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提交 f8842359 authored 作者: Frederic Bastien's avatar Frederic Bastien
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remove print in tests.

上级 f9bd4d9f
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正在显示 包含 55 行增加 和 55 行删除
theano/sandbox/test_neighbours.py
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......@@ -102,7 +102,7 @@ def test_neibs_manual():
#print images.get_value(borrow=True)
neibs = f()
print neibs
#print neibs
assert numpy.allclose(neibs,[[ 0, 1, 4, 5],
[ 2, 3, 6, 7],
[ 8, 9, 12, 13],
......@@ -410,7 +410,7 @@ def tes_neibs2images_crash_on_grad():
to_images = T.sum(neibs2images(neibs, (2, 2), (2, 3, 20, 20)))
g = T.grad(to_images, neibs)
fn = theano.function([neibs], to_images, mode=mode_without_gpu)
print "Compiled"
#print "Compiled"
fn(neibs_val)
if __name__ == '__main__':
......
theano/sandbox/test_rng_mrg.py
浏览文件 @ f8842359
......@@ -52,7 +52,7 @@ def test_deterministic():
test_use_cuda.append(True)
for use_cuda in test_use_cuda:
print 'use_cuda =', use_cuda
#print 'use_cuda =', use_cuda
R = MRG_RandomStreams(seed=seed, use_cuda=use_cuda)
u = R.uniform(size=sample_size)
f = theano.function([], u)
......@@ -85,7 +85,7 @@ def test_consistency_randomstreams():
test_use_cuda.append(True)
for use_cuda in test_use_cuda:
print 'use_cuda =', use_cuda
#print 'use_cuda =', use_cuda
samples = []
rng = MRG_RandomStreams(seed=seed, use_cuda=False)
for i in range(n_streams):
......@@ -335,22 +335,22 @@ def basictest(f, steps, sample_size, prefix="", allow_01=False, inputs=None,
if hasattr(target_avg, 'shape'): # looks if target_avg is an array
diff = numpy.mean(abs(mean - target_avg))
print prefix, 'mean diff with mean', diff
#print prefix, 'mean diff with mean', diff
assert diff < mean_rtol, 'bad mean? %f %f' % (mean, target_avg)
else: # if target_avg is a scalar, then we can do the mean of
# `mean` to get something more precise
mean = numpy.mean(mean)
print prefix, 'mean', mean
#print prefix, 'mean', mean
assert abs(mean - target_avg) < mean_rtol, 'bad mean? %f %f' % (
numpy.mean(mean), target_avg)
print prefix, 'std', avg_std
#print prefix, 'std', avg_std
if target_std is not None:
assert abs(avg_std - target_std) < .01, 'bad std? %f %f' % (avg_std,
target_std)
print prefix, 'time', dt
print prefix, 'elements', steps * sample_size[0] * sample_size[1]
print prefix, 'samples/sec', steps * sample_size[0] * sample_size[1] / dt
print prefix, 'min', min_, 'max', max_
#print prefix, 'time', dt
#print prefix, 'elements', steps * sample_size[0] * sample_size[1]
#print prefix, 'samples/sec', steps * sample_size[0] * sample_size[1] / dt
#print prefix, 'min', min_, 'max', max_
def test_uniform():
......@@ -374,8 +374,8 @@ def test_uniform():
#### TEST CPU IMPLEMENTATION ####
# The python and C implementation are tested with DebugMode
print ''
print 'ON CPU with size=(%s):' % str(size)
#print ''
#print 'ON CPU with size=(%s):' % str(size)
x = tensor.matrix()
R = MRG_RandomStreams(234, use_cuda=False)
# Note: we specify `nstreams` to avoid a warning.
......@@ -387,17 +387,17 @@ def test_uniform():
f = theano.function(var_input, u, mode=mode)
assert any([isinstance(node.op, theano.sandbox.rng_mrg.mrg_uniform)
for node in f.maker.env.toposort()])
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
cpu_out = f(*input)
print 'CPU: random?[:10], random?[-10:]'
print cpu_out[0, 0:10]
print cpu_out[-1, -10:]
#print 'CPU: random?[:10], random?[-10:]'
#print cpu_out[0, 0:10]
#print cpu_out[-1, -10:]
basictest(f, steps, sample_size, prefix='mrg cpu', inputs=input)
if mode != 'FAST_COMPILE' and cuda_available:
print ''
print 'ON GPU with size=(%s):' % str(size)
#print ''
#print 'ON GPU with size=(%s):' % str(size)
R = MRG_RandomStreams(234, use_cuda=True)
u = R.uniform(size=size, dtype='float32',
nstreams=rng_mrg.guess_n_streams(size, warn=False))
......@@ -409,19 +409,19 @@ def test_uniform():
assert any([isinstance(node.op,
theano.sandbox.rng_mrg.GPU_mrg_uniform)
for node in f.maker.env.toposort()])
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
gpu_out = numpy.asarray(f(*input))
print 'GPU: random?[:10], random?[-10:]'
print gpu_out[0, 0:10]
print gpu_out[-1, -10:]
#print 'GPU: random?[:10], random?[-10:]'
#print gpu_out[0, 0:10]
#print gpu_out[-1, -10:]
basictest(f, steps, sample_size, prefix='mrg gpu', inputs=input)
numpy.testing.assert_array_almost_equal(cpu_out, gpu_out,
decimal=6)
print ''
print 'ON CPU w Numpy with size=(%s):' % str(size)
#print ''
#print 'ON CPU w Numpy with size=(%s):' % str(size)
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
uu = RR.uniform(size=size)
......@@ -457,24 +457,24 @@ def test_binomial():
(x.shape, [x], [numpy.zeros(sample_size, dtype=config.floatX)])
]:
print ''
print 'ON CPU with size=(%s) and mean(%d):' % (str(size), mean)
#print ''
#print 'ON CPU with size=(%s) and mean(%d):' % (str(size), mean)
R = MRG_RandomStreams(234, use_cuda=False)
# Note: we specify `nstreams` to avoid a warning.
u = R.binomial(size=size, p=mean,
nstreams=rng_mrg.guess_n_streams(size, warn=False))
f = theano.function(var_input, u, mode=mode)
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
out = f(*input)
print 'random?[:10]\n', out[0, 0:10]
print 'random?[-1,-10:]\n', out[-1, -10:]
#print 'random?[:10]\n', out[0, 0:10]
#print 'random?[-1,-10:]\n', out[-1, -10:]
basictest(f, steps, sample_size, prefix='mrg cpu',
inputs=input, allow_01=True,
target_avg=mean, mean_rtol=rtol)
if mode != 'FAST_COMPILE' and cuda_available:
print ''
print 'ON GPU with size=(%s) and mean(%d):' % (str(size), mean)
#print ''
#print 'ON GPU with size=(%s) and mean(%d):' % (str(size), mean)
R = MRG_RandomStreams(234, use_cuda=True)
u = R.binomial(size=size, p=mean, dtype='float32',
nstreams=rng_mrg.guess_n_streams(size,
......@@ -484,19 +484,19 @@ def test_binomial():
f = theano.function(var_input, theano.Out(
theano.sandbox.cuda.basic_ops.gpu_from_host(u),
borrow=True), mode=mode_with_gpu)
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
gpu_out = numpy.asarray(f(*input))
print 'random?[:10]\n', gpu_out[0, 0:10]
print 'random?[-1,-10:]\n', gpu_out[-1, -10:]
#print 'random?[:10]\n', gpu_out[0, 0:10]
#print 'random?[-1,-10:]\n', gpu_out[-1, -10:]
basictest(f, steps, sample_size, prefix='mrg gpu',
inputs=input, allow_01=True,
target_avg=mean, mean_rtol=rtol)
numpy.testing.assert_array_almost_equal(out, gpu_out,
decimal=6)
print ''
print 'ON CPU w NUMPY with size=(%s) and mean(%d):' % (str(size),
mean)
#print ''
#print 'ON CPU w NUMPY with size=(%s) and mean(%d):' % (str(size),
# mean)
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
uu = RR.binomial(size=size, p=mean)
......@@ -534,25 +534,25 @@ def test_normal0():
dtype='float32').reshape(sample_size),
10. * std / numpy.sqrt(steps)),
]:
print ''
print 'ON CPU:'
#print ''
#print 'ON CPU:'
R = MRG_RandomStreams(234, use_cuda=False)
# Note: we specify `nstreams` to avoid a warning.
n = R.normal(size=size, avg=avg, std=std,
nstreams=rng_mrg.guess_n_streams(size, warn=False))
f = theano.function(var_input, n, mode=mode)
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
out = f(*input)
print 'random?[:10]\n', out[0, 0:10]
#print 'random?[:10]\n', out[0, 0:10]
basictest(f, steps, const_size, target_avg=avg, target_std=std,
prefix='mrg ', allow_01=True, inputs=input, mean_rtol=rtol)
sys.stdout.flush()
if mode != 'FAST_COMPILE' and cuda_available:
print ''
print 'ON GPU:'
#print ''
#print 'ON GPU:'
R = MRG_RandomStreams(234, use_cuda=True)
n = R.normal(size=size, avg=avg, std=std, dtype='float32',
nstreams=rng_mrg.guess_n_streams(size, warn=False))
......@@ -562,11 +562,11 @@ def test_normal0():
theano.sandbox.cuda.basic_ops.gpu_from_host(n),
borrow=True), mode=mode_with_gpu)
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
sys.stdout.flush()
gpu_out = numpy.asarray(f(*input))
print 'random?[:10]\n', gpu_out[0, 0:10]
print '----'
#print 'random?[:10]\n', gpu_out[0, 0:10]
#print '----'
sys.stdout.flush()
basictest(f, steps, const_size, target_avg=avg, target_std=std,
prefix='gpu mrg ', allow_01=True, inputs=input,
......@@ -575,8 +575,8 @@ def test_normal0():
# computation that are done on the gpu vs cpu
assert numpy.allclose(out, gpu_out, rtol=5e-6, atol=5e-6)
print ''
print 'ON CPU w NUMPY:'
#print ''
#print 'ON CPU w NUMPY:'
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
nn = RR.normal(size=size, avg=avg, std=std)
......@@ -622,8 +622,8 @@ def test_multinomial():
else:
sample_size = (450, 6)
mode_ = theano.compile.mode.get_mode(mode_)
print ''
print 'ON CPU:'
#print ''
#print 'ON CPU:'
pvals = numpy.asarray(numpy.random.uniform(size=sample_size))
pvals = numpy.apply_along_axis(lambda row: row / numpy.sum(row), 1, pvals)
......@@ -631,15 +631,15 @@ def test_multinomial():
# Note: we specify `nstreams` to avoid a warning.
m = R.multinomial(pvals=pvals, dtype=config.floatX, nstreams=30 * 256)
f = theano.function([], m, mode=mode_)
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
out = f()
basic_multinomialtest(f, steps, sample_size, pvals, prefix='mrg ')
sys.stdout.flush()
if mode != 'FAST_COMPILE' and cuda_available:
print ''
print 'ON GPU:'
#print ''
#print 'ON GPU:'
R = MRG_RandomStreams(234, use_cuda=True)
pvals = numpy.asarray(pvals, dtype='float32')
# We give the number of streams to avoid a warning.
......@@ -651,7 +651,7 @@ def test_multinomial():
theano.sandbox.cuda.basic_ops.gpu_from_host(n),
mode=mode_.including('gpu'))
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
gpu_out = f()
sys.stdout.flush()
basic_multinomialtest(f, steps, sample_size, pvals, prefix='gpu mrg ')
......
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