In [1]:
import sys, os
import h2o
from h2o.estimators.deepwater import H2ODeepWaterEstimator
from h2o.estimators import H2OGeneralizedLinearEstimator
from requests import get
import numpy as np
h2o.init()
if not H2ODeepWaterEstimator.available(): exit
Checking whether there is an H2O instance running at http://localhost:54321..... not found.
Attempting to start a local H2O server...
Java Version: java version "1.8.0_101"; Java(TM) SE Runtime Environment (build 1.8.0_101-b13); Java HotSpot(TM) 64-Bit Server VM (build 25.101-b13, mixed mode)
Starting server from /home/ubuntu/h2o-3/build/h2o.jar
Ice root: /tmp/tmpmpiQGE
JVM stdout: /tmp/tmpmpiQGE/h2o_ubuntu_started_from_python.out
JVM stderr: /tmp/tmpmpiQGE/h2o_ubuntu_started_from_python.err
Server is running at http://127.0.0.1:54321
Connecting to H2O server at http://127.0.0.1:54321... successful.
H2O cluster uptime:
03 secs H2O cluster version:
3.11.0.99999 H2O cluster version age:
22 hours and 40 minutes H2O cluster name:
H2O_from_python_ubuntu_89du55 H2O cluster total nodes:
1 H2O cluster free memory:
13.33 Gb H2O cluster total cores:
4 H2O cluster allowed cores:
4 H2O cluster status:
accepting new members, healthy H2O connection url:
http://127.0.0.1:54321 H2O connection proxy:
None H2O internal security:
False Python version:
2.7.12 final
In [2]:
print("Downloading the model")
## http://data.dmlc.ml/mxnet/models/imagenet/inception-bn_old.tar.gz
## !gunzip ...
print("Importing the model architecture for scoring in H2O")
model = H2ODeepWaterEstimator(epochs=0, ## no training - just load the state - NOTE: training for this 3-class problem wouldn't work since the model has 1k classes
mini_batch_size=32, ## mini-batch size is used for scoring
## all parameters below are needed
network='user',
network_definition_file=os.getcwd() + "/Inception_BN-symbol.json",
network_parameters_file=os.getcwd() + "/Inception_BN-0039.params",
mean_image_file= os.getcwd() + "/mean_224.nd",
image_shape=[224,224],
channels=3
)
frame = h2o.import_file("bigdata/laptop/deepwater/imagenet/cat_dog_mouse.csv")
print(frame.head(5))
nclasses = frame[1].nlevels()[0]
model.train(x=[0],y=1, training_frame=frame) ## must call train() to initialize the model, but it isn't training
Downloading the model
Importing the model architecture for scoring in H2O
Parse progress: |█████████████████████████████████████████████████████████| 100%
C1 C2 /home/ubuntu/h2o-3/bigdata/laptop/deepwater/imagenet/cat/102194502_49f003abd9.jpg cat /home/ubuntu/h2o-3/bigdata/laptop/deepwater/imagenet/cat/11146807_00a5f35255.jpg cat /home/ubuntu/h2o-3/bigdata/laptop/deepwater/imagenet/cat/1140846215_70e326f868.jpg cat /home/ubuntu/h2o-3/bigdata/laptop/deepwater/imagenet/cat/114170569_6cbdf4bbdb.jpg cat /home/ubuntu/h2o-3/bigdata/laptop/deepwater/imagenet/cat/1217664848_de4c7fc296.jpg cat
deepwater Model Build progress: |█████████████████████████████████████████| 100%
In [3]:
## Extract deep features from final layer before going into Softmax.
extracted_features = model.deepfeatures(frame, "global_pool_output")
#extracted_features = model.deepfeatures(frame, "conv_5b_double_3x3_1_output")
print(extracted_features.shape)
print(extracted_features)
#assert extracted_features.ncol == 1024
deepfeatures progress: |██████████████████████████████████████████████████| 100%
(267, 1024)
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0.0169071 0.679781 0.594898 0.319288 0.974714 0.0698717 0.234051 0.078514 0.646865 0.658881 0.0507115 1.36688 0.40877 0.337767 0.0322085 0.247714 2.21834 0.490276 1.35399 0.0181318 0.0153164 0.0180441 0.0868358 0.308364 0.420711 0.525601 2.08359 0.481449 0.864755 0.0718321 0.114393 0.174271 0.619865 0.8873 0.0602401 0.385753 0.225039 0.921793 0.127746 0.287251 0.672764 0.123171 0.011431 0.615813 0.339739 0.02415 0.851785 0.140933 0.017569 0.383286 0.0849528 0.25875 0.43779 0.0333275 0.655435 0.0577315 0.198671 2.17354 1.0431 0.306462 0.0237272 0.133664 0.8463 0.144425 0.610598 0.289533 0.258783 0.219417 0.250636 0.5624 0.00157631 1.22291 0.0442142 0.0166786 1.07423 0.599789 1.25289 0.230705 0.101577 0.812775 0.0447784 1.80489 0.28022 0.0496337 0.216077 1.60415 0.0192524 0.838368 0.0624907 0.658942 0.301128 0.17984 0.0332779 0.404772 0.416771 0.313009 0.50171 0.0456997 0.177226 0.305367 0.621072 1.40789 0.663004 0.104465 0.274619 0.0296859 0.121249 0.148401 0.287624 0.0776708 0.515115 0.116072 0.73189 0.862255 1.04483 2.22274 0 0.15285 0.460751 0.278204 0.0103585 0.316823 0.0153496 0.0712004 0.299474 0.468864 1.1573 0.536572 0.765382 0.133169 0.0619138 0.00656087 2.28218 0.171888 0.0860357 0.364987 0.00167703 1.13489 0.296248 0.0220257 0.392822 0.568267 0.615368 0.982159 0.151234 1.76574 1.38333 0.0461752 0.252697 0.244168 1.66419 0.312529 1.06143 0.231678 0.445658 0.715378 0 0.466263 0.66117 2.52104 0 0.0334166 0.36899 1.61737 0.00274637 4.07362 0.836076 0.174178 0.0733521 5.10419 0.0085694 0.00481724 1.17116 0.0528554 0.717141 0.470162 0.0489558 0 0.127651 1.3391 0.0725176 0.558788 0.37415 0.160523 2.09292 1.45956 0.400996 0.803772 0.439865 0.00566193 0.160621 0.13087 1.39462 1.01361 0.321262 1.40735 0.629474 0.0889721 0.704005 0.0226171 0.192107 0.352014 0.0931799 2.0928 0.00872363 0.210156 0.107754 1.98531 0.102493 0.107043 1.13181 0.028586 0.59131 0.0563034 0.95406 1.10134 0.411067 1.09125 0.622774 0.78452 0.0471134 0.283015 1.29234 0.137425 2.28227 0.748857 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0 0.100357 0.588703 0.200076 0.506824 0.349638 0.232314 0.76381 2.31536 0.0345533 0.474909 0.062936 0.248325 0.891326 0.201505 1.52189 1.138 0.162147 0.0141059 0.606916 0.0591541 1.08084 0.223329 1.25336 0.432629 0.602163 0.167254 0.925809 0.400269 0.859578 2.04149 1.45899 1.72835 1.34348 0.0029553 0.178232 1.90069 2.63895 0.773371 0.32585 1.20069 2.99477 0.957154 0.263286 1.64791 0.505406 2.77334 1.20551 1.92565 1.77904 0.368846 0.903391 0.908242 1.13975 1.48286 2.7398 0.659005 1.08679 0.30389 2.30084 0.680815 2.96331 3.24166 0.0237629 0.533341 0.740411 0.204472 0.545806 0.946494 1.04669 0.83554 1.84631 0.708602 0.797938 0.753578 0.266081 2.06215 1.12894 0.182284 1.90474 1.11741 1.37227 2.41732 0.957683 0.574398 0.576557 1.4063 0.702866 0.690765 1.12596 0.921559 1.22935 0.897589 1.37866 1.24874 0.419084 0.307918 0.42339 0.455464 0.0695865 1.28335 1.39374 0.302109 0.0401453 0.32346 2.00086 0.245027 1.13167 1.51978 1.95038 2.42259 3.31588 2.09709 0.962325 1.47452 1.14199 1.84548 0.350738 1.61112 2.38043 0.229216 2.83294 0.802553 2.83271 1.98689 1.0425 0.219427 0.739591 0.289371 1.31942 0.835953 2.24182 2.52577 1.9495 1.38017 1.2734 0.0360556 0.545416 1.25901 0.277423 1.29449 0.459828 0.712246 0.606575 2.30032 1.09746 0.243126 3.13209 0.303532 0.506456 1.04441 0.482649 0.190933 1.92948 0.0394131 0.280714 0.0237203 0.0468308 0.160543 0.261121 1.34334 0.436816 1.45836 0.817119 0.174134 1.2773 0.238256 0.481667 0.940183 0.291937 0.67033 0.114712 0.529791 1.02319 0.103654 0.204289 0.0728228 0.142505 0.248454 0.184231 0.0899315 0.282496 0.66944 0.155886 0.200496 0.914333 1.74368 0.79278 0.0719713 0.167552 0.672666 0.938171 0.750196 0.387399 0.818091 0.450262 0.265402 1.54703 0.0536194 0.26161 0.157657 0.979715 0.800318 0.098144 0.831855 0.0894789 0.129349 0.0864341 0.0220536 0.354049 0.0723247 0.153172 0.0220935 0.462803 0.171001 0.110185 0.629752 1.96272 0.769842 1.21993 1.17047 0.346843 0.329565 1.60898 0.314002 1.24802 0.237049 0.0119597 0.116115 0.905532 1.08062 0.300603 0.441853 0.182715 0.618135 0.97997 0.0893032 0.00642589 0.0413229 0.0458888 0.746748 0.0286615 1.37407 1.38483 0.591037 2.54062 1.53542 0.111194 0.325074 1.34044 0.564034 0.0396167 0.0968732 0.0316905 0.174899 0.139636 0.00759267 0 0.260751 0.393545 0.569395 0.307691 0.267842 0.344294 0.0995979 0.360621 1.54019 1.02597 0.238855 0.432316 0.141878 0.402495 0.464443 0.10544 0.787153 0.102254 0.531444 1.39222 0.10267 0.264885 0.186633 1.892 0 0.828789 0.220483 0.997824 0.443561 0.553831 0.0605481 0.665061 1.68263 0.997841 3.21999 1.24695 0.0501561 0.00267788 0.474806 0.971953 0.0004549 0 0.00283003 0.0327954 0.582157 0.281987 0.427671 1.56248 1.56136 0.340542 0.161713 0.140063 0.0413731 0.0614186 0.00557631 0.129545 0.320445 1.16918 1.68112 2.06451 0.174435 0.106225 1.32078 0.0978727 0.387574 0.104558 0.414352 0.710788 1.12652 0.686439 0.0210663 0.180352 0.147746 0.368632 1.18895 0.324532 0.651616 1.20316 0.0642757 1.47541 0.179357 0.0619181 0.586754 0.165191 0.256866 0.0935998 1.99897 0.100316 2.10573 0.689594 0.13908 1.73298 0.23067 0.171918 0.363488 0.553812 0.0400497 1.15308 1.28498 0.20447 0.126987 0.883186 0.022275 0.552921 0.926645 0.0147383 0.890748 0.0293742 0.0487911 0.428503 0.244631 0.50127 0.171071 0.361929 0.11582 0.846529 0.273699 0.254324 0.954728 0.145119 0.295291 1.25161 0.019526 0.203133 0.114186 0.120684 0.525071 0.172451 0.245595 0.106807 0.1472 0.859025 1.75264 0 0.292731 0.201034 1.11145 3.28676 0.655319 3.46626 0.142513 0.352103 0.0161966 0.0485144 0.0880327 0.110351 0.0918486 1.53992 1.32432 0.51235 0.185866 0.247103 0.396061 0.186619 1.0782 0.166075 0.172891 0.0448694 0.0391395 0.511683 0.0038095 0.219205 0.468309 0.757205 0.496415 0.197962 0.945979 0.527767 0.0150634 0.598919 0.391569 0.402858 0.108467 1.90337 0.22517 0.62574 0.291754 0.839819 0.592593 0.15587 0.313111 1.4403 0.111205 0.0511677 0.108947 0.546512 0.18443 0.0450094 2.92194 0.49958 0.117093 0.035271 1.02978 0.578462 1.31052 0.630301 0.111564 1.13134 0.0871303 0.0491259 1.50587 0.412412 0.0458398 0.303305 0.272506 1.30869 0.33374 0.151798 0.437284 0.67168 0.178193 0.963055 0.196168 0.212308 1.29819 0.120079 0.40506 1.63545 0.547615 0.31275 0.00684948 0.479432 0.243162 0.144964 0.0566716 0 0.732228 0.0528387 1.99716 0.115347 0.520785 0.780071 0.142934 0 0.156641 0.24402 0.258828 1.13594 0.110574 0.230356 0.722023 1.22415 0.0287569 0.0501262 0.0088722 0.0915832 0.0452431 0.978248 0.234408 0 0.148561 0.00827926 0.133667 0.862544 0.0164292 0.392301 0.0225176 0.173367 0.00465067 0.125193 0.346667 0.107338 0.304447 0.338289 0.0632564 0.559189 0.0126419 0.0380145 0.0311651 0.00714434 1.02369 0.0514098 0.0730187 0.257601 0.0250453 0.0156435 0.0425174 0 0.350154 0.300615 0.352264 0.180447 0.460866 0.684468 0.00889775 1.03424 0.257178 1.24404 1.30449 0.0528334 1.29962 0.0773398 0.00208712 0.209172 0.051073 0.561197 0.194372 0.526245 0.277582 0.138444 0.285569 0.254322 0.098949 0.0150071 0.765173 0.187056 2.95679 0.27312 0.18003 0.184028 0.443599 0.826864 0.815915 1.57675 1.53489 0.12723 0.810316 0.00175789 0.0260398 0.0601948 1.02958 0.786207 0.0037228 0.0192877 0.206765 0.0750072 0.222791 0.6581 0.0204984 0.27797 0.116088 0.0847756 0.610511 0.612468 0.234424 0.0710226 0.082252 0.0116116 0.43251 4.41242e-05 0.00199691 0.133985 0.0913105 0.810171 0.112063 0.666156 0.323321 0 0.014181 0.19838 0.342144 0.105441 0.0146501 0.815906 0.0837253 0.0132798 0.00864743 1.53396 0.488223 0.0369898 0.110747 0.547945 0.275945 0.0171165 0.729389 0.180914 0.163462 0.00380276 0.0805576 0.455811 0.000277532 0.0121703 0.076658 0.473338 0.00044538 0.0487279 0.0200674 0.535534 0 0.80249 0.0338581 0.162152 0.342969 0.716225 0.0758918 0.284557 0.541546 0.124817 0.624136 0.177633 0.607902 0.907194 0.0612247 0.136227 0.609867 0 0.251816 0.362494 0.277898 1.20646 0.00273281 0 0.0697499 0.496381 0.328081 0.0680869 0.815925 0.00263169 0.249621 0.00443054 1.0957 0.0187207 0.108736 0.298921 1.44098 0.515092 0.325045 0.780801 0 0.432136 0.343777 0.159181 0.191431 0.371547 0.367825 0.179841 0.493854 0.119306 0.122397 0.234685 0.256807 0.520006 0.359854 0.321937 0.0381972 0.859172 0.55195 1.03089 0.438556 0.184526 0.0469247 0.203237 0.56891 0.506318 0.82959 0.142864 0.268586 0 0.0927484 1.44809 0.583462 1.8431 0.542987 1.26789 0.562225 0.207424 0.222953 0.772943 0.480365 2.14863 0.229518 0.105617 0.514394 0.195613 0.118393 0.2063 0.0395324 0.587929 0.372656 0.444002 0.699176 0.0123287 0.183401 0.0900214 0.0649364 0.284379 0.170193 0.1688 0 0.904051 0.192753 1.31891 0.032042 0.662897 0.302745 0.222141 0.431669 0.395661 0.0136333 0.283517 0 0.0304227 0.0159028 0 0.351444 0.75268 0.445128 0.772583 0.474082 0.00176097 0.123415 0.163091 0 0.0544286 0.618982 1.33616 0 0.782793 0.262853 0.667972 0.117727 0.0693369 0.607812 0.0233956 0.945644 0.0079543 0.191634 0.0519449 0.377561 0.163421 0.048622 0.995227 0.847124 0.786634 0.792375 0.920877 0.238232 0.24103 0.0118094 0.0369902 0.606319 0.539337 0.234976 0.463616 0.0203092 1.04182 0.0905824 0.00942397 0.144275 1.0409 1.15484 0.0416408 0.0273756 0.0493219 0.0904581 0.523541 0.0423419 0.389617 0.0675736 0.357221 0.290033 0.00145006 0.0820087 0.142646 0.0605014 0.20016 0.396281 0.325975 0.475849 0.211415 1.60997 0.0404918 0.176681 1.09934 0.122114 0.118955 0.649526 0.309568 0.389228 0.800143 0.610537 0.268756 0.941441 1.03049 0.539123 2.00342 0.503291 0.723978 0.141466 0.146647 0.360765 0.584876 0.559671 0.645504 0.0290002 0.0648059 0 1.87914 0.668768 0.711984 0.344792 0.317834 0.182645 0.126559 0.318977 0 0.90954 0.418296 0.158618 0.223818 0.0656502 0.41322 0.142997 0.421826 0.155208 0.0287081 1.67023 0.7625 0.0919445 0.373089 1.52587 0.662989 0.178187 0.177437 0.626731 0.691754 1.08433 0.106494 0.732089 0.00217091 0.361034 0.062957 0.108889 1.52849 0.275499 0.0672191 0.235905 0.615374 0.0546829 1.24325 1.24416 0.115416 0.51643 0.65666 0.00145059 0.909432 0.0339945 0.195238 0.00369681 0.351003 0.114604 0.492072 1.23475 1.14494 0.0645746 1.86221 2.12783 0.220653 0.613612 0.109329 0.273107 1.01324 0.150573 0.61617 0.346672 0.388964 0.0243165 1.20956 2.93831 0.188876 0.413882 0.097197 0.412927 0.103176 0.673821 1.93592 0.0818368 0.545145 0.628106 0.012509 0.600246 0.159091 1.51653 0.110555 0.0288581 1.12347 0.182456 0.156935 1.25743 0.512939 0.355979 2.14535 0.0900695 0.279196 0.313677 0.236322 0.015547 0.683992 1.06643 0.055313 0.0800715 0.0651653 0.435704 2.43521 0.952599 0.603073 0.10632 0.323405 0.14465 0.133088 0.107424 0.0265638 0.78161 0.2028 0.63762 0.105877 0.229962 0.64219 0.0023134 0.297089 0.608854 0.532365 0.10992 0.291677 0.0837724 0.00405838 0.499638 0.0322244 0.318587 0.775829 0.12271 0.300506 0.103862 0.0432799 0.11394 0.731146 0.330673 0.408719 0.517582 1.51001 0.101083 1.26215 2.5915 1.01875 2.18841 0.414411 0.328375 0 1.22391 1.02748 0.131866 0.287905 0.0468126 0.132779 0.250183 1.59852 0.187457 2.39831e-05 0.796339 2.1617 0.869491 1.13627 0.541417 0.0386291 1.17719 0.561877 0.313347 0.0912182 2.05741 0.902778 0.225295 0.504547 0.0339052 0.0965651 0.0286419 0.320601 0.275377 0.699506 0.344503 0.22273 0.559987 0.36295 0.207333 0.0847466 0.243284 0.0495543 2.42211 1.68931 1.30513 1.02091 0.296041 0.954728 3.89833 1.16124 1.82533 0.0877521 0.364245 0.137682 0.539992 0.586569 0.277454 0.389967 0.189167 0.672771 1.64045 0.0157283 1.05215 0.498317 0.800926 1.26347 0.740585 0.817171 0.210264 1.58293 0.0819437 0 1.73202 1.65262 1.71285 2.28763 0.976888 1.08722 1.03789 0.869824 0.0148238 0.137868 0.293444 0.37432 0.9924 3.21354 1.0174 0.531381 0.30946 1.10249 2.11029 0.0208615 0.90126 0.538788 1.07411 0.732944 1.48451 2.02971 0.40505 0.583712 0.708335 0.599471 0.0157871 3.96611 2.16791 1.4347 0.0112722 0.914691 1.59257 0.322007 1.13234 0.0351356 3.81943 1.27813 1.3632 0.931642 0.0994819 1.24562 0.570774 0.685748 1.47149 1.63411 1.38464 0.351443 1.7807 1.44854 0.480036 0.171892 0.786399 0.157511 1.6222 0.842218 1.32782 1.73238 0.0467577 0.785192 0.943064 0.953832 1.49482 1.34002 0.838362 1.3039 1.60838 1.18036 1.01572 0.0604782 1.94618 0.382247 0.237953 2.11116 1.65383 1.18157 0.945864 1.11407 2.58124 0.428322 2.81176 0.634002 1.07086 1.27461 0.407622 0.970373 2.406 0.234443 2.0304 0.970729 0.0221257 1.66642 0.480858 0.193375 1.32684 0.267179 0.14133 0.162765 0.325756 1.11341 0.505189 0.781766 1.34685 0.82583 0.683145 2.38998 0.550754 0.025169 0.599643 1.71303
In [4]:
extracted_features["target"] = frame["C2"]
train,valid, test = extracted_features.split_frame(ratios=[.7, .15])
model = H2OGeneralizedLinearEstimator(model_id = "deep_features_glm", family = "multinomial")
model.train(y = "target", x = [x for x in extracted_features.columns if x != "target"],
training_frame = train,
validation_frame = valid)
glm Model Build progress: |███████████████████████████████████████████████| 100%
In [5]:
model_perf = model.model_performance(test)
model_perf.confusion_matrix()
Confusion Matrix: vertical: actual; across: predicted
cat
dog
mouse
Error
Rate 11.0
1.0
1.0
0.1538462
2 / 13 1.0
10.0
0.0
0.0909091
1 / 11 0.0
0.0
15.0
0.0
0 / 15 12.0
11.0
16.0
0.0769231
3 / 39
Out[5]:
In [6]:
model_perf.logloss()
Out[6]:
0.27333032002769736
In [ ]:
Content source: spennihana/h2o-3
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