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import convolutional_mlp, logistic_cg, logistic_sgd, mlp, SdA, dA, rbm , DBN
from nose.plugins.skip import SkipTest
import numpy, theano
import time, sys
def test_logistic_sgd():
logistic_sgd.sgd_optimization_mnist(n_epochs=10)
def test_logistic_cg():
logistic_cg.cg_optimization_mnist(n_epochs=10)
def test_mlp():
mlp.test_mlp(n_epochs=5)
def test_convolutional_mlp():
convolutional_mlp.evaluate_lenet5(n_epochs=5,nkerns=[5,5])
def test_dA():
dA.test_dA(training_epochs = 3, output_folder = 'tmp_dA_plots')
def test_SdA():
SdA.test_SdA(pretraining_epochs = 2, training_epochs = 3, batch_size = 300)
def test_dbn():
DBN.test_DBN(pretraining_epochs = 1, training_epochs = 2, batch_size =300)
def test_rbm():
rbm.test_rbm(training_epochs = 1, batch_size = 300, n_chains = 1, n_samples = 1,
output_folder = 'tmp_rbm_plots')
def speed():
"""
This fonction modify the configuration theano and don't restore it!
I want it to be compatible with python2.4 so using try: finaly: is not an option.
"""
import theano
algo=['logistic_sgd','logistic_cg','mlp','convolutional_mlp','dA','SdA','DBN','rbm']
to_exec=[True]*len(algo)
# to_exec=[False]*len(algo)
# to_exec[-1]=True
expected_times_64=numpy.asarray([ 12.42313051, 28.09523582, 106.35365391, 116.79225969, 153.12310314,
425.09175086, 642.72824597, 652.52828193])
expected_times_32=numpy.asarray([ 13.29699826, 32.42813158, 68.03559947, 105.54640913, 107.00527334,
242.41721797, 490.40798998, 528.88854146])
expected_times_gpu=numpy.asarray([ 3.07663488, 7.55523491, 18.99226785, 9.58915591, 24.13007045,
24.77524018, 92.66246653, 322.34032917])
def time_test(m,l,idx,f,**kwargs):
if not to_exec[idx]:
l[idx]=float('nan')
return
print algo[idx]
ts=m.call_time
try:
f(**kwargs)
except Exception, e:
print >> sys.stderr, 'test', algo[idx], 'FAILED', e
l[idx]=float('nan')
return
te=m.call_time
l[idx]=te-ts
def do_tests():
m=theano.compile.mode.get_default_mode()
l=numpy.zeros(len(algo))
time_test(m,l,0, logistic_sgd.sgd_optimization_mnist,n_epochs=30)
time_test(m,l,1, logistic_cg.cg_optimization_mnist,n_epochs=30)
time_test(m,l,2, mlp.test_mlp, n_epochs=5)
time_test(m,l,3, convolutional_mlp.evaluate_lenet5, n_epochs=5,nkerns=[5,5])
time_test(m,l,4, dA.test_dA, training_epochs = 2, output_folder = 'tmp_dA_plots')
time_test(m,l,5, SdA.test_SdA, pretraining_epochs = 1, training_epochs = 2, batch_size = 300)
time_test(m,l,6, DBN.test_DBN, pretraining_epochs = 1, training_epochs = 2, batch_size = 300)
time_test(m,l,7, rbm.test_rbm, training_epochs = 1, batch_size = 300, n_chains = 1, n_samples = 1, output_folder = 'tmp_rbm_plots')
return l
#test in float64 in FAST_RUN mode on the cpu
print >> sys.stderr, algo
theano.config.floatX='float64'
theano.config.mode='FAST_RUN'
float64_times=numpy.zeros(len(algo))
float64_times=do_tests()
print >> sys.stderr, 'float64 times',float64_times
print >> sys.stderr, 'float64 expected',expected_times_64
print >> sys.stderr, 'float64 % expected/get',expected_times_64/float64_times
#test in float32 in FAST_RUN mode on the cpu
theano.config.floatX='float32'
float32_times=numpy.zeros(len(algo))
float32_times=do_tests()
print >> sys.stderr, 'float32 times',float32_times
print >> sys.stderr, 'float32 expected',expected_times_32
print >> sys.stderr, 'float32 % expected/get',expected_times_32/float32_times
print >> sys.stderr, 'float64/float32',float64_times/float32_times
print >> sys.stderr
print >> sys.stderr, 'Duplicate the timing to have everything in one place'
print >> sys.stderr, algo
print >> sys.stderr, 'float64 times',float64_times
print >> sys.stderr, 'float64 expected',expected_times_64
print >> sys.stderr, 'float64 % expected/get',expected_times_64/float64_times
print >> sys.stderr, 'float32 times',float32_times
print >> sys.stderr, 'float32 expected',expected_times_32
print >> sys.stderr, 'float32 % expected/get',expected_times_32/float32_times
print >> sys.stderr, 'float64/float32',float64_times/float32_times
print >> sys.stderr, 'expected float64/float32',expected_times_64/float32_times
#test in float64 in FAST_RUN mode on the gpu
theano.config.device='gpu0'
import theano.sandbox.cuda
theano.sandbox.cuda.use('gpu')
gpu_times=do_tests()
print >> sys.stderr, 'gpu times',gpu_times
print >> sys.stderr, 'gpu expected',expected_times_gpu
print >> sys.stderr, 'gpu % expected/get',expected_times_gpu/gpu_times
print >> sys.stderr, 'float64/gpu',float64_times/gpu_times
print >> sys.stderr
print >> sys.stderr, 'Duplicate the timing to have everything in one place'
print >> sys.stderr, algo
print >> sys.stderr, 'float64 times',float64_times
print >> sys.stderr, 'float64 expected',expected_times_64
print >> sys.stderr, 'float64 % expected/get',expected_times_64/float64_times
print >> sys.stderr, 'float32 times',float32_times
print >> sys.stderr, 'float32 expected',expected_times_32
print >> sys.stderr, 'float32 % expected/get',expected_times_32/float32_times
print >> sys.stderr, 'gpu times',gpu_times
print >> sys.stderr, 'gpu expected',expected_times_gpu
print >> sys.stderr, 'gpu % expected/get',expected_times_gpu/gpu_times
print >> sys.stderr, 'float64/float32',float64_times/float32_times
print >> sys.stderr, 'float64/gpu',float64_times/gpu_times
print >> sys.stderr, 'float32/gpu',float32_times/gpu_times
print >> sys.stderr, 'expected float64/float32',expected_times_64/float32_times
print >> sys.stderr, 'expected float64/gpu',expected_times_64/gpu_times
print >> sys.stderr, 'expected float32/gpu',expected_times_32/gpu_times
assert not numpy.isnan(gpu_times).any()