AIB*_*all 18 python-3.x pytorch
torch.mm阅读完 pytorch 文档后，我仍然需要帮助来理解,torch.matmul和之间的区别torch.mul。由于我不完全理解它们，所以我无法简明地解释这一点。
B = torch.tensor([[ 1.1207],
[-0.3137],
[ 0.0700],
[ 0.8378]])
C = torch.tensor([[ 0.5146, 0.1216, -0.5244, 2.2382]])
print(torch.mul(B,C))
print(torch.matmul(B,C))
print(torch.mm(B,C))
所有三个都会产生以下输出（即它们执行矩阵乘法）：
tensor([[ 0.5767, 0.1363, -0.5877, 2.5084],
[-0.1614, -0.0381, 0.1645, -0.7021],
[ 0.0360, 0.0085, -0.0367, 0.1567],
[ 0.4311, 0.1019, -0.4393, 1.8752]])
A = torch.tensor([[1.8351,2.1536], [-0.8320,-1.4578]])
B = torch.tensor([[2.9355, 0.3450], [0.5708, 1.9957]])
print(torch.mul(A,B))
print(torch.matmul(A,B))
print(torch.mm(A,B))
产生不同的输出。torch.mm 不再执行矩阵乘法（而是广播并执行逐元素乘法，而其他两个仍然执行矩阵乘法。
tensor([[ 5.3869, 0.7430],
[-0.4749, -2.9093]])
tensor([[ 6.6162, 4.9310],
[-3.2744, -3.1964]])
tensor([[ 6.6162, 4.9310],
[-3.2744, -3.1964]])
输入
tensor1 = torch.randn(10, 3, 4)
tensor2 = torch.randn(4)
tensor1 =
tensor([[[-0.2267, 0.6311, -0.5689, 1.2712],
[-0.0241, -0.5362, 0.5481, -0.4534],
[-0.9773, -0.6842, 0.6927, 0.3363]],
[[-2.6759, 0.7817, 2.6821, 0.7037],
[ 0.1804, 0.3938, -1.2235, 0.8729],
[-1.9873, -0.5030, 0.0945, 0.2688]],
[[ 0.4244, 1.7350, 0.0558, -0.1861],
[-0.9063, -0.4737, -0.4284, -0.3883],
[ 0.4827, -0.2628, 1.0084, 0.2769]],
[[ 0.2939, 0.4604, 0.8014, -1.8760],
[ 1.8807, 0.1623, 0.2344, -0.6221],
[ 1.3964, 3.1637, 0.7889, 0.1195]],
[[-0.7202, 1.4250, 2.4302, 1.4811],
[-0.2301, 0.6280, 0.5379, 0.5178],
[-2.1073, -1.4399, -0.9451, 0.8534]],
[[ 2.8178, -0.4451, -0.7871, -0.5198],
[ 0.2825, 1.0692, 0.1559, 1.2945],
[-0.5828, -1.6287, -2.0661, -0.4107]],
[[ 0.5077, -0.6349, -0.0160, -0.4477],
[-0.8070, 0.3746, 1.1852, 0.0351],
[-0.6454, 1.5877, 0.8561, 1.1021]],
[[ 0.1191, 1.0116, 0.5807, 1.2105],
[-0.5403, 1.2404, 1.1532, 0.6537],
[ 1.4757, -1.3648, -1.7158, -1.0289]],
[[-0.1326, 0.3715, 0.2429, -0.0794],
[ 0.3224, -0.3064, 0.1963, 0.7276],
[ 0.9098, 1.5984, -1.4953, 0.0420]],
[[ 0.1511, 0.9691, -0.5204, 0.3858],
[ 0.4566, 1.5482, -0.3401, 0.5960],
[-0.9998, 0.7198, 0.9286, 0.4498]]])
tensor2 =
tensor([-1.6350, 1.0335, -0.9023, 0.0696])
print(torch.mul(tensor1,tensor2))
print(torch.matmul(tensor1,tensor2))
print(torch.mm(tensor1,tensor2))
输出都不同。我认为torch.mul将矩阵的每4个元素广播并乘以向量，tensor2，即[-0.2267, 0.6311, -0.5689, 1.2712] x tensor 2 逐元素、[-0.0241, -0.5362, 0.5481, -0.4534] x tensor 2逐元素等等。我不明白 torch.matmul在做什么。我认为这与文档的第五个要点有关（如果两个参数......），但我无法理解这一点。https://pytorch.org/docs/stable/ generated/torch.matmul.html
torch.mm我认为无法产生输出的原因是它无法广播（如果我错了，请纠正我）。
tensor([[[ 3.7071e-01, 6.5221e-01, 5.1335e-01, 8.8437e-02],
[ 3.9400e-02, -5.5417e-01, -4.9460e-01, -3.1539e-02],
[ 1.5979e+00, -7.0715e-01, -6.2499e-01, 2.3398e-02]],
[[ 4.3752e+00, 8.0790e-01, -2.4201e+00, 4.8957e-02],
[-2.9503e-01, 4.0699e-01, 1.1040e+00, 6.0723e-02],
[ 3.2494e+00, -5.1981e-01, -8.5253e-02, 1.8701e-02]],
[[-6.9397e-01, 1.7931e+00, -5.0379e-02, -1.2945e-02],
[ 1.4818e+00, -4.8954e-01, 3.8657e-01, -2.7010e-02],
[-7.8920e-01, -2.7163e-01, -9.0992e-01, 1.9265e-02]],
[[-4.8055e-01, 4.7582e-01, -7.2309e-01, -1.3051e-01],
[-3.0750e+00, 1.6770e-01, -2.1146e-01, -4.3281e-02],
[-2.2832e+00, 3.2697e+00, -7.1183e-01, 8.3139e-03]],
[[ 1.1775e+00, 1.4727e+00, -2.1928e+00, 1.0304e-01],
[ 3.7617e-01, 6.4900e-01, -4.8534e-01, 3.6025e-02],
[ 3.4455e+00, -1.4882e+00, 8.5277e-01, 5.9369e-02]],
[[-4.6072e+00, -4.6005e-01, 7.1024e-01, -3.6160e-02],
[-4.6191e-01, 1.1051e+00, -1.4067e-01, 9.0053e-02],
[ 9.5283e-01, -1.6833e+00, 1.8643e+00, -2.8571e-02]],
[[-8.3005e-01, -6.5622e-01, 1.4461e-02, -3.1148e-02],
[ 1.3195e+00, 3.8716e-01, -1.0694e+00, 2.4421e-03],
[ 1.0553e+00, 1.6409e+00, -7.7250e-01, 7.6669e-02]],
[[-1.9477e-01, 1.0455e+00, -5.2398e-01, 8.4209e-02],
[ 8.8343e-01, 1.2820e+00, -1.0405e+00, 4.5478e-02],
[-2.4128e+00, -1.4106e+00, 1.5482e+00, -7.1578e-02]],
[[ 2.1675e-01, 3.8391e-01, -2.1914e-01, -5.5219e-03],
[-5.2707e-01, -3.1668e-01, -1.7711e-01, 5.0619e-02],
[-1.4876e+00, 1.6520e+00, 1.3493e+00, 2.9198e-03]],
[[-2.4706e-01, 1.0015e+00, 4.6955e-01, 2.6842e-02],
[-7.4663e-01, 1.6001e+00, 3.0685e-01, 4.1462e-02],
[ 1.6347e+00, 7.4395e-01, -8.3792e-01, 3.1291e-02]]])
tensor([[ 1.6247, -1.0409, 0.2891],
[ 2.8120, 1.2767, 2.6630],
[ 1.0358, 1.3518, -1.9515],
[-0.8583, -3.1620, 0.2830],
[ 0.5605, 0.5759, 2.8694],
[-4.3932, 0.5925, 1.1053],
[-1.5030, 0.6397, 2.0004],
[ 0.4109, 1.1704, -2.3467],
[ 0.3760, -0.9702, 1.5165],
[ 1.2509, 1.2018, 1.5720]])
小智 30
简而言之：
\ntorch.mm- 执行矩阵乘法而不广播- (2D 张量) by (2D 张量)torch.mul-通过广播执行元素乘法- （张量）乘以（张量或数字）torch.matmul-具有广播的矩阵乘积- （张量）乘以（张量），具有不同的行为，具体取决于张量形状（点积、矩阵乘积、批量矩阵乘积）。一些细节：
\ntorch.mm- 执行矩阵乘法而不广播它需要两个 2D 张量，因此n\xc3\x97m * m\xc3\x97p = n\xc3\x97p
\n从文档https://pytorch.org/docs/stable/ generated/torch.mm.html ：
\nThis function does not broadcast. For broadcasting matrix products, see torch.matmul().\ntorch.mul-通过广播执行元素乘法- （张量）乘以（张量或数字）文档： https: //pytorch.org/docs/stable/ generated/torch.mul.html
\ntorch.mul不执行矩阵乘法。它广播两个张量并执行元素乘法。因此，当您将其与张量 1x4 * 4x1 一起使用时，其工作原理类似于：
This function does not broadcast. For broadcasting matrix products, see torch.matmul().\ntensor([[1., 1., 1.],\n [2., 2., 2.],\n [3., 3., 3.]])\ntensor([[ 1., 10., 100.],\n [ 1., 10., 100.],\n [ 1., 10., 100.]])\ntensor([[ 1., 10., 100.],\n [ 2., 20., 200.],\n [ 3., 30., 300.]])\ntorch.matmul最好查看官方文档https://pytorch.org/docs/stable/ generated/torch.matmul.html 因为它根据输入张量使用不同的模式。它可以通过广播执行点积、矩阵-矩阵积或批量矩阵积。
\n至于您关于以下产品的问题：
\ntensor1 = torch.randn(10, 3, 4)\ntensor2 = torch.randn(4)\n它是产品的批量版本。请检查这个简单的例子来理解：
\nimport torch\n\n# 3x1x3\na = torch.FloatTensor([[[1, 2, 3]], [[3, 4, 5]], [[6, 7, 8]]])\n# 3\nb = torch.FloatTensor([1, 10, 100])\nr1 = torch.matmul(a, b)\n\nr2 = torch.stack((\n torch.matmul(a[0], b),\n torch.matmul(a[1], b),\n torch.matmul(a[2], b),\n))\nassert torch.allclose(r1, r2)\n\n因此它可以看作是跨批次维度堆叠在一起的多个操作。
\n阅读有关广播的内容也可能有用：
\nhttps://pytorch.org/docs/stable/notes/broadcasting.html#broadcasting-semantics
\n| 归档时间： |
|
| 查看次数： |
20881 次 |
| 最近记录： |