try:

import scipy

logistic_cg.cg_optimization_mnist(n_epochs=10)

except ImportError:

from nose.plugins.skip import SkipTest

raise SkipTest(

"""

algo = ['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp',

'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm', 'rnnslu', 'lstm']

to_exec = [True] * len(algo)

do_float64 = True

do_float32 = True

do_gpu = True

algo_executed = [s for idx, s in enumerate(algo) if to_exec[idx]]

#Timming expected are from the buildbot that have an i7-920 @

# 2.67GHz with hyperthread enabled for the cpu, 12G of ram. An GeForce GTX

# 580 for the GPU. OS=Fedora 14, gcc=4.5.1, python/BLAS from EPD

# 7.1-2 (python 2.7.2, mkl unknow). BLAS with only 1 thread.

expected_times_64 = numpy.asarray([9.8, 22.0, 76.1, 73.7, 116.4,

346.9, 355.0, 558.1, 130.4, 23.2, 106])

expected_times_32 = numpy.asarray([8.1, 17.9, 42.5, 66.5, 71,

191.2, 199.0, 432.8, 119.5, 36.9, 78.0])

# Number with just 1 decimal are new value that are faster with

# the Theano version 0.5rc2 Other number are older. They are not

# updated, as we where faster in the past!

# TODO: find why and fix this!

expected_times_gpu = numpy.asarray([3.0, 7.55523491, 18.99226785,

5.8, 20.0,

11.2, 17.2, 257.7, 118.8, 34.2, 8.7])

expected_times_64 = [s for idx, s in enumerate(expected_times_64)

if to_exec[idx]]

expected_times_32 = [s for idx, s in enumerate(expected_times_32)

if to_exec[idx]]

expected_times_gpu = [s for idx, s in enumerate(expected_times_gpu)

if to_exec[idx]]

def time_test(m, l, idx, f, **kwargs):

if not to_exec[idx]:

return

print algo[idx]

ts = m.call_time

try:

f(**kwargs)

except Exception, e:

print >> sys.stderr, 'test', algo[idx], 'FAILED', e

l.append(numpy.nan)

return

te = m.call_time

l.append(te - ts)

def do_tests():

m = theano.compile.mode.get_default_mode()

l = []

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')

time_test(m, l, 8, rnnrbm.test_rnnrbm, num_epochs=1)

s = {'fold': 3,

# 5 folds 0,1,2,3,4

'data': 'atis',

'lr': 0.0970806646812754,

'verbose': 1,

'decay': True,

# decay on the learning rate if improvement stops

'win': 7,

# number of words in the context window

'nhidden': 200,

# number of hidden units

'seed': 345,

'emb_dimension': 50,

# dimension of word embedding

'nepochs': 1,

# 60 is recommended

'savemodel': False}

time_test(m, l, 9, rnnslu.main, param=s)

time_test(m, l, 10, lstm.train_lstm, max_epochs=1, test_size=1000,

saveto='')

return numpy.asarray(l)

#test in float64 in FAST_RUN mode on the cpu

import theano

if do_float64:

theano.config.floatX = 'float64'

theano.config.mode = 'FAST_RUN'

float64_times = do_tests()

print >> sys.stderr, algo_executed

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'

if do_float32:

float32_times = do_tests()

print >> sys.stderr, algo_executed

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)

if do_float64:

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_executed

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 float32 in FAST_RUN mode on the gpu

import theano.sandbox.cuda

if do_gpu:

theano.sandbox.cuda.use('gpu')

gpu_times = do_tests()

print >> sys.stderr, algo_executed

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)

if do_float64:

print >> sys.stderr, 'float64/gpu', float64_times / gpu_times

if (do_float64 + do_float32 + do_gpu) > 1:

print >> sys.stderr

print >> sys.stderr, ('Duplicate the timing to have everything '

'in one place')

print >> sys.stderr, algo_executed

if do_float64:

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)

if do_float32:

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)

if do_gpu:

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

if do_float64 and do_float32:

print >> sys.stderr, 'float64/float32', (

float64_times / float32_times)

print >> sys.stderr, 'expected float64/float32', (

expected_times_64 / float32_times)

if do_float64 and do_gpu:

print >> sys.stderr, 'float64/gpu', float64_times / gpu_times

print >> sys.stderr, 'expected float64/gpu', (

expected_times_64 / gpu_times)

if do_float32 and do_gpu:

print >> sys.stderr, 'float32/gpu', float32_times / gpu_times

print >> sys.stderr, 'expected float32/gpu', (

expected_times_32 / gpu_times)

def compare(x, y):

ratio = x / y

# If there is more then 5% difference between the expected

# time and the real time, we consider this an error.

return sum((ratio < 0.95) + (ratio > 1.05))

print

if do_float64:

err = compare(expected_times_64, float64_times)

print >> sys.stderr, 'speed_failure_float64=' + str(err)

if do_float32:

err = compare(expected_times_32, float32_times)

print >> sys.stderr, 'speed_failure_float32=' + str(err)

if do_gpu:

err = compare(expected_times_gpu, gpu_times)

print >> sys.stderr, 'speed_failure_gpu=' + str(err)