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slurm-6841215.out
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running script: tf_script_yisha.py
#####################
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.
#####################
#1:Script starts
SUBMIT DIR: /global/u1/y/yisha/scripts
Tensorflow location
/usr/common/software/python/2.7-anaconda/envs/deeplearning/lib/python2.7/site-packages/tensorflow/__init__.pyc
2017-09-15 19:13:33.720132: 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.
2017-09-15 19:13:33.720875: 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.
2017-09-15 19:13:33.720966: 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.
2017-09-15 19:13:33.721052: 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.
2017-09-15 19:13:33.721139: 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.
We have 3 classes to predict
We have the following 62 features: ['ra', 'dec', 'psfMag_u', 'psfMagErr_u', 'psfMag_g', 'psfMagErr_g', 'psfMag_r', 'psfMagErr_r', 'psfMag_i', 'psfMagErr_i', 'psfMag_z', 'psfMagErr_z', 'modelMag_u', 'modelMagErr_u', 'modelMag_g', 'modelMagErr_g', 'modelMag_r', 'modelMagErr_r', 'modelMag_i', 'modelMagErr_i', 'modelMag_z', 'modelMagErr_z', 'petroRad_u', 'petroRadErr_u', 'petroRad_g', 'petroRadErr_g', 'petroRad_r', 'petroRadErr_r', 'petroRad_i', 'petroRadErr_i', 'petroRad_z', 'petroRadErr_z', 'q_u', 'qErr_u', 'q_g', 'qErr_g', 'q_r', 'qErr_r', 'q_i', 'qErr_i', 'q_z', 'qErr_z', 'u_u', 'uErr_u', 'u_g', 'uErr_g', 'u_r', 'uErr_r', 'u_i', 'uErr_i', 'u_z', 'uErr_z', 'mE1_u', 'mE1_g', 'mE1_r', 'mE1_i', 'mE1_z', 'mE2_u', 'mE2_g', 'mE2_r', 'mE2_i', 'mE2_z']
Training set size: 2342365
Validation set size: 292794
Test set size: 292799
max steps: 468473
Training Loss epoch 1 : 0.26843815467
Validation Accuracy epoch 1 : 0.923322
Training Loss epoch 2 : 0.171505879209
Validation Accuracy epoch 2 : 0.941867
Training Loss epoch 3 : 0.151329917519
Validation Accuracy epoch 3 : 0.947328
Training Loss epoch 4 : 0.155905595654
Validation Accuracy epoch 4 : 0.950829
Training Loss epoch 5 : 0.141335369119
Validation Accuracy epoch 5 : 0.955586
Training Loss epoch 6 : 0.131678036921
Validation Accuracy epoch 6 : 0.958589
Training Loss epoch 7 : 0.151548888162
Validation Accuracy epoch 7 : 0.960761
Training Loss epoch 8 : 0.140446444789
Validation Accuracy epoch 8 : 0.961755
Training Loss epoch 9 : 0.145546530799
Validation Accuracy epoch 9 : 0.962957
Training Loss epoch 10 : 0.152934047204
Validation Accuracy epoch 10 : 0.963534
Training Loss epoch 11 : 0.117699279307
Validation Accuracy epoch 11 : 0.963869
Training Loss epoch 12 : 0.142125538035
Validation Accuracy epoch 12 : 0.964924
Training Loss epoch 13 : 0.135147220796
Validation Accuracy epoch 13 : 0.965167
Training Loss epoch 14 : 0.157582775633
Validation Accuracy epoch 14 : 0.96615
Training Loss epoch 15 : 0.156532767249
Validation Accuracy epoch 15 : 0.966321
Training Loss epoch 16 : 0.138478735552
Validation Accuracy epoch 16 : 0.96629
Training Loss epoch 17 : 0.158386458569
Validation Accuracy epoch 17 : 0.966922
Training Loss epoch 18 : 0.152333450639
Validation Accuracy epoch 18 : 0.966628
Training Loss epoch 19 : 0.156551727662
Validation Accuracy epoch 19 : 0.967171
Training Loss epoch 20 : 0.160328754222
Validation Accuracy epoch 20 : 0.966929
Final Validation accuracy ML: 0.966929
Final Validation accuracy SDSS: 0.834413
printing weights, 62*62, 62*62: <tf.Variable 'W_fc1:0' shape=(62, 62) dtype=float32_ref> <tf.Variable 'W_fc2:0' shape=(62, 62) dtype=float32_ref>