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{ | |
"result":{ | |
"IL":"8Mk2kEDuOeeCjjrPf7sqag==_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 |
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mport torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
# Test double grad for convolution | |
embed_layer = nn.Embedding(100, 256) | |
conv_layer = nn.Conv1d(256, 256, 3, padding=1) | |
linear_layer = nn.Linear(256, 100) | |
energy_layer = nn.Linear(256, 1) | |
embed_layer.cuda() | |
conv_layer.cuda() |
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import torch | |
import torch.nn as nn | |
from torch.autograd import Variable, grad | |
import torch.utils.data as Data | |
import torchvision | |
train_data = torchvision.datasets.MNIST( | |
root='./mnist/', | |
train=True, | |
transform=torchvision.transforms.ToTensor(), |
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<null> | |
<s> | |
</s> | |
UNK | |
the | |
, | |
. | |
of | |
and | |
to |
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<null> | |
<s> | |
</s> | |
UNK | |
, | |
. | |
die | |
der | |
und | |
in |
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# TRAINS SDK configuration file | |
api { | |
# Notice: 'host' is the api server (default port 8008), not the web server. | |
api_server: http://trains.deeplearn.org:8008 | |
web_server: http://trains.deeplearn.org:8080 | |
files_server: http://trains.deeplearn.org:8081 | |
# Credentials are generated using the webapp, http://trains.deeplearn.org:8080/profile | |
credentials {"access_key": "R9ZERNX530SMXD1UP3LT", "secret_key": "W^xIFt_xb9pnJ9iqVB_YAH$+XzW!sY0+-pfDM-lV-sts83%^-Q"} | |
} | |
sdk { |
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-------------------------------------------------------------------------- | |
[[49815,1],0]: A high-performance Open MPI point-to-point messaging module | |
was unable to find any relevant network interfaces: | |
Module: OpenFabrics (openib) | |
Host: presto | |
Another transport will be used instead, although this may result in | |
lower performance. | |
-------------------------------------------------------------------------- |
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(S NP VP .) -0.03 -0.55 -0.03 -0.76 -0.35 0.40 1.01 -0.58 -0.81 0.80 -0.04 -0.25 -0.12 -0.94 1.17 0.57 -0.42 -1.55 1.20 1.51 0.07 0.07 0.02 -0.10 0.07 -0.80 0.31 0.04 -0.35 0.81 -0.46 -0.10 -0.89 0.09 -0.23 0.77 -0.81 4.04 0.20 0.39 -0.86 -0.12 0.04 -1.01 -0.77 0.35 0.92 0.04 0.68 -0.00 1.11 -1.16 0.70 -0.30 -0.50 -0.07 0.04 0.03 0.51 0.77 -0.22 0.83 0.03 -0.67 0.62 -0.21 0.35 0.13 0.77 -0.74 0.67 -0.02 -0.73 0.57 -0.01 -0.01 0.06 -0.02 -1.06 -0.46 -0.78 0.13 -0.46 -0.11 -0.02 0.93 -0.97 0.21 -0.86 -0.88 -0.10 1.05 1.21 0.08 0.61 -0.96 -0.05 0.29 1.73 -0.73 0.32 -0.12 -0.19 -0.19 -2.88 -0.82 0.01 -0.62 -2.54 0.24 -0.91 0.64 0.12 0.10 -0.74 -1.48 0.84 -1.03 0.21 -0.70 -0.25 0.22 0.17 0.72 0.79 -1.01 -0.09 0.49 0.71 1.31 0.79 -0.59 0.48 -0.04 0.85 0.07 0.69 0.85 -0.28 0.38 0.04 0.51 -0.24 -0.30 -0.44 1.96 0.05 0.09 0.10 -0.79 0.93 0.48 -0.04 -0.22 -1.19 -0.71 0.11 1.72 0.69 -0.52 -1.40 0.47 0.86 -0.80 0.74 -1.34 -0.06 0.18 -0.25 -1.87 -0.54 -0.13 -0.51 -0.97 0.02 -0.81 0.87 0.02 0.86 0.20 0.73 -0.03 1.76 -0.31 |
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5 (ROOT (S NP VP .)) | |
5 (ROOT (S NP VP .)) | |
5 (ROOT (S NP VP .)) | |
10 (ROOT (NP NP PP :)) | |
4 (ROOT (S NP VP .)) | |
5 (ROOT (S NP VP .)) | |
5 (ROOT (S NP VP .)) | |
5 (ROOT (S NP VP .)) | |
3 (ROOT (S NP VP .)) | |
17 (ROOT (S S CC S : .)) |
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103 (ROOT (S NP VP .)) | |
103 (ROOT (S NP VP .)) | |
69 (ROOT (S NP VP .)) | |
29 (ROOT (NP NP PP :)) | |
76 (ROOT (S NP VP .)) | |
103 (ROOT (S NP VP .)) | |
102 (ROOT (S NP VP .)) | |
103 (ROOT (S NP VP .)) | |
230 (ROOT (S NP VP .)) | |
48 (ROOT (S S CC S : .)) |