nn.AdaptiveAvgPool1d(5)

自適應的平均池化，應該是在最低維上轉為5個數，即
9---->5
(1, 9, 8)---->(1, 5, 8)

import torch from torch import nn from torch import autograd m = nn.AdaptiveAvgPool1d(5)# target output size of 5 input = autograd.Variable(torch.randn(1, 9, 8)) print(input) tensor([[[ 3.0320e-01, 3.6879e-01, -1.0896e+00, -1.3251e+00, 2.1113e-01,-1.3839e+00, -1.2036e+00, -1.5154e+00],[ 4.2374e-01, -1.2755e+00, 2.0100e-01, 1.0388e+00, 1.0022e+00,-7.3641e-01, -1.3196e+00, 2.3717e-01],[ 2.9847e-01, -7.8317e-01, -1.1153e+00, 1.2325e+00, -6.0868e-01,-6.0629e-01, -1.0107e+00, -1.8141e-01],[ 9.8106e-01, 1.8035e+00, 1.5790e-01, -7.5658e-01, -1.5404e+00,-4.7695e-01, 3.9196e-01, 7.1803e-01],[-1.4173e+00, 6.5709e-01, 5.9748e-01, -2.3872e+00, -1.3445e+00,-5.9272e-01, -2.0092e-01, -2.0429e+00],[-3.7274e-01, -6.3365e-01, -3.1410e-01, -5.2788e-01, 2.9406e-01,4.7489e-01, -3.2312e-01, 2.5546e+00],[-4.9002e-01, -5.4810e-01, 1.4601e+00, 3.2844e-01, -3.7427e-01,-4.9377e-01, 2.8525e-01, -2.7228e+00],[-2.3692e-03, 2.1962e+00, 9.3386e-02, 4.1071e-01, -8.9220e-01,-2.4525e-01, -6.0414e-01, 4.3239e-01],[-9.7711e-01, -4.6109e-01, -3.6924e-01, 8.7918e-02, -1.1675e+00,-6.8003e-01, -6.2466e-01, -6.5148e-01]]]) output = m(input) print(output) tensor([[[ 0.3360, -0.6820, -0.5570, -0.7921, -1.3595],[-0.4259, -0.0119, 1.0205, -0.3513, -0.5412],[-0.2423, -0.2220, 0.3119, -0.7419, -0.5961],[ 1.3923, 0.4016, -1.1485, -0.5418, 0.5550],[-0.3801, -0.3776, -1.8658, -0.7127, -1.1219],[-0.5032, -0.4919, -0.1169, 0.1486, 1.1157],[-0.5191, 0.4135, -0.0229, -0.1943, -1.2188],[ 1.0969, 0.9001, -0.2407, -0.5805, -0.0859],[-0.7191, -0.2475, -0.5398, -0.8241, -0.6381]]]) m = nn.AdaptiveAvgPool1d(1)# target output size of 1 input = autograd.Variable(torch.randn(1, 9, 8)) print(input) tensor([[[-0.4052, -0.2062, -0.3630, 0.5443, -1.1570, 0.9105, -1.7502,-2.0864],[ 0.1581, -1.6536, -0.9496, -0.5045, 0.4973, -1.6026, 1.8087,-1.5534],[-0.5572, 2.0890, -1.3753, -0.5857, 0.9093, -0.3246, 0.5703,0.7315],[ 0.5320, 0.2400, -1.8946, 1.2201, -0.8956, 0.3155, -0.4960,0.2246],[-0.7937, 0.1326, 1.5602, -0.1684, 1.3426, 0.3997, -0.7715,-0.1143],[-1.2648, -0.9803, 1.2850, -0.5430, 1.0204, -0.6017, -0.3234,0.8067],[ 1.4721, 0.4670, 0.3708, 1.1734, -0.1523, 2.5045, -0.8696,0.8504],[-0.0640, -1.6002, 0.8113, -0.5312, -0.4878, 1.7936, -0.1291,0.2794],[-0.1011, -0.9912, -0.5175, -0.3998, 0.7953, -0.9220, -0.8127,-1.2444]]]) output = m(input) print(output)

[-0.4052, -0.2062, -0.3630, 0.5443, -1.1570, 0.9105, -1.7502, -2.0864]相加除以9等于-0.48不等于-0.5642
說明不是這樣算的，不知道nn.AdaptiveAvgPool1d()運算過程是如何計算的

tensor([[[-0.5642],[-0.4749],[ 0.1821],[-0.0942],[ 0.1984],[-0.0751],[ 0.7270],[ 0.0090],[-0.5242]]])

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