor([0.0033, 0.0033, 0.0033])    grad:  2.0 4.0 tensor([-2.8414, -1.4207, -0.7103])    grad:  3.0 6.0 tensor([3.0377, 1.0126, 0.3375])progress:  10 0.02848036028444767    grad:  1.0 2.0 tensor([0.0148, 0.0148, 0.0148])    grad:  2.0 4.0 tensor([-2.8174, -1.4087, -0.7043])    grad:  3.0 6.0 tensor([2.9260, 0.9753, 0.3251])progress:  11 0.02642466314136982    grad:  1.0 2.0 tensor([0.0280, 0.0280, 0.0280])    grad:  2.0 4.0 tensor([-2.7682, -1.3841, -0.6920])    grad:  3.0 6.0 tensor([2.8915, 0.9638, 0.3213])progress:  12 0.025804826989769936    grad:  1.0 2.0 tensor([0.0397, 0.0397, 0.0397])    grad:  2.0 4.0 tensor([-2.7330, -1.3665, -0.6832])    grad:  3.0 6.0 tensor([2.8243, 0.9414, 0.3138])progress:  13 0.02462013065814972    grad:  1.0 2.0 tensor([0.0514, 0.0514, 0.0514])    grad:  2.0 4.0 tensor([-2.6934, -1.3467, -0.6734])    grad:  3.0 6.0 tensor([2.7756, 0.9252, 0.3084])progress:  14 0.023777369409799576    grad:  1.0 2.0 tensor([0.0624, 0.0624, 0.0624])    grad:  2.0 4.0 tensor([-2.6580, -1.3290, -0.6645])    grad:  3.0 6.0 tensor([2.7213, 0.9071, 0.3024])progress:  15 0.0228563379496336    grad:  1.0 2.0 tensor([0.0731, 0.0731, 0.0731])    grad:  2.0 4.0 tensor([-2.6227, -1.3113, -0.6557])    grad:  3.0 6.0 tensor([2.6725, 0.8908, 0.2969])progress:  16 0.022044027224183083    grad:  1.0 2.0 tensor([0.0833, 0.0833, 0.0833])    grad:  2.0 4.0 tensor([-2.5893, -1.2946, -0.6473])    grad:  3.0 6.0 tensor([2.6240, 0.8747, 0.2916])progress:  17 0.02125072106719017    grad:  1.0 2.0 tensor([0.0931, 0.0931, 0.0931])    grad:  2.0 4.0 tensor([-2.5568, -1.2784, -0.6392])    grad:  3.0 6.0 tensor([2.5780, 0.8593, 0.2864])progress:  18 0.020513182505965233    grad:  1.0 2.0 tensor([0.1025, 0.1025, 0.1025])    grad:  2.0 4.0 tensor([-2.5258, -1.2629, -0.6314])    grad:  3.0 6.0 tensor([2.5335, 0.8445, 0.2815])progress:  19 0.019810274243354797    grad:  1.0 2.0 tensor([0.1116, 0.1116, 0.1116])    grad:  2.0 4.0 tensor([-2.4958, -1.2479, -0.6239])    grad:  3.0 6.0 tensor([2.4908, 0.8303, 0.2768])progress:  20 0.019148115068674088    grad:  1.0 2.0 tensor([0.1203, 0.1203, 0.1203])    grad:  2.0 4.0 tensor([-2.4669, -1.2335, -0.6167])    grad:  3.0 6.0 tensor([2.4496, 0.8165, 0.2722])progress:  21 0.018520694226026535    grad:  1.0 2.0 tensor([0.1286, 0.1286, 0.1286])    grad:  2.0 4.0 tensor([-2.4392, -1.2196, -0.6098])    grad:  3.0 6.0 tensor([2.4101, 0.8034, 0.2678])progress:  22 0.017927465960383415    grad:  1.0 2.0 tensor([0.1367, 0.1367, 0.1367])    grad:  2.0 4.0 tensor([-2.4124, -1.2062, -0.6031])    grad:  3.0 6.0 tensor([2.3720, 0.7907, 0.2636])progress:  23 0.01736525259912014    grad:  1.0 2.0 tensor([0.1444, 0.1444, 0.1444])    grad:  2.0 4.0 tensor([-2.3867, -1.1933, -0.5967])    grad:  3.0 6.0 tensor([2.3354, 0.7785, 0.2595])progress:  24 0.016833148896694183    grad:  1.0 2.0 tensor([0.1518, 0.1518, 0.1518])    grad:  2.0 4.0 tensor([-2.3619, -1.1810, -0.5905])    grad:  3.0 6.0 tensor([2.3001, 0.7667, 0.2556])progress:  25 0.01632905937731266    grad:  1.0 2.0 tensor([0.1589, 0.1589, 0.1589])    grad:  2.0 4.0 tensor([-2.3380, -1.1690, -0.5845])    grad:  3.0 6.0 tensor([2.2662, 0.7554, 0.2518])progress:  26 0.01585075818002224    grad:  1.0 2.0 tensor([0.1657, 0.1657, 0.1657])    grad:  2.0 4.0 tensor([-2.3151, -1.1575, -0.5788])    grad:  3.0 6.0 tensor([2.2336, 0.7445, 0.2482])progress:  27 0.015397666022181511    grad:  1.0 2.0 tensor([0.1723, 0.1723, 0.1723])    grad:  2.0 4.0 tensor([-2.2929, -1.1465, -0.5732])    grad:  3.0 6.0 tensor([2.2022, 0.7341, 0.2447])progress:  28 0.014967591501772404    grad:  1.0 2.0 tensor([0.1786, 0.1786, 0.1786])    grad:  2.0 4.0 tensor([-2.2716, -1.1358, -0.5679])    grad:  3.0 6.0 tensor([2.1719, 0.7240, 0.2413])progress:  29 0.014559715054929256    grad:  1.0 2.0 tensor([0.1846, 0.1846, 0.1846])    grad:  2.0 4.0 tensor([-2.2511, -1.1255, -0.5628])    grad:  3.0 6.0 tensor([2.1429, 0.7143, 0.2381])progress:  30 0.014172340743243694    grad:  1.0 2.0 tensor([0.1904, 0.1904, 0.1904])    grad:  2.0 4.0 tensor([-2.2313, -1.1157, -0.5578])    grad:  3.0 6.0 tensor([2.1149, 0.7050, 0.2350])progress:  31 0.013804304413497448    grad:  1.0 2.0 tensor([0.1960, 0.1960, 0.1960])    grad:  2.0 4.0 tensor([-2.2123, -1.1061, -0.5531])    grad:  3.0 6.0 tensor([2.0879, 0.6960, 0.2320])progress:  32 0.013455045409500599    grad:  1.0 2.0 tensor([0.2014, 0.2014, 0.2014])    grad:  2.0 4.0 tensor([-2.1939, -1.0970, -0.5485])    grad:  3.0 6.0 tensor([2.0620, 0.6873, 0.2291])progress:  33 0.013122711330652237    grad:  1.0 2.0 tensor([0.2065, 0.2065, 0.2065])    grad:  2.0 4.0 tensor([-2.1763, -1.0881, -0.5441])    grad:  3.0 6.0 tensor([2.0370, 0.6790, 0.2263])progress:  34 0.01280694268643856    grad:  1.0 2.0 tensor([0.2114, 0.2114, 0.2114])    grad:  2.0 4.0 tensor([-2.1592, -1.0796, -0.5398])    grad:  3.0 6.0 tensor([2.0130, 0.6710, 0.2237])progress:  35 0.012506747618317604    grad:  1.0 2.0 tensor([0.2162, 0.2162, 0.2162])    grad:  2.0 4.0 tensor([-2.1428, -1.0714, -0.5357])    grad:  3.0 6.0 tensor([1.9899, 0.6633, 0.2211])progress:  36 0.012220758944749832    grad:  1.0 2.0 tensor([0.2207, 0.2207, 0.2207])    grad:  2.0 4.0 tensor([-2.1270, -1.0635, -0.5317])    grad:  3.0 6.0 tensor([1.9676, 0.6559, 0.2186])progress:  37 0.01194891706109047    grad:  1.0 2.0 tensor([0.2251, 0.2251, 0.2251])    grad:  2.0 4.0 tensor([-2.1118, -1.0559, -0.5279])    grad:  3.0 6.0 tensor([1.9462, 0.6487, 0.2162])progress:  38 0.011689926497638226    grad:  1.0 2.0 tensor([0.2292, 0.2292, 0.2292])    grad:  2.0 4.0 tensor([-2.0971, -1.0485, -0.5243])    grad:  3.0 6.0 tensor([1.9255, 0.6418, 0.2139])progress:  39 0.01144315768033266    grad:  1.0 2.0 tensor([0.2333, 0.2333, 0.2333])    grad:  2.0 4.0 tensor([-2.0829, -1.0414, -0.5207])    grad:  3.0 6.0 tensor([1.9057, 0.6352, 0.2117])progress:  40 0.011208509095013142    grad:  1.0 2.0 tensor([0.2371, 0.2371, 0.2371])    grad:  2.0 4.0 tensor([-2.0693, -1.0346, -0.5173])    grad:  3.0 6.0 tensor([1.8865, 0.6288, 0.2096])progress:  41 0.0109840864315629    grad:  1.0 2.0 tensor([0.2408, 0.2408, 0.2408])    grad:  2.0 4.0 tensor([-2.0561, -1.0280, -0.5140])    grad:  3.0 6.0 tensor([1.8681, 0.6227, 0.2076])progress:  42 0.010770938359200954    grad:  1.0 2.0 tensor([0.2444, 0.2444, 0.2444])    grad:  2.0 4.0 tensor([-2.0434, -1.0217, -0.5108])    grad:  3.0 6.0 tensor([1.8503, 0.6168, 0.2056])progress:  43 0.010566935874521732    grad:  1.0 2.0 tensor([0.2478, 0.2478, 0.2478])    grad:  2.0 4.0 tensor([-2.0312, -1.0156, -0.5078])    grad:  3.0 6.0 tensor([1.8332, 0.6111, 0.2037])progress:  44 0.010372749529778957    grad:  1.0 2.0 tensor([0.2510, 0.2510, 0.2510])    grad:  2.0 4.0 tensor([-2.0194, -1.0097, -0.5048])    grad:  3.0 6.0 tensor([1.8168, 0.6056, 0.2019])progress:  45 0.010187389329075813    grad:  1.0 2.0 tensor([0.2542, 0.2542, 0.2542])    grad:  2.0 4.0 tensor([-2.0080, -1.0040, -0.5020])    grad:  3.0 6.0 tensor([1.8009, 0.6003, 0.2001])progress:  46 0.010010283440351486    grad:  1.0 2.0 tensor([0.2572, 0.2572, 0.2572])    grad:  2.0 4.0 tensor([-1.9970, -0.9985, -0.4992])    grad:  3.0 6.0 tensor([1.7856, 0.5952, 0.1984])progress:  47 0.00984097272157669    grad:  1.0 2.0 tensor([0.2600, 0.2600, 0.2600])    grad:  2.0 4.0 tensor([-1.9864, -0.9932, -0.4966])    grad:  3.0 6.0 tensor([1.7709, 0.5903, 0.1968])progress:  48 0.009679674170911312    grad:  1.0 2.0 tensor([0.2628, 0.2628, 0.2628])    grad:  2.0 4.0 tensor([-1.9762, -0.9881, -0.4940])    grad:  3.0 6.0 tensor([1.7568, 0.5856, 0.1952])progress:  49 0.009525291621685028    grad:  1.0 2.0 tensor([0.2655, 0.2655, 0.2655])    grad:  2.0 4.0 tensor([-1.9663, -0.9832, -0.4916])    grad:  3.0 6.0 tensor([1.7431, 0.5810, 0.1937])progress:  50 0.00937769003212452    grad:  1.0 2.0 tensor([0.2680, 0.2680, 0.2680])    grad:  2.0 4.0 tensor([-1.9568, -0.9784, -0.4892])    grad:  3.0 6.0 tensor([1.7299, 0.5766, 0.1922])progress:  51 0.009236648678779602    grad:  1.0 2.0 tensor([0.2704, 0.2704, 0.2704])    grad:  2.0 4.0 tensor([-1.9476, -0.9738, -0.4869])    grad:  3.0 6.0 tensor([1.7172, 0.5724, 0.1908])progress:  52 0.00910158734768629    grad:  1.0 2.0 tensor([0.2728, 0.2728, 0.2728])    grad:  2.0 4.0 tensor([-1.9387, -0.9694, -0.4847])    grad:  3.0 6.0 tensor([1.7050, 0.5683, 0.1894])progress:  53 0.00897257961332798    grad:  1.0 2.0 tensor([0.2750, 0.2750, 0.2750])    grad:  2.0 4.0 tensor([-1.9301, -0.9651, -0.4825])    grad:  3.0 6.0 tensor([1.6932, 0.5644, 0.1881])progress:  54 0.008848887868225574    grad:  1.0 2.0 tensor([0.2771, 0.2771, 0.2771])    grad:  2.0 4.0 tensor([-1.9219, -0.9609, -0.4805])    grad:  3.0 6.0 tensor([1.6819, 0.5606, 0.1869])progress:  55 0.008730598725378513    grad:  1.0 2.0 tensor([0.2792, 0.2792, 0.2792])    grad:  2.0 4.0 tensor([-1.9139, -0.9569, -0.4785])    grad:  3.0 6.0 tensor([1.6709, 0.5570, 0.1857])progress:  56 0.00861735362559557    grad:  1.0 2.0 tensor([0.2811, 0.2811, 0.2811])    grad:  2.0 4.0 tensor([-1.9062, -0.9531, -0.4765])    grad:  3.0 6.0 tensor([1.6604, 0.5535, 0.1845])progress:  57 0.008508718572556973    grad:  1.0 2.0 tensor([0.2830, 0.2830, 0.2830])    grad:  2.0 4.0 tensor([-1.8987, -0.9493, -0.4747])    grad:  3.0 6.0 tensor([1.6502, 0.5501, 0.1834])progress:  58 0.008404706604778767    grad:  1.0 2.0 tensor([0.2848, 0.2848, 0.2848])    grad:  2.0 4.0 tensor([-1.8915, -0.9457, -0.4729])    grad:  3.0 6.0 tensor([1.6404, 0.5468, 0.1823])progress:  59 0.008305158466100693    grad:  1.0 2.0 tensor([0.2865, 0.2865, 0.2865])    grad:  2.0 4.0 tensor([-1.8845, -0.9423, -0.4711])    grad:  3.0 6.0 tensor([1.6309, 0.5436, 0.1812])progress:  60 0.00820931326597929    grad:  1.0 2.0 tensor([0.2882, 0.2882, 0.2882])    grad:  2.0 4.0 tensor([-1.8778, -0.9389, -0.4694])    grad:  3.0 6.0 tensor([1.6218, 0.5406, 0.1802])progress:  61 0.008117804303765297    grad:  1.0 2.0 tensor([0.2898, 0.2898, 0.2898])    grad:  2.0 4.0 tensor([-1.8713, -0.9356, -0.4678])    grad:  3.0 6.0 tensor([1.6130, 0.5377, 0.1792])progress:  62 0.008029798977077007    grad:  1.0 2.0 tensor([0.2913, 0.2913, 0.2913])    grad:  2.0 4.0 tensor([-1.8650, -0.9325, -0.4662])    grad:  3.0 6.0 tensor([1.6045, 0.5348, 0.1783])progress:  63 0.007945418357849121    grad:  1.0 2.0 tensor([0.2927, 0.2927, 0.2927])    grad:  2.0 4.0 tensor([-1.8589, -0.9294, -0.4647])    grad:  3.0 6.0 tensor([1.5962, 0.5321, 0.1774])progress:  64 0.007864190265536308    grad:  1.0 2.0 tensor([0.2941, 0.2941, 0.2941])    grad:  2.0 4.0 tensor([-1.8530, -0.9265, -0.4632])    grad:  3.0 6.0 tensor([1.5884, 0.5295, 0.1765])progress:  65 0.007786744274199009    grad:  1.0 2.0 tensor([0.2954, 0.2954, 0.2954])    grad:  2.0 4.0 tensor([-1.8473, -0.9236, -0.4618])    grad:  3.0 6.0 tensor([1.5807, 0.5269, 0.1756])progress:  66 0.007711691781878471    grad:  1.0 2.0 tensor([0.2967, 0.2967, 0.2967])    grad:  2.0 4.0 tensor([-1.8417, -0.9209, -0.4604])    grad:  3.0 6.0 tensor([1.5733, 0.5244, 0.1748])progress:  67 0.007640169933438301    grad:  1.0 2.0 tensor([0.2979, 0.2979, 0.2979])    grad:  2.0 4.0 tensor([-1.8364, -0.9182, -0.4591])    grad:  3.0 6.0 tensor([1.5662, 0.5221, 0.1740])progress:  68 0.007570972666144371    grad:  1.0 2.0 tensor([0.2991, 0.2991, 0.2991])    grad:  2.0 4.0 tensor([-1.8312, -0.9156, -0.4578])    grad:  3.0 6.0 tensor([1.5593, 0.5198, 0.1733])progress:  69 0.007504733745008707    grad:  1.0 2.0 tensor([0.3002, 0.3002, 0.3002])    grad:  2.0 4.0 tensor([-1.8262, -0.9131, -0.4566])    grad:  3.0 6.0 tensor([1.5527, 0.5176, 0.1725])progress:  70 0.007440924644470215    grad:  1.0 2.0 tensor([0.3012, 0.3012, 0.3012])    grad:  2.0 4.0 tensor([-1.8214, -0.9107, -0.4553])    grad:  3.0 6.0 tensor([1.5463, 0.5154, 0.1718])progress:  71 0.007379599846899509    grad:  1.0 2.0 tensor([0.3022, 0.3022, 0.3022])    grad:  2.0 4.0 tensor([-1.8167, -0.9083, -0.4542])    grad:  3.0 6.0 tensor([1.5401, 0.5134, 0.1711])progress:  72 0.007320486940443516    grad:  1.0 2.0 tensor([0.3032, 0.3032, 0.3032])    grad:  2.0 4.0 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4.0 tensor([-1.7415, -0.8708, -0.4354])    grad:  3.0 6.0 tensor([1.4448, 0.4816, 0.1605])progress:  94 0.006442630663514137    grad:  1.0 2.0 tensor([0.3156, 0.3156, 0.3156])    grad:  2.0 4.0 tensor([-1.7391, -0.8695, -0.4348])    grad:  3.0 6.0 tensor([1.4418, 0.4806, 0.1602])progress:  95 0.006416172254830599    grad:  1.0 2.0 tensor([0.3159, 0.3159, 0.3159])    grad:  2.0 4.0 tensor([-1.7366, -0.8683, -0.4342])    grad:  3.0 6.0 tensor([1.4389, 0.4796, 0.1599])progress:  96 0.006390606984496117    grad:  1.0 2.0 tensor([0.3161, 0.3161, 0.3161])    grad:  2.0 4.0 tensor([-1.7343, -0.8671, -0.4336])    grad:  3.0 6.0 tensor([1.4361, 0.4787, 0.1596])progress:  97 0.0063657015562057495    grad:  1.0 2.0 tensor([0.3164, 0.3164, 0.3164])    grad:  2.0 4.0 tensor([-1.7320, -0.8660, -0.4330])    grad:  3.0 6.0 tensor([1.4334, 0.4778, 0.1593])progress:  98 0.0063416799530386925    grad:  1.0 2.0 tensor([0.3166, 0.3166, 0.3166])    grad:  2.0 4.0 tensor([-1.7297, -0.8649, -0.4324])    grad:  3.0 6.0 tensor([1.4308, 0.4769, 0.1590])progress:  99 0.00631808303296566predict (after tranining)  4 8.544171333312988

损失值随着迭代次数的增加呈递减趋势，如下图所示：

可以看出：x=4时的预测值约为8.5，与真实值8有所差距，可通过提高迭代次数或者调整学习率、初始参数等方法来减小差距。

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