计算如下
egin{array}{l}{x_{1}=w_{1} * ext { input }} \ {x_{2}=w_{2} * x_{1}} \ {x_{3}=w_{3} * x_{2}}end{array}
其中$w_{1}$，$w_{2}$，$w_{3}$是权重参数，是需要梯度的。在初始化时，三个值分别为1，0，1。
程序代码如下：
import torch import torch.nn as nn input_data = torch.randn(1) weight1 = torch.ones(1,requires_grad=True) weight2 = torch.zeros(1,requires_grad=True) weight3 = torch.ones(1,requires_grad=True) x_1 = weight1 * input_data x_2 = weight2 * x_1 x_3 = weight3 * x_2 one = torch.ones(1) x_3 = x_3 * one x_3.backward() print("x1:{},x2{},x3{},weight1_gard:{},weight2_gard:{},weight3_gard:{}".format(x_1,x_2,x_3, weight1.grad,weight2.grad,weight3.grad))
运行时，随机产生的Input_data为1.688，三个权重的梯度值分别为0，1.688，0。
梯度的计算公式如下：
egin{equation}
frac{partial x_{3}}{partial w_{3}}=x_{2}
end{equation}
egin{equation}
frac{partial x_{3}}{partial x_{2}}=w_{3}
end{equation}
egin{equation}
frac{partial x_{3}}{partial w_{2}}=frac{partial x_{3}}{partial x_{2}} frac{partial x_{2}}{partial w_{2}}=w_{3} * x_{1}
end{equation}
egin{equation}
frac{partial x_{3}}{partial x_{1}}=frac{partial x_{3}}{partial x_{2}} frac{partial x_{2}}{partial x_{1}}=w_{3} * w_{2}
end{equation}
egin{equation}
frac{partial x_{3}}{partial w_{1}}=frac{partial x_{3}}{partial x_{1}} frac{partial x_{1}}{partial w_{1}}=w_{3} * w_{2} * input
end{equation}
由此可以看出一个问题是，权重数据为0，不代表其梯度也会等于0，权重数据不为0，不代表其梯度就不会为0.
在进行一些模型修改的时候常常会将一些卷积核置为零，但是如果这些卷积核仍然requires_grad=True，那么在反向梯度传播的时候这些卷积核还是有可能会更新改变值的。
下面分析一段程序：
class Net(nn.Module):
def __init__(self):
super(Net,self).__init__()
self.conv1 = nn.Conv2d(3,3,kernel_size=2,padding=1,bias=False)
self.conv2 = nn.Conv2d(3,3,kernel_size=2,padding=1,bias=False)
def forward(self,x):
x = self.conv1(x)
print("x(1):",x)
x = self.conv2(x)
print("x(2):",x)
x = x.view(x.shape[0],-1)
x = torch.sum(x,dim=1)
return x
mask = torch.tensor([1.0,0.0,1.0])
model = Net()
for name,weight in model.named_parameters():
weight.data = weight.data.transpose(0,3) * mask #注意此处是将输出的channels数目和最后一个维度进行对调
weight.data = weight.data.transpose(0,3)
break
input_data = torch.randn(1,3,4,4)
model.train()
out = model(input_data)
print(out)
out.backward()
for name,weight in model.named_parameters():
print("weight:",weight)
print("weight.grad:",weight.grad)
这段代码想通过将权重值置为0的形式实现上图中的卷积核选择，结果如下，主要关注于卷积核的梯度值：
tensor([-5.7640], grad_fn=<SumBackward2>)
weight: Parameter containing:
tensor([[[[ 0.0543, -0.1514],
[ 0.1190, -0.1161]],
[[-0.0760, -0.1224],
[-0.1884, 0.1472]],
[[-0.1482, 0.0413],
[ 0.0735, -0.2729]]],
[[[-0.0000, 0.0000],
[ 0.0000, -0.0000]],
[[-0.0000, 0.0000],
[-0.0000, -0.0000]],
[[ 0.0000, 0.0000],
[-0.0000, 0.0000]]],
[[[ 0.1193, 0.0289],
[ 0.1296, 0.2184]],
[[-0.2156, -0.0562],
[ 0.1257, 0.2109]],
[[ 0.2618, 0.1946],
[-0.2667, 0.1019]]]], requires_grad=True)
weight.grad: tensor([[[[ 1.3665, 1.3665],
[ 1.3665, 1.3665]],
[[-0.1851, -0.1851],
[-0.1851, -0.1851]],
[[ 0.8171, 0.8171],
[ 0.8171, 0.8171]]],
[[[-1.3336, -1.3336],
[-1.3336, -1.3336]],
[[ 0.1807, 0.1807],
[ 0.1807, 0.1807]],
[[-0.7974, -0.7974],
[-0.7974, -0.7974]]],
[[[-8.2042, -8.2042],
[-8.2042, -8.2042]],
[[ 1.1116, 1.1116],
[ 1.1116, 1.1116]],
[[-4.9058, -4.9058],
[-4.9058, -4.9058]]]])
weight: Parameter containing:
tensor([[[[ 0.1661, -0.2404],
[-0.2504, 0.0886]],
[[-0.1079, 0.2199],
[ 0.0405, -0.2834]],
[[-0.1478, -0.1596],
[ 0.0747, -0.0178]]],
[[[ 0.2699, 0.0623],
[-0.0816, 0.1588]],
[[ 0.0798, -0.1606],
[-0.2531, 0.2330]],
[[-0.1956, 0.1329],
[ 0.1160, -0.0881]]],
[[[ 0.0497, 0.0830],
[ 0.0780, -0.1898]],
[[ 0.1204, -0.0770],
[ 0.0008, -0.0018]],
[[-0.2224, -0.2384],
[-0.1398, -0.2800]]]], requires_grad=True)
weight.grad: tensor([[[[-1.7231, -1.7231],
[-1.7231, -1.7231]],
[[ 0.0000, 0.0000],
[ 0.0000, 0.0000]],
[[ 4.6560, 4.6560],
[ 4.6560, 4.6560]]],
[[[-1.7231, -1.7231],
[-1.7231, -1.7231]],
[[ 0.0000, 0.0000],
[ 0.0000, 0.0000]],
[[ 4.6560, 4.6560],
[ 4.6560, 4.6560]]],
[[[-1.7231, -1.7231],
[-1.7231, -1.7231]],
[[ 0.0000, 0.0000],
[ 0.0000, 0.0000]],
[[ 4.6560, 4.6560],
[ 4.6560, 4.6560]]]])
这样梯度值还是在更新的。
另外一种mask的选择的方式，程序如下：
class Net(nn.Module): def __init__(self): super(Net,self).__init__() self.conv1 = nn.Conv2d(3,3,kernel_size=3,padding=1,bias=False) self.mask = torch.tensor([1.0,0.0,1.0]) self.conv2 = nn.Conv2d(3,3,kernel_size=3,padding=1,bias=False) def forward(self,x): x = self.conv1(x) x = x.transpose(1,3) # 此处的是将channels的维度和最后一个维度对调 x = self.mask * x x = x.transpose(1,3) x = self.conv2(x) x = x.view(x.shape[0],-1) x = torch.sum(x,dim=1) return x model = Net() input_data = torch.randn(1,3,4,4) model.train() out = model(input_data) print(out) out.backward() for name,weight in model.named_parameters(): print("weight:",weight) print("weight.grad:",weight.grad)
结果如下：
tensor([0.2569], grad_fn=<SumBackward2>) weight: Parameter containing: tensor([[[[-0.0880, -0.1685, -0.0367], [-0.0882, 0.0551, -0.0204], [-0.0213, 0.1404, 0.1892]], [[ 0.0056, 0.1266, 0.0108], [-0.1146, 0.1275, 0.1070], [-0.1756, -0.1015, 0.1670]], [[ 0.1145, 0.0617, 0.0290], [ 0.0034, -0.0688, -0.0720], [-0.1227, -0.1408, -0.0095]]], [[[ 0.1335, -0.1492, -0.0962], [-0.1691, -0.1726, 0.1218], [ 0.1924, 0.0165, 0.1454]], [[-0.1302, -0.1700, 0.1157], [ 0.0050, -0.0149, -0.0506], [-0.0059, 0.0439, 0.1396]], [[-0.0524, 0.0682, -0.0892], [-0.1708, -0.0117, -0.0379], [-0.0459, 0.0743, -0.0160]]], [[[ 0.1923, 0.0397, -0.1278], [-0.0590, -0.1523, 0.1832], [ 0.0136, -0.0047, 0.1030]], [[ 0.1912, 0.1178, -0.0915], [ 0.0639, -0.0495, -0.0504], [-0.1025, 0.0448, -0.1506]], [[ 0.0784, 0.0163, 0.0904], [ 0.1349, -0.0998, -0.0801], [ 0.1837, -0.1003, -0.1355]]]], requires_grad=True) weight.grad: tensor([[[[ 1.2873, 2.1295, 1.7824], [ 1.8223, 2.5102, 1.4646], [ 1.0475, 1.5060, 0.7760]], [[ 0.3358, 1.0468, 0.7017], [ 1.2767, 3.0948, 2.3152], [ 1.5075, 3.1297, 2.0517]], [[-0.1350, -0.0052, 0.5159], [-0.5552, -0.2897, -0.2935], [-0.8585, -0.3860, -0.6307]]], [[[ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000]], [[ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000]], [[ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000]]], [[[-0.5196, -0.5230, -1.4080], [-1.0556, -1.3059, -1.9042], [-0.9298, -1.3619, -0.9315]], [[-0.1840, -0.3386, -0.1826], [-0.4814, -0.6702, -1.3245], [-0.6749, -0.9496, -1.7621]], [[-0.5977, -0.0242, -0.8976], [-0.7431, -0.0033, -0.8301], [-0.5861, 0.1346, -0.1433]]]]) weight: Parameter containing: tensor([[[[-0.1239, 0.1291, 0.1867], [-0.1434, 0.1218, 0.0452], [ 0.0722, 0.0830, -0.1149]], [[-0.0145, -0.1000, -0.0537], [ 0.1225, -0.0513, -0.0325], [-0.0796, -0.1129, 0.0850]], [[-0.0283, -0.0441, 0.0508], [ 0.0523, -0.1224, 0.0353], [ 0.1726, 0.0695, 0.0078]]], [[[ 0.0371, 0.1536, -0.0583], [ 0.0471, 0.0636, 0.1264], [-0.0544, 0.1420, 0.0421]], [[-0.1213, 0.1672, -0.0086], [ 0.1251, -0.1603, -0.0988], [ 0.1399, -0.0367, -0.1656]], [[ 0.0279, -0.1697, 0.0959], [-0.1719, -0.0208, 0.0677], [-0.1116, -0.0659, 0.1343]]], [[[-0.0840, -0.0361, -0.1864], [ 0.1757, 0.1003, -0.0931], [-0.1388, -0.0980, -0.0236]], [[ 0.0761, 0.0710, -0.1916], [ 0.0159, 0.1678, -0.1378], [ 0.1372, -0.1410, -0.0596]], [[-0.1344, 0.0832, 0.0147], [ 0.0531, -0.1044, 0.0755], [-0.1519, 0.1288, -0.1672]]]], requires_grad=True) weight.grad: tensor([[[[ 0.3392, 0.4461, -0.4528], [ 0.6036, 0.7465, -0.0223], [ 1.0414, 0.8226, -0.3322]], [[ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000]], [[ 0.4305, -0.6932, -0.6299], [ 0.4147, -0.5902, -0.2296], [-0.0378, -0.8272, -0.2457]]], [[[ 0.3392, 0.4461, -0.4528], [ 0.6036, 0.7465, -0.0223], [ 1.0414, 0.8226, -0.3322]], [[ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000]], [[ 0.4305, -0.6932, -0.6299], [ 0.4147, -0.5902, -0.2296], [-0.0378, -0.8272, -0.2457]]], [[[ 0.3392, 0.4461, -0.4528], [ 0.6036, 0.7465, -0.0223], [ 1.0414, 0.8226, -0.3322]], [[ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000]], [[ 0.4305, -0.6932, -0.6299], [ 0.4147, -0.5902, -0.2296], [-0.0378, -0.8272, -0.2457]]]])
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为什么会有这种不同呢？这个与反向梯度传播的计算有关系，对于权重的梯度的计算，在链式求导法则当中其实与中间节点的值并没有什么关系，反而与权重之前的节点值有关系，如果有疑问可以画个图分析一下。