def test_NN2_params_rand2(self):
h2o.beta_features = True
csvPathname = 'covtype/covtype.20k.data'
hex_key = 'covtype.20k.hex'
parseResult = h2i.import_parse(bucket='smalldata',
path=csvPathname,
hex_key=hex_key,
schema='put')
paramDict = define_params()
for trial in range(3):
# params is mutable. This is default.
params = {'response': 'C55', 'epochs': '1'}
h2o_nn.pickRandDeepLearningParams(paramDict, params)
kwargs = params.copy()
start = time.time()
nn = h2o_cmd.runDeepLearning(timeoutSecs=500,
parseResult=parseResult,
**kwargs)
print "nn result:", h2o.dump_json(nn)
h2o.check_sandbox_for_errors()
# FIX! simple check?
print "Deep Learning end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
print "Trial #", trial, "completed\n"
def test_NN2_params_rand2(self):
csvPathname = 'covtype/covtype.20k.data'
hex_key = 'covtype.20k.hex'
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=hex_key, schema='put')
paramDict = define_params()
for trial in range(5):
# params is mutable. This is default.
params = {'response': 'C55'}
h2o_nn.pickRandDeepLearningParams(paramDict, params)
kwargs = params.copy()
start = time.time()
nn = h2o_cmd.runDeepLearning(timeoutSecs=300, parseResult=parseResult, **kwargs)
print "nn result:", h2o.dump_json(nn)
h2o.check_sandbox_for_errors()
# FIX! simple check?
print "Deep Learning end on ", csvPathname, 'took', time.time() - start, 'seconds'
print "Trial #", trial, "completed\n"
def test_NN2_params_rand2(self):
csvPathname = 'covtype/covtype.20k.data'
hex_key = 'covtype.20k.hex'
parseResult = h2i.import_parse(bucket='smalldata',
path=csvPathname,
hex_key=hex_key,
schema='put')
paramDict = define_params()
for trial in range(3):
# params is mutable. This is default.
params = {'response': 'C55', 'epochs': '1'}
h2o_nn.pickRandDeepLearningParams(paramDict, params)
kwargs = params.copy()
start = time.time()
nn = h2o_cmd.runDeepLearning(timeoutSecs=500,
parseResult=parseResult,
**kwargs)
print "nn result:", h2o.dump_json(nn)
h2o.check_sandbox_for_errors()
deeplearning_model = nn['deeplearning_model']
errors = deeplearning_model['errors']
# print "errors", h2o.dump_json(errors)
# print "errors, classification", errors['classification']
# assert 1==0
# unstable = nn['model_info']['unstable']
# unstable case caused by :
# normal initial distribution with amplitude 1 and input_dropout_ratio=1.
# blowing up numerically during propagation of all zeroes as input repeatedly.
# arnon added logging to stdout in addition to html in 7899b92ad67.
# Will have to check that first before making predictions.
# print "unstable:", unstable
# FIX! simple check?
print "Deep Learning end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
print "Trial #", trial, "completed\n"
def test_NN2_params_rand2(self):
h2o.beta_features = True
csvPathname = 'covtype/covtype.20k.data'
hex_key = 'covtype.20k.hex'
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=hex_key, schema='put')
paramDict = define_params()
for trial in range(3):
# params is mutable. This is default.
params = {'response': 'C55', 'epochs': '1'}
h2o_nn.pickRandDeepLearningParams(paramDict, params)
kwargs = params.copy()
start = time.time()
nn = h2o_cmd.runDeepLearning(timeoutSecs=500, parseResult=parseResult, **kwargs)
print "nn result:", h2o.dump_json(nn)
h2o.check_sandbox_for_errors()
deeplearning_model = nn['deeplearning_model']
errors = deeplearning_model['errors']
# print "errors", h2o.dump_json(errors)
# print "errors, classification", errors['classification']
# assert 1==0
# unstable = nn['model_info']['unstable']
# unstable case caused by :
# normal initial distribution with amplitude 1 and input_dropout_ratio=1.
# blowing up numerically during propagation of all zeroes as input repeatedly.
# arnon added logging to stdout in addition to html in 7899b92ad67.
# Will have to check that first before making predictions.
# print "unstable:", unstable
# FIX! simple check?
print "Deep Learning end on ", csvPathname, 'took', time.time() - start, 'seconds'
print "Trial #", trial, "completed\n"
