In [1]:
import torch
import torch.nn as nn
import torch.nn.init as init
from model import RecurrentModel
reg = RecurrentModel(inputSize=4096, nHidden=[4096,1024, 64*32], \
noutputs=64*32, batchSize=1, ship2gpu=True, \
numLayers=1)
# for m in reg.modules():
# if(isinstance(m, nn.LSTM)):
# mkeys = m.state_dict().keys()
# mvals = m.state_dict().values()
# init.uniform(mvals[1], 0, 1)
# print(mvals[1])
"""
for m in reg.lstm3.modules():
for t in m.state_dict().values():
print init.uniform(t, 0, 1)
"""
# for m in reg.modules():
# if (isinstance, nn.LSTM):
# keys = m.state_dict().keys
#weight_ih_l
Out[1]:
'\nfor m in reg.lstm3.modules(): \n for t in m.state_dict().values():\n print init.uniform(t, 0, 1)\n'
In [4]:
params = list(reg.parameters())
print params
[Parameter containing:
-6.1764e-03 1.0196e-02 -4.8158e-03 ... -1.3005e-02 1.2572e-02 1.0006e-02
5.8714e-03 -1.0709e-02 -1.4068e-02 ... 4.7597e-03 -1.3672e-02 -1.0446e-02
3.8936e-03 1.5572e-02 1.0738e-02 ... 1.9629e-03 8.2544e-04 1.5378e-02
... ⋱ ...
-9.6988e-03 5.6114e-03 7.2494e-03 ... -9.7322e-03 -1.4987e-03 -1.5181e-02
9.1930e-03 -7.7751e-04 1.0126e-02 ... 7.0993e-03 -9.6946e-03 9.2351e-03
1.7392e-03 -3.1567e-03 1.1008e-02 ... 4.8549e-03 -1.4407e-02 1.3779e-02
[torch.DoubleTensor of size 16384x4096]
, Parameter containing:
3.0674e-03 -1.4418e-02 -1.4713e-02 ... -1.3884e-02 1.5112e-02 -1.5605e-03
6.5186e-03 9.7675e-03 -1.0814e-02 ... 7.6088e-03 1.2152e-02 -3.0384e-03
-1.2199e-02 9.5358e-03 -2.4002e-03 ... -1.1985e-02 -6.3747e-03 1.2077e-02
... ⋱ ...
-8.7445e-03 3.0171e-03 -3.5812e-03 ... 1.2019e-02 1.3638e-02 -8.1813e-03
-1.3924e-02 1.3511e-02 1.4339e-02 ... -1.0296e-02 -4.0513e-03 -4.2915e-03
-1.5116e-02 -7.9915e-03 7.7508e-03 ... 1.4098e-02 1.0286e-02 -1.4088e-02
[torch.DoubleTensor of size 16384x4096]
, Parameter containing:
-7.9938e-03 -6.8149e-03 -2.8361e-02 ... 2.2732e-04 8.0911e-03 2.7480e-02
-5.7078e-03 -3.0018e-02 1.6093e-02 ... -1.0365e-02 -6.9713e-03 2.5716e-02
-1.3081e-02 2.1478e-02 3.7763e-03 ... 9.1951e-03 2.9470e-02 -1.6416e-02
... ⋱ ...
1.3396e-02 1.3351e-02 6.1546e-04 ... -3.0222e-02 2.6748e-02 2.6073e-02
-2.0555e-02 -3.0876e-02 2.3071e-02 ... -9.0544e-03 2.0729e-02 -8.2354e-03
1.7304e-02 -1.8979e-02 1.4538e-02 ... -2.9897e-02 -1.4413e-02 -2.1524e-02
[torch.DoubleTensor of size 4096x4096]
, Parameter containing:
2.4741e-02 -1.5301e-02 2.5316e-02 ... -1.2111e-02 1.0435e-02 -2.8739e-02
-2.3048e-02 -2.6921e-02 -2.0897e-02 ... -4.4201e-03 3.0935e-02 3.0540e-02
2.5862e-02 -1.9326e-02 -2.2926e-02 ... -1.0019e-02 -1.3795e-02 9.1855e-03
... ⋱ ...
2.8649e-02 2.5118e-02 1.8011e-02 ... -2.0008e-03 1.4374e-02 -2.8311e-02
2.0291e-02 4.5106e-03 -1.1679e-02 ... 1.2991e-02 1.5746e-02 -2.6977e-02
-2.7820e-02 1.8272e-02 -1.6348e-02 ... -1.7788e-02 -7.7549e-03 -1.3663e-02
[torch.DoubleTensor of size 4096x1024]
, Parameter containing:
-1.9138e-02 -9.4376e-03 -2.1603e-02 ... -2.1392e-02 -1.3221e-03 -2.1732e-02
7.2356e-03 6.0557e-03 -1.2403e-02 ... -2.3860e-03 1.1154e-02 -3.3996e-03
-1.0013e-02 3.2242e-03 7.5778e-03 ... 1.1446e-02 1.2351e-02 1.4997e-02
... ⋱ ...
-5.0316e-03 -1.2850e-02 6.2053e-03 ... 2.1595e-02 -2.5359e-03 -6.5198e-03
1.9525e-02 -5.4727e-03 -1.0127e-02 ... 5.0508e-03 -1.4952e-03 -5.4373e-03
-2.7979e-03 -1.5377e-02 -1.3318e-03 ... -7.3602e-03 -8.9475e-03 -3.5579e-03
[torch.DoubleTensor of size 8192x1024]
, Parameter containing:
1.6421e-05 1.3183e-03 2.9429e-03 ... 1.5533e-02 1.1309e-02 -1.4332e-02
3.5590e-03 9.1467e-03 -1.1816e-02 ... -2.8441e-03 1.9235e-02 2.3587e-03
1.6672e-02 -1.8863e-02 -1.0777e-02 ... -1.9488e-02 -1.5322e-02 1.6784e-02
... ⋱ ...
1.3867e-02 -1.6166e-02 4.7729e-03 ... 9.2436e-03 -3.4599e-03 1.4893e-04
1.8080e-02 1.3096e-02 -1.9225e-02 ... 8.4135e-03 7.3216e-04 2.1693e-02
3.4534e-03 -9.0428e-03 1.6899e-02 ... -2.5820e-04 1.5144e-02 -1.3443e-02
[torch.DoubleTensor of size 8192x2048]
]
In [13]:
for i in range(len(params)):
init.uniform(params[i], 0, 1)
print(list(reg.parameters()))
[Parameter containing:
7.8732e-01 8.7127e-03 2.5498e-01 ... 9.7151e-01 7.0045e-01 8.0179e-01
5.9271e-01 8.7199e-01 2.8282e-01 ... 9.2077e-01 2.3692e-02 4.6481e-01
3.1488e-01 6.8871e-01 9.7602e-01 ... 5.4705e-01 1.8657e-01 4.4125e-01
... ⋱ ...
2.3685e-01 4.8616e-01 5.9912e-01 ... 6.3499e-01 5.5828e-01 3.4472e-01
4.3522e-01 1.7198e-01 9.3313e-01 ... 3.6230e-01 8.7210e-01 2.7585e-01
1.9875e-01 4.4363e-01 2.8850e-01 ... 5.6620e-01 2.1519e-01 3.7712e-01
[torch.DoubleTensor of size 16384x4096]
, Parameter containing:
5.0426e-01 5.4531e-02 7.8249e-01 ... 8.5079e-01 7.6380e-01 5.5872e-01
1.7001e-01 4.9714e-01 1.8019e-01 ... 5.3173e-01 6.0125e-02 5.9271e-01
5.6888e-01 1.6904e-01 3.3299e-01 ... 2.0617e-01 8.9110e-01 2.1650e-01
... ⋱ ...
2.6615e-01 6.7556e-01 9.8385e-04 ... 2.6373e-01 4.0696e-01 5.5842e-01
5.8705e-01 9.2699e-01 5.7709e-01 ... 4.3778e-01 1.7824e-01 6.7693e-01
5.2773e-01 8.3292e-01 6.0711e-01 ... 5.6245e-01 6.1239e-01 7.6092e-01
[torch.DoubleTensor of size 16384x4096]
, Parameter containing:
4.5064e-01 2.3110e-01 3.3303e-01 ... 3.3549e-01 8.6444e-01 3.5573e-01
4.8742e-02 7.3461e-01 7.5073e-01 ... 2.5214e-01 2.7292e-01 5.6398e-01
1.3773e-01 1.6539e-03 5.2152e-01 ... 4.9379e-01 8.0150e-01 6.1325e-01
... ⋱ ...
3.0222e-01 9.3854e-01 9.8024e-01 ... 2.2727e-01 5.9206e-01 4.1467e-01
2.8625e-01 1.5102e-01 6.7757e-01 ... 7.3678e-01 8.7848e-01 3.4654e-01
1.3479e-01 5.3564e-02 5.7687e-02 ... 6.4621e-01 7.0807e-01 7.2826e-01
[torch.DoubleTensor of size 4096x4096]
, Parameter containing:
7.0781e-01 6.3431e-02 3.8171e-01 ... 2.1702e-01 8.9114e-01 6.9316e-01
5.5395e-01 7.9579e-02 9.9122e-01 ... 7.3171e-01 5.2518e-01 4.0740e-01
8.0593e-01 6.6224e-01 9.1686e-01 ... 1.9911e-01 3.2145e-01 6.8971e-01
... ⋱ ...
7.4174e-01 6.5799e-01 3.1992e-03 ... 1.8419e-01 6.7892e-01 8.7800e-01
4.3848e-01 7.2543e-01 9.0873e-02 ... 6.9933e-01 4.1620e-01 9.5101e-01
2.0775e-01 9.0825e-01 3.4861e-01 ... 6.4109e-01 4.0008e-01 6.1257e-01
[torch.DoubleTensor of size 4096x1024]
, Parameter containing:
9.6045e-01 9.7580e-01 1.7768e-01 ... 4.5329e-01 6.3651e-01 5.2336e-01
2.3537e-01 6.6579e-01 3.0231e-01 ... 2.4843e-01 2.8533e-01 7.2911e-01
4.2500e-01 2.4578e-02 1.8030e-01 ... 5.0404e-01 2.5446e-01 3.1119e-01
... ⋱ ...
5.4096e-01 8.7669e-01 7.9158e-01 ... 6.3855e-01 6.9397e-01 9.8296e-01
2.1805e-01 1.8647e-01 5.6119e-01 ... 3.8049e-01 9.7390e-01 8.2169e-01
8.7761e-01 3.5373e-01 7.3494e-01 ... 5.1408e-01 2.1345e-01 3.5590e-01
[torch.DoubleTensor of size 8192x1024]
, Parameter containing:
2.1546e-01 5.3919e-01 6.8705e-01 ... 4.3722e-01 9.3209e-01 6.6289e-01
5.1668e-01 5.4269e-01 1.6900e-01 ... 7.2303e-01 9.3884e-01 2.8335e-01
5.6254e-01 8.1803e-01 1.9909e-01 ... 6.5482e-02 8.0398e-02 5.9281e-01
... ⋱ ...
1.3541e-01 8.8949e-01 1.7041e-01 ... 6.9329e-01 1.4280e-01 4.4319e-01
4.4677e-01 4.0816e-01 6.2033e-01 ... 8.1556e-01 4.9535e-01 7.7868e-01
1.1293e-01 7.8041e-01 9.4200e-01 ... 4.3383e-01 2.2709e-01 9.6178e-01
[torch.DoubleTensor of size 8192x2048]
]
Content source: lakehanne/ensenso
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