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slurm-6739011.out
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slurm-6739011.out
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#####################
This deeplearning module will soon be deprecated - please consider using the default python module instead!
It is accessible via "module load python/2.7-anaconda-4.4" (for python3 use python/3.6-anaconda-4.4
The Anaconda distribution contains all the deeplearning frameworks in this module (apart from Lasagne), fully integrated with all other python modules.
#####################
2017-09-11 00:44:53.046950: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-11 00:44:53.048858: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-11 00:44:53.048912: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-09-11 00:44:53.048966: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-11 00:44:53.049027: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
(2927958, 64)
[ 1. 2. 2. ..., 2. 2. 2.]
[[0 0 0 ..., 0 0 0]
[0 0 0 ..., 0 0 0]
[0 0 0 ..., 0 0 0]
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[[0 0 0 ..., 0 0 0]
[1 0 0 ..., 0 0 0]
[0 1 1 ..., 1 1 1]
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(2927958, 64)
max steps: 210000
Training Accuracy:
0.904676
Training Accuracy:
0.971421
Training Accuracy:
0.979773
Training Accuracy:
0.985055
Training Accuracy:
0.985028
Training Accuracy:
0.986694
Training Accuracy:
0.991178
Training Accuracy:
0.995619
Training Accuracy:
0.997168
Training Accuracy:
0.997268
Test Accuracy:
0.997038
Shapes:
[2 1 0 ..., 2 2 2]
[ 2. 1. 0. ..., 2. 2. 2.]
(827958,)
(827958,)
[[105863 200 96]
[ 823 213854 554]
[ 541 238 505789]]