pytorch model参数解析
以vggnet16为例:
mod = models.vgg16(pretrained = True)ps:在pytorch中，torch.nn.Module模块中的state_dict变量存放训练过程中需要学习的权重和偏执系数，state_dict作为python的字典对象将每一层的参数映射成tensor张量，
1.
print("mod:",mod)输出:
('mod:', VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU(inplace)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU(inplace)
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace)
(16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU(inplace)
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU(inplace)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU(inplace)
(23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(25): ReLU(inplace)
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU(inplace)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU(inplace)
(30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU(inplace)
(2): Dropout(p=0.5)
(3): Linear(in_features=4096, out_features=4096, bias=True)
(4): ReLU(inplace)
(5): Dropout(p=0.5)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
))
2.
for param_tensor in mod.state_dict():
print(param_tensor,'\t',mod.state_dict()[param_tensor].size())输出:
0('features.0.weight', '\t', (64, 3, 3, 3))
1('features.0.bias', '\t', (64,))
2('features.2.weight', '\t', (64, 64, 3, 3))
3('features.2.bias', '\t', (64,))
4('features.5.weight', '\t', (128, 64, 3, 3))
5('features.5.bias', '\t', (128,))
6('features.7.weight', '\t', (128, 128, 3, 3))
7('features.7.bias', '\t', (128,))
8('features.10.weight', '\t', (256, 128, 3, 3))
9('features.10.bias', '\t', (256,))
10('features.12.weight', '\t', (256, 256, 3, 3))
11('features.12.bias', '\t', (256,))
12('features.14.weight', '\t', (256, 256, 3, 3))
13('features.14.bias', '\t', (256,))
14('features.17.weight', '\t', (512, 256, 3, 3))
15('features.17.bias', '\t', (512,))
16('features.19.weight', '\t', (512, 512, 3, 3))
17('features.19.bias', '\t', (512,))
18('features.21.weight', '\t', (512, 512, 3, 3))
19('features.21.bias', '\t', (512,))
20('features.24.weight', '\t', (512, 512, 3, 3))
21('features.24.bias', '\t', (512,))
22('features.26.weight', '\t', (512, 512, 3, 3))
23('features.26.bias', '\t', (512,))
24('features.28.weight', '\t', (512, 512, 3, 3))
25('features.28.bias', '\t', (512,))
26('classifier.0.weight', '\t', (4096, 25088))
27('classifier.0.bias', '\t', (4096,))
28('classifier.3.weight', '\t', (4096, 4096))
29('classifier.3.bias', '\t', (4096,))
30('classifier.6.weight', '\t', (1000, 4096))
31('classifier.6.bias', '\t', (1000,))
3.
for param_tensor in mod.state_dict():
print(param_tensor,'\t',mod.state_dict()[param_tensor])输出:
('features.26.weight', '\t', tensor([[[[ 5.3396e-03, -2.6011e-03, 2.0471e-03],
[-5.5203e-04, -5.6705e-03, -1.6622e-02],
[-5.3414e-03, -4.3450e-03, -1.1729e-02]],
[[ 2.8564e-02, 2.7208e-02, 3.0957e-02],
[ 2.9747e-02, 2.6193e-02, 3.5095e-02],
[ 2.9610e-02, 4.1583e-02, 3.3541e-02]],
[[-4.8096e-03, 3.1882e-03, 1.0270e-03],
[ 6.3651e-03, 1.7796e-02, 1.4464e-02],
[-2.8096e-03, -5.9703e-03, 2.8339e-03]],
...,
[[ 1.8245e-03, 8.2978e-03, -4.4093e-04],
[ 3.4736e-04, -7.0685e-03, -1.1561e-02],
[-8.2451e-03, -2.0998e-02, -1.2935e-02]],
[[ 1.1445e-02, 1.9142e-02, 1.2391e-02],
[ 5.1714e-03, 1.4061e-02, 1.2852e-02],
[-2.2861e-03, 3.0864e-03, 2.8760e-03]],
[[ 4.0615e-03, 1.2981e-02, 3.9028e-03],
[ 1.0133e-02, 2.2851e-02, 1.2542e-02],
[-1.8526e-02, 4.0812e-03, -7.3445e-03]]],
[[[ 6.6611e-03, 1.6469e-02, 1.8038e-02],
[ 4.2513e-03, 1.3071e-02, 1.3559e-02],
[ 5.8119e-05, -5.8924e-03, 2.7461e-03]],
[[-1.9168e-02, -1.1762e-02, -6.3617e-03],
[-4.4906e-03, 5.5111e-03, 2.2184e-02],
[ 1.5402e-02, 3.0761e-02, 2.4692e-02]],
[[ 2.4585e-02, 1.4311e-03, 1.1614e-02],
[ 3.0728e-03, 3.2102e-03, -2.3297e-03],
[ 8.8638e-03, 2.1141e-04, -6.4401e-03]],
...,
[[-1.1725e-03, -1.5327e-02, -9.5785e-03],
[-3.2860e-03, -1.0098e-02, -9.1001e-03],
[-8.7265e-03, -1.0286e-02, -1.2795e-02]],
[[-2.2210e-03, -1.1368e-02, -1.6937e-02],
[-4.1622e-03, -1.7335e-02, -1.9301e-02],
[ 1.7208e-02, -5.8893e-03, -3.5251e-03]],
[[ 4.2934e-04, -3.1749e-02, -1.1264e-02],
[-1.3598e-02, -2.6306e-02, -1.2232e-02],
[-1.0754e-02, -6.1461e-03, 1.9349e-03]]],
[[[-7.4869e-03, -1.0894e-02, -2.3739e-02],
[ 1.2003e-02, -4.4158e-03, -2.6271e-02],
[ 1.0297e-02, -1.4211e-02, -2.0285e-02]],
[[-7.8340e-03, -2.3295e-02, -2.0594e-02],
[-4.7429e-03, -1.5618e-02, -1.8468e-02],
[ 2.5025e-03, -1.0162e-02, 2.7711e-03]],
[[ 8.0493e-03, -1.5987e-02, 4.0480e-03],
[ 5.0285e-04, -1.5592e-02, -9.4860e-04],
[ 1.8756e-03, -1.6222e-03, 2.6918e-02]],
...,
[[-6.0127e-03, -4.9466e-03, 9.5168e-03],
[-5.3006e-03, -7.0202e-03, 6.6488e-03],
[ 2.5722e-03, -5.2046e-03, 7.1014e-04]],
[[-2.1340e-02, -1.3918e-02, -1.2257e-02],
[-1.9682e-02, -2.2466e-02, -1.6505e-02],
[ 1.6370e-03, -1.8450e-03, -1.6151e-02]],
[[-6.9218e-03, -9.6376e-03, -6.8654e-03],
[-2.6699e-02, -8.4984e-03, -1.5467e-02],
[-2.1957e-02, -3.2741e-03, -1.0307e-02]]],
...,
[[[-9.1366e-03, -6.9445e-03, -1.6918e-03],
[-1.4680e-02, -7.3195e-03, -1.8997e-03],
[-1.7819e-02, -6.0907e-03, -3.8606e-03]],
[[-2.6625e-03, -4.5790e-03, -5.2848e-03],
[ 2.7805e-03, 1.2579e-02, -3.2661e-03],
[ 1.6539e-02, 1.5941e-02, 2.5934e-03]],
[[-1.7372e-02, -1.5669e-02, -1.7184e-02],
[-1.0523e-02, -8.0881e-03, -3.5002e-03],
[ 2.0012e-02, 1.3992e-02, 2.2567e-02]],
...,
[[-1.8450e-03, 1.4391e-03, -4.8037e-03],
[ 3.1779e-03, -2.1945e-03, -1.8111e-03],
[-8.2320e-04, -5.2492e-03, 5.9751e-03]],
[[-1.8042e-02, -1.9381e-02, -2.0262e-02],
[-1.2160e-02, -1.6965e-02, -1.1955e-02],
[-1.2115e-02, -2.2049e-02, -7.6491e-03]],
[[-1.5930e-02, -1.5632e-02, -1.4184e-02],
[-2.6613e-02, -2.4581e-02, -2.5748e-02],
[-4.3730e-02, -2.4319e-02, -3.5868e-02]]],
[[[ 2.5428e-02, 1.7873e-02, 3.8022e-03],
[ 3.0997e-02, 2.7097e-02, 1.3571e-02],
[ 4.2717e-02, 3.4481e-02, 1.6175e-02]],
[[-1.5489e-02, -5.0627e-03, 1.2652e-03],
[-1.0676e-02, -1.4705e-04, 6.0551e-03],
[-1.5833e-02, -1.6241e-02, -8.2176e-03]],
[[ 5.4370e-03, -9.6808e-03, -1.1396e-02],
[ 8.2531e-03, 3.7010e-03, 1.4806e-02],
[ 2.5946e-02, 3.4422e-02, 2.8250e-02]],
...,
[[ 4.0576e-02, 4.6424e-02, 6.3164e-03],
[ 2.3792e-02, 3.9974e-02, 1.2829e-02],
[-2.2258e-02, -1.0163e-02, -2.6070e-02]],
[[ 3.2659e-02, 3.8465e-02, 5.0303e-02],
[ 1.5540e-02, 2.3043e-02, 2.1985e-02],
[ 6.8735e-04, -1.1371e-02, -8.7360e-03]],
[[-2.5060e-02, -2.7326e-02, -2.7092e-02],
[-1.7381e-02, -2.3803e-02, -2.5497e-02],
[-3.5137e-02, -3.5637e-02, -4.1381e-02]]],
[[[ 6.4722e-03, 2.6112e-02, 1.4285e-02],
[ 1.5717e-02, 1.4589e-02, 1.0102e-02],
[ 3.1282e-04, 1.3287e-02, -1.4117e-03]],
[[-3.0626e-02, -1.7090e-02, -1.8398e-02],
[-1.3602e-02, -1.3641e-02, -1.7517e-05],
[-4.3749e-03, -1.6990e-02, -5.9113e-03]],
[[ 3.5263e-02, 6.0833e-02, 4.4315e-02],
[ 2.2339e-02, 6.8715e-02, 2.7959e-02],
[-3.1752e-03, 2.2407e-02, 1.5560e-02]],
...,
[[ 9.8860e-04, -3.1928e-03, 1.3426e-02],
[-3.6923e-03, -3.4468e-03, 3.3096e-02],
[-1.2766e-02, -1.6451e-02, 8.1307e-03]],
[[-7.9840e-03, 3.2893e-02, 4.4818e-02],
[ 1.8329e-02, 3.8900e-02, 3.9412e-02],
[-1.0024e-02, 5.2262e-03, -1.6413e-03]],
[[ 7.7319e-03, 1.6902e-02, -3.2354e-03],
[ 6.6349e-03, -1.1266e-02, 1.1672e-03],
[ 3.5068e-03, 2.4610e-03, 4.2446e-02]]]]))
('features.26.bias', '\t', tensor([-1.8666e-03, 5.7095e-02, 2.9342e-01, -4.1835e-02, -1.4559e-01,
9.7580e-02, 2.3287e-03, -4.6849e-02, -1.1053e-01, 3.6631e-02,
-2.3670e-02, 1.9662e-01, 6.2942e-03, 1.0368e-01, -1.2430e-01,
-5.5525e-02, 1.0667e-01, 2.8836e-01, 7.5442e-02, 4.5309e-02,
7.1352e-02, 9.4569e-02, -1.0453e-01, -3.0553e-01, -1.4769e-01,
1.5424e-01, -3.6026e-02, -1.6674e-02, 5.0073e-02, -1.0362e-01,
1.6252e-01, 3.6867e-02, -5.6979e-02, -8.1772e-02, 2.0144e-01,
7.0015e-02, -2.2859e-01, 1.3822e-01, 2.6113e-01, 2.1136e-02,
1.0234e-01, 8.6473e-02, 6.1111e-02, 7.2767e-02, 6.9773e-03,
-4.2054e-02, 1.0137e-01, 1.8389e-01, 9.8832e-02, 2.6407e-01,
5.3378e-02, 1.6710e-01, 2.5089e-01, 1.1613e-01, 2.1970e-01,
1.3894e-01, -4.7889e-02, -9.7952e-02, 1.0368e-01, 1.6410e-01,
1.9025e-01, 7.4526e-02, 2.2013e-01, 9.4355e-02, -4.7882e-02,
4.6833e-02, -3.9320e-03, 1.8785e-01, -2.2729e-02, 1.4750e-01,
1.4188e-01, 1.8834e-01, 2.4944e-02, 2.1748e-01, 1.0304e-01,
1.5125e-01, 1.7675e-01, 6.0383e-02, 1.6431e-01, 3.4881e-02,
3.8004e-02, -6.8401e-02, 6.2248e-03, 1.3423e-02, -1.3956e-01,
7.8097e-02, 1.9550e-01, -1.2594e-01, 9.6037e-02, -2.0711e-03,
6.9357e-02, -1.6091e-01, 8.9944e-02, 2.5616e-01, -9.8200e-02,
2.6589e-01, -2.3143e-02, -1.7001e-01, -2.4942e-04, 2.9673e-02,
1.0278e-01, 1.5479e-01, 1.9948e-01, -2.8051e-01, 1.0574e-01,
7.8208e-02, 6.1702e-02, 7.9237e-02, 1.7736e-03, -9.6903e-02,
2.1890e-01, -3.3631e-02, 3.0116e-02, 2.6728e-01, 7.2655e-02,
3.8347e-02, 4.4684e-02, 2.9473e-01, 2.6413e-02, 2.6462e-01,
2.2294e-01, -1.7687e-01, 1.4487e-01, 2.5277e-01, 9.5677e-02,
7.3535e-02, 1.2854e-01, 3.4688e-01, 1.1824e-01, 3.1590e-01,
-8.2523e-02, -2.8264e-01, 2.5187e-01, -4.4773e-02, 8.9053e-02,
2.1587e-03, 1.8896e-01, 1.6210e-01, -1.9770e-01, 1.5449e-01,
-1.7553e-02, -1.6512e-02, 1.8990e-01, 6.1404e-02, 1.6946e-01,
-2.7088e-02, 5.3145e-02, 1.0964e-01, 1.3800e-01, 6.7332e-02,
-5.0629e-03, -1.7180e-01, 7.6077e-02, -1.2958e-02, 1.3120e-01,
-8.2870e-02, 1.0818e-01, 1.1670e-01, 1.9102e-01, -2.6032e-01,
1.2425e-01, -6.4174e-01, -9.4544e-02, 1.4883e-01, -2.2177e-02,
1.9388e-01, 2.2708e-01, -3.0475e-02, 4.3636e-02, 2.5054e-02,
4.8016e-02, 2.4943e-01, 8.6237e-02, 9.5355e-03, 6.0247e-03,
1.5412e-01, 1.5840e-01, 1.3796e-01, -1.2680e-01, 1.3226e-01,
4.6432e-02, 2.2256e-01, 2.6363e-01, -3.5001e-03, 1.8687e-01,
-3.5546e-02, 2.9883e-04, 7.7087e-02, 7.4261e-02, 1.9941e-01,
-2.8899e-02, 2.8666e-02, 2.2292e-03, 6.0504e-02, 1.2704e-01,
-1.9055e-01, 1.4168e-01, 5.8124e-02, 2.5609e-02, 1.1290e-01,
3.1867e-01, -6.8704e-02, 2.2565e-01, 1.5933e-01, 3.9752e-02,
1.2825e-01, 1.4174e-01, 4.1308e-01, 4.6752e-02, 1.2245e-01,
2.1968e-01, 4.9856e-02, 7.1631e-02, -3.9455e-02, -2.3028e-01,
1.3458e-01, -4.2816e-02, 1.5429e-02, -6.6889e-01, 1.6270e-01,
2.2208e-02, -1.7758e-01, 1.2783e-01, 2.1327e-02, -5.5212e-01,
2.6010e-01, 8.0355e-02, 9.4295e-02, -7.3130e-02, 1.6812e-01,
5.4281e-02, 1.4536e-01, 1.9376e-01, 3.5577e-01, 5.1165e-02,
2.2360e-01, 2.3526e-01, 1.6229e-01, 9.5042e-03, -2.0074e-01,
1.4703e-02, -2.0507e-01, 1.5991e-01, -1.1455e-01, 2.4596e-01,
6.5557e-02, 1.0167e-01, -8.4635e-02, -3.6976e-01, 1.2340e-03,
-5.0580e-02, 8.3523e-02, -2.8552e-01, 1.3614e-01, 1.2556e-01,
2.7155e-01, -1.1871e-01, 1.4263e-02, 8.3877e-02, -2.1675e-02,
-1.0315e-01, 4.0777e-02, 1.6204e-02, -6.4240e-02, -8.4088e-03,
1.0339e-01, -3.2666e-02, -7.4288e-02, -5.5998e-02, 1.3324e-01,
-1.2704e-01, 2.5314e-01, 2.7311e-01, -7.6727e-02, 1.6360e-01,
1.4462e-01, 4.8776e-02, 1.2075e-01, -3.1966e-01, 6.8700e-02,
-3.0422e-01, 5.1718e-02, 6.9389e-02, -5.4538e-03, 1.4129e-01,
-2.4315e-01, 1.3872e-01, 7.0531e-02, -1.9635e-01, 2.3215e-01,
5.3497e-02, 1.2996e-01, 2.3196e-01, 5.3783e-02, -3.2011e-01,
9.7505e-02, 6.9466e-02, 1.2734e-01, -6.5295e-02, 1.3896e-01,
9.7967e-02, 3.6168e-03, -4.9335e-02, 2.2809e-01, -1.8391e-01,
8.4771e-02, -4.9345e-02, 3.6355e-02, -1.4925e-02, -3.3247e-02,
-2.8801e-02, 7.3432e-02, 1.0934e-01, 2.1105e-01, 7.4307e-02,
-3.3863e-02, -9.7055e-03, -4.4074e-02, 5.2093e-02, 7.3870e-02,
2.2864e-01, 4.4315e-01, -2.6606e-01, 2.4656e-01, 8.8123e-03,
2.6059e-01, 2.6270e-02, 4.9199e-03, -2.0948e-02, 2.3407e-01,
4.6750e-02, -1.8637e-01, -6.0074e-02, 1.9932e-02, 1.2411e-01,
6.9108e-02, 8.7628e-02, 6.0595e-02, 1.9305e-02, 7.3282e-03,
2.7907e-01, 1.5810e-01, 1.4193e-01, 7.2068e-02, 1.2330e-01,
2.2972e-01, 3.7316e-02, 2.7085e-01, 2.6735e-01, 2.9073e-01,
-1.2664e-01, -1.7979e-01, 1.4586e-01, -1.6734e-01, 3.0067e-01,
5.5059e-02, 3.3035e-02, -1.1998e-01, 9.2903e-02, 1.1212e-01,
3.7626e-03, 1.4395e-01, 6.1938e-02, -1.2140e-02, -6.1718e-02,
2.5250e-01, 1.3300e-01, 1.6234e-01, 8.2953e-02, 2.6083e-02,
1.3916e-01, 2.0513e-01, 6.7912e-04, -1.7945e-01, 1.5764e-01,
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-2.5930e-02, 1.3437e-03, 2.8364e-02, -1.3526e-02, -6.8687e-02,
1.7460e-02, 5.0171e-02, 1.7448e-02, 3.8029e-02, 9.8575e-03,
2.2886e-02, 1.2857e-02, -2.6916e-02, 4.8951e-02, -9.6445e-03,
-3.5893e-02, -2.6359e-02, -2.0544e-02, 3.6961e-03, -9.1653e-03,
2.1354e-02, -4.2778e-02, -4.1528e-02, -3.8514e-02, -3.1796e-02,
3.4630e-02, 4.7508e-02, 2.4460e-02, -6.2255e-02, -5.2742e-02,
8.7197e-03, -4.4129e-03, -1.8273e-02, -3.5542e-02, -8.3854e-03,
7.8142e-02, 1.6183e-02, -2.9596e-02, 4.5440e-03, -1.6409e-02,
-6.8854e-02, 5.5268e-03, -9.1066e-04, 7.5423e-03, 3.0180e-04,
-4.5678e-02, 2.2746e-02, 1.7497e-03, -3.3456e-02, -9.0392e-03,
-6.2202e-02, 5.3786e-02, 3.8939e-04, 4.0257e-02, 3.1605e-03,
-6.9488e-02, -3.2256e-02, 2.3048e-02, 2.6307e-03, -3.9207e-02,
-5.0876e-02, -4.5844e-02, 3.2221e-02, -2.7124e-02, -7.5084e-03,
-4.4676e-03, 4.1103e-02, -5.6786e-03, -3.9598e-02, 1.4338e-02,
2.3843e-03, 1.4621e-02, 4.6011e-02, 7.8583e-04, 1.3014e-02,
1.4411e-02, -2.8963e-02, -4.4605e-02, 8.5262e-03, -2.8862e-02,
-5.3216e-03, 2.0222e-02, 3.5987e-03, 1.5160e-02, 8.0866e-02,
-1.5476e-02, 4.4872e-02, 6.0198e-04, -1.2368e-02, 8.1617e-03,
-3.7857e-03, -4.2203e-02, 2.6076e-02, 2.1671e-02, -1.6940e-02,
1.3443e-02, -2.6324e-02, 3.6311e-02, 4.8506e-02, -3.7339e-03,
1.4108e-02, 7.7902e-04, -6.0017e-02, 3.7350e-03, -2.6110e-02,
5.0281e-03, -2.4351e-02, -2.7377e-02, -3.7669e-02, 3.8070e-02,
1.0932e-02, 2.8160e-02, -3.5762e-02, 2.3261e-02, 1.9776e-02,
-3.8261e-03, -6.5859e-03, -1.8929e-02, -4.5027e-02, -1.1969e-02,
8.7756e-03, -2.6216e-02, -2.1189e-02, 1.2913e-02, -1.8512e-02,
-2.6655e-02, 4.0894e-03, 1.6387e-03, -1.6818e-02, -1.0804e-02,
3.9582e-02, 1.8490e-02, 2.5662e-02, -4.7438e-02, 1.8842e-02,
-3.9618e-02, -2.2390e-02, -4.7946e-03, 6.2678e-04, 1.4640e-03,
-2.9613e-02, -2.3728e-02, 5.8741e-03, 3.9343e-02, -4.4972e-03,
1.2785e-03, 1.9657e-02, 8.6271e-03, 9.4731e-03, -8.3145e-02,
-9.5304e-03, 5.8406e-03, -2.7983e-02, 1.1320e-02, 5.2982e-03,
2.1871e-02, -8.8594e-04, -3.8309e-03, -5.7810e-02, 3.0410e-03,
-2.1917e-02, -7.4865e-02, -4.3740e-02, 2.0407e-02, -3.4062e-03,
1.8807e-02, -1.7270e-02, 1.7385e-02, 4.7496e-02, 6.6395e-02,
-1.7039e-02, -1.0944e-02, 7.3278e-03, 2.6257e-02, -2.1868e-02,
-1.0651e-02, 2.5665e-02, -3.9925e-02, 9.3388e-03, 6.9712e-02,
-1.0000e-02, -3.2376e-02, -7.8124e-02, -2.6672e-02, -1.0220e-04,
2.9822e-02, 6.2931e-03, -1.5316e-02, -2.4690e-02, -2.3386e-02,
-8.5112e-03, -7.5672e-03, -3.5472e-02, 8.3757e-03, -4.4933e-02,
-2.5905e-02, -3.5467e-02, -7.1374e-02, 9.2666e-03, -7.3159e-02,
-9.6658e-02, -3.1080e-02, -3.5131e-02, -1.6773e-02, -2.2672e-02,
7.4318e-03, -9.8196e-03, -4.4741e-02, -2.3274e-02, -4.2749e-02,
-1.0063e-02, 4.5505e-03, -1.6073e-02, -4.8172e-02, -4.4076e-02,
6.1854e-02, -9.5113e-03, 2.6095e-02, -8.6355e-02, 1.6138e-02,
-8.2407e-03, 5.0933e-02, 1.8536e-03, -5.1323e-02, 2.2101e-02,
-3.0345e-03, 3.6723e-02, 2.7383e-02, -1.4681e-02, 1.0468e-02,
1.5814e-02, 1.8042e-02, 5.9351e-02, -1.9994e-04, -5.2148e-02,
-2.8923e-02, -3.6363e-02, -6.5360e-02, -6.3748e-04, -2.7562e-02,
-4.4246e-02, -4.8769e-02, -4.5221e-02, -5.8486e-02, -3.1612e-02,
-1.4006e-02, -5.0941e-02, 2.0858e-02, -3.2510e-02, -7.1847e-03]))
4.
print("mod.state_dict():",mod.state_dict())输出:
('features.26.weight', tensor([[[[ 5.3396e-03, -2.6011e-03, 2.0471e-03],
[-5.5203e-04, -5.6705e-03, -1.6622e-02],
[-5.3414e-03, -4.3450e-03, -1.1729e-02]],
[[ 2.8564e-02, 2.7208e-02, 3.0957e-02],
[ 2.9747e-02, 2.6193e-02, 3.5095e-02],
[ 2.9610e-02, 4.1583e-02, 3.3541e-02]],
[[-4.8096e-03, 3.1882e-03, 1.0270e-03],
[ 6.3651e-03, 1.7796e-02, 1.4464e-02],
[-2.8096e-03, -5.9703e-03, 2.8339e-03]],
...,
[[ 1.8245e-03, 8.2978e-03, -4.4093e-04],
[ 3.4736e-04, -7.0685e-03, -1.1561e-02],
[-8.2451e-03, -2.0998e-02, -1.2935e-02]],
[[ 1.1445e-02, 1.9142e-02, 1.2391e-02],
[ 5.1714e-03, 1.4061e-02, 1.2852e-02],
[-2.2861e-03, 3.0864e-03, 2.8760e-03]],
[[ 4.0615e-03, 1.2981e-02, 3.9028e-03],
[ 1.0133e-02, 2.2851e-02, 1.2542e-02],
[-1.8526e-02, 4.0812e-03, -7.3445e-03]]],
[[[ 6.6611e-03, 1.6469e-02, 1.8038e-02],
[ 4.2513e-03, 1.3071e-02, 1.3559e-02],
[ 5.8119e-05, -5.8924e-03, 2.7461e-03]],
[[-1.9168e-02, -1.1762e-02, -6.3617e-03],
[-4.4906e-03, 5.5111e-03, 2.2184e-02],
[ 1.5402e-02, 3.0761e-02, 2.4692e-02]],
[[ 2.4585e-02, 1.4311e-03, 1.1614e-02],
[ 3.0728e-03, 3.2102e-03, -2.3297e-03],
[ 8.8638e-03, 2.1141e-04, -6.4401e-03]],
...,
[[-1.1725e-03, -1.5327e-02, -9.5785e-03],
[-3.2860e-03, -1.0098e-02, -9.1001e-03],
[-8.7265e-03, -1.0286e-02, -1.2795e-02]],
[[-2.2210e-03, -1.1368e-02, -1.6937e-02],
[-4.1622e-03, -1.7335e-02, -1.9301e-02],
[ 1.7208e-02, -5.8893e-03, -3.5251e-03]],
[[ 4.2934e-04, -3.1749e-02, -1.1264e-02],
[-1.3598e-02, -2.6306e-02, -1.2232e-02],
[-1.0754e-02, -6.1461e-03, 1.9349e-03]]],
[[[-7.4869e-03, -1.0894e-02, -2.3739e-02],
[ 1.2003e-02, -4.4158e-03, -2.6271e-02],
[ 1.0297e-02, -1.4211e-02, -2.0285e-02]],
[[-7.8340e-03, -2.3295e-02, -2.0594e-02],
[-4.7429e-03, -1.5618e-02, -1.8468e-02],
[ 2.5025e-03, -1.0162e-02, 2.7711e-03]],
[[ 8.0493e-03, -1.5987e-02, 4.0480e-03],
[ 5.0285e-04, -1.5592e-02, -9.4860e-04],
[ 1.8756e-03, -1.6222e-03, 2.6918e-02]],
...,
[[-6.0127e-03, -4.9466e-03, 9.5168e-03],
[-5.3006e-03, -7.0202e-03, 6.6488e-03],
[ 2.5722e-03, -5.2046e-03, 7.1014e-04]],
[[-2.1340e-02, -1.3918e-02, -1.2257e-02],
[-1.9682e-02, -2.2466e-02, -1.6505e-02],
[ 1.6370e-03, -1.8450e-03, -1.6151e-02]],
[[-6.9218e-03, -9.6376e-03, -6.8654e-03],
[-2.6699e-02, -8.4984e-03, -1.5467e-02],
[-2.1957e-02, -3.2741e-03, -1.0307e-02]]],
...,
[[[-9.1366e-03, -6.9445e-03, -1.6918e-03],
[-1.4680e-02, -7.3195e-03, -1.8997e-03],
[-1.7819e-02, -6.0907e-03, -3.8606e-03]],
[[-2.6625e-03, -4.5790e-03, -5.2848e-03],
[ 2.7805e-03, 1.2579e-02, -3.2661e-03],
[ 1.6539e-02, 1.5941e-02, 2.5934e-03]],
[[-1.7372e-02, -1.5669e-02, -1.7184e-02],
[-1.0523e-02, -8.0881e-03, -3.5002e-03],
[ 2.0012e-02, 1.3992e-02, 2.2567e-02]],
...,
[[-1.8450e-03, 1.4391e-03, -4.8037e-03],
[ 3.1779e-03, -2.1945e-03, -1.8111e-03],
[-8.2320e-04, -5.2492e-03, 5.9751e-03]],
[[-1.8042e-02, -1.9381e-02, -2.0262e-02],
[-1.2160e-02, -1.6965e-02, -1.1955e-02],
[-1.2115e-02, -2.2049e-02, -7.6491e-03]],
[[-1.5930e-02, -1.5632e-02, -1.4184e-02],
[-2.6613e-02, -2.4581e-02, -2.5748e-02],
[-4.3730e-02, -2.4319e-02, -3.5868e-02]]],
[[[ 2.5428e-02, 1.7873e-02, 3.8022e-03],
[ 3.0997e-02, 2.7097e-02, 1.3571e-02],
[ 4.2717e-02, 3.4481e-02, 1.6175e-02]],
[[-1.5489e-02, -5.0627e-03, 1.2652e-03],
[-1.0676e-02, -1.4705e-04, 6.0551e-03],
[-1.5833e-02, -1.6241e-02, -8.2176e-03]],
[[ 5.4370e-03, -9.6808e-03, -1.1396e-02],
[ 8.2531e-03, 3.7010e-03, 1.4806e-02],
[ 2.5946e-02, 3.4422e-02, 2.8250e-02]],
...,
[[ 4.0576e-02, 4.6424e-02, 6.3164e-03],
[ 2.3792e-02, 3.9974e-02, 1.2829e-02],
[-2.2258e-02, -1.0163e-02, -2.6070e-02]],
[[ 3.2659e-02, 3.8465e-02, 5.0303e-02],
[ 1.5540e-02, 2.3043e-02, 2.1985e-02],
[ 6.8735e-04, -1.1371e-02, -8.7360e-03]],
[[-2.5060e-02, -2.7326e-02, -2.7092e-02],
[-1.7381e-02, -2.3803e-02, -2.5497e-02],
[-3.5137e-02, -3.5637e-02, -4.1381e-02]]],
[[[ 6.4722e-03, 2.6112e-02, 1.4285e-02],
[ 1.5717e-02, 1.4589e-02, 1.0102e-02],
[ 3.1282e-04, 1.3287e-02, -1.4117e-03]],
[[-3.0626e-02, -1.7090e-02, -1.8398e-02],
[-1.3602e-02, -1.3641e-02, -1.7517e-05],
[-4.3749e-03, -1.6990e-02, -5.9113e-03]],
[[ 3.5263e-02, 6.0833e-02, 4.4315e-02],
[ 2.2339e-02, 6.8715e-02, 2.7959e-02],
[-3.1752e-03, 2.2407e-02, 1.5560e-02]],
...,
[[ 9.8860e-04, -3.1928e-03, 1.3426e-02],
[-3.6923e-03, -3.4468e-03, 3.3096e-02],
[-1.2766e-02, -1.6451e-02, 8.1307e-03]],
[[-7.9840e-03, 3.2893e-02, 4.4818e-02],
[ 1.8329e-02, 3.8900e-02, 3.9412e-02],
[-1.0024e-02, 5.2262e-03, -1.6413e-03]],
[[ 7.7319e-03, 1.6902e-02, -3.2354e-03],
[ 6.6349e-03, -1.1266e-02, 1.1672e-03],
[ 3.5068e-03, 2.4610e-03, 4.2446e-02]]]])), ('features.26.bias', tensor([-1.8666e-03, 5.7095e-02, 2.9342e-01, -4.1835e-02, -1.4559e-01,
9.7580e-02, 2.3287e-03, -4.6849e-02, -1.1053e-01, 3.6631e-02,
-2.3670e-02, 1.9662e-01, 6.2942e-03, 1.0368e-01, -1.2430e-01,
-5.5525e-02, 1.0667e-01, 2.8836e-01, 7.5442e-02, 4.5309e-02,
7.1352e-02, 9.4569e-02, -1.0453e-01, -3.0553e-01, -1.4769e-01,
1.5424e-01, -3.6026e-02, -1.6674e-02, 5.0073e-02, -1.0362e-01,
1.6252e-01, 3.6867e-02, -5.6979e-02, -8.1772e-02, 2.0144e-01,
7.0015e-02, -2.2859e-01, 1.3822e-01, 2.6113e-01, 2.1136e-02,
1.0234e-01, 8.6473e-02, 6.1111e-02, 7.2767e-02, 6.9773e-03,
-4.2054e-02, 1.0137e-01, 1.8389e-01, 9.8832e-02, 2.6407e-01,
5.3378e-02, 1.6710e-01, 2.5089e-01, 1.1613e-01, 2.1970e-01,
1.3894e-01, -4.7889e-02, -9.7952e-02, 1.0368e-01, 1.6410e-01,
1.9025e-01, 7.4526e-02, 2.2013e-01, 9.4355e-02, -4.7882e-02,
4.6833e-02, -3.9320e-03, 1.8785e-01, -2.2729e-02, 1.4750e-01,
1.4188e-01, 1.8834e-01, 2.4944e-02, 2.1748e-01, 1.0304e-01,
1.5125e-01, 1.7675e-01, 6.0383e-02, 1.6431e-01, 3.4881e-02,
3.8004e-02, -6.8401e-02, 6.2248e-03, 1.3423e-02, -1.3956e-01,
7.8097e-02, 1.9550e-01, -1.2594e-01, 9.6037e-02, -2.0711e-03,
6.9357e-02, -1.6091e-01, 8.9944e-02, 2.5616e-01, -9.8200e-02,
2.6589e-01, -2.3143e-02, -1.7001e-01, -2.4942e-04, 2.9673e-02,
1.0278e-01, 1.5479e-01, 1.9948e-01, -2.8051e-01, 1.0574e-01,
7.8208e-02, 6.1702e-02, 7.9237e-02, 1.7736e-03, -9.6903e-02,
2.1890e-01, -3.3631e-02, 3.0116e-02, 2.6728e-01, 7.2655e-02,
3.8347e-02, 4.4684e-02, 2.9473e-01, 2.6413e-02, 2.6462e-01,
2.2294e-01, -1.7687e-01, 1.4487e-01, 2.5277e-01, 9.5677e-02,
7.3535e-02, 1.2854e-01, 3.4688e-01, 1.1824e-01, 3.1590e-01,
-8.2523e-02, -2.8264e-01, 2.5187e-01, -4.4773e-02, 8.9053e-02,
2.1587e-03, 1.8896e-01, 1.6210e-01, -1.9770e-01, 1.5449e-01,
-1.7553e-02, -1.6512e-02, 1.8990e-01, 6.1404e-02, 1.6946e-01,
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4.8016e-02, 2.4943e-01, 8.6237e-02, 9.5355e-03, 6.0247e-03,
1.5412e-01, 1.5840e-01, 1.3796e-01, -1.2680e-01, 1.3226e-01,
4.6432e-02, 2.2256e-01, 2.6363e-01, -3.5001e-03, 1.8687e-01,
-3.5546e-02, 2.9883e-04, 7.7087e-02, 7.4261e-02, 1.9941e-01,
-2.8899e-02, 2.8666e-02, 2.2292e-03, 6.0504e-02, 1.2704e-01,
-1.9055e-01, 1.4168e-01, 5.8124e-02, 2.5609e-02, 1.1290e-01,
3.1867e-01, -6.8704e-02, 2.2565e-01, 1.5933e-01, 3.9752e-02,
1.2825e-01, 1.4174e-01, 4.1308e-01, 4.6752e-02, 1.2245e-01,
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1.3458e-01, -4.2816e-02, 1.5429e-02, -6.6889e-01, 1.6270e-01,
2.2208e-02, -1.7758e-01, 1.2783e-01, 2.1327e-02, -5.5212e-01,
2.6010e-01, 8.0355e-02, 9.4295e-02, -7.3130e-02, 1.6812e-01,
5.4281e-02, 1.4536e-01, 1.9376e-01, 3.5577e-01, 5.1165e-02,
2.2360e-01, 2.3526e-01, 1.6229e-01, 9.5042e-03, -2.0074e-01,
1.4703e-02, -2.0507e-01, 1.5991e-01, -1.1455e-01, 2.4596e-01,
6.5557e-02, 1.0167e-01, -8.4635e-02, -3.6976e-01, 1.2340e-03,
-5.0580e-02, 8.3523e-02, -2.8552e-01, 1.3614e-01, 1.2556e-01,
2.7155e-01, -1.1871e-01, 1.4263e-02, 8.3877e-02, -2.1675e-02,
-1.0315e-01, 4.0777e-02, 1.6204e-02, -6.4240e-02, -8.4088e-03,
1.0339e-01, -3.2666e-02, -7.4288e-02, -5.5998e-02, 1.3324e-01,
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