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from __future__ import absolute_import, print_function, division
import sys
import numpy
import convolutional_mlp
import dA
import DBN
import logistic_cg
import logistic_sgd
import mlp
import rbm
import rnnrbm
import SdA
import rnnslu
import lstm
def test_logistic_sgd():
logistic_sgd.sgd_optimization_mnist(n_epochs=10)
def test_logistic_cg():
try:
import scipy
logistic_cg.cg_optimization_mnist(n_epochs=10)
except ImportError:
from nose.plugins.skip import SkipTest
raise SkipTest(
'SciPy not available. Needed for the logistic_cg example.')
def test_mlp():
mlp.test_mlp(n_epochs=1)
def test_convolutional_mlp():
convolutional_mlp.evaluate_lenet5(n_epochs=1, nkerns=[5, 5])
def test_dA():
dA.test_dA(training_epochs=1, output_folder='tmp_dA_plots')
def test_SdA():
SdA.test_SdA(pretraining_epochs=1, training_epochs=1, batch_size=300)
def test_dbn():
DBN.test_DBN(pretraining_epochs=1, training_epochs=1, batch_size=300)
def test_rbm():
rbm.test_rbm(training_epochs=1, batch_size=300, n_chains=1, n_samples=1,
n_hidden=20, output_folder='tmp_rbm_plots')
def test_rnnrbm():
rnnrbm.test_rnnrbm(num_epochs=1)
def test_rnnslu():
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, # CHANGED
'savemodel': False}
rnnslu.main(s)
def test_lstm():
lstm.train_lstm(max_epochs=1, test_size=1000, saveto='')
def speed():
"""
This fonction modify the configuration theano and don't restore it!
"""
algo = ['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp',
'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm', 'rnnslu', 'lstm']
to_exec = [True] * len(algo)
# to_exec = [False] * len(algo)
# to_exec[-1] = True
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([9.3, 21.0, 76.1, 73.7, 116.4,
346.9, 355.0, 268.2, 115.8, 16.8, 91.6])
expected_times_32 = numpy.asarray([6.4, 14.7, 42.5, 63.1, 71,
191.2, 199.0, 201.9, 107, 12.6, 61.3])
# 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!
# Here is the value for the buildbot on February 3th 2012 with a GTX 285
# sgd, cg mlp conv da
# sda dbn rbm
# gpu times[3.72957802, 9.94316864, 29.1772666, 9.13857198, 25.91144657,
# 18.30802011, 53.38651466, 285.41386175]
# expected [3.076634879, 7.555234910, 18.99226785, 9.58915591, 24.130070450,
# 24.77524018, 92.66246653, 322.340329170]
# sgd, cg mlp conv da
# sda dbn rbm
#expected/get [0.82492841, 0.75984178, 0.65092691, 1.04930573, 0.93125138
# 1.35324519 1.7356905 1.12937868]
expected_times_gpu = numpy.asarray([2.9, 7.55523491, 18.99226785,
5.8, 19.2,
11.2, 7.3, 122, 112.5, 31.1, 8.3])
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 as e:
print('test', algo[idx], 'FAILED', e, file=sys.stderr)
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(algo_executed, file=sys.stderr)
print('float64 times', float64_times, file=sys.stderr)
print('float64 expected', expected_times_64, file=sys.stderr)
print('float64 % expected/get', (
expected_times_64 / float64_times), file=sys.stderr)
#test in float32 in FAST_RUN mode on the cpu
theano.config.floatX = 'float32'
if do_float32:
float32_times = do_tests()
print(algo_executed, file=sys.stderr)
print('float32 times', float32_times, file=sys.stderr)
print('float32 expected', expected_times_32, file=sys.stderr)
print('float32 % expected/get', (
expected_times_32 / float32_times), file=sys.stderr)
if do_float64:
print('float64/float32', (
float64_times / float32_times), file=sys.stderr)
print(file=sys.stderr)
print(('Duplicate the timing to have everything '
'in one place'), file=sys.stderr)
print(algo_executed, file=sys.stderr)
print('float64 times', float64_times, file=sys.stderr)
print('float64 expected', expected_times_64, file=sys.stderr)
print('float64 % expected/get', (
expected_times_64 / float64_times), file=sys.stderr)
print('float32 times', float32_times, file=sys.stderr)
print('float32 expected', expected_times_32, file=sys.stderr)
print('float32 % expected/get', (
expected_times_32 / float32_times), file=sys.stderr)
print('float64/float32', (
float64_times / float32_times), file=sys.stderr)
print('expected float64/float32', (
expected_times_64 / float32_times), file=sys.stderr)
#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(algo_executed, file=sys.stderr)
print('gpu times', gpu_times, file=sys.stderr)
print('gpu expected', expected_times_gpu, file=sys.stderr)
print('gpu % expected/get', (
expected_times_gpu / gpu_times), file=sys.stderr)
if do_float64:
print('float64/gpu', float64_times / gpu_times, file=sys.stderr)
if (do_float64 + do_float32 + do_gpu) > 1:
print(file=sys.stderr)
print(('Duplicate the timing to have everything '
'in one place'), file=sys.stderr)
print(algo_executed, file=sys.stderr)
if do_float64:
print('float64 times', float64_times, file=sys.stderr)
print('float64 expected', expected_times_64, file=sys.stderr)
print('float64 % expected/get', (
expected_times_64 / float64_times), file=sys.stderr)
if do_float32:
print('float32 times', float32_times, file=sys.stderr)
print('float32 expected', expected_times_32, file=sys.stderr)
print('float32 % expected/get', (
expected_times_32 / float32_times), file=sys.stderr)
if do_gpu:
print('gpu times', gpu_times, file=sys.stderr)
print('gpu expected', expected_times_gpu, file=sys.stderr)
print('gpu % expected/get', (
expected_times_gpu / gpu_times), file=sys.stderr)
print()
if do_float64 and do_float32:
print('float64/float32', (
float64_times / float32_times), file=sys.stderr)
print('expected float64/float32', (
expected_times_64 / float32_times), file=sys.stderr)
if do_float64 and do_gpu:
print('float64/gpu', float64_times / gpu_times, file=sys.stderr)
print('expected float64/gpu', (
expected_times_64 / gpu_times), file=sys.stderr)
if do_float32 and do_gpu:
print('float32/gpu', float32_times / gpu_times, file=sys.stderr)
print('expected float32/gpu', (
expected_times_32 / gpu_times), file=sys.stderr)
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(file=sys.stderr)
if do_float64:
err = compare(expected_times_64, float64_times)
print('speed_failure_float64=' + str(err), file=sys.stderr)
if do_float32:
err = compare(expected_times_32, float32_times)
print('speed_failure_float32=' + str(err), file=sys.stderr)
if do_gpu:
err = compare(expected_times_gpu, gpu_times)
print('speed_failure_gpu=' + str(err), file=sys.stderr)
assert not numpy.isnan(gpu_times).any()
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