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提交 5f0aec41 authored 作者: amrithasuresh's avatar amrithasuresh
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Updated numpy as np

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正在显示 包含 111 行增加 和 111 行删除
theano/sandbox/tests/test_rng_mrg.py
浏览文件 @ 5f0aec41
......@@ -8,7 +8,7 @@ import functools
from nose.plugins.skip import SkipTest
from nose.tools import assert_raises
import numpy
import numpy as np
from six.moves import xrange
import theano
......@@ -44,7 +44,7 @@ utt.seed_rng()
# 12 streams
# 7 substreams for each stream
# 5 samples drawn from each substream
java_samples = numpy.loadtxt(os.path.join(os.path.split(theano.__file__)[0],
java_samples = np.loadtxt(os.path.join(os.path.split(theano.__file__)[0],
'sandbox',
'samples_MRG31k3p_12_7_5.txt'))
......@@ -65,15 +65,15 @@ def test_deterministic():
fsample1 = f()
fsample2 = f()
assert not numpy.allclose(fsample1, fsample2)
assert not np.allclose(fsample1, fsample2)
R2 = MRG_RandomStreams(seed=seed, use_cuda=use_cuda)
u2 = R2.uniform(size=sample_size)
g = theano.function([], u2)
gsample1 = g()
gsample2 = g()
assert numpy.allclose(fsample1, gsample1)
assert numpy.allclose(fsample2, gsample2)
assert np.allclose(fsample1, gsample1)
assert np.allclose(fsample2, gsample2)
def test_consistency_randomstreams():
......@@ -102,12 +102,12 @@ def test_consistency_randomstreams():
for j in range(n_samples):
s = f()
stream_samples.append(s)
stream_samples = numpy.array(stream_samples)
stream_samples = np.array(stream_samples)
stream_samples = stream_samples.T.flatten()
samples.append(stream_samples)
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
samples = np.array(samples).flatten()
assert(np.allclose(samples, java_samples))
def test_get_substream_rstates():
......@@ -117,7 +117,7 @@ def test_get_substream_rstates():
n_streams = 100
dtype = 'float32'
rng = MRG_RandomStreams(numpy.random.randint(2147462579))
rng = MRG_RandomStreams(np.random.randint(2147462579))
rng.get_substream_rstates(n_streams, dtype)
......@@ -137,12 +137,12 @@ def test_consistency_cpu_serial():
n_substreams = 7
samples = []
curr_rstate = numpy.array([seed] * 6, dtype='int32')
curr_rstate = np.array([seed] * 6, dtype='int32')
for i in range(n_streams):
stream_rstate = curr_rstate.copy()
for j in range(n_substreams):
rstate = theano.shared(numpy.array([stream_rstate.copy()],
rstate = theano.shared(np.array([stream_rstate.copy()],
dtype='int32'))
new_rstate, sample = rng_mrg.mrg_uniform.new(rstate, ndim=None,
dtype=config.floatX,
......@@ -164,8 +164,8 @@ def test_consistency_cpu_serial():
# next stream
curr_rstate = rng_mrg.ff_2p134(curr_rstate)
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
samples = np.array(samples).flatten()
assert(np.allclose(samples, java_samples))
def test_consistency_cpu_parallel():
......@@ -180,14 +180,14 @@ def test_consistency_cpu_parallel():
n_substreams = 7 # 7 samples will be drawn in parallel
samples = []
curr_rstate = numpy.array([seed] * 6, dtype='int32')
curr_rstate = np.array([seed] * 6, dtype='int32')
for i in range(n_streams):
stream_samples = []
rstate = [curr_rstate.copy()]
for j in range(1, n_substreams):
rstate.append(rng_mrg.ff_2p72(rstate[-1]))
rstate = numpy.asarray(rstate)
rstate = np.asarray(rstate)
rstate = theano.shared(rstate)
new_rstate, sample = rng_mrg.mrg_uniform.new(rstate, ndim=None,
......@@ -205,13 +205,13 @@ def test_consistency_cpu_parallel():
s = f()
stream_samples.append(s)
samples.append(numpy.array(stream_samples).T.flatten())
samples.append(np.array(stream_samples).T.flatten())
# next stream
curr_rstate = rng_mrg.ff_2p134(curr_rstate)
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
samples = np.array(samples).flatten()
assert(np.allclose(samples, java_samples))
def test_consistency_GPU_serial():
......@@ -233,15 +233,15 @@ def test_consistency_GPU_serial():
n_substreams = 7
samples = []
curr_rstate = numpy.array([seed] * 6, dtype='int32')
curr_rstate = np.array([seed] * 6, dtype='int32')
for i in range(n_streams):
stream_rstate = curr_rstate.copy()
for j in range(n_substreams):
substream_rstate = numpy.array(stream_rstate.copy(), dtype='int32')
substream_rstate = np.array(stream_rstate.copy(), dtype='int32')
# HACK - we transfer these int32 to the GPU memory as float32
# (reinterpret_cast)
tmp_float_buf = numpy.frombuffer(substream_rstate.data,
tmp_float_buf = np.frombuffer(substream_rstate.data,
dtype='float32')
# Transfer to device
rstate = float32_shared_constructor(tmp_float_buf)
......@@ -269,8 +269,8 @@ def test_consistency_GPU_serial():
# next stream
curr_rstate = rng_mrg.ff_2p134(curr_rstate)
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
samples = np.array(samples).flatten()
assert(np.allclose(samples, java_samples))
def test_consistency_GPU_parallel():
......@@ -293,17 +293,17 @@ def test_consistency_GPU_parallel():
n_substreams = 7 # 7 samples will be drawn in parallel
samples = []
curr_rstate = numpy.array([seed] * 6, dtype='int32')
curr_rstate = np.array([seed] * 6, dtype='int32')
for i in range(n_streams):
stream_samples = []
rstate = [curr_rstate.copy()]
for j in range(1, n_substreams):
rstate.append(rng_mrg.ff_2p72(rstate[-1]))
rstate = numpy.asarray(rstate).flatten()
rstate = np.asarray(rstate).flatten()
# HACK - transfer these int32 to the GPU memory as float32
# (reinterpret_cast)
tmp_float_buf = numpy.frombuffer(rstate.data, dtype='float32')
tmp_float_buf = np.frombuffer(rstate.data, dtype='float32')
# Transfer to device
rstate = float32_shared_constructor(tmp_float_buf)
......@@ -325,13 +325,13 @@ def test_consistency_GPU_parallel():
s = f()
stream_samples.append(s)
samples.append(numpy.array(stream_samples).T.flatten())
samples.append(np.array(stream_samples).T.flatten())
# next stream
curr_rstate = rng_mrg.ff_2p134(curr_rstate)
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
samples = np.array(samples).flatten()
assert(np.allclose(samples, java_samples))
def test_GPU_nstreams_limit():
......@@ -373,12 +373,12 @@ def test_consistency_GPUA_serial():
n_substreams = 7
samples = []
curr_rstate = numpy.array([seed] * 6, dtype='int32')
curr_rstate = np.array([seed] * 6, dtype='int32')
for i in range(n_streams):
stream_rstate = curr_rstate.copy()
for j in range(n_substreams):
substream_rstate = numpy.array([stream_rstate.copy()],
substream_rstate = np.array([stream_rstate.copy()],
dtype='int32')
# Transfer to device
rstate = gpuarray_shared_constructor(substream_rstate)
......@@ -407,8 +407,8 @@ def test_consistency_GPUA_serial():
# next stream
curr_rstate = rng_mrg.ff_2p134(curr_rstate)
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
samples = np.array(samples).flatten()
assert(np.allclose(samples, java_samples))
def test_consistency_GPUA_parallel():
......@@ -424,14 +424,14 @@ def test_consistency_GPUA_parallel():
n_substreams = 7 # 7 samples will be drawn in parallel
samples = []
curr_rstate = numpy.array([seed] * 6, dtype='int32')
curr_rstate = np.array([seed] * 6, dtype='int32')
for i in range(n_streams):
stream_samples = []
rstate = [curr_rstate.copy()]
for j in range(1, n_substreams):
rstate.append(rng_mrg.ff_2p72(rstate[-1]))
rstate = numpy.asarray(rstate)
rstate = np.asarray(rstate)
rstate = gpuarray_shared_constructor(rstate)
new_rstate, sample = rng_mrg.GPUA_mrg_uniform.new(rstate, ndim=None,
......@@ -452,13 +452,13 @@ def test_consistency_GPUA_parallel():
s = f()
stream_samples.append(s)
samples.append(numpy.array(stream_samples).T.flatten())
samples.append(np.array(stream_samples).T.flatten())
# next stream
curr_rstate = rng_mrg.ff_2p134(curr_rstate)
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
samples = np.array(samples).flatten()
assert(np.allclose(samples, java_samples))
def test_GPUA_full_fill():
......@@ -496,16 +496,16 @@ def basictest(f, steps, sample_size, prefix="", allow_01=False, inputs=None,
ival = f(*inputs)
assert ival.shape == sample_size
dt += time.time() - t0
ival = numpy.asarray(ival)
ival = np.asarray(ival)
if i == 0:
mean = numpy.array(ival, copy=True)
avg_var = numpy.mean((ival - target_avg) ** 2)
mean = np.array(ival, copy=True)
avg_var = np.mean((ival - target_avg) ** 2)
min_ = ival.min()
max_ = ival.max()
else:
alpha = 1.0 / (1 + i)
mean = alpha * ival + (1 - alpha) * mean
avg_var = (alpha * numpy.mean((ival - target_avg) ** 2) +
avg_var = (alpha * np.mean((ival - target_avg) ** 2) +
(1 - alpha) * avg_var)
min_ = min(min_, ival.min())
max_ = max(max_, ival.max())
......@@ -514,19 +514,19 @@ def basictest(f, steps, sample_size, prefix="", allow_01=False, inputs=None,
assert max_ < 1
if hasattr(target_avg, 'shape'): # looks if target_avg is an array
diff = numpy.mean(abs(mean - target_avg))
diff = np.mean(abs(mean - target_avg))
# print prefix, 'mean diff with mean', diff
assert numpy.all(diff < mean_rtol * (1 + abs(target_avg))), (
assert np.all(diff < mean_rtol * (1 + abs(target_avg))), (
'bad mean? %s %s' % (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)
mean = np.mean(mean)
# print prefix, 'mean', mean
assert abs(mean - target_avg) < mean_rtol * (1 + abs(target_avg)), (
'bad mean? %f %f' % (mean, target_avg))
std = numpy.sqrt(avg_var)
std = np.sqrt(avg_var)
# print prefix, 'var', avg_var
# print prefix, 'std', std
if target_std is not None:
......@@ -556,9 +556,9 @@ def test_uniform():
for size, const_size, var_input, input in [
(sample_size, sample_size, [], []),
(x.shape, sample_size, [x],
[numpy.zeros(sample_size, dtype=config.floatX)]),
[np.zeros(sample_size, dtype=config.floatX)]),
((x.shape[0], sample_size[1]), sample_size, [x],
[numpy.zeros(sample_size, dtype=config.floatX)]),
[np.zeros(sample_size, dtype=config.floatX)]),
# test empty size (scalar)
((), (), [], []),
]:
......@@ -586,7 +586,7 @@ def test_uniform():
# print cpu_out[-1, -10:]
# Increase the number of steps if sizes implies only a few samples
if numpy.prod(const_size) < 10:
if np.prod(const_size) < 10:
steps_ = steps * 100
else:
steps_ = steps
......@@ -607,14 +607,14 @@ def test_uniform():
theano.sandbox.rng_mrg.GPU_mrg_uniform)
for node in f.maker.fgraph.toposort()])
# theano.printing.debugprint(f)
gpu_out = numpy.asarray(f(*input))
gpu_out = np.asarray(f(*input))
# print 'GPU: random?[:10], random?[-10:]'
# print gpu_out[0, 0:10]
# print gpu_out[-1, -10:]
basictest(f, steps_, const_size, prefix='mrg gpu', inputs=input)
numpy.testing.assert_array_almost_equal(cpu_out, gpu_out,
np.testing.assert_array_almost_equal(cpu_out, gpu_out,
decimal=6)
# print ''
......@@ -633,7 +633,7 @@ def test_broadcastable():
x = tensor.matrix()
size1 = (10, 1)
size2 = (x.shape[0], 1)
pvals_1 = numpy.random.uniform(0, 1, size=size1)
pvals_1 = np.random.uniform(0, 1, size=size1)
pvals_1 = pvals_1 / sum(pvals_1)
pvals_2 = R.uniform(size=size2)
pvals_2 = pvals_2 / tensor.sum(pvals_2)
......@@ -684,9 +684,9 @@ def test_binomial():
for size, const_size, var_input, input in [
(sample_size, sample_size, [], []),
(x.shape, sample_size, [x],
[numpy.zeros(sample_size, dtype=config.floatX)]),
[np.zeros(sample_size, dtype=config.floatX)]),
((x.shape[0], sample_size[1]), sample_size, [x],
[numpy.zeros(sample_size, dtype=config.floatX)]),
[np.zeros(sample_size, dtype=config.floatX)]),
# test empty size (scalar)
((), (), [], []),
]:
......@@ -701,7 +701,7 @@ def t_binomial(mean, size, const_size, var_input, input, steps, rtol):
out = f(*input)
# Increase the number of steps if sizes implies only a few samples
if numpy.prod(const_size) < 10:
if np.prod(const_size) < 10:
steps_ = steps * 100
else:
steps_ = steps
......@@ -717,12 +717,12 @@ def t_binomial(mean, size, const_size, var_input, input, steps, rtol):
f = theano.function(var_input, theano.Out(
theano.sandbox.cuda.basic_ops.gpu_from_host(u),
borrow=True), mode=mode_with_gpu)
gpu_out = numpy.asarray(f(*input))
gpu_out = np.asarray(f(*input))
basictest(f, steps_, const_size, prefix='mrg gpu',
inputs=input, allow_01=True,
target_avg=mean, mean_rtol=rtol)
numpy.testing.assert_array_almost_equal(out, gpu_out,
np.testing.assert_array_almost_equal(out, gpu_out,
decimal=6)
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
......@@ -752,22 +752,22 @@ def test_normal0():
for size, const_size, var_input, input, avg, rtol, std_tol in [
(sample_size, sample_size, [], [], -5., default_rtol, default_rtol),
(x.shape, sample_size, [x],
[numpy.zeros(sample_size, dtype=config.floatX)],
[np.zeros(sample_size, dtype=config.floatX)],
-5., default_rtol, default_rtol),
((x.shape[0], sample_size[1]), sample_size, [x],
[numpy.zeros(sample_size, dtype=config.floatX)],
[np.zeros(sample_size, dtype=config.floatX)],
-5., default_rtol, default_rtol),
# test odd value
(sample_size_odd, sample_size_odd, [], [], -5.,
default_rtol, default_rtol),
# test odd value
(x.shape, sample_size_odd, [x],
[numpy.zeros(sample_size_odd, dtype=config.floatX)],
[np.zeros(sample_size_odd, dtype=config.floatX)],
-5., default_rtol, default_rtol),
(sample_size, sample_size, [], [],
numpy.arange(numpy.prod(sample_size),
np.arange(np.prod(sample_size),
dtype='float32').reshape(sample_size),
10. * std / numpy.sqrt(steps), default_rtol),
10. * std / np.sqrt(steps), default_rtol),
# test empty size (scalar)
((), (), [], [], -5., default_rtol, 0.02),
# test with few samples at the same time
......@@ -788,7 +788,7 @@ def test_normal0():
# print 'random?[:10]\n', out[0, 0:10]
# Increase the number of steps if size implies only a few samples
if numpy.prod(const_size) < 10:
if np.prod(const_size) < 10:
steps_ = steps * 50
else:
steps_ = steps
......@@ -812,7 +812,7 @@ def test_normal0():
# theano.printing.debugprint(f)
sys.stdout.flush()
gpu_out = numpy.asarray(f(*input))
gpu_out = np.asarray(f(*input))
# print 'random?[:10]\n', gpu_out[0, 0:10]
# print '----'
sys.stdout.flush()
......@@ -821,7 +821,7 @@ def test_normal0():
mean_rtol=rtol, std_tol=std_tol)
# Need to allow some rounding error as their is float
# computation that are done on the gpu vs cpu
assert numpy.allclose(out, gpu_out, rtol=5e-6, atol=5e-6)
assert np.allclose(out, gpu_out, rtol=5e-6, atol=5e-6)
# print ''
# print 'ON CPU w NUMPY:'
......@@ -838,26 +838,26 @@ def basic_multinomialtest(f, steps, sample_size, target_pvals, n_samples,
prefix="", mean_rtol=0.04):
dt = 0.0
avg_pvals = numpy.zeros(target_pvals.shape, dtype=config.floatX)
avg_pvals = np.zeros(target_pvals.shape, dtype=config.floatX)
for i in xrange(steps):
t0 = time.time()
ival = f()
assert ival.shape == sample_size
assert numpy.all(numpy.sum(ival, axis=1) == n_samples)
assert np.all(np.sum(ival, axis=1) == n_samples)
dt += time.time() - t0
avg_pvals += ival
avg_pvals /= (steps * n_samples)
assert numpy.mean(abs(avg_pvals - target_pvals)) < mean_rtol
assert np.mean(abs(avg_pvals - target_pvals)) < mean_rtol
print('random?[:10]\n', numpy.asarray(f()[:10]))
print('random?[:10]\n', np.asarray(f()[:10]))
print(prefix, 'mean', avg_pvals)
# < mean_rtol, 'bad mean? %s %s' % (str(avg_pvals), str(target_pvals))
print(numpy.mean(abs(avg_pvals - target_pvals)))
print(np.mean(abs(avg_pvals - target_pvals)))
print(prefix, 'time', dt)
print(prefix, 'elements', steps * numpy.prod(target_pvals.shape))
print(prefix, 'samples/sec', steps * numpy.prod(target_pvals.shape) / dt)
print(prefix, 'elements', steps * np.prod(target_pvals.shape))
print(prefix, 'samples/sec', steps * np.prod(target_pvals.shape) / dt)
def test_multinomial():
......@@ -875,8 +875,8 @@ def test_multinomial():
# 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)
pvals = np.asarray(np.random.uniform(size=sample_size))
pvals = np.apply_along_axis(lambda row: row / np.sum(row), 1, pvals)
R = MRG_RandomStreams(234, use_cuda=False)
# Note: we specify `nstreams` to avoid a warning.
m = R.multinomial(pvals=pvals, dtype=config.floatX, nstreams=30 * 256)
......@@ -892,7 +892,7 @@ def test_multinomial():
# print ''
# print 'ON GPU:'
R = MRG_RandomStreams(234, use_cuda=True)
pvals = numpy.asarray(pvals, dtype='float32')
pvals = np.asarray(pvals, dtype='float32')
# We give the number of streams to avoid a warning.
n = R.multinomial(pvals=pvals, dtype='float32', nstreams=30 * 256)
# well, it's really that this test w GPU doesn't make sense otw
......@@ -907,7 +907,7 @@ def test_multinomial():
sys.stdout.flush()
basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1,
prefix='gpu mrg ')
numpy.testing.assert_array_almost_equal(out, gpu_out, decimal=6)
np.testing.assert_array_almost_equal(out, gpu_out, decimal=6)
def test_multinomial_n_samples():
......@@ -922,8 +922,8 @@ def test_multinomial_n_samples():
sample_size = (450, 6)
mode_ = theano.compile.mode.get_mode(mode_)
pvals = numpy.asarray(numpy.random.uniform(size=sample_size))
pvals = numpy.apply_along_axis(lambda row: row / numpy.sum(row), 1, pvals)
pvals = np.asarray(np.random.uniform(size=sample_size))
pvals = np.apply_along_axis(lambda row: row / np.sum(row), 1, pvals)
R = MRG_RandomStreams(234, use_cuda=False)
for n_samples, steps in zip([5, 10, 100, 1000], [20, 10, 1, 1]):
......@@ -936,7 +936,7 @@ def test_multinomial_n_samples():
if mode != 'FAST_COMPILE' and cuda_available:
R = MRG_RandomStreams(234, use_cuda=True)
pvals = numpy.asarray(pvals, dtype='float32')
pvals = np.asarray(pvals, dtype='float32')
n = R.multinomial(pvals=pvals, n=n_samples,
dtype='float32', nstreams=30 * 256)
assert n.dtype == 'float32'
......@@ -999,14 +999,14 @@ def test_random_state_transfer():
for (su1, su2) in zip(g1.rng.state_updates, g2.rng.state_updates):
su2[0].set_value(su1[0].get_value())
numpy.testing.assert_array_almost_equal(f1(), f2(), decimal=6)
np.testing.assert_array_almost_equal(f1(), f2(), decimal=6)
def test_gradient_scan():
# Test for a crash when using MRG inside scan and taking the gradient
# See https://groups.google.com/d/msg/theano-dev/UbcYyU5m-M8/UO9UgXqnQP0J
theano_rng = MRG_RandomStreams(10)
w = theano.shared(numpy.ones(1, dtype='float32'))
w = theano.shared(np.ones(1, dtype='float32'))
def one_step(x):
return x + theano_rng.uniform((1,), dtype='float32') * w
......@@ -1015,7 +1015,7 @@ def test_gradient_scan():
values, updates = theano.scan(one_step, outputs_info=x, n_steps=10)
gw = theano.grad(tensor.sum(values[-1]), w)
f = theano.function([x], gw)
f(numpy.arange(1, dtype='float32'))
f(np.arange(1, dtype='float32'))
def test_multMatVect():
......@@ -1029,14 +1029,14 @@ def test_multMatVect():
g0 = rng_mrg.DotModulo()(A1, s1, m1, A2, s2, m2)
f0 = theano.function([A1, s1, m1, A2, s2, m2], g0)
i32max = numpy.iinfo(numpy.int32).max
i32max = np.iinfo(np.int32).max
A1 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
s1 = numpy.random.randint(0, i32max, 3).astype('int32')
m1 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
A2 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
s2 = numpy.random.randint(0, i32max, 3).astype('int32')
m2 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
A1 = np.random.randint(0, i32max, (3, 3)).astype('int64')
s1 = np.random.randint(0, i32max, 3).astype('int32')
m1 = np.asarray(np.random.randint(i32max), dtype="int32")
A2 = np.random.randint(0, i32max, (3, 3)).astype('int64')
s2 = np.random.randint(0, i32max, 3).astype('int32')
m2 = np.asarray(np.random.randint(i32max), dtype="int32")
f0.input_storage[0].storage[0] = A1
f0.input_storage[1].storage[0] = s1
......@@ -1050,8 +1050,8 @@ def test_multMatVect():
f0.fn()
r_b = f0.output_storage[0].value
assert numpy.allclose(r_a1, r_b[:3])
assert numpy.allclose(r_a2, r_b[3:])
assert np.allclose(r_a1, r_b[:3])
assert np.allclose(r_a2, r_b[3:])
def test_seed_fn():
......@@ -1079,13 +1079,13 @@ def test_seed_fn():
fn1_val0 = fn1()
fn1_val1 = fn1()
assert not numpy.allclose(fn1_val0, fn1_val1)
assert not np.allclose(fn1_val0, fn1_val1)
fn2_val0 = fn2()
fn2_val1 = fn2()
assert not numpy.allclose(fn2_val0, fn2_val1)
assert not np.allclose(fn2_val0, fn2_val1)
fn3_val0 = fn3([4])
fn3_val1 = fn3([4])
assert not numpy.allclose(fn3_val0, fn3_val1)
assert not np.allclose(fn3_val0, fn3_val1)
assert fn1_val0.size == 4
assert fn2_val0.size == 9
......@@ -1097,12 +1097,12 @@ def test_seed_fn():
fn2_val3 = fn2()
fn3_val2 = fn3([4])
fn3_val3 = fn3([4])
assert numpy.allclose(fn1_val0, fn1_val2) == same
assert numpy.allclose(fn1_val1, fn1_val3) == same
assert numpy.allclose(fn2_val0, fn2_val2) == same
assert numpy.allclose(fn2_val1, fn2_val3) == same
assert numpy.allclose(fn3_val0, fn3_val2) == same
assert numpy.allclose(fn3_val1, fn3_val3) == same
assert np.allclose(fn1_val0, fn1_val2) == same
assert np.allclose(fn1_val1, fn1_val3) == same
assert np.allclose(fn2_val0, fn2_val2) == same
assert np.allclose(fn2_val1, fn2_val3) == same
assert np.allclose(fn3_val0, fn3_val2) == same
assert np.allclose(fn3_val1, fn3_val3) == same
def rng_mrg_overflow(sizes, fct, mode, should_raise_error):
......@@ -1118,7 +1118,7 @@ def rng_mrg_overflow(sizes, fct, mode, should_raise_error):
def test_overflow_cpu():
# run with THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32
rng = MRG_RandomStreams(numpy.random.randint(1234))
rng = MRG_RandomStreams(np.random.randint(1234))
fct = rng.uniform
# should raise error as the size overflows
sizes = [(2**31, ), (2**32, ), (2**15, 2**16,), (2, 2**15, 2**15)]
......@@ -1127,8 +1127,8 @@ def test_overflow_cpu():
sizes = [(2**5, ), (2**5, 2**5), (2**5, 2**5, 2**5)]
rng_mrg_overflow(sizes, fct, config.mode, should_raise_error=False)
# should support int32 sizes
sizes = [(numpy.int32(2**10), ),
(numpy.int32(2), numpy.int32(2**10), numpy.int32(2**10))]
sizes = [(np.int32(2**10), ),
(np.int32(2), np.int32(2**10), np.int32(2**10))]
rng_mrg_overflow(sizes, fct, config.mode, should_raise_error=False)
......@@ -1147,8 +1147,8 @@ def test_overflow_gpu_old_backend():
sizes = [(2**5, ), (2**5, 2**5), (2**5, 2**5, 2**5)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
# should support int32 sizes
sizes = [(numpy.int32(2**10), ),
(numpy.int32(2), numpy.int32(2**10), numpy.int32(2**10))]
sizes = [(np.int32(2**10), ),
(np.int32(2), np.int32(2**10), np.int32(2**10))]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
......@@ -1159,11 +1159,11 @@ def test_overflow_gpu_new_backend():
from theano.gpuarray.type import gpuarray_shared_constructor
seed = 12345
n_substreams = 7
curr_rstate = numpy.array([seed] * 6, dtype='int32')
curr_rstate = np.array([seed] * 6, dtype='int32')
rstate = [curr_rstate.copy()]
for j in range(1, n_substreams):
rstate.append(rng_mrg.ff_2p72(rstate[-1]))
rstate = numpy.asarray(rstate)
rstate = np.asarray(rstate)
rstate = gpuarray_shared_constructor(rstate)
fct = functools.partial(rng_mrg.GPUA_mrg_uniform.new, rstate,
ndim=None, dtype='float32')
......@@ -1174,8 +1174,8 @@ def test_overflow_gpu_new_backend():
sizes = [(2**5, ), (2**5, 2**5), (2**5, 2**5, 2**5)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
# should support int32 sizes
sizes = [(numpy.int32(2**10), ),
(numpy.int32(2), numpy.int32(2**10), numpy.int32(2**10))]
sizes = [(np.int32(2**10), ),
(np.int32(2), np.int32(2**10), np.int32(2**10))]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
......@@ -1185,12 +1185,12 @@ def test_validate_input_types_gpuarray_backend():
from theano.configparser import change_flags
with change_flags(compute_test_value="raise"):
rstate = numpy.zeros((7, 6), dtype="int32")
rstate = np.zeros((7, 6), dtype="int32")
rstate = gpuarray_shared_constructor(rstate)
mrg_uniform.new(rstate, ndim=None, dtype="float32", size=(3,))
if __name__ == "__main__":
rng = MRG_RandomStreams(numpy.random.randint(2147462579))
rng = MRG_RandomStreams(np.random.randint(2147462579))
print(theano.__file__)
pvals = theano.tensor.fmatrix()
for i in range(10):
......
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