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提交 bea31470 authored 作者: Frédéric Bastien's avatar Frédéric Bastien 提交者： GitHub
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Merge pull request #5782 from Amrithasuresh/master

Updated numpy as np #4218
上级 56da8ca8 65a48d98
隐藏空白字符变更
内嵌 并排
正在显示 包含 719 行增加 和 719 行删除
theano/sandbox/fourier.py
浏览文件 @ bea31470
......@@ -4,7 +4,7 @@ Provides Ops for FFT and DCT.
"""
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import numpy.fft
from six.moves import xrange
......@@ -126,13 +126,13 @@ def dct_matrix(rows, cols, unitary=True):
This algorithm is adapted from Dan Ellis' Rastmat spec2cep.m, lines 15-20.
"""
rval = numpy.zeros((rows, cols))
col_range = numpy.arange(cols)
scale = numpy.sqrt(2.0 / cols)
rval = np.zeros((rows, cols))
col_range = np.arange(cols)
scale = np.sqrt(2.0 / cols)
for i in xrange(rows):
rval[i] = numpy.cos(
i * (col_range * 2 + 1) / (2.0 * cols) * numpy.pi) * scale
rval[i] = np.cos(
i * (col_range * 2 + 1) / (2.0 * cols) * np.pi) * scale
if unitary:
rval[0] *= numpy.sqrt(0.5)
rval[0] *= np.sqrt(0.5)
return rval
theano/sandbox/linalg/tests/test_linalg.py
浏览文件 @ bea31470
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import numpy.linalg
import theano
......@@ -39,9 +39,9 @@ def test_rop_lop():
non_sequences=[y, mx, mv])
scan_f = function([mx, mv], sy)
rng = numpy.random.RandomState(utt.fetch_seed())
vx = numpy.asarray(rng.randn(4, 4), theano.config.floatX)
vv = numpy.asarray(rng.randn(4, 4), theano.config.floatX)
rng = np.random.RandomState(utt.fetch_seed())
vx = np.asarray(rng.randn(4, 4), theano.config.floatX)
vv = np.asarray(rng.randn(4, 4), theano.config.floatX)
v1 = rop_f(vx, vv)
v2 = scan_f(vx, vv)
......@@ -61,7 +61,7 @@ def test_rop_lop():
'Op did not raised an error even though the function'
' is not differentiable'))
vv = numpy.asarray(rng.uniform(size=(4,)), theano.config.floatX)
vv = np.asarray(rng.uniform(size=(4,)), theano.config.floatX)
yv = tensor.Lop(y, mx, v)
lop_f = function([mx, v], yv)
......@@ -75,21 +75,21 @@ def test_rop_lop():
def test_spectral_radius_bound():
tol = 10 ** (-6)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
x = theano.tensor.matrix()
radius_bound = spectral_radius_bound(x, 5)
f = theano.function([x], radius_bound)
shp = (3, 4)
m = rng.rand(*shp)
m = numpy.cov(m).astype(config.floatX)
m = np.cov(m).astype(config.floatX)
radius_bound_theano = f(m)
# test the approximation
mm = m
for i in range(5):
mm = numpy.dot(mm, mm)
radius_bound_numpy = numpy.trace(mm) ** (2 ** (-5))
mm = np.dot(mm, mm)
radius_bound_numpy = np.trace(mm) ** (2 ** (-5))
assert abs(radius_bound_numpy - radius_bound_theano) < tol
# test the bound
......
theano/sandbox/minimal.py
浏览文件 @ bea31470
......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import unittest
import numpy
import numpy as np
from theano import gof, tensor, function
from theano.tests import unittest_tools as utt
......@@ -39,11 +39,11 @@ class Minimal(gof.Op):
# but do not modify any of the arguments [inplace].
print("perform got %i arguments" % len(inputs))
print("Max of input[0] is ", numpy.max(inputs[0]))
print("Max of input[0] is ", np.max(inputs[0]))
# return some computed value.
# do not return something that is aliased to one of the inputs.
output[0] = numpy.asarray(0, dtype='int64')
output[0] = np.asarray(0, dtype='int64')
minimal = Minimal()
......@@ -55,7 +55,7 @@ minimal = Minimal()
class T_minimal(unittest.TestCase):
def setUp(self):
self.rng = numpy.random.RandomState(utt.fetch_seed(666))
self.rng = np.random.RandomState(utt.fetch_seed(666))
def test0(self):
A = tensor.matrix()
......@@ -66,6 +66,6 @@ class T_minimal(unittest.TestCase):
print('built')
Aval = self.rng.randn(5, 5)
bval = numpy.arange(5, dtype=float)
bval = np.arange(5, dtype=float)
f(Aval, bval)
print('done')
theano/sandbox/multinomial.py
浏览文件 @ bea31470
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import warnings
import theano
......@@ -172,7 +172,7 @@ class MultinomialFromUniform(Op):
raise ValueError("unis.shape[0] != pvals.shape[0] * n_samples",
unis.shape[0], pvals.shape[0], n_samples)
if z[0] is None or z[0].shape != pvals.shape:
z[0] = numpy.zeros(pvals.shape, dtype=node.outputs[0].dtype)
z[0] = np.zeros(pvals.shape, dtype=node.outputs[0].dtype)
else:
z[0].fill(0)
......@@ -209,7 +209,7 @@ class MultinomialFromUniform(Op):
# have the same answer as the c code as in the c code
# the cumul is in double precission.
cumsum = pvals[n].cumsum(dtype='float64')
z[0][n, numpy.searchsorted(cumsum, unis_n)] += 1
z[0][n, np.searchsorted(cumsum, unis_n)] += 1
class ChoiceFromUniform(MultinomialFromUniform):
......@@ -380,8 +380,8 @@ class ChoiceFromUniform(MultinomialFromUniform):
else:
odtype = self.odtype
if (z[0] is None or
not numpy.all(z[0].shape == [pvals.shape[0], n_samples])):
z[0] = -1 * numpy.ones((pvals.shape[0], n_samples), dtype=odtype)
not np.all(z[0].shape == [pvals.shape[0], n_samples])):
z[0] = -1 * np.ones((pvals.shape[0], n_samples), dtype=odtype)
nb_multi = pvals.shape[0]
nb_outcomes = pvals.shape[1]
......
theano/sandbox/rng_mrg.py
浏览文件 @ bea31470
......@@ -8,7 +8,7 @@ http://www.iro.umontreal.ca/~simardr/ssj/indexe.html
from __future__ import absolute_import, print_function, division
import warnings
import numpy
import numpy as np
from six import integer_types
from six.moves import xrange
......@@ -38,7 +38,7 @@ if theano.sandbox.cuda.cuda_available:
def matVecModM(A, s, m):
# TODO : need description for method, parameter and return
assert A.dtype == 'int64'
return numpy.int32(numpy.sum((A * s) % m, 1) % m)
return np.int32(np.sum((A * s) % m, 1) % m)
def multMatVect(v, A, m1, B, m2):
......@@ -97,7 +97,7 @@ class DotModulo(Op):
(out,) = outputs
o1 = matVecModM(A, s, m)
o2 = matVecModM(A2, s2, m2)
out[0] = numpy.concatenate((o1, o2))
out[0] = np.concatenate((o1, o2))
def c_code_cache_version(self):
return (6,)
......@@ -198,39 +198,39 @@ class DotModulo(Op):
# MRG31k3p
# generator constants :
M1 = numpy.asarray(numpy.int32(2147483647)) # 2^31 - 1
M2 = numpy.asarray(numpy.int32(2147462579)) # 2^31 - 21069
MASK12 = numpy.int32(511) # 2^9 - 1
MASK13 = numpy.int32(16777215) # 2^24 - 1
MASK2 = numpy.int32(65535) # 2^16 - 1
MULT2 = numpy.int32(21069)
M1 = np.asarray(np.int32(2147483647)) # 2^31 - 1
M2 = np.asarray(np.int32(2147462579)) # 2^31 - 21069
MASK12 = np.int32(511) # 2^9 - 1
MASK13 = np.int32(16777215) # 2^24 - 1
MASK2 = np.int32(65535) # 2^16 - 1
MULT2 = np.int32(21069)
NORM = 4.656612873077392578125e-10 # 1./2^31
# A1p0 = numpy.asarray([[0, 4194304, 129], [1, 0, 0], [0, 1, 0]],
# A1p0 = np.asarray([[0, 4194304, 129], [1, 0, 0], [0, 1, 0]],
# dtype='int64')
# A2p0 = numpy.asarray([[32768, 0, 32769], [1, 0, 0], [0, 1, 0]],
# A2p0 = np.asarray([[32768, 0, 32769], [1, 0, 0], [0, 1, 0]],
# dtype='int64')
A1p72 = numpy.asarray([[1516919229, 758510237, 499121365],
[1884998244, 1516919229, 335398200],
[601897748, 1884998244, 358115744]],
dtype='int64')
A2p72 = numpy.asarray([[1228857673, 1496414766, 954677935],
[1133297478, 1407477216, 1496414766],
[2002613992, 1639496704, 1407477216]],
dtype='int64')
A1p72 = np.asarray([[1516919229, 758510237, 499121365],
[1884998244, 1516919229, 335398200],
[601897748, 1884998244, 358115744]],
dtype='int64')
A2p72 = np.asarray([[1228857673, 1496414766, 954677935],
[1133297478, 1407477216, 1496414766],
[2002613992, 1639496704, 1407477216]],
dtype='int64')
A1p134 = numpy.asarray(
A1p134 = np.asarray(
[[1702500920, 1849582496, 1656874625],
[828554832, 1702500920, 1512419905],
[1143731069, 828554832, 102237247]],
dtype='int64')
A2p134 = numpy.asarray(
A2p134 = np.asarray(
[[796789021, 1464208080, 607337906],
[1241679051, 1431130166, 1464208080],
[1401213391, 1178684362, 1431130166]],
dtype='int64')
np_int32_vals = [numpy.int32(i) for i in (0, 7, 9, 15, 16, 22, 24)]
np_int32_vals = [np.int32(i) for i in (0, 7, 9, 15, 16, 22, 24)]
def ff_2p134(rstate):
......@@ -246,14 +246,14 @@ def ff_2p72(rstate):
def mrg_next_value(rstate, new_rstate):
# TODO : need description for method, parameter and return
x11, x12, x13, x21, x22, x23 = rstate
assert type(x11) == numpy.int32
assert type(x11) == np.int32
i0, i7, i9, i15, i16, i22, i24 = np_int32_vals
# first component
y1 = (((x12 & MASK12) << i22) + (x12 >> i9) +
((x13 & MASK13) << i7) + (x13 >> i24))
assert type(y1) == numpy.int32
assert type(y1) == np.int32
if (y1 < 0 or y1 >= M1): # must also check overflow
y1 -= M1
y1 += x13
......@@ -266,11 +266,11 @@ def mrg_next_value(rstate, new_rstate):
# second component
y1 = ((x21 & MASK2) << i15) + (MULT2 * (x21 >> i16))
assert type(y1) == numpy.int32
assert type(y1) == np.int32
if (y1 < 0 or y1 >= M2):
y1 -= M2
y2 = ((x23 & MASK2) << i15) + (MULT2 * (x23 >> i16))
assert type(y2) == numpy.int32
assert type(y2) == np.int32
if (y2 < 0 or y2 >= M2):
y2 -= M2
y2 += x23
......@@ -286,7 +286,7 @@ def mrg_next_value(rstate, new_rstate):
# Must never return either 0 or M1+1
new_rstate[...] = [x11, x12, x13, x21, x22, x23]
assert new_rstate.dtype == numpy.int32
assert new_rstate.dtype == np.int32
if (x11 <= x21):
return (x11 - x21 + M1) * NORM
else:
......@@ -360,22 +360,22 @@ class mrg_uniform(mrg_uniform_base):
# some rng_mrg tests) I also add this limit here.
raise ValueError("rng_mrg does not support more then (2**31 -1) samples")
rstate = numpy.asarray(rstate) # bring state from GPU if necessary
rstate = np.asarray(rstate) # bring state from GPU if necessary
if not self.inplace:
rstate = rstate.copy()
n_streams, _ = rstate.shape
rval = numpy.zeros(n_elements, dtype=self.output_type.dtype)
rval = np.zeros(n_elements, dtype=self.output_type.dtype)
err_orig = numpy.seterr(over='ignore')
err_orig = np.seterr(over='ignore')
try:
for i in xrange(n_elements):
sample = mrg_next_value(rstate[i % n_streams],
rstate[i % n_streams])
rval[i] = sample
finally:
numpy.seterr(**err_orig)
np.seterr(**err_orig)
# send to GPU if necessary
o_rstate[0] = node.outputs[0].type.filter(rstate)
......@@ -396,13 +396,13 @@ class mrg_uniform(mrg_uniform_base):
'NPY_ARRAY_ENSURECOPY|NPY_ARRAY_C_CONTIGUOUS|'
'NPY_ARRAY_ALIGNED')
ndim = self.output_type.ndim
o_type_num = numpy.asarray(0, dtype=self.output_type.dtype).dtype.num
o_type_num = np.asarray(0, dtype=self.output_type.dtype).dtype.num
fail = sub['fail']
if self.output_type.dtype == 'float32':
otype = 'float'
NORM = '4.6566126e-10f' # numpy.float32(1.0/(2**31+65))
NORM = '4.6566126e-10f' # np.float32(1.0/(2**31+65))
# this was determined by finding the biggest number such that
# numpy.float32(number * M1) < 1.0
# np.float32(number * M1) < 1.0
else:
otype = 'double'
NORM = '4.656612873077392578125e-10'
......@@ -590,9 +590,9 @@ class GPU_mrg_uniform(mrg_uniform_base, GpuOp):
def c_support_code_apply(self, node, nodename):
if self.output_type.dtype == 'float32':
otype = 'float'
NORM = '4.6566126e-10f' # numpy.float32(1.0/(2**31+65))
NORM = '4.6566126e-10f' # np.float32(1.0/(2**31+65))
# this was determined by finding the biggest number such that
# numpy.float32(number * M1) < 1.0
# np.float32(number * M1) < 1.0
else:
otype = 'double'
NORM = '4.656612873077392578125e-10'
......@@ -686,7 +686,7 @@ class GPU_mrg_uniform(mrg_uniform_base, GpuOp):
o_rstate, o_sample = out
inplace = int(self.inplace)
ndim = self.output_type.ndim
o_type_num = numpy.asarray(0, dtype=self.output_type.dtype).dtype.num
o_type_num = np.asarray(0, dtype=self.output_type.dtype).dtype.num
fail = sub['fail']
if self.output_type.dtype == 'float32':
......@@ -858,15 +858,15 @@ class GPUA_mrg_uniform(GpuKernelBase, mrg_uniform_base):
otype = 'ga_half'
# limit the values of the state that we use.
mask = '& 0x7fff'
NORM = '3.0518e-05f' # numpy.float16(1.0/(2**15+8))
NORM = '3.0518e-05f' # np.float16(1.0/(2**15+8))
# this was determined by finding the biggest number such that
# numpy.float16(number * (M1 & 0x7fff)) < 1.0
# np.float16(number * (M1 & 0x7fff)) < 1.0
elif self.output_type.dtype == 'float32':
otype = 'float'
mask = ''
NORM = '4.6566126e-10f' # numpy.float32(1.0/(2**31+65))
NORM = '4.6566126e-10f' # np.float32(1.0/(2**31+65))
# this was determined by finding the biggest number such that
# numpy.float32(number * M1) < 1.0
# np.float32(number * M1) < 1.0
elif self.output_type.dtype == 'float64':
otype = 'double'
mask = ''
......@@ -969,7 +969,7 @@ class GPUA_mrg_uniform(GpuKernelBase, mrg_uniform_base):
o_rstate, o_sample = out
inplace = int(self.inplace)
ndim = self.output_type.ndim
o_type_num = numpy.asarray(0, dtype=self.output_type.dtype).dtype.num
o_type_num = np.asarray(0, dtype=self.output_type.dtype).dtype.num
fail = sub['fail']
ctx = sub['params']
kname = self.gpu_kernels(node, nodename)[0].objvar
......@@ -1176,7 +1176,7 @@ class MRG_RandomStreams(object):
raise ValueError('seed should not be 0', seed)
elif seed >= M2:
raise ValueError('seed should be less than %i' % M2, seed)
self.rstate = numpy.asarray([seed] * 6, dtype='int32')
self.rstate = np.asarray([seed] * 6, dtype='int32')
elif len(seed) == 6:
if seed[0] == 0 and seed[1] == 0 and seed[2] == 0:
raise ValueError(
......@@ -1192,7 +1192,7 @@ class MRG_RandomStreams(object):
raise ValueError(
'The last 3 values of seed should be less than %i' % M2,
seed)
self.rstate = numpy.asarray(seed, dtype='int32')
self.rstate = np.asarray(seed, dtype='int32')
else:
raise TypeError("seed should be 1 integer or 6 integers")
......@@ -1234,7 +1234,7 @@ class MRG_RandomStreams(object):
"""
# self.rstate = ff_2p134(self.rstate)
self.rstate = multMatVect(self.rstate, A1p134, M1, A2p134, M2)
assert self.rstate.dtype == numpy.int32
assert self.rstate.dtype == np.int32
@theano.configparser.change_flags(compute_test_value='off')
def get_substream_rstates(self, n_streams, dtype, inc_rstate=True):
......@@ -1247,7 +1247,7 @@ class MRG_RandomStreams(object):
assert isinstance(dtype, str)
assert n_streams < 2**72
assert n_streams > 0
rval = numpy.zeros((n_streams, 6), dtype='int32')
rval = np.zeros((n_streams, 6), dtype='int32')
rval[0] = self.rstate
# If multMatVect.dot_modulo isn't compiled, compile it.
......@@ -1276,7 +1276,7 @@ class MRG_RandomStreams(object):
# HACK - we use fact that int32 and float32 have same size to
# sneak ints into the CudaNdarray type.
# these *SHOULD NEVER BE USED AS FLOATS*
tmp_float_buf = numpy.frombuffer(rval.data, dtype='float32')
tmp_float_buf = np.frombuffer(rval.data, dtype='float32')
assert tmp_float_buf.shape == rval.shape
assert (tmp_float_buf.view('int32') == rval).all()
rval = tmp_float_buf
......@@ -1334,9 +1334,9 @@ class MRG_RandomStreams(object):
if isinstance(size, tuple):
msg = "size must be a tuple of int or a Theano variable"
assert all([isinstance(i, (numpy.integer, integer_types, Variable))
assert all([isinstance(i, (np.integer, integer_types, Variable))
for i in size]), msg
if any([isinstance(i, (numpy.integer, integer_types)) and i <= 0
if any([isinstance(i, (np.integer, integer_types)) and i <= 0
for i in size]):
raise ValueError(
"The specified size contains a dimension with value <= 0",
......@@ -1558,10 +1558,10 @@ class MRG_RandomStreams(object):
evened = False
constant = False
if (isinstance(size, tuple) and
all([isinstance(i, (numpy.integer, integer_types)) for i in size])):
all([isinstance(i, (np.integer, integer_types)) for i in size])):
constant = True
# Force dtype because it defaults to float when size is empty
n_samples = numpy.prod(size, dtype='int64')
n_samples = np.prod(size, dtype='int64')
if n_samples % 2 == 1:
n_samples += 1
......@@ -1583,14 +1583,14 @@ class MRG_RandomStreams(object):
sqrt_ln_U1 = sqrt(-2.0 * log(U1))
# TypeError: 'TensorVariable' object does not support item assignment
# so this doesn't work...
# normal_samples[:n_samples/2] = sqrt_ln_U1 * cos(2.0*numpy.pi*U2)
# normal_samples[n_samples/2:] = sqrt_ln_U1 * sin(2.0*numpy.pi*U2)
# normal_samples[:n_samples/2] = sqrt_ln_U1 * cos(2.0*np.pi*U2)
# normal_samples[n_samples/2:] = sqrt_ln_U1 * sin(2.0*np.pi*U2)
# so trying this instead
first_half = sqrt_ln_U1 * cos(
numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
np.array(2.0 * np.pi, dtype=dtype) * U2)
second_half = sqrt_ln_U1 * sin(
numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
np.array(2.0 * np.pi, dtype=dtype) * U2)
normal_samples = join(0, first_half, second_half)
final_samples = None
......
theano/sandbox/tests/test_multinomial.py
浏览文件 @ bea31470
......@@ -4,7 +4,7 @@ import sys
from six import reraise
from nose.plugins.skip import SkipTest
import numpy
import numpy as np
import theano
from theano import config, function, tensor
......@@ -40,9 +40,9 @@ def test_n_samples_1():
f = function([p, u, n], m, allow_input_downcast=True)
numpy.random.seed(12345)
np.random.seed(12345)
for i in [1, 5, 10, 100, 1000, 10000]:
uni = numpy.random.rand(2 * i).astype(config.floatX)
uni = np.random.rand(2 * i).astype(config.floatX)
res = f([[1.0, 0.0], [0.0, 1.0]], uni, i)
utt.assert_allclose(res, [[i * 1.0, 0.0], [0.0, i * 1.0]])
......@@ -55,17 +55,17 @@ def test_n_samples_2():
f = function([p, u, n], m, allow_input_downcast=True)
numpy.random.seed(12345)
np.random.seed(12345)
for i in [1, 5, 10, 100, 1000]:
uni = numpy.random.rand(i).astype(config.floatX)
pvals = numpy.random.randint(1, 1000, (1, 1000)).astype(config.floatX)
uni = np.random.rand(i).astype(config.floatX)
pvals = np.random.randint(1, 1000, (1, 1000)).astype(config.floatX)
pvals /= pvals.sum(1)
res = f(pvals, uni, i)
assert res.sum() == i
for i in [1, 5, 10, 100, 1000]:
uni = numpy.random.rand(i).astype(config.floatX)
pvals = numpy.random.randint(
uni = np.random.rand(i).astype(config.floatX)
pvals = np.random.randint(
1, 1000000, (1, 1000000)).astype(config.floatX)
pvals /= pvals.sum(1)
res = f(pvals, uni, i)
......@@ -104,8 +104,8 @@ def test_n_samples_compatibility():
raise
f = theano.function([X], samples)
res = f(numpy.random.randn(20, 10))
assert numpy.all(res.sum(axis=1) == 1)
res = f(np.random.randn(20, 10))
assert np.all(res.sum(axis=1) == 1)
def test_multinomial_0():
......@@ -160,9 +160,9 @@ def test_multinomial_large():
assert any([type(node.op) is multinomial.GpuMultinomialFromUniform
for node in f.maker.fgraph.toposort()])
pval = numpy.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
pval = np.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
pval = pval / pval.sum(axis=1)[:, None]
uval = numpy.ones_like(pval[:, 0]) * 0.5
uval = np.ones_like(pval[:, 0]) * 0.5
mval = f(pval, uval)
assert mval.shape == pval.shape
......@@ -175,7 +175,7 @@ def test_multinomial_large():
else:
raise NotImplementedError(config.cast_policy)
utt.assert_allclose(mval.sum(axis=1), 2)
asdf = numpy.asarray([0, 0, 2, 0]) + 0 * pval
asdf = np.asarray([0, 0, 2, 0]) + 0 * pval
utt.assert_allclose(mval, asdf) # broadcast over all rows
run_with_c(body)
if cuda.cuda_available:
......@@ -216,9 +216,9 @@ def test_gpu_opt():
f = function([p, u], m_gpu, allow_input_downcast=True, mode=get_mode(True))
assert any([type(node.op) is multinomial.GpuMultinomialFromUniform
for node in f.maker.fgraph.toposort()])
pval = numpy.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
pval = np.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
pval = pval / pval.sum(axis=1)[:, None]
uval = numpy.ones_like(pval[:, 0]) * 0.5
uval = np.ones_like(pval[:, 0]) * 0.5
f(pval, uval)
# Test with a row, it was failing in the past.
......@@ -230,7 +230,7 @@ def test_gpu_opt():
f = function([r, u], m_gpu, allow_input_downcast=True, mode=get_mode(True))
assert any([type(node.op) is multinomial.GpuMultinomialFromUniform
for node in f.maker.fgraph.toposort()])
pval = numpy.arange(1 * 4, dtype='float32').reshape((1, 4)) + 0.1
pval = np.arange(1 * 4, dtype='float32').reshape((1, 4)) + 0.1
pval = pval / pval.sum(axis=1)[:, None]
uval = numpy.ones_like(pval[:, 0]) * 0.5
uval = np.ones_like(pval[:, 0]) * 0.5
f(pval, uval)
theano/sandbox/tests/test_multinomial_wo_replacement.py
浏览文件 @ bea31470
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import os
from theano import config, function, tensor
from theano.compat import PY3
......@@ -24,25 +24,25 @@ class test_OP(unittest.TestCase):
n_elements = 1000
all_indices = range(n_elements)
numpy.random.seed(12345)
np.random.seed(12345)
expected = [
numpy.asarray([[931, 318, 185, 209, 559]]),
numpy.asarray([[477, 887, 2, 717, 333, 665, 159, 559, 348, 136]]),
numpy.asarray([[546, 28, 79, 665, 295, 779, 433, 531, 411, 716, 244, 234, 70, 88, 612, 639, 383, 335,
451, 100, 175, 492, 848, 771, 559, 214, 568, 596, 370, 486, 855, 925, 138, 300, 528, 507,
730, 199, 882, 357, 58, 195, 705, 900, 66, 468, 513, 410, 816, 672]])]
np.asarray([[931, 318, 185, 209, 559]]),
np.asarray([[477, 887, 2, 717, 333, 665, 159, 559, 348, 136]]),
np.asarray([[546, 28, 79, 665, 295, 779, 433, 531, 411, 716, 244, 234, 70, 88, 612, 639, 383, 335,
451, 100, 175, 492, 848, 771, 559, 214, 568, 596, 370, 486, 855, 925, 138, 300, 528, 507,
730, 199, 882, 357, 58, 195, 705, 900, 66, 468, 513, 410, 816, 672]])]
for i in [5, 10, 50, 100, 500, n_elements]:
uni = numpy.random.rand(i).astype(config.floatX)
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
uni = np.random.rand(i).astype(config.floatX)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
res = f(pvals, uni, i)
for ii in range(len(expected)):
if expected[ii].shape == res.shape:
assert (expected[ii] == res).all()
res = numpy.squeeze(res)
res = np.squeeze(res)
assert len(res) == i
assert numpy.all(numpy.in1d(numpy.unique(res), all_indices)), res
assert np.all(np.in1d(np.unique(res), all_indices)), res
def test_fail_select_alot(self):
"""
......@@ -58,9 +58,9 @@ class test_OP(unittest.TestCase):
n_elements = 100
n_selected = 200
numpy.random.seed(12345)
uni = numpy.random.rand(n_selected).astype(config.floatX)
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
np.random.seed(12345)
uni = np.random.rand(n_selected).astype(config.floatX)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
self.assertRaises(ValueError, f, pvals, uni, n_selected)
......@@ -79,18 +79,18 @@ class test_OP(unittest.TestCase):
n_elements = 100
n_selected = 10
mean_rtol = 0.0005
numpy.random.seed(12345)
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
np.random.seed(12345)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
avg_pvals = numpy.zeros((n_elements,), dtype=config.floatX)
avg_pvals = np.zeros((n_elements,), dtype=config.floatX)
for rep in range(10000):
uni = numpy.random.rand(n_selected).astype(config.floatX)
uni = np.random.rand(n_selected).astype(config.floatX)
res = f(pvals, uni, n_selected)
res = numpy.squeeze(res)
res = np.squeeze(res)
avg_pvals[res] += 1
avg_pvals /= avg_pvals.sum()
avg_diff = numpy.mean(abs(avg_pvals - pvals))
avg_diff = np.mean(abs(avg_pvals - pvals))
assert avg_diff < mean_rtol, avg_diff
......@@ -110,14 +110,14 @@ class test_function(unittest.TestCase):
n_elements = 1000
all_indices = range(n_elements)
numpy.random.seed(12345)
np.random.seed(12345)
for i in [5, 10, 50, 100, 500, n_elements]:
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
res = f(pvals, i)
res = numpy.squeeze(res)
res = np.squeeze(res)
assert len(res) == i
assert numpy.all(numpy.in1d(numpy.unique(res), all_indices)), res
assert np.all(np.in1d(np.unique(res), all_indices)), res
def test_fail_select_alot(self):
"""
......@@ -134,8 +134,8 @@ class test_function(unittest.TestCase):
n_elements = 100
n_selected = 200
numpy.random.seed(12345)
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
np.random.seed(12345)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
self.assertRaises(ValueError, f, pvals, n_selected)
......@@ -155,17 +155,17 @@ class test_function(unittest.TestCase):
n_elements = 100
n_selected = 10
mean_rtol = 0.0005
numpy.random.seed(12345)
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
np.random.seed(12345)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
avg_pvals = numpy.zeros((n_elements,), dtype=config.floatX)
avg_pvals = np.zeros((n_elements,), dtype=config.floatX)
for rep in range(10000):
res = f(pvals, n_selected)
res = numpy.squeeze(res)
res = np.squeeze(res)
avg_pvals[res] += 1
avg_pvals /= avg_pvals.sum()
avg_diff = numpy.mean(abs(avg_pvals - pvals))
avg_diff = np.mean(abs(avg_pvals - pvals))
assert avg_diff < mean_rtol
def test_unpickle_legacy_op(self):
......
theano/sandbox/tests/test_rng_mrg.py
浏览文件 @ bea31470
......@@ -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
......@@ -44,9 +44,9 @@ 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],
'sandbox',
'samples_MRG31k3p_12_7_5.txt'))
java_samples = np.loadtxt(os.path.join(os.path.split(theano.__file__)[0],
'sandbox',
'samples_MRG31k3p_12_7_5.txt'))
def test_deterministic():
......@@ -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)
......@@ -137,13 +137,13 @@ 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()],
dtype='int32'))
rstate = theano.shared(np.array([stream_rstate.copy()],
dtype='int32'))
new_rstate, sample = rng_mrg.mrg_uniform.new(rstate, ndim=None,
dtype=config.floatX,
size=(1,))
......@@ -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():
......@@ -233,16 +233,16 @@ 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,
dtype='float32')
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():
......@@ -373,13 +373,13 @@ 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()],
dtype='int32')
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
......@@ -607,15 +607,15 @@ 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,
decimal=6)
np.testing.assert_array_almost_equal(cpu_out, gpu_out,
decimal=6)
# print ''
# print 'ON CPU w Numpy with size=(%s):' % str(size)
......@@ -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
......@@ -717,13 +717,13 @@ 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,
decimal=6)
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),
dtype='float32').reshape(sample_size),
10. * std / numpy.sqrt(steps), default_rtol),
np.arange(np.prod(sample_size),
dtype='float32').reshape(sample_size),
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):
......
theano/scan_module/scan.py
浏览文件 @ bea31470
......@@ -45,7 +45,7 @@ __contact__ = "Razvan Pascanu <r.pascanu@gmail>"
import logging
import numpy
import numpy as np
import warnings
from collections import OrderedDict
......@@ -488,8 +488,8 @@ def scan(fn,
# a sequence, though is highly unlikely in practice
if 'taps' in seq:
# go through the indicated slice
mintap = numpy.min(seq['taps'])
maxtap = numpy.max(seq['taps'])
mintap = np.min(seq['taps'])
maxtap = np.max(seq['taps'])
for k in seq['taps']:
# create one slice of the input
# Later on, if we decide not to use scan because we are
......@@ -670,15 +670,15 @@ def scan(fn,
elif init_out.get('taps', None):
if numpy.any(numpy.array(init_out.get('taps', [])) > 0):
if np.any(np.array(init_out.get('taps', [])) > 0):
# Make sure we do not have requests for future values of a
# sequence we can not provide such values
raise ValueError('Can not use future taps of outputs',
init_out)
# go through the taps
mintap = abs(numpy.min(init_out['taps']))
mintap = abs(np.min(init_out['taps']))
mit_sot_tap_array.append(init_out['taps'])
idx_offset = abs(numpy.min(init_out['taps']))
idx_offset = abs(np.min(init_out['taps']))
# Sequence
mit_sot_scan_inputs.append(
scan_utils.expand_empty(init_out['initial'][:mintap],
......@@ -725,9 +725,9 @@ def scan(fn,
# a map); in that case we do not have to do anything ..
# Re-order args
max_mit_sot = numpy.max([-1] + mit_sot_rightOrder) + 1
max_sit_sot = numpy.max([-1] + sit_sot_rightOrder) + 1
n_elems = numpy.max([max_mit_sot, max_sit_sot])
max_mit_sot = np.max([-1] + mit_sot_rightOrder) + 1
max_sit_sot = np.max([-1] + sit_sot_rightOrder) + 1
n_elems = np.max([max_mit_sot, max_sit_sot])
_ordered_args = [[] for x in xrange(n_elems)]
offset = 0
for idx in xrange(n_mit_sot):
......@@ -1101,7 +1101,7 @@ def scan(fn,
return out_ls
offset = n_mit_mot
offsets = [abs(numpy.min(x)) for x in mit_sot_tap_array]
offsets = [abs(np.min(x)) for x in mit_sot_tap_array]
mit_sot_outs = remove_dimensions(
scan_outs[offset:offset + n_mit_sot],
mit_sot_return_steps,
......
theano/scan_module/scan_op.py
浏览文件 @ bea31470
......@@ -54,7 +54,7 @@ import logging
import time
from collections import OrderedDict
import numpy
import numpy as np
from six import iteritems, integer_types, raise_from
from six.moves import xrange
......@@ -193,7 +193,7 @@ class Scan(PureOp):
self.info['name'] = self.name
# Pre-computing some values to speed up perform
self.mintaps = [numpy.min(x) for x in self.tap_array]
self.mintaps = [np.min(x) for x in self.tap_array]
self.mintaps += [0 for x in xrange(self.n_nit_sot)]
self.seqs_arg_offset = 1 + self.n_seqs
self.shared_arg_offset = (self.seqs_arg_offset +
......@@ -336,7 +336,7 @@ class Scan(PureOp):
the inner function)
"""
assert numpy.all(isinstance(i, gof.Variable) for i in inputs)
assert np.all(isinstance(i, gof.Variable) for i in inputs)
# Check that the number of inputs to the Scan node corresponds to
# the number of inputs of the inner function of scan
n_outer_ins = len(inputs) - len(self.outer_nitsot(inputs)) - 1
......@@ -901,53 +901,53 @@ class Scan(PureOp):
try:
if impl == 'py':
raise theano.gof.cmodule.MissingGXX
cython_mintaps = numpy.asarray(self.mintaps, dtype='int32')
cython_mintaps = np.asarray(self.mintaps, dtype='int32')
cython_tap_array_len = \
numpy.asarray([len(x) for x in self.tap_array],
dtype='int32')
np.asarray([len(x) for x in self.tap_array],
dtype='int32')
if len(self.tap_array) == 0:
d1 = 0
else:
d1 = numpy.max(cython_tap_array_len)
d1 = np.max(cython_tap_array_len)
d0 = len(self.tap_array)
cython_tap_array = numpy.zeros((d0, d1), dtype='int32')
cython_tap_array = np.zeros((d0, d1), dtype='int32')
for _d0 in xrange(d0):
for _d1 in xrange(cython_tap_array_len[_d0]):
cython_tap_array[_d0, _d1] = self.tap_array[_d0][_d1]
cython_mit_mot_out_nslices = \
numpy.asarray([len(x) for x in self.mit_mot_out_slices],
dtype='int32')
np.asarray([len(x) for x in self.mit_mot_out_slices],
dtype='int32')
if len(self.mit_mot_out_slices) == 0:
d1 = 0
else:
d1 = numpy.max(cython_mit_mot_out_nslices)
d1 = np.max(cython_mit_mot_out_nslices)
d0 = len(self.mit_mot_out_slices)
cython_mit_mot_out_slices = numpy.zeros((d0, d1),
dtype='int32')
cython_mit_mot_out_slices = np.zeros((d0, d1),
dtype='int32')
for _d0 in xrange(d0):
for _d1 in xrange(cython_mit_mot_out_nslices[_d0]):
cython_mit_mot_out_slices[_d0, _d1] = \
self.mit_mot_out_slices[_d0][_d1]
cython_vector_seqs = numpy.asarray(self.vector_seqs,
cython_vector_seqs = np.asarray(self.vector_seqs,
dtype='int32')
cython_vector_outs = np.asarray(self.vector_outs,
dtype='int32')
cython_mitmots_preallocated = np.asarray(self.mitmots_preallocated,
dtype='int32')
cython_inps_is_tensor = np.asarray(self.inps_is_tensor,
dtype='int32')
cython_vector_outs = numpy.asarray(self.vector_outs,
cython_outs_is_tensor = np.asarray(self.outs_is_tensor,
dtype='int32')
cython_mitmots_preallocated = numpy.asarray(self.mitmots_preallocated,
dtype='int32')
cython_inps_is_tensor = numpy.asarray(self.inps_is_tensor,
dtype='int32')
cython_outs_is_tensor = numpy.asarray(self.outs_is_tensor,
dtype='int32')
if hasattr(self, 'destroy_map'):
cython_destroy_map = [x in self.destroy_map
for x in xrange(len(node.outputs))]
else:
cython_destroy_map = [0 for x in xrange(len(node.outputs))]
cython_destroy_map = numpy.asarray(cython_destroy_map,
dtype='int32')
cython_destroy_map = np.asarray(cython_destroy_map,
dtype='int32')
from . import scan_perform_ext
def p(node, args, outs):
......@@ -2200,9 +2200,9 @@ class Scan(PureOp):
# Seqs
outer_inp_seqs = [x[::-1] for x in inputs[1:1 + self.n_seqs]]
for idx in xrange(self.n_mit_mot + self.n_mit_sot):
mintap = numpy.min(self.tap_array[idx])
mintap = np.min(self.tap_array[idx])
if idx < self.n_mit_mot:
outmaxtap = numpy.max(self.mitmot_out_taps()[idx])
outmaxtap = np.max(self.mitmot_out_taps()[idx])
else:
outmaxtap = 0
seq = outs[idx]
......@@ -2226,7 +2226,7 @@ class Scan(PureOp):
# that.
for taps, x in zip(self.mitsot_taps(),
self.outer_mitsot_outs(outs)):
mintap = numpy.min(taps)
mintap = np.min(taps)
if hasattr(x[::-1][:mintap], 'test_value'):
assert (x[::-1][:mintap].tag.test_value.shape[0] ==
inputs[0].tag.test_value)
......@@ -2238,7 +2238,7 @@ class Scan(PureOp):
if hasattr(x[::-1].tag, 'test_value'):
assert (x[::-1].tag.test_value.shape[0] ==
inputs[0].tag.test_value)
outer_inp_seqs += [x[::-1][:numpy.min(taps)]
outer_inp_seqs += [x[::-1][:np.min(taps)]
for taps, x in zip(self.mitsot_taps(),
self.outer_mitsot_outs(outs))]
outer_inp_seqs += [x[::-1][:-1] for x in self.outer_sitsot_outs(outs)]
......@@ -2726,8 +2726,8 @@ class Scan(PureOp):
b = e
e = e + self.n_mit_mot
ib = ie
ie = ie + int(numpy.sum([len(x) for x in
self.tap_array[:self.n_mit_mot]]))
ie = ie + int(np.sum([len(x) for x in
self.tap_array[:self.n_mit_mot]]))
clean_eval_points = []
for inp, evp in zip(inputs[b:e], eval_points[b:e]):
if evp is not None:
......@@ -2742,9 +2742,9 @@ class Scan(PureOp):
b = e
e = e + self.n_mit_sot
ib = ie
ie = ie + int(numpy.sum([len(x) for x in
self.tap_array[self.n_mit_mot:
self.n_mit_mot + self.n_mit_sot]]))
ie = ie + int(np.sum([len(x) for x in
self.tap_array[self.n_mit_mot:
self.n_mit_mot + self.n_mit_sot]]))
clean_eval_points = []
for inp, evp in zip(inputs[b:e], eval_points[b:e]):
if evp is not None:
......@@ -2795,8 +2795,8 @@ class Scan(PureOp):
inner_other = self_inputs[ie:] + inner_eval_points[ib:]
# Outputs
n_mit_mot_outs = int(numpy.sum([len(x) for x in
self.mit_mot_out_slices]))
n_mit_mot_outs = int(np.sum([len(x) for x in
self.mit_mot_out_slices]))
info['n_mit_mot_outs'] = n_mit_mot_outs * 2
b = 0
e = n_mit_mot_outs
......
theano/scan_module/scan_opt.py
浏览文件 @ bea31470
......@@ -54,7 +54,7 @@ import logging
import copy
from sys import maxsize
from collections import OrderedDict
import numpy
import numpy as np
import theano
from theano import tensor, scalar
......@@ -636,7 +636,7 @@ class PushOutSeqScan(gof.Optimizer):
if out in op.inner_mitsot_outs(ls):
odx = op.inner_mitsot_outs(ls).index(out)
inp = op.outer_mitsot(node)[odx]
st = abs(numpy.min(op.mitsot_taps()))
st = abs(np.min(op.mitsot_taps()))
y = tensor.set_subtensor(inp[st:], _y)
elif out in op.inner_sitsot_outs(ls):
odx = op.inner_sitsot_outs(ls).index(out)
......@@ -1373,7 +1373,7 @@ class ScanSaveMem(gof.Optimizer):
# TODO: Simplify the number of steps needed.
# FB: This need good testing, left to later.
# call get_scalar_constant_value()? it can
# return python/numpy scalar or numpy.ndarray
# return python/numpy scalar or np.ndarray
# currently.
# pval = pre_greedy_local_optimizer(list_opt_slice,
# pval)
......
theano/scan_module/scan_perform_ext.py
浏览文件 @ bea31470
......@@ -12,7 +12,7 @@ import os
import sys
import warnings
import numpy
import numpy as np
import theano
from theano import config
......@@ -103,7 +103,7 @@ except ImportError:
# During scan cython development, it is helpful to keep the old interface, to don't manually edit the c file each time.
preargs.remove('-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION')
else:
numpy_ver = [int(n) for n in numpy.__version__.split('.')[:2]]
numpy_ver = [int(n) for n in np.__version__.split('.')[:2]]
# Add add some macro to lower the number of edit
# needed to the c file.
if bool(numpy_ver >= [1, 7]):
......
theano/scan_module/scan_utils.py
浏览文件 @ bea31470
......@@ -20,7 +20,7 @@ import logging
import warnings
from collections import OrderedDict
import numpy
import numpy as np
import theano
from theano.compat import izip
......@@ -589,8 +589,8 @@ def get_updates_and_outputs(ls):
def isNaN_or_Inf_or_None(x):
isNone = x is None
try:
isNaN = numpy.isnan(x)
isInf = numpy.isinf(x)
isNaN = np.isnan(x)
isInf = np.isinf(x)
isStr = isinstance(x, string_types)
except Exception:
isNaN = False
......@@ -599,8 +599,8 @@ def isNaN_or_Inf_or_None(x):
if not isNaN and not isInf:
try:
val = get_scalar_constant_value(x)
isInf = numpy.isinf(val)
isNaN = numpy.isnan(val)
isInf = np.isinf(val)
isNaN = np.isnan(val)
except Exception:
isNaN = False
isInf = False
......@@ -959,7 +959,7 @@ def scan_can_remove_outs(op, out_idxs):
added = False
for pos, idx in enumerate(out_idxs):
if (out_idxs_mask[pos] and
numpy.any([x in required_inputs for x in out_ins[idx]])):
np.any([x in required_inputs for x in out_ins[idx]])):
# This output is required ..
out_idxs_mask[pos] = 0
required_inputs += gof.graph.inputs([op.outputs[idx]])
......
theano/scan_module/tests/test_scan.py
浏览文件 @ bea31470
......@@ -11,7 +11,7 @@ from collections import OrderedDict
import six.moves.cPickle as pickle
from six.moves import xrange
import numpy
import numpy as np
from nose.plugins.skip import SkipTest
from nose.tools import assert_raises
from nose.tools import raises
......@@ -89,7 +89,7 @@ class multiple_outputs_numeric_grad:
for i, p in enumerate(pt):
if ndarray_mask[i]:
pt[i] = numpy.array(p)
pt[i] = np.array(p)
_eps = type_eps[str(pt[i].dtype)]
if _eps > dtype_eps:
dtype_eps = _eps
......@@ -116,12 +116,12 @@ class multiple_outputs_numeric_grad:
t[pos] += _eps
t = t.reshape(pt[i].shape)
f_eps = f(*(pt[:i] + [t] + pt[i + 1:]))
_g.append(numpy.asarray((f_eps - f_x) / _eps))
gx.append(numpy.asarray(_g).reshape(pt[i].shape))
_g.append(np.asarray((f_eps - f_x) / _eps))
gx.append(np.asarray(_g).reshape(pt[i].shape))
else:
t = numpy.array(pt[i] + _eps)
t = np.array(pt[i] + _eps)
f_eps = f(*(pt[:i] + [t] + pt[i + 1:]))
gx.append(numpy.asarray((f_eps - f_x) / _eps))
gx.append(np.asarray((f_eps - f_x) / _eps))
self.gx = gx
@staticmethod
......@@ -137,8 +137,8 @@ class multiple_outputs_numeric_grad:
for i in xrange(len(_g_pt)):
if self.ndarray_mask[i]:
g_pt.append(_g_pt[i])
elif isinstance(_g_pt[i], numpy.ndarray):
assert numpy.all(_g_pt[i] == 0)
elif isinstance(_g_pt[i], np.ndarray):
assert np.all(_g_pt[i] == 0)
if len(g_pt) != len(self.gx):
raise ValueError('argument has wrong number of elements',
len(g_pt))
......@@ -149,12 +149,12 @@ class multiple_outputs_numeric_grad:
raise ValueError('argument element %i has wrong shape %s' %
(i, str((a.shape, b.shape))))
vv = multiple_outputs_numeric_grad.abs_rel_err(a, b)
errs.append(numpy.max(
errs.append(np.max(
multiple_outputs_numeric_grad.abs_rel_err(a, b)))
if numpy.all(numpy.isfinite(errs)):
return numpy.max(errs), numpy.argmax(errs)
if np.all(np.isfinite(errs)):
return np.max(errs), np.argmax(errs)
else:
return numpy.inf, 0
return np.inf, 0
# TODO: Test this function, and if it works,
......@@ -262,11 +262,11 @@ class T_Scan(unittest.TestCase):
if tmpdir is not None:
shutil.rmtree(tmpdir)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
state = rng.uniform()
steps = 5
numpy_values = numpy.array([state * (2 ** (k + 1)) for k
numpy_values = np.array([state * (2 ** (k + 1)) for k
in xrange(steps)])
theano_values = my_f(state, steps)
utt.assert_allclose(numpy_values, theano_values)
......@@ -300,7 +300,7 @@ class T_Scan(unittest.TestCase):
assert all(i.value is None for i in scan_node.op.fn.input_storage)
assert all(o.value is None for o in scan_node.op.fn.output_storage)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
state = rng.uniform()
steps = 5
......@@ -332,11 +332,11 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
state = rng.uniform()
steps = 5
numpy_values = numpy.array([state * (2 ** (k + 1)) for k
numpy_values = np.array([state * (2 ** (k + 1)) for k
in xrange(steps)])
theano_values = my_f(state, steps)
utt.assert_allclose(numpy_values, theano_values[0])
......@@ -370,10 +370,10 @@ class T_Scan(unittest.TestCase):
# This assertation fails if savemem optimization failed on scan
if theano.config.mode != "FAST_COMPILE":
assert nodes[0].op._scan_savemem_visited
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
my_f(rng.uniform(size=(3,)),
4,
numpy.int64([2, 2, 3]))
np.int64([2, 2, 3]))
@attr('slow')
def test_only_nonseq_inputs(self):
......@@ -388,9 +388,9 @@ class T_Scan(unittest.TestCase):
fun = theano.function([inp], [broadcasted_inp, gr])
# Execute the Theano function and compare outputs to the expected outputs
inputs = numpy.array([[1, 2], [3, 4]], dtype=theano.config.floatX)
expected_out1 = numpy.repeat(inputs[None], n_steps, axis=0)
expected_out2 = numpy.ones(inputs.shape, dtype="int8") * n_steps
inputs = np.array([[1, 2], [3, 4]], dtype=theano.config.floatX)
expected_out1 = np.repeat(inputs[None], n_steps, axis=0)
expected_out2 = np.ones(inputs.shape, dtype="int8") * n_steps
out1, out2 = fun(inputs)
utt.assert_allclose(out1, expected_out1)
......@@ -420,14 +420,14 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
# compute the output in numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[0] * W_in + v_x0 * W
for step in xrange(1, 4):
v_out[step] = v_u[step] * W_in + v_out[step - 1] * W
......@@ -437,7 +437,7 @@ class T_Scan(unittest.TestCase):
# simple rnn, one input, one state, weights for each; input/state
# are vectors, weights are scalars; using shared variables
def test_one_sequence_one_output_weights_shared(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
u = theano.tensor.vector('u')
x0 = theano.tensor.scalar('x0')
W_in = theano.shared(asarrayX(rng.uniform()), name='w_in')
......@@ -462,19 +462,19 @@ class T_Scan(unittest.TestCase):
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
# compute the output i numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[0] * W_in.get_value() + v_x0 * W.get_value()
for step in xrange(1, 4):
v_out[step] = (v_u[step] * W_in.get_value() +
v_out[step - 1] * W.get_value())
theano_values = f3(v_u, v_x0)
assert numpy.allclose(theano_values, v_out)
assert np.allclose(theano_values, v_out)
# some rnn with multiple outputs and multiple inputs; other
# dimension instead of scalars/vectors
def test_multiple_inputs_multiple_outputs(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
vW = asarrayX(rng.uniform(size=(2, 2), low=-5., high=5.))
vWout = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
......@@ -511,15 +511,15 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# compute the values in numpy
v_x = numpy.zeros((3, 2), dtype=theano.config.floatX)
v_y = numpy.zeros((3,), dtype=theano.config.floatX)
v_x[0] = (numpy.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 +
numpy.dot(v_x0, vW))
v_y[0] = numpy.dot(v_x0, vWout)
v_x = np.zeros((3, 2), dtype=theano.config.floatX)
v_y = np.zeros((3,), dtype=theano.config.floatX)
v_x[0] = (np.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 +
np.dot(v_x0, vW))
v_y[0] = np.dot(v_x0, vWout)
for i in xrange(1, 3):
v_x[i] = (numpy.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 +
numpy.dot(v_x[i - 1], vW))
v_y[i] = numpy.dot(v_x[i - 1], vWout)
v_x[i] = (np.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 +
np.dot(v_x[i - 1], vW))
v_y[i] = np.dot(v_x[i - 1], vWout)
(theano_x, theano_y) = f4(v_u1, v_u2, v_x0, v_y0, vW_in1)
utt.assert_allclose(theano_x, v_x)
......@@ -527,7 +527,7 @@ class T_Scan(unittest.TestCase):
def test_multiple_outs_taps(self):
l = 5
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
vW = asarrayX(rng.uniform(size=(2, 2), low=-.2, high=.2))
vWout = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
......@@ -579,40 +579,40 @@ class T_Scan(unittest.TestCase):
v_y0,
vW_in1)
ny0 = numpy.zeros((5, 2))
ny1 = numpy.zeros((5,))
ny2 = numpy.zeros((5, 2))
ny0[0] = numpy.dot(v_u1[0], vW_in1) + \
(v_u2[1] + v_u2[0] * v_u2[2]) * vW_in2 + numpy.dot(v_x0, vW)
ny0 = np.zeros((5, 2))
ny1 = np.zeros((5,))
ny2 = np.zeros((5, 2))
ny0[0] = np.dot(v_u1[0], vW_in1) + \
(v_u2[1] + v_u2[0] * v_u2[2]) * vW_in2 + np.dot(v_x0, vW)
ny1[0] = (v_y0[2] + v_y0[0]) * numpy.dot(v_x0, vWout)
ny2[0] = numpy.dot(v_u1[0], vW_in1)
ny1[0] = (v_y0[2] + v_y0[0]) * np.dot(v_x0, vWout)
ny2[0] = np.dot(v_u1[0], vW_in1)
ny0[1] = numpy.dot(v_u1[1], vW_in1) + \
(v_u2[2] + v_u2[1] * v_u2[3]) * vW_in2 + numpy.dot(ny0[0], vW)
ny0[1] = np.dot(v_u1[1], vW_in1) + \
(v_u2[2] + v_u2[1] * v_u2[3]) * vW_in2 + np.dot(ny0[0], vW)
ny1[1] = (ny1[0] + v_y0[1]) * numpy.dot(ny0[0], vWout)
ny2[1] = numpy.dot(v_u1[1], vW_in1)
ny1[1] = (ny1[0] + v_y0[1]) * np.dot(ny0[0], vWout)
ny2[1] = np.dot(v_u1[1], vW_in1)
ny0[2] = numpy.dot(v_u1[2], vW_in1) + \
ny0[2] = np.dot(v_u1[2], vW_in1) + \
(v_u2[3] + v_u2[2] * v_u2[4]) * vW_in2 + \
numpy.dot(ny0[1], vW)
ny1[2] = (ny1[1] + v_y0[2]) * numpy.dot(ny0[1], vWout)
ny2[2] = numpy.dot(v_u1[2], vW_in1)
np.dot(ny0[1], vW)
ny1[2] = (ny1[1] + v_y0[2]) * np.dot(ny0[1], vWout)
ny2[2] = np.dot(v_u1[2], vW_in1)
ny0[3] = numpy.dot(v_u1[3], vW_in1) + \
ny0[3] = np.dot(v_u1[3], vW_in1) + \
(v_u2[4] + v_u2[3] * v_u2[5]) * vW_in2 + \
numpy.dot(ny0[2], vW)
np.dot(ny0[2], vW)
ny1[3] = (ny1[2] + ny1[0]) * numpy.dot(ny0[2], vWout)
ny2[3] = numpy.dot(v_u1[3], vW_in1)
ny1[3] = (ny1[2] + ny1[0]) * np.dot(ny0[2], vWout)
ny2[3] = np.dot(v_u1[3], vW_in1)
ny0[4] = numpy.dot(v_u1[4], vW_in1) + \
ny0[4] = np.dot(v_u1[4], vW_in1) + \
(v_u2[5] + v_u2[4] * v_u2[6]) * vW_in2 + \
numpy.dot(ny0[3], vW)
np.dot(ny0[3], vW)
ny1[4] = (ny1[3] + ny1[1]) * numpy.dot(ny0[3], vWout)
ny2[4] = numpy.dot(v_u1[4], vW_in1)
ny1[4] = (ny1[3] + ny1[1]) * np.dot(ny0[3], vWout)
ny2[4] = np.dot(v_u1[4], vW_in1)
def test_using_taps_sequence(self):
# this test refers to a bug reported by Nicolas
......@@ -621,9 +621,9 @@ class T_Scan(unittest.TestCase):
y, updates = theano.scan(lambda x: [x],
sequences=dict(input=x, taps=[-1]),
outputs_info=[None])
inp = numpy.arange(5).astype('float64')
inp = np.arange(5).astype('float64')
rval = theano.function([x], y, updates=updates)(inp)
assert numpy.all(rval == inp[:-1])
assert np.all(rval == inp[:-1])
def test_using_negative_taps_sequence(self):
# This test refers to a bug reported on github on May 22 2015 by
......@@ -636,7 +636,7 @@ class T_Scan(unittest.TestCase):
f = theano.function([x], res, updates = upd)
output = f([1, 2, 3, 4, 5])
expected_output = numpy.array([1, 2, 3], dtype="float32")
expected_output = np.array([1, 2, 3], dtype="float32")
utt.assert_allclose(output, expected_output)
def test_connection_pattern(self):
......@@ -649,8 +649,8 @@ class T_Scan(unittest.TestCase):
def fn(a_m2, a_m1, b_m2, b_m1):
return a_m1, b_m1
a0 = theano.shared(numpy.arange(2))
b0 = theano.shared(numpy.arange(2))
a0 = theano.shared(np.arange(2))
b0 = theano.shared(np.arange(2))
(a, b), _ = theano.scan(fn,
outputs_info=[{'initial': a0, 'taps': [-2, -1]},
......@@ -741,7 +741,7 @@ class T_Scan(unittest.TestCase):
# Call verify_grad to ensure the correctness of the second gradients
floatX = theano.config.floatX
inputs_test_values = [numpy.random.random((3)).astype(floatX)]
inputs_test_values = [np.random.random((3)).astype(floatX)]
theano.tests.unittest_tools.verify_grad(get_sum_of_grad,
inputs_test_values)
......@@ -768,8 +768,8 @@ class T_Scan(unittest.TestCase):
# Call verify_grad to ensure the correctness of the second gradients
floatX = theano.config.floatX
inputs_test_values = [numpy.random.random((2, 3)).astype(floatX),
numpy.random.random((3)).astype(floatX)]
inputs_test_values = [np.random.random((2, 3)).astype(floatX),
np.random.random((3)).astype(floatX)]
theano.tests.unittest_tools.verify_grad(get_sum_of_grad,
inputs_test_values)
......@@ -781,7 +781,7 @@ class T_Scan(unittest.TestCase):
# forward pass
W = theano.shared(
numpy.random.randn(2, 2).astype('float32'),
np.random.randn(2, 2).astype('float32'),
name="W", borrow=True)
def forward_scanner(x_t):
......@@ -807,7 +807,7 @@ class T_Scan(unittest.TestCase):
# vectors, weights are scalars; using shared variables and past
# taps (sequences and outputs)
def test_using_taps_input_output(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW = asarrayX(rng.uniform())
vW_in = asarrayX(rng.uniform())
vu = asarrayX(rng.uniform(size=(4,), low=-5., high=5.))
......@@ -843,7 +843,7 @@ class T_Scan(unittest.TestCase):
# in scan) which might seem strange, but then again why not use
# v_0[t] instead of v_0[t-2] in a real application ??
# also vx0[0] corresponds to vx0[-2], vx0[1] to vx0[-1]
numpy_out = numpy.zeros((2,))
numpy_out = np.zeros((2,))
numpy_out[0] = vu[0] * vW_in + vx0[1] * vW + vx0[0]
numpy_out[1] = vu[1] * vW_in + numpy_out[0] * vW + vx0[1]
utt.assert_allclose(numpy_out, theano_out)
......@@ -852,7 +852,7 @@ class T_Scan(unittest.TestCase):
# vectors, weights are scalars; using shared variables and past
# taps (sequences and outputs) and future taps for sequences
def test_past_future_taps_shared(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW = asarrayX(rng.uniform())
vW_in = asarrayX(rng.uniform())
vu = asarrayX(rng.uniform(size=(6,), low=-5., high=5.))
......@@ -880,7 +880,7 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
theano_out = f8(vu, vx0)
# compute output in numpy
numpy_out = numpy.zeros(2)
numpy_out = np.zeros(2)
# think of vu[0] as vu[-2], vu[4] as vu[2]
# and vx0[0] as vx0[-2], vx0[1] as vx0[-1]
numpy_out[0] = (vu[0] + vu[4]) * vW_in + vx0[1] * vW + vx0[0]
......@@ -889,9 +889,9 @@ class T_Scan(unittest.TestCase):
# simple rnn ; compute inplace version 1
def test_inplace1(self):
rng = numpy.random.RandomState(utt.fetch_seed())
vW = asarrayX(numpy.random.uniform())
vW_in = asarrayX(numpy.random.uniform())
rng = np.random.RandomState(utt.fetch_seed())
vW = asarrayX(np.random.uniform())
vW_in = asarrayX(np.random.uniform())
vu0 = asarrayX(rng.uniform(size=(3,), low=-5., high=5.))
vu1 = asarrayX(rng.uniform(size=(3,), low=-5., high=5.))
vu2 = asarrayX(rng.uniform(size=(3,), low=-5., high=5.))
......@@ -934,8 +934,8 @@ class T_Scan(unittest.TestCase):
assert 0 in scan_node[0].op.destroy_map.keys()
assert 1 in scan_node[0].op.destroy_map.keys()
# compute output in numpy
numpy_x0 = numpy.zeros((3,))
numpy_x1 = numpy.zeros((3,))
numpy_x0 = np.zeros((3,))
numpy_x1 = np.zeros((3,))
numpy_x0[0] = vu0[0] * vW_in + vx0 * vW + vu1[0] * vu2[0]
numpy_x1[0] = vu0[0] * vW_in + vx1 * vW + vu1[0] + vu2[0]
for i in xrange(1, 3):
......@@ -953,9 +953,9 @@ class T_Scan(unittest.TestCase):
# simple rnn ; compute inplace version 2
def test_inplace2(self):
rng = numpy.random.RandomState(utt.fetch_seed())
vW = asarrayX(numpy.random.uniform())
vW_in = asarrayX(numpy.random.uniform())
rng = np.random.RandomState(utt.fetch_seed())
vW = asarrayX(np.random.uniform())
vW_in = asarrayX(np.random.uniform())
vu0 = asarrayX(rng.uniform(size=(3,), low=-5., high=5.))
vu1 = asarrayX(rng.uniform(size=(4,), low=-5., high=5.))
vu2 = asarrayX(rng.uniform(size=(5,), low=-5., high=5.))
......@@ -1006,8 +1006,8 @@ class T_Scan(unittest.TestCase):
assert 0 in scan_node[0].op.destroy_map.keys()
assert 1 in scan_node[0].op.destroy_map.keys()
# compute output in numpy
numpy_x0 = numpy.zeros((3,))
numpy_x1 = numpy.zeros((3,))
numpy_x0 = np.zeros((3,))
numpy_x1 = np.zeros((3,))
numpy_x0[0] = vu0[0] * vW_in + vx0 * vW + vu1[0] * vu1[1]
numpy_x1[0] = vu0[0] * vW_in + vx1 * vW + vu2[0] + vu2[1] + vu2[2]
for i in xrange(1, 3):
......@@ -1024,7 +1024,7 @@ class T_Scan(unittest.TestCase):
utt.assert_allclose(theano_x1, numpy_x1)
def test_inplace3(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vx0 = asarrayX(rng.uniform())
vx1 = asarrayX(rng.uniform())
......@@ -1035,7 +1035,7 @@ class T_Scan(unittest.TestCase):
[],
[x0, x1],
n_steps=3)
x0 = asarrayX(numpy.zeros((3,)))
x0 = asarrayX(np.zeros((3,)))
x0[0] = vx0
x0 = theano.tensor.constant(x0)
to_replace = outputs[0].owner.inputs[0].owner.inputs[1]
......@@ -1053,7 +1053,7 @@ class T_Scan(unittest.TestCase):
# Shared variable with updates
def test_shared_arguments_with_updates(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW1 = asarrayX(rng.rand(2, 3))
vW2 = asarrayX(rng.rand(3, 2))
......@@ -1128,22 +1128,22 @@ class T_Scan(unittest.TestCase):
theano_y0, theano_y1, theano_y2 = allstuff
# do things in numpy
numpy_y0 = numpy.zeros((6, 2))
numpy_y1 = numpy.zeros((4, 2))
numpy_y2 = numpy.zeros((3, 3))
numpy_y0 = np.zeros((6, 2))
numpy_y1 = np.zeros((4, 2))
numpy_y2 = np.zeros((3, 3))
numpy_y0[:3] = vy0
numpy_y1[0] = vy1
numpy_W1 = vW1.copy()
numpy_W2 = vW2.copy()
for idx in xrange(3):
numpy_y0[idx + 3] = numpy.dot(numpy.dot(vu1[idx, :], numpy_W1),
numpy_y0[idx + 3] = np.dot(np.dot(vu1[idx, :], numpy_W1),
numpy_W2) + \
0.1 * numpy_y0[idx + 2] + \
0.33 * numpy_y0[idx + 1] + \
0.17 * numpy_y0[idx]
numpy_y1[idx + 1] = (numpy.dot(vu2[idx, :], numpy_W2) +
numpy_y1[idx + 1] = (np.dot(vu2[idx, :], numpy_W2) +
numpy_y1[idx])
numpy_y2[idx] = numpy.dot(vu1[idx, :], numpy_W1)
numpy_y2[idx] = np.dot(vu1[idx, :], numpy_W1)
numpy_W1 = numpy_W1 + .1
numpy_W2 = numpy_W2 + .05
......@@ -1174,7 +1174,7 @@ class T_Scan(unittest.TestCase):
f = theano.function([c, x, y], [gX, gY],
allow_input_downcast=True)
# Check for runtime errors
f(numpy.int32(0), numpy.float32(1.), numpy.float32(.5))
f(np.int32(0), np.float32(1.), np.float32(.5))
def test_simple_shared_mrg_random(self):
theano_rng = theano.sandbox.rng_mrg.MRG_RandomStreams(utt.fetch_seed())
......@@ -1211,10 +1211,10 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2 ** 30)
rng = numpy.random.RandomState(int(rng_seed)) # int() is for 32bit
rng_seed = np.random.RandomState(utt.fetch_seed()).randint(2 ** 30)
rng = np.random.RandomState(int(rng_seed)) # int() is for 32bit
numpy_v = numpy.zeros((10, 2))
numpy_v = np.zeros((10, 2))
for i in xrange(10):
numpy_v[i] = rng.uniform(-1, 1, size=(2,))
......@@ -1224,12 +1224,12 @@ class T_Scan(unittest.TestCase):
utt.assert_allclose(theano_v, numpy_v[5:, :])
def test_gibbs_chain(self):
rng = numpy.random.RandomState(utt.fetch_seed())
v_W = numpy.array(rng.rand(20, 30) - .5, dtype='float32')
v_vsample = numpy.array(rng.binomial(1, .5, size=(3, 20),),
rng = np.random.RandomState(utt.fetch_seed())
v_W = np.array(rng.rand(20, 30) - .5, dtype='float32')
v_vsample = np.array(rng.binomial(1, .5, size=(3, 20),),
dtype='float32')
v_bvis = numpy.array(rng.rand(20) - .5, dtype='float32')
v_bhid = numpy.array(rng.rand(30) - .5, dtype='float32')
v_bvis = np.array(rng.rand(20) - .5, dtype='float32')
v_bhid = np.array(rng.rand(30) - .5, dtype='float32')
W = theano.shared(v_W, 'vW')
bhid = theano.shared(v_bhid, 'vbhid')
bvis = theano.shared(v_bvis, 'vbvis')
......@@ -1261,24 +1261,24 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
_rng = numpy.random.RandomState(utt.fetch_seed())
_rng = np.random.RandomState(utt.fetch_seed())
rng_seed = _rng.randint(2 ** 30)
nrng1 = numpy.random.RandomState(int(rng_seed)) # int() is for 32bit
nrng1 = np.random.RandomState(int(rng_seed)) # int() is for 32bit
rng_seed = _rng.randint(2 ** 30)
nrng2 = numpy.random.RandomState(int(rng_seed)) # int() is for 32bit
nrng2 = np.random.RandomState(int(rng_seed)) # int() is for 32bit
def numpy_implementation(vsample):
for idx in range(10):
hmean = 1. / (1. + numpy.exp(-(numpy.dot(vsample, v_W) +\
hmean = 1. / (1. + np.exp(-(np.dot(vsample, v_W) +\
v_bhid)))
hsample = numpy.array(nrng1.binomial(1,
hsample = np.array(nrng1.binomial(1,
hmean,
size=hmean.shape),
dtype='float32')
vmean = 1. / (1. + numpy.exp(-(numpy.dot(hsample, v_W.T) +\
vmean = 1. / (1. + np.exp(-(np.dot(hsample, v_W.T) +\
v_bvis)))
vsample = numpy.array(nrng2.binomial(1,
vsample = np.array(nrng2.binomial(1,
vmean,
size=vmean.shape),
dtype='float32')
......@@ -1290,7 +1290,7 @@ class T_Scan(unittest.TestCase):
utt.assert_allclose(t_result, n_result)
def test_only_shared_no_input_no_output(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_state = asarrayX(rng.uniform())
state = theano.shared(v_state, 'vstate')
......@@ -1331,7 +1331,7 @@ class T_Scan(unittest.TestCase):
outputs,
updates=updates,
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(5,), low=-5., high=5.)
numpy_result = v_u + 3
......@@ -1352,7 +1352,7 @@ class T_Scan(unittest.TestCase):
updates=abs_updates,
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vals = rng.uniform(size=(10,), low=-5., high=5.)
abs_vals = abs(vals)
theano_vals = f(vals)
......@@ -1380,14 +1380,14 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
# compute the output in numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[3] * W_in + v_x0 * W
for step in xrange(1, 4):
v_out[step] = v_u[3 - step] * W_in + v_out[step - 1] * W
......@@ -1404,9 +1404,9 @@ class T_Scan(unittest.TestCase):
result,
updates=updates,
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_v = rng.uniform(size=(5,), low=-5., high=5.)
assert abs(numpy.sum(v_v) - f(v_v, 0.)) < 1e-3
assert abs(np.sum(v_v) - f(v_v, 0.)) < 1e-3
def test_grad_one_output(self):
def f_rnn(u_t, x_tm1, W_in, W):
......@@ -1440,12 +1440,12 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
v_u = numpy.array(rng.uniform(size=(10,), low=-.5, high=.5),
rng = np.random.RandomState(utt.fetch_seed())
v_u = np.array(rng.uniform(size=(10,), low=-.5, high=.5),
dtype=theano.config.floatX)
v_x0 = numpy.array(rng.uniform(), dtype=theano.config.floatX)
W = numpy.array(rng.uniform(), dtype=theano.config.floatX)
W_in = numpy.array(rng.uniform(), dtype=theano.config.floatX)
v_x0 = np.array(rng.uniform(), dtype=theano.config.floatX)
W = np.array(rng.uniform(), dtype=theano.config.floatX)
W_in = np.array(rng.uniform(), dtype=theano.config.floatX)
analytic_grad = grad_fn(v_u, v_x0, W_in, W)
num_grad = multiple_outputs_numeric_grad(
......@@ -1459,7 +1459,7 @@ class T_Scan(unittest.TestCase):
num_grad.gx[max_err_pos]))
def test_grad_multiple_outs(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-.1, high=.1))
vW = asarrayX(rng.uniform(size=(2, 2), low=-.1, high=.1))
vWout = asarrayX(rng.uniform(size=(2,), low=-.1, high=.1))
......@@ -1524,7 +1524,7 @@ class T_Scan(unittest.TestCase):
@attr('slow')
def test_grad_multiple_outs_taps(self):
l = 5
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
vW = asarrayX(rng.uniform(size=(2, 2), low=-.2, high=.2))
vWout = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
......@@ -1618,7 +1618,7 @@ class T_Scan(unittest.TestCase):
@attr('slow')
def test_grad_multiple_outs_taps_backwards(self):
l = 5
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
vW = asarrayX(rng.uniform(size=(2, 2), low=-.2, high=.2))
vWout = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
......@@ -1685,10 +1685,10 @@ class T_Scan(unittest.TestCase):
num_grad.gx[max_err_pos]))
def test_grad_multiple_outs_some_uncomputable(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in = asarrayX(rng.uniform(size=(2, 2), low=-3., high=3.))
v_u = asarrayX(rng.uniform(size=(5, 2), low=-3., high=3.))
v_u2 = numpy.array([1, 3, 4, 6, 8], dtype='int32')
v_u2 = np.array([1, 3, 4, 6, 8], dtype='int32')
v_x0 = asarrayX(rng.uniform(size=(2,), low=-3., high=3.))
W_in = theano.tensor.matrix('win')
......@@ -1730,9 +1730,9 @@ class T_Scan(unittest.TestCase):
def reset_rng_fn(fn, *args):
for idx, arg in enumerate(fn.maker.expanded_inputs):
if (arg.value and type(arg.value.data) == \
type(numpy.random.RandomState(123))):
type(np.random.RandomState(123))):
obj = fn.maker.expanded_inputs[idx].value
obj.data = numpy.random.RandomState(123)
obj.data = np.random.RandomState(123)
fn.maker.expanded_inputs[idx].value = obj
return fn(*args)
......@@ -1764,7 +1764,7 @@ class T_Scan(unittest.TestCase):
assert(result == expected_result)
def test_grad_multiple_outs_some_truncate(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in = asarrayX(rng.uniform(size=(2, 2), low=-.1, high=.1))
v_u = asarrayX(rng.uniform(size=(5, 2), low=-.1, high=.1))
v_x0 = asarrayX(rng.uniform(size=(2,), low=-.1, high=.1))
......@@ -1807,9 +1807,9 @@ class T_Scan(unittest.TestCase):
def reset_rng_fn(fn, *args):
for idx, arg in enumerate(fn.maker.expanded_inputs):
if (arg.value and
isinstance(arg.value.data, numpy.random.RandomState)):
isinstance(arg.value.data, np.random.RandomState)):
obj = fn.maker.expanded_inputs[idx].value
obj.data = numpy.random.RandomState(123)
obj.data = np.random.RandomState(123)
fn.maker.expanded_inputs[idx].value = obj
out = fn(*args)
return out
......@@ -1819,7 +1819,7 @@ class T_Scan(unittest.TestCase):
num_grad = multiple_outputs_numeric_grad(
reset_rng_cost_fn, [v_u, v_x0, vW_in])
analytic_grad = reset_rng_grad_fn(v_u, v_x0, vW_in)
utt.assert_allclose(analytic_grad[0][:2], numpy.zeros((2, 2)))
utt.assert_allclose(analytic_grad[0][:2], np.zeros((2, 2)))
def test_grad_multiple_outs_some_disconnected(self):
final_cost = self._grad_mout_helper(100, mode_nodebug)
......@@ -1833,7 +1833,7 @@ class T_Scan(unittest.TestCase):
def _grad_mout_helper(self, n_iters, mode):
# Created on Tue Oct 07 13:28:51 2014
# @author: vaneetke
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
n_hid = 3
n_in = 1
n_out = 1
......@@ -1897,10 +1897,10 @@ class T_Scan(unittest.TestCase):
mode=mode)
# artificial data
x_v = numpy.arange(0., 10.49, 0.21, dtype=theano.config.floatX)
x_v = np.arange(0., 10.49, 0.21, dtype=theano.config.floatX)
x_v = x_v.reshape(len(x_v), 1)
s_v = numpy.sin(x_v)
t_v = numpy.roll(s_v, -1)[:-1]
s_v = np.sin(x_v)
t_v = np.roll(s_v, -1)[:-1]
s_v = s_v[:-1]
for i in xrange(n_iters):
cost = learn_rnn_fn(s_v, t_v)
......@@ -1919,14 +1919,14 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
nx = rng.uniform(size=(10, 10))
ny1, nz1 = f(nx)
ny2, nz2 = f(nx)
utt.assert_allclose([ny1, ny1], nz1)
utt.assert_allclose([ny2, ny2], nz2)
assert not numpy.allclose(ny1, ny2)
assert not np.allclose(ny1, ny2)
def test_grad_of_shared(self):
x1 = theano.shared(3.)
......@@ -1942,7 +1942,7 @@ class T_Scan(unittest.TestCase):
def test_computing_gradient(self):
x1 = theano.tensor.scalar('x1')
x2 = theano.shared(numpy.array([1, 2, 3, 4, 5]), name='x2')
x2 = theano.shared(np.array([1, 2, 3, 4, 5]), name='x2')
K = x2 * x1
out, updates = theano.scan(lambda i, v: theano.tensor.grad(K[i], v),
......@@ -1950,10 +1950,10 @@ class T_Scan(unittest.TestCase):
non_sequences=x1)
f = theano.function([x1], out, allow_input_downcast=True)
assert numpy.all(f(3.) != 0.)
assert np.all(f(3.) != 0.)
def test_shared_updates(self):
X = theano.shared(numpy.array(1))
X = theano.shared(np.array(1))
out, updates = theano.scan(
lambda: OrderedDict([(X, (X + 1))]),
......@@ -1967,8 +1967,8 @@ class T_Scan(unittest.TestCase):
assert X.get_value() == 11
def test_memory_aliasing_updates(self):
x = theano.shared(numpy.array(1))
y = theano.shared(numpy.array(1))
x = theano.shared(np.array(1))
y = theano.shared(np.array(1))
out, updates = theano.scan(
lambda: OrderedDict([(x, x + 1), (y, x)]),
......@@ -1979,7 +1979,7 @@ class T_Scan(unittest.TestCase):
f = theano.function([], [], updates=updates)
f()
assert not numpy.may_share_memory(x.container.storage[0],
assert not np.may_share_memory(x.container.storage[0],
y.container.storage[0])
assert x.get_value() != y.get_value()
......@@ -1998,7 +1998,7 @@ class T_Scan(unittest.TestCase):
"""
a = theano.tensor.vector()
init_a = theano.tensor.vector()
b = theano.shared(numpy.random.rand(5, 4))
b = theano.shared(np.random.rand(5, 4))
def inner_func(a):
return a + 1, OrderedDict([(b, 2 * b)])
......@@ -2032,9 +2032,9 @@ class T_Scan(unittest.TestCase):
non_sequences=[gy, x])
f = theano.function([x, A], hy, allow_input_downcast=True)
vx = numpy.array([1., 1.], dtype=theano.config.floatX)
vA = numpy.array([[1., 1.], [1., 0.]], dtype=theano.config.floatX)
vR = numpy.array([[3.6, 1.8], [1.8, 0.9]], dtype=theano.config.floatX)
vx = np.array([1., 1.], dtype=theano.config.floatX)
vA = np.array([[1., 1.], [1., 0.]], dtype=theano.config.floatX)
vR = np.array([[3.6, 1.8], [1.8, 0.9]], dtype=theano.config.floatX)
out = f(vx, vA)
utt.assert_allclose(out, vR)
......@@ -2157,7 +2157,7 @@ class T_Scan(unittest.TestCase):
# some rnn with multiple outputs and multiple inputs; other
# dimension instead of scalars/vectors
def test_reordering(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
vW = asarrayX(rng.uniform(size=(2, 2), low=-5., high=5.))
vWout = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
......@@ -2200,15 +2200,15 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# compute the values in numpy
v_x = numpy.zeros((3, 2), dtype=theano.config.floatX)
v_y = numpy.zeros((3,), dtype=theano.config.floatX)
v_x[0] = numpy.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
numpy.dot(v_x0, vW)
v_y[0] = numpy.dot(v_x0, vWout) + v_y0[2]
v_x = np.zeros((3, 2), dtype=theano.config.floatX)
v_y = np.zeros((3,), dtype=theano.config.floatX)
v_x[0] = np.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
np.dot(v_x0, vW)
v_y[0] = np.dot(v_x0, vWout) + v_y0[2]
for i in xrange(1, 3):
v_x[i] = numpy.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
numpy.dot(v_x[i - 1], vW)
v_y[i] = numpy.dot(v_x[i - 1], vWout) + v_y[i - 1]
v_x[i] = np.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
np.dot(v_x[i - 1], vW)
v_y[i] = np.dot(v_x[i - 1], vWout) + v_y[i - 1]
(theano_dump1, theano_dump2, theano_x, theano_y) = f4(v_u1,
v_u2,
......@@ -2247,7 +2247,7 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
def test_save_mem(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
vW = asarrayX(rng.uniform(size=(2, 2), low=-5., high=5.))
vWout = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
......@@ -2288,16 +2288,16 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# compute the values in numpy
v_x = numpy.zeros((8, 2), dtype=theano.config.floatX)
v_y = numpy.zeros((8,), dtype=theano.config.floatX)
v_x[0] = numpy.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
numpy.dot(v_x0, vW)
v_y[0] = numpy.dot(v_x0, vWout) + v_y0[2]
v_x = np.zeros((8, 2), dtype=theano.config.floatX)
v_y = np.zeros((8,), dtype=theano.config.floatX)
v_x[0] = np.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
np.dot(v_x0, vW)
v_y[0] = np.dot(v_x0, vWout) + v_y0[2]
for i in xrange(1, 8):
v_x[i] = numpy.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
numpy.dot(v_x[i - 1], vW)
v_y[i] = numpy.dot(v_x[i - 1], vWout) + v_y[i - 1]
v_x[i] = np.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
np.dot(v_x[i - 1], vW)
v_y[i] = np.dot(v_x[i - 1], vWout) + v_y[i - 1]
(theano_dump, theano_x, theano_y) = f4(v_u1, v_u2, v_x0, v_y0, vW_in1)
......@@ -2321,24 +2321,24 @@ class T_Scan(unittest.TestCase):
sh = expr.shape[0]
v1 = theano.shared(numpy.ones(5, dtype=theano.config.floatX))
v2 = theano.shared(numpy.ones((5, 5), dtype=theano.config.floatX))
v1 = theano.shared(np.ones(5, dtype=theano.config.floatX))
v2 = theano.shared(np.ones((5, 5), dtype=theano.config.floatX))
shapef = theano.function([W],
expr,
givens=OrderedDict([(initial, v1),
(inpt, v2)]))
# First execution to cache n_steps
shapef(numpy.ones((5, 5), dtype=theano.config.floatX))
shapef(np.ones((5, 5), dtype=theano.config.floatX))
cost = expr.sum()
d_cost_wrt_W = tensor.grad(cost, [W])
f = theano.function(
[W, inpt], d_cost_wrt_W,
givens=OrderedDict([(initial, theano.shared(numpy.zeros(5)))]))
givens=OrderedDict([(initial, theano.shared(np.zeros(5)))]))
rval = numpy.asarray([[5187989] * 5] * 5, dtype=theano.config.floatX)
arg1 = numpy.ones((5, 5), dtype=theano.config.floatX)
arg2 = numpy.ones((10, 5), dtype=theano.config.floatX)
rval = np.asarray([[5187989] * 5] * 5, dtype=theano.config.floatX)
arg1 = np.ones((5, 5), dtype=theano.config.floatX)
arg2 = np.ones((10, 5), dtype=theano.config.floatX)
utt.assert_allclose(f(arg1, arg2), rval)
def test_save_mem_reduced_number_of_steps(self):
......@@ -2372,7 +2372,7 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(20,), low=-5., high=5.)
# compute the output in numpy
......@@ -2428,7 +2428,7 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(20,), low=-5., high=5.)
# compute the output in numpy
......@@ -2474,7 +2474,7 @@ class T_Scan(unittest.TestCase):
floatX = theano.config.floatX
init_value = 5.0
seq_value = numpy.arange(4, dtype=floatX)
seq_value = np.arange(4, dtype=floatX)
output1, output2 = fct(init_value, seq_value)
expected_output1 = [init_value]
......@@ -2509,13 +2509,13 @@ class T_Scan(unittest.TestCase):
[out1_direct, out2_direct])
# Test that the function returns valid outputs
x_val = numpy.arange(0, 4)[:, None]
seq_val = numpy.arange(4, 8)[:, None]
x_val = np.arange(0, 4)[:, None]
seq_val = np.arange(4, 8)[:, None]
out1, out2 = fct(x_val, seq_val)
expected_out1 = numpy.zeros((5, 4, 1))
expected_out2 = numpy.zeros((5, 4, 1))
expected_out1 = np.zeros((5, 4, 1))
expected_out2 = np.zeros((5, 4, 1))
for i in range(4):
expected_out2[i + 1] = expected_out2[i] + seq_val[i]
for i in range(5):
......@@ -2565,7 +2565,7 @@ class T_Scan(unittest.TestCase):
diff = mitsot_m1 + seq1
next_mitsot_val = mitsot_m2 + diff
next_sitsot_val = sitsot_m1 - diff
nitsot_out = tensor.alloc(numpy.asarray(0., 'float32'),
nitsot_out = tensor.alloc(np.asarray(0., 'float32'),
next_mitsot_val +
next_sitsot_val)
return next_sitsot_val, next_mitsot_val, nitsot_out
......@@ -2584,7 +2584,7 @@ class T_Scan(unittest.TestCase):
assert(len(scan_nodes_from_fct(f)) == 1)
# This generate a scan crash during execution.
# output_shape = f(numpy.arange(5), 5, [1, 2])
# output_shape = f(np.arange(5), 5, [1, 2])
# assert(all(output_shape == (5, 6)))
# The following test will fail in DebugMode if there are
......@@ -2608,7 +2608,7 @@ class T_Scan(unittest.TestCase):
go1 = theano.tensor.grad(o1.mean(), wrt=x)
f = theano.function([x], go1, updates=updates,
allow_input_downcast=True, mode=mode_with_opt)
self.assertTrue(numpy.allclose(f([1, 2, 3]), 2. / 3))
self.assertTrue(np.allclose(f([1, 2, 3]), 2. / 3))
topo = f.maker.fgraph.toposort()
# this new assert is here to test if scan_merging works ..
......@@ -2711,7 +2711,7 @@ class T_Scan(unittest.TestCase):
n.op, theano.scan_module.scan_op.Scan)]
self.assertTrue(len(scans) == 2)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
x_val = rng.uniform(size=(4,)).astype(theano.config.floatX)
y_val = rng.uniform(size=(4,)).astype(theano.config.floatX)
# Run it so DebugMode can detect optimization problems.
......@@ -2752,7 +2752,7 @@ class T_Scan(unittest.TestCase):
return M
# some initializations
hypx = numpy.log(numpy.tile([1,1,1,1,1,1,0.01], (3,1)))
hypx = np.log(np.tile([1,1,1,1,1,1,0.01], (3,1)))
# variables used in the following expressions
hyp = theano.shared(hypx)
......@@ -2763,10 +2763,10 @@ class T_Scan(unittest.TestCase):
M = init_predictive_output(inputs,targets,hyp,x_star,s_star)
X = numpy.random.random((10,4))
Y = numpy.random.random((10,3))
test_m = numpy.random.random((4,))
test_s = numpy.eye(4)
X = np.random.random((10,4))
Y = np.random.random((10,3))
test_m = np.random.random((4,))
test_s = np.eye(4)
# Compute expected outputs (jacobian of M wrt x_star)
dfdm = theano.function([inputs,targets,x_star,s_star],
......@@ -2851,9 +2851,9 @@ class T_Scan(unittest.TestCase):
x = theano.tensor.fmatrix('x')
mem_val = numpy.zeros((2,), dtype='float32')
mem_val = np.zeros((2,), dtype='float32')
memory = theano.shared(mem_val)
W = theano.shared(numpy.random.random((5, 2)).astype('float32'))
W = theano.shared(np.random.random((5, 2)).astype('float32'))
def f(inp, mem):
i = theano.tensor.join(0, inp, mem)
......@@ -2867,7 +2867,7 @@ class T_Scan(unittest.TestCase):
f = theano.function([x], outs[0])
f2 = theano.function([x], outs[1])
x_val = numpy.random.random((4, 3)).astype('float32')
x_val = np.random.random((4, 3)).astype('float32')
f_vals = f(x_val)
memory.set_value(mem_val)
......@@ -2876,12 +2876,12 @@ class T_Scan(unittest.TestCase):
def test_reduce_memory_consumption(self):
x = theano.shared(numpy.asarray(
numpy.random.uniform(size=(10,)), dtype=theano.config.floatX))
x = theano.shared(np.asarray(
np.random.uniform(size=(10,)), dtype=theano.config.floatX))
o, _ = theano.reduce(lambda v, acc: acc + v,
x,
theano.tensor.constant(
numpy.asarray(0.,
np.asarray(0.,
dtype=theano.config.floatX)))
mode = theano.compile.mode.FAST_RUN
mode = mode.excluding('inplace')
......@@ -2905,15 +2905,15 @@ class T_Scan(unittest.TestCase):
gx = theano.tensor.grad(o, x)
f2 = theano.function([], gx)
utt.assert_allclose(f2(), numpy.ones((10,)))
utt.assert_allclose(f2(), np.ones((10,)))
def test_foldl_memory_consumption(self):
x = theano.shared(numpy.asarray(
numpy.random.uniform(size=(10,)), dtype=theano.config.floatX))
x = theano.shared(np.asarray(
np.random.uniform(size=(10,)), dtype=theano.config.floatX))
o, _ = theano.foldl(lambda v, acc: acc + v,
x,
theano.tensor.constant(
numpy.asarray(0.,
np.asarray(0.,
dtype=theano.config.floatX)))
mode = theano.compile.mode.FAST_RUN
......@@ -2938,16 +2938,16 @@ class T_Scan(unittest.TestCase):
gx = theano.tensor.grad(o, x)
f2 = theano.function([], gx)
utt.assert_allclose(f2(), numpy.ones((10,)))
utt.assert_allclose(f2(), np.ones((10,)))
def test_foldr_memory_consumption(self):
x = theano.shared(numpy.asarray(
numpy.random.uniform(size=(10,)), dtype=theano.config.floatX))
x = theano.shared(np.asarray(
np.random.uniform(size=(10,)), dtype=theano.config.floatX))
o, _ = theano.foldr(lambda v, acc: acc + v,
x,
theano.tensor.constant(
numpy.asarray(0.,
np.asarray(0.,
dtype=theano.config.floatX)))
mode = theano.compile.mode.FAST_RUN
......@@ -2972,26 +2972,26 @@ class T_Scan(unittest.TestCase):
gx = theano.tensor.grad(o, x)
f2 = theano.function([], gx)
utt.assert_allclose(f2(), numpy.ones((10,)))
utt.assert_allclose(f2(), np.ones((10,)))
@attr('slow')
def test_rop2(self):
seed = utt.fetch_seed()
rng = numpy.random.RandomState(seed)
rng = np.random.RandomState(seed)
floatX = theano.config.floatX
v_u = numpy.array(rng.uniform(size=(3, 5)) - .5, dtype=floatX)
v_W = numpy.array(rng.uniform(size=(5, 5)) - .5, dtype=floatX)
v_h0 = numpy.array(rng.uniform(size=(5,)) - .5, dtype=floatX)
v_u = np.array(rng.uniform(size=(3, 5)) - .5, dtype=floatX)
v_W = np.array(rng.uniform(size=(5, 5)) - .5, dtype=floatX)
v_h0 = np.array(rng.uniform(size=(5,)) - .5, dtype=floatX)
v_eu = numpy.array(rng.uniform(size=(3, 5)) - .5, dtype=floatX)
v_eW = numpy.array(rng.uniform(size=(5, 5)) - .5, dtype=floatX)
v_eh0 = numpy.array(rng.uniform(size=(5,)) - .5, dtype=floatX)
v_eu = np.array(rng.uniform(size=(3, 5)) - .5, dtype=floatX)
v_eW = np.array(rng.uniform(size=(5, 5)) - .5, dtype=floatX)
v_eh0 = np.array(rng.uniform(size=(5,)) - .5, dtype=floatX)
def rnn_fn(_u, _y, _W):
srng = theano.tensor.shared_randomstreams.RandomStreams(seed)
tmp_val = _u + _y + srng.uniform(size=v_h0.shape) *\
numpy.asarray(1e-6, dtype=floatX)
np.asarray(1e-6, dtype=floatX)
sl_o = theano.tensor.tanh(theano.tensor.dot(_W, tmp_val))
return sl_o, tmp_val
......@@ -3053,15 +3053,15 @@ class T_Scan(unittest.TestCase):
def test_rop(self):
seed = utt.fetch_seed()
rng = numpy.random.RandomState(seed)
rng = np.random.RandomState(seed)
floatX = theano.config.floatX
v_u = numpy.array(rng.uniform(size=(20, 5)), dtype=floatX)
v_W = numpy.array(rng.uniform(size=(5, 5)), dtype=floatX)
v_h0 = numpy.array(rng.uniform(size=(5,)), dtype=floatX)
v_u = np.array(rng.uniform(size=(20, 5)), dtype=floatX)
v_W = np.array(rng.uniform(size=(5, 5)), dtype=floatX)
v_h0 = np.array(rng.uniform(size=(5,)), dtype=floatX)
v_eu = numpy.array(rng.uniform(size=(20, 5)), dtype=floatX)
v_eW = numpy.array(rng.uniform(size=(5, 5)), dtype=floatX)
v_eh0 = numpy.array(rng.uniform(size=(5,)), dtype=floatX)
v_eu = np.array(rng.uniform(size=(20, 5)), dtype=floatX)
v_eW = np.array(rng.uniform(size=(5, 5)), dtype=floatX)
v_eh0 = np.array(rng.uniform(size=(5,)), dtype=floatX)
def rnn_fn(_u, _y, _W):
sl_o = theano.tensor.tanh(theano.tensor.dot(_W, (_u + _y)))
......@@ -3163,14 +3163,14 @@ class T_Scan(unittest.TestCase):
assert len(scan_nodes) == 0
seed = utt.fetch_seed()
rng = numpy.random.RandomState(seed)
rng = np.random.RandomState(seed)
floatX = theano.config.floatX
v_h = numpy.array(rng.uniform(size=(2,)), dtype=floatX)
v_W1 = numpy.array(rng.uniform(size=(2, 2)), dtype=floatX)
v_W2 = numpy.array(rng.uniform(size=(2, 2)), dtype=floatX)
v_h = np.array(rng.uniform(size=(2,)), dtype=floatX)
v_W1 = np.array(rng.uniform(size=(2, 2)), dtype=floatX)
v_W2 = np.array(rng.uniform(size=(2, 2)), dtype=floatX)
v_out = numpy.dot(v_h, v_W1 + v_W2)
sol = numpy.zeros((5, 2))
v_out = np.dot(v_h, v_W1 + v_W2)
sol = np.zeros((5, 2))
# This line is here to make sol have the same shape as the output of
# theano. Note that what we ask theano to do is to repeat the 2
# elements vector v_out 5 times
......@@ -3206,9 +3206,9 @@ class T_Scan(unittest.TestCase):
f_ref = theano.function([W1, W2, step_indices], o, mode='FAST_COMPILE')
# Compare the results of the two implementations
input_values = [numpy.random.random((5, 5)).astype("float32"),
numpy.random.random((5, 5)).astype("float32"),
numpy.arange(5).astype("float32")]
input_values = [np.random.random((5, 5)).astype("float32"),
np.random.random((5, 5)).astype("float32"),
np.arange(5).astype("float32")]
out = f(*input_values)
out_ref = f_ref(*input_values)
......@@ -3243,10 +3243,10 @@ class T_Scan(unittest.TestCase):
([i_t, i_tm1], _) = theano.scan(
fn, sequences=[inp],
outputs_info=[numpy.asarray([0.0, 0.0], theano.config.floatX),
outputs_info=[np.asarray([0.0, 0.0], theano.config.floatX),
None])
f = theano.function([inp], [i_t, i_tm1])
val = numpy.arange(10).reshape(5, 2).astype(theano.config.floatX)
val = np.arange(10).reshape(5, 2).astype(theano.config.floatX)
ret = f(val)
utt.assert_allclose(ret[0], val + 10)
utt.assert_allclose(ret[1], [[0., 0.],
......@@ -3330,7 +3330,7 @@ class T_Scan(unittest.TestCase):
return x_t + 1, theano.scan_module.until(x_t > 3)
o, _ = theano.scan(lambda_fn, x)
f = theano.function([x], o)
vx = numpy.zeros((50,), dtype=theano.config.floatX)
vx = np.zeros((50,), dtype=theano.config.floatX)
vx[23] = 4
out = f(vx)
assert len(out) == 24
......@@ -3344,11 +3344,11 @@ class T_Scan(unittest.TestCase):
o2, _ = theano.scan(lambda x_t: x_t + 2, x)
f = theano.function([x], [o, o2], mode=mode_with_opt)
vx = numpy.zeros((50,), dtype=theano.config.floatX)
vx = np.zeros((50,), dtype=theano.config.floatX)
vx[23] = 4
out, out2 = f(vx)
assert len(out) == 24
assert numpy.all(out2 == vx + 2)
assert np.all(out2 == vx + 2)
lssc = [x for x in f.maker.fgraph.toposort()
if isinstance(x.op, theano.scan_module.scan_op.Scan)]
# One scan node gets optimnized out
......@@ -3402,7 +3402,7 @@ class T_Scan(unittest.TestCase):
polynomial3[-1],
polynomial4[-1]])
test_coeff = numpy.asarray([1, 0, 2], dtype=theano.config.floatX)
test_coeff = np.asarray([1, 0, 2], dtype=theano.config.floatX)
# This will be tested by DEBUG_MODE
out = calculate_polynomial(test_coeff, 3)
assert out[0] == 19
......@@ -3480,7 +3480,7 @@ class T_Scan(unittest.TestCase):
x)
f = theano.function([x], [o, o2], mode=mode_with_opt)
vx = numpy.zeros((50,), dtype=theano.config.floatX)
vx = np.zeros((50,), dtype=theano.config.floatX)
vx[23] = 4
out, out2 = f(vx)
assert len(out) == 24
......@@ -3497,7 +3497,7 @@ class T_Scan(unittest.TestCase):
o, _ = theano.scan(lambda_fn, x)
f = theano.function([x], o.shape[0], mode=mode_with_opt)
vx = numpy.zeros((50,), dtype=theano.config.floatX)
vx = np.zeros((50,), dtype=theano.config.floatX)
vx[23] = 4
out = f(vx)
assert out == 24
......@@ -3516,7 +3516,7 @@ class T_Scan(unittest.TestCase):
[o1.shape[0], o2.shape[0]],
mode=mode_with_opt)
vx = numpy.ones((10,), dtype=theano.config.floatX)
vx = np.ones((10,), dtype=theano.config.floatX)
out1, out2 = f(vx)
assert out1 == 10
assert out2 == 10
......@@ -3535,7 +3535,7 @@ class T_Scan(unittest.TestCase):
[o1.shape[0], o2.shape[0]],
mode=mode_with_opt)
vx = numpy.ones((30,), dtype=theano.config.floatX)
vx = np.ones((30,), dtype=theano.config.floatX)
o1, o2 = f(vx)
assert o1 == 20
assert o2 == 20
......@@ -3635,13 +3635,13 @@ class T_Scan(unittest.TestCase):
# Run the function and validate the outputs
dtype = theano.config.floatX
seq_value = numpy.random.random((10, 3)).astype(dtype)
out_init_value = numpy.random.random((3, 3)).astype(dtype)
non_seq_value = numpy.random.random((3)).astype(dtype)
seq_value = np.random.random((10, 3)).astype(dtype)
out_init_value = np.random.random((3, 3)).astype(dtype)
non_seq_value = np.random.random((3)).astype(dtype)
outputs = fct(seq_value, out_init_value, non_seq_value)
expected_g_seq = numpy.array([[4, 4, 4],
expected_g_seq = np.array([[4, 4, 4],
[3, 3, 3],
[3, 3, 3],
[3, 3, 3],
......@@ -3652,7 +3652,7 @@ class T_Scan(unittest.TestCase):
[1, 1, 1],
[1, 1, 1]])
expected_g_out_init = expected_g_seq[:3]
expected_g_non_seq = numpy.array([22, 22, 22])
expected_g_non_seq = np.array([22, 22, 22])
utt.assert_allclose(outputs[0], expected_g_seq)
utt.assert_allclose(outputs[1], expected_g_out_init)
......@@ -3729,7 +3729,7 @@ class T_Scan(unittest.TestCase):
assert tf([1.0, 2.0, -3.0, 4.0], 2.0) == 42
def test_return_steps(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
vW = asarrayX(rng.uniform(size=(2, 2), low=-5., high=5.))
vWout = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
......@@ -3774,16 +3774,16 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# compute the values in numpy
v_x = numpy.zeros((8, 2), dtype=theano.config.floatX)
v_y = numpy.zeros((8,), dtype=theano.config.floatX)
v_x[0] = numpy.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
numpy.dot(v_x0, vW)
v_y[0] = numpy.dot(v_x0, vWout) + v_y0[2]
v_x = np.zeros((8, 2), dtype=theano.config.floatX)
v_y = np.zeros((8,), dtype=theano.config.floatX)
v_x[0] = np.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
np.dot(v_x0, vW)
v_y[0] = np.dot(v_x0, vWout) + v_y0[2]
for i in xrange(1, 8):
v_x[i] = numpy.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
numpy.dot(v_x[i - 1], vW)
v_y[i] = numpy.dot(v_x[i - 1], vWout) + v_y[i - 1]
v_x[i] = np.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
np.dot(v_x[i - 1], vW)
v_y[i] = np.dot(v_x[i - 1], vWout) + v_y[i - 1]
(theano_dump, theano_x, theano_y) = f4(v_u1, v_u2, v_x0, v_y0, vW_in1)
......@@ -3811,14 +3811,14 @@ class T_Scan(unittest.TestCase):
assert any([isinstance(node.op, tensor.blas.Dot22)
for node in topo])
vx = numpy.array([[1., 1.], [2., 2.]], dtype=theano.config.floatX)
vA = numpy.array([[1., 1.], [1., 0.]], dtype=theano.config.floatX)
vR = numpy.array([[[2, 1], [4, 2]], [[2, 1], [4, 2]]],
vx = np.array([[1., 1.], [2., 2.]], dtype=theano.config.floatX)
vA = np.array([[1., 1.], [1., 0.]], dtype=theano.config.floatX)
vR = np.array([[[2, 1], [4, 2]], [[2, 1], [4, 2]]],
dtype=theano.config.floatX)
utt.assert_allclose(f(vx, vA), vR)
def test_savemem_opt(self):
y0 = theano.shared(numpy.ones((2, 10)))
y0 = theano.shared(np.ones((2, 10)))
[y1, y2], updates = theano.scan(lambda y: [y, y],
outputs_info=[dict(initial=y0,
taps=[-2]), None],
......@@ -3860,9 +3860,9 @@ class T_Scan(unittest.TestCase):
f = theano.function(inputs=[x, w], outputs=get_outputs(x, w))
# Test the function to ensure it returns valid results
x_value = numpy.random.random((2, 2, 3)).astype(theano.config.floatX)
w_value = numpy.random.random((3, 3)).astype(theano.config.floatX)
expected_output = numpy.tile(x_value[:, 0].sum(0), (3, 1)).transpose()
x_value = np.random.random((2, 2, 3)).astype(theano.config.floatX)
w_value = np.random.random((3, 3)).astype(theano.config.floatX)
expected_output = np.tile(x_value[:, 0].sum(0), (3, 1)).transpose()
output = f(x_value, w_value)
utt.assert_allclose(output, expected_output)
......@@ -3891,17 +3891,17 @@ class T_Scan(unittest.TestCase):
gw, gx = tensor.grad(loss, [w, xinit])
grad_fn = theano.function([xinit, w], [gx, gw],
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# If numbers are small, the gradients with respect to x are small
# and the numeric differentiation becomes unstable.
# To fix this issue I ensure we are sampling numbers larger in
# absolute value than 1.
v_x = numpy.array(rng.uniform(size=(5, 2, 2), low=1., high=3.),
v_x = np.array(rng.uniform(size=(5, 2, 2), low=1., high=3.),
dtype=theano.config.floatX)
# Making some entries to be negative.
pos = rng.uniform(size=(5, 2, 2), low=0., high=1) < .5
v_x[pos] = -1 * v_x[pos]
v_w = numpy.array(rng.uniform(size=(2, 2), low=1., high=3.),
v_w = np.array(rng.uniform(size=(2, 2), low=1., high=3.),
dtype=theano.config.floatX)
pos = rng.uniform(size=(2, 2), low=0., high=1.) < .5
v_w[pos] = -1 * v_w[pos]
......@@ -3916,11 +3916,11 @@ class T_Scan(unittest.TestCase):
num_grad.gx[max_err_pos]))
def test_grad_numeric_shared(self):
shared_var = theano.shared(numpy.float32(1.))
shared_var = theano.shared(np.float32(1.))
def inner_fn():
return [], OrderedDict(
[(shared_var, shared_var + numpy.float32(1.))])
[(shared_var, shared_var + np.float32(1.))])
_, updates = theano.scan(inner_fn,
n_steps=10,
truncate_gradient=-1,
......@@ -3940,7 +3940,7 @@ class T_Scan(unittest.TestCase):
n_pars = 1 * 3 + 3 * 3
# Allocate big parameter array.
pars = theano.shared(numpy.empty(n_pars))
pars = theano.shared(np.empty(n_pars))
# Assign slices.
W1 = pars[:3].reshape(W1shape)
......@@ -3983,15 +3983,15 @@ class T_Scan(unittest.TestCase):
Hp = tensor.Rop(d_cost_wrt_pars, pars, p)
def test_seq_tap_bug_jeremiah(self):
inp = numpy.arange(10).reshape(-1, 1).astype(theano.config.floatX)
exp_out = numpy.zeros((10, 1)).astype(theano.config.floatX)
inp = np.arange(10).reshape(-1, 1).astype(theano.config.floatX)
exp_out = np.zeros((10, 1)).astype(theano.config.floatX)
exp_out[4:] = inp[:-4]
def onestep(x, x_tm4):
return x, x_tm4
seq = tensor.matrix()
initial_value = theano.shared(numpy.zeros((4, 1),
initial_value = theano.shared(np.zeros((4, 1),
dtype=theano.config.floatX))
outputs_info = [OrderedDict(
[('initial', initial_value), ('taps', [-4])]), None]
......@@ -4000,7 +4000,7 @@ class T_Scan(unittest.TestCase):
outputs_info=outputs_info)
f = theano.function([seq], results[1])
assert numpy.all(exp_out == f(inp))
assert np.all(exp_out == f(inp))
def test_borrow_bug_jeremiah(self):
# This tests two things. The first is a bug occuring when scan wrongly
......@@ -4008,29 +4008,29 @@ class T_Scan(unittest.TestCase):
# method will be able to remove the Scan node from the graph in this
# case.
inp = numpy.arange(10).reshape(-1, 1).astype(theano.config.floatX)
exp_out = numpy.zeros((10, 1)).astype(theano.config.floatX)
inp = np.arange(10).reshape(-1, 1).astype(theano.config.floatX)
exp_out = np.zeros((10, 1)).astype(theano.config.floatX)
exp_out[4:] = inp[:-4]
def onestep(x, x_tm4):
return x, x_tm4
seq = tensor.matrix()
initial_value = theano.shared(numpy.zeros((4, 1),
initial_value = theano.shared(np.zeros((4, 1),
dtype=theano.config.floatX))
outputs_info = [OrderedDict([('initial', initial_value),
('taps', [-4])]), None]
results, _ = theano.scan(fn=onestep,
sequences=seq,
outputs_info=outputs_info)
sharedvar = theano.shared(numpy.zeros((1, 1),
sharedvar = theano.shared(np.zeros((1, 1),
dtype=theano.config.floatX))
updates = OrderedDict([(sharedvar, results[0][-1:])])
f = theano.function([seq], results[1], updates=updates)
# This fails if scan uses wrongly the borrow flag
assert numpy.all(exp_out == f(inp))
assert np.all(exp_out == f(inp))
# This fails if Scan's infer_shape() is unable to remove the Scan
# node from the graph.
......@@ -4070,9 +4070,9 @@ class T_Scan(unittest.TestCase):
# Compare obtained outputs with expected outputs
floatX = theano.config.floatX
outputs = fct(numpy.arange(9, dtype=floatX).reshape(3,3))
outputs = fct(np.arange(9, dtype=floatX).reshape(3,3))
states = numpy.array([[0, 1, 2],
states = np.array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8],
[9, 12, 15],
......@@ -4144,8 +4144,8 @@ class T_Scan(unittest.TestCase):
f = theano.function([v], gv)
# Ensure the output of the function is valid
output = f(numpy.random.random(5))
utt.assert_allclose(output, numpy.ones(5))
output = f(np.random.random(5))
utt.assert_allclose(output, np.ones(5))
def test_dot_optimization(self):
A = tensor.matrix('A')
......@@ -4155,10 +4155,10 @@ class T_Scan(unittest.TestCase):
B.dimshuffle(0, 'x', 1)],
outputs_info=[tensor.zeros_like(A)])
f = theano.function([A, B], S.owner.inputs[0][-1])
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vA = rng.uniform(size=(5, 5)).astype(theano.config.floatX)
vB = rng.uniform(size=(5, 5)).astype(theano.config.floatX)
utt.assert_allclose(f(vA, vB), numpy.dot(vA.T, vB))
utt.assert_allclose(f(vA, vB), np.dot(vA.T, vB))
def test_pregreedy_optimizer(self):
W = tensor.zeros((5, 4))
......@@ -4171,7 +4171,7 @@ class T_Scan(unittest.TestCase):
lambda x: tensor.dot(tensor.dot(x, W) + bh_t, W.T) + bv_t,
outputs_info=v,
n_steps=2)
theano.function([v], chain)(numpy.zeros((3, 5),
theano.function([v], chain)(np.zeros((3, 5),
dtype=theano.config.floatX))
def test_savemem_does_not_duplicate_number_of_scan_nodes(self):
......@@ -4210,7 +4210,7 @@ class T_Scan(unittest.TestCase):
updates=updates,
mode=theano.Mode(linker='py'),
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = asarrayX(rng.uniform(size=(5,)))
outs = f(v_u, [0, 0, 0], 0)
utt.assert_allclose(outs[0], v_u + 1)
......@@ -4243,7 +4243,7 @@ class T_Scan(unittest.TestCase):
updates=updates,
mode=theano.Mode(linker='py'),
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_w = asarrayX(rng.uniform())
outs = f(v_w, [0, 0, 0], 0)
utt.assert_allclose(outs[0], v_w + 1)
......@@ -4252,7 +4252,7 @@ class T_Scan(unittest.TestCase):
utt.assert_allclose(sh.get_value(), v_w + 4)
def test_grad_bug_disconnected_input(self):
W = theano.shared(numpy.zeros((3, 3)), name='W')
W = theano.shared(np.zeros((3, 3)), name='W')
v = theano.tensor.ivector(name='v')
y, _ = theano.scan(lambda i, W: W[i], sequences=v, outputs_info=None, non_sequences=W)
......@@ -4270,14 +4270,14 @@ class T_Scan(unittest.TestCase):
# theano.printing.debugprint(out)
return theano.function([], out)()
x = theano.shared(numpy.asarray(0., dtype=theano.config.floatX))
x = theano.shared(np.asarray(0., dtype=theano.config.floatX))
utt.assert_allclose(test(x, tensor.sum((x+1)**2), mention_y=False),
1.21000003815)
utt.assert_allclose(test(x, tensor.sum((x+1)**2), mention_y=True),
1.21000003815)
def test_grad_find_input(self):
w = theano.shared(numpy.array(0, dtype='float32'), name='w')
w = theano.shared(np.array(0, dtype='float32'), name='w')
init = tensor.fscalar('init')
out, _ = theano.scan(
......@@ -4333,7 +4333,7 @@ class T_Scan(unittest.TestCase):
for out in [y1, y2, y3, y4, y5, y6]:
# This used to raise an exception
f = theano.function([W, v], out, mode=mode_with_opt)
f(numpy.zeros((3, 3), dtype=theano.config.floatX), [1, 2])
f(np.zeros((3, 3), dtype=theano.config.floatX), [1, 2])
scan_nodes = scan_nodes_from_fct(f)
assert len(scan_nodes) == 1
......@@ -4375,9 +4375,9 @@ class T_Scan(unittest.TestCase):
# This used to raise an exception
f = theano.function([W, v, vv], out, on_unused_input='ignore',
mode=mode_with_opt)
f(numpy.zeros((3, 3), theano.config.floatX),
f(np.zeros((3, 3), theano.config.floatX),
[1, 2],
numpy.zeros((3, 3), theano.config.floatX))
np.zeros((3, 3), theano.config.floatX))
scan_nodes = scan_nodes_from_fct(f)
assert len(scan_nodes) == 1
......@@ -4413,7 +4413,7 @@ class T_Scan(unittest.TestCase):
result_inner, _ = theano.scan(
fn=loss_inner,
outputs_info=tensor.as_tensor_variable(
numpy.asarray(0, dtype=numpy.float32)),
np.asarray(0, dtype=np.float32)),
non_sequences=[W],
n_steps=1,
)
......@@ -4422,7 +4422,7 @@ class T_Scan(unittest.TestCase):
result_outer, _ = theano.scan(
fn=loss_outer,
outputs_info=tensor.as_tensor_variable(
numpy.asarray(0, dtype=numpy.float32)),
np.asarray(0, dtype=np.float32)),
non_sequences=[W],
n_steps=n_steps,
return_list=True,
......@@ -4432,14 +4432,14 @@ class T_Scan(unittest.TestCase):
H = theano.gradient.hessian(cost, W)
print(".", file=sys.stderr)
f = theano.function([W, n_steps], H)
f(numpy.ones((8,), dtype='float32'), 1)
f(np.ones((8,), dtype='float32'), 1)
def test_strict_mode(self):
n = 10
w = numpy.array([[-1, 2], [3, -4]]).astype(theano.config.floatX)
w = np.array([[-1, 2], [3, -4]]).astype(theano.config.floatX)
w_ = theano.shared(w)
x0 = numpy.array([1, 2]).astype(theano.config.floatX)
x0 = np.array([1, 2]).astype(theano.config.floatX)
x0_ = tensor.vector(name='x0', dtype=theano.config.floatX)
def _scan_loose(x):
......@@ -4474,9 +4474,9 @@ class T_Scan(unittest.TestCase):
def test_strict_mode_ex(self):
n = 10
w = numpy.array([[-1, 2], [3, -4]]).astype(theano.config.floatX)
w = np.array([[-1, 2], [3, -4]]).astype(theano.config.floatX)
w_ = theano.shared(w)
x0 = numpy.array([1, 2]).astype(theano.config.floatX)
x0 = np.array([1, 2]).astype(theano.config.floatX)
x0_ = tensor.vector(name='x0', dtype=theano.config.floatX)
def _scan_loose(x):
......@@ -4497,7 +4497,7 @@ class T_Scan(unittest.TestCase):
# Build a MonitorMode that counts how many values are greater than 10
def detect_large_outputs(i, node, fn):
for output in fn.outputs:
if isinstance(output[0], numpy.ndarray):
if isinstance(output[0], np.ndarray):
detect_large_outputs.large_count += (output[0] > 10).sum()
detect_large_outputs.large_count = 0
......@@ -4516,7 +4516,7 @@ class T_Scan(unittest.TestCase):
f = theano.function(inputs=[A, k],
outputs=final_result,
updates=updates)
f(numpy.asarray([2, 3, .1, 0, 1], dtype=theano.config.floatX), 4)
f(np.asarray([2, 3, .1, 0, 1], dtype=theano.config.floatX), 4)
# There should be 3 outputs greater than 10: prior_result[0] at step 3,
# and prior_result[1] at steps 2 and 3.
......@@ -4574,19 +4574,19 @@ class ScanGpuTests:
mode=self.mode_with_gpu)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
v_u = numpy.asarray(v_u, dtype='float32')
v_x0 = numpy.asarray(v_x0, dtype='float32')
W = numpy.asarray(W, dtype='float32')
W_in = numpy.asarray(W_in, dtype='float32')
v_u = np.asarray(v_u, dtype='float32')
v_x0 = np.asarray(v_x0, dtype='float32')
W = np.asarray(W, dtype='float32')
W_in = np.asarray(W_in, dtype='float32')
# compute the output in numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[0] * W_in + v_x0 * W
for step in xrange(1, 4):
v_out[step] = v_u[step] * W_in + v_out[step - 1] * W
......@@ -4646,14 +4646,14 @@ class ScanGpuTests:
mode=self.mode_with_gpu)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
# compute the output in numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[0] * W_in + v_x0 * W
for step in xrange(1, 4):
v_out[step] = v_u[step] * W_in + v_out[step - 1] * W
......@@ -4708,20 +4708,20 @@ class ScanGpuTests:
mode=self.mode_with_gpu)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
# compute the output in numpy
v_out1 = numpy.zeros((4,))
v_out2 = numpy.zeros((4,), dtype='int64')
v_out1 = np.zeros((4,))
v_out2 = np.zeros((4,), dtype='int64')
v_out1[0] = v_u[0] * W_in + v_x0 * W
v_out2[0] = v_u[0] + v_x0
for step in xrange(1, 4):
v_out1[step] = v_u[step] * W_in + v_out1[step - 1] * W
v_out2[step] = numpy.int64(v_u[step] + v_out1[step - 1])
v_out2[step] = np.int64(v_u[step] + v_out1[step - 1])
theano_out1, theano_out2 = f2(v_u, v_x0, W_in, W)
utt.assert_allclose(theano_out1, v_out1)
......@@ -4735,8 +4735,8 @@ class ScanGpuTests:
assert self.is_scan_on_gpu(scan_node)
def test_gibbs_chain(self):
rng = numpy.random.RandomState(utt.fetch_seed())
v_vsample = numpy.array(rng.binomial(1, .5, size=(3, 20),),
rng = np.random.RandomState(utt.fetch_seed())
v_vsample = np.array(rng.binomial(1, .5, size=(3, 20),),
dtype='float32')
vsample = theano.shared(v_vsample)
trng = theano.sandbox.rng_mrg.MRG_RandomStreams(
......@@ -4788,11 +4788,11 @@ class ScanGpuTests:
# Initialize the network parameters
floatX = theano.config.floatX
U = theano.shared(numpy.zeros((n_in, n_hid), dtype="float32"),
U = theano.shared(np.zeros((n_in, n_hid), dtype="float32"),
name='W_xin_to_l1')
V = theano.shared(numpy.zeros((n_hid, n_hid), dtype="float32"),
V = theano.shared(np.zeros((n_hid, n_hid), dtype="float32"),
name='W_l1_to_l1')
W = theano.shared(numpy.zeros((n_hid, n_out), dtype="float32"),
W = theano.shared(np.zeros((n_hid, n_out), dtype="float32"),
name='W_l1_to_l2')
nparams = [U, V, W]
......@@ -4802,7 +4802,7 @@ class ScanGpuTests:
def scan_l(baseline, last_step):
return baseline + tensor.dot(last_step, V)
zero_output = tensor.alloc(numpy.asarray(0., dtype="float32"),
zero_output = tensor.alloc(np.asarray(0., dtype="float32"),
mb_size, n_hid)
l1_out, _ = theano.scan(scan_l, sequences=[l1_base],
......@@ -4833,9 +4833,9 @@ class ScanGpuTests:
assert len(grad_scan_node.outputs) == 2, len(grad_scan_node.outputs)
# Call the theano function to ensure the absence of a memory error
feval_backprop(numpy.zeros((mb_length, mb_size, n_in),
feval_backprop(np.zeros((mb_length, mb_size, n_in),
dtype="float32"),
numpy.zeros((mb_length, mb_size, n_out),
np.zeros((mb_length, mb_size, n_out),
dtype="float32"))
def test_memory_reuse_gpudimshuffle(self):
......@@ -4864,11 +4864,11 @@ class ScanGpuTests:
fct = theano.function([input1, init], [out1, out2],
mode=self.mode_with_gpu)
output = fct(numpy.ones((2, 1, 1), dtype="float32"),
numpy.ones((1, 1, 1), dtype="float32"))
output = fct(np.ones((2, 1, 1), dtype="float32"),
np.ones((1, 1, 1), dtype="float32"))
expected_output = (numpy.array([2, 4], dtype="float32"),
numpy.array([3, 7], dtype="float32"))
expected_output = (np.array([2, 4], dtype="float32"),
np.array([3, 7], dtype="float32"))
utt.assert_allclose(output, expected_output)
......@@ -4985,7 +4985,7 @@ def test_speed():
if not theano.config.cxx:
raise SkipTest("G++ not available, so we need to skip this test.")
r = numpy.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
r = np.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
t0 = time.time()
for i in xrange(1, 1000):
......@@ -4993,7 +4993,7 @@ def test_speed():
t1 = time.time()
print('python', t1 - t0)
r = numpy.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
r = np.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
t0 = time.time()
r_i = iter(r[1:])
r_ii = iter(r[:-1])
......@@ -5015,7 +5015,7 @@ def test_speed():
print('python with builtin iterator', t1 - t0)
if 1:
r = numpy.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
r = np.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
s_r = tensor.matrix()
s_y, updates = theano.scan(fn=lambda ri, rii: ri + rii,
sequences=[s_r[1:]],
......@@ -5030,9 +5030,9 @@ def test_speed():
print('theano (scan, cvm)', t3 - t2)
if 1:
r = numpy.arange(10000).astype(theano.config.floatX).reshape(-1, 10)
r = np.arange(10000).astype(theano.config.floatX).reshape(-1, 10)
shared_r = theano.shared(r)
s_i = theano.shared(numpy.array(1))
s_i = theano.shared(np.array(1))
s_rinc = tensor.inc_subtensor(shared_r[s_i], shared_r[s_i - 1],
tolerate_inplace_aliasing=True)
# theano.printing.debugprint(s_rinc)
......@@ -5075,18 +5075,18 @@ def test_speed_rnn():
L = 10000
N = 50
numpy.random.seed(2523452)
r = numpy.arange(L * N).astype(theano.config.floatX).reshape(L, N)
w = numpy.random.randn(N, N).astype(theano.config.floatX)
np.random.seed(2523452)
r = np.arange(L * N).astype(theano.config.floatX).reshape(L, N)
w = np.random.randn(N, N).astype(theano.config.floatX)
t0 = time.time()
for i in xrange(1, L):
r[i] = numpy.tanh(numpy.dot(r[i - 1], w))
r[i] = np.tanh(np.dot(r[i - 1], w))
t1 = time.time()
print('python', t1 - t0)
if 1:
r = numpy.arange(L * N).astype(theano.config.floatX).reshape(L, N)
r = np.arange(L * N).astype(theano.config.floatX).reshape(L, N)
s_r = tensor.matrix()
s_y, updates = theano.scan(
fn=lambda ri, rii: tensor.tanh(tensor.dot(rii, w)),
......@@ -5102,7 +5102,7 @@ def test_speed_rnn():
print('theano (scan, cvm)', t3 - t2)
if 1:
r = numpy.arange(L * N).astype(theano.config.floatX).reshape(L, N)
r = np.arange(L * N).astype(theano.config.floatX).reshape(L, N)
s_w = theano.shared(w)
shared_r = theano.shared(r)
s_i = theano.scalar.sharedvar.shared(1)
......@@ -5154,18 +5154,18 @@ def test_speed_batchrnn():
B = 50
N = 400
numpy.random.seed(2523452)
r = numpy.arange(B * L * N).astype(theano.config.floatX).reshape(L, B, N)
w = numpy.random.randn(N, N).astype(theano.config.floatX)
np.random.seed(2523452)
r = np.arange(B * L * N).astype(theano.config.floatX).reshape(L, B, N)
w = np.random.randn(N, N).astype(theano.config.floatX)
t0 = time.time()
for i in xrange(1, L):
r[i] = numpy.tanh(numpy.dot(r[i - 1], w))
r[i] = np.tanh(np.dot(r[i - 1], w))
t1 = time.time()
print('python', t1 - t0)
if 1:
r = numpy.arange(B * L * N).astype(
r = np.arange(B * L * N).astype(
theano.config.floatX).reshape(L, B, N)
s_w = theano.shared(w)
shared_r = theano.shared(r)
......@@ -5328,9 +5328,9 @@ def test_compute_test_value():
theano.config.compute_test_value = 'raise'
try:
x = tensor.vector('x')
xv = numpy.ones(3, dtype=theano.config.floatX)
xv = np.ones(3, dtype=theano.config.floatX)
x.tag.test_value = xv
y = theano.shared(numpy.arange(3, dtype=theano.config.floatX),
y = theano.shared(np.arange(3, dtype=theano.config.floatX),
name='y')
z, updates = theano.scan(
fn=lambda u, v: u + v,
......@@ -5351,10 +5351,10 @@ def test_compute_test_value_nonseq():
theano.config.compute_test_value = 'raise'
try:
x = tensor.vector('x')
xv = numpy.ones(3, dtype=theano.config.floatX)
xv = np.ones(3, dtype=theano.config.floatX)
x.tag.test_value = xv
y = theano.shared(
numpy.arange(9, dtype=theano.config.floatX).reshape(3, 3),
np.arange(9, dtype=theano.config.floatX).reshape(3, 3),
name='y')
z, updates = theano.scan(
fn=lambda u, v: u + v,
......@@ -5373,7 +5373,7 @@ def test_compute_test_value_nonseq():
def test_compute_test_value_grad():
# Test case originally reported by Bitton Tenessi
# https://groups.google.com/d/msg/theano-users/fAP3i2CbskQ/3OgBf4yjqiQJ
WEIGHT = numpy.array([1, 2, 1, 3, 4, 1, 5, 6, 1, 7, 8, 1],
WEIGHT = np.array([1, 2, 1, 3, 4, 1, 5, 6, 1, 7, 8, 1],
dtype='float32')
old_compute_test_val = theano.config.compute_test_value
......@@ -5387,13 +5387,13 @@ def test_compute_test_value_grad():
W = W_flat.reshape((2, 2, 3))
outputs_mi = tensor.as_tensor_variable(
numpy.asarray(0, dtype='float32'))
outputs_mi.tag.test_value = numpy.asarray(0, dtype='float32')
np.asarray(0, dtype='float32'))
outputs_mi.tag.test_value = np.asarray(0, dtype='float32')
def loss_mi(mi, sum_mi, W):
outputs_ti = tensor.as_tensor_variable(
numpy.asarray(0, dtype='float32'))
outputs_ti.tag.test_value = numpy.asarray(0, dtype='float32')
np.asarray(0, dtype='float32'))
outputs_ti.tag.test_value = np.asarray(0, dtype='float32')
def loss_ti(ti, sum_ti, mi, W):
return W.sum().sum().sum() + sum_ti
......@@ -5430,10 +5430,10 @@ def test_compute_test_value_grad_cast():
theano.config.compute_test_value = 'raise'
try:
h = tensor.matrix('h')
h.tag.test_value = numpy.array([[1, 2, 3, 4], [5, 6, 7, 8]],
h.tag.test_value = np.array([[1, 2, 3, 4], [5, 6, 7, 8]],
dtype=floatX)
w = theano.shared(numpy.random.randn(4, 3).astype(floatX), name='w')
w = theano.shared(np.random.randn(4, 3).astype(floatX), name='w')
outputs, _ = theano.scan(lambda i, h, w: (theano.dot(h[i], w), i),
outputs_info=[None, 0], non_sequences=[h, w],
......@@ -5473,10 +5473,10 @@ def test_outputs_taps_check():
def test_default_value_broadcasted():
def floatx(X):
return numpy.asarray(X, dtype=theano.config.floatX)
return np.asarray(X, dtype=theano.config.floatX)
def init_weights(shape, name):
return theano.shared(floatx(numpy.random.randn(*shape) * 0.1), name)
return theano.shared(floatx(np.random.randn(*shape) * 0.1), name)
X = theano.tensor.matrix('X')
in_size = 2
......@@ -5494,14 +5494,14 @@ def test_default_value_broadcasted():
gW_x = theano.tensor.grad(cost, W_x)
updates = [(W_x, W_x - 0.1 * gW_x)]
f = theano.function([X], outputs=cost, updates=updates)
f(numpy.random.rand(10, in_size).astype(X.dtype))
f(np.random.rand(10, in_size).astype(X.dtype))
class TestInconsistentBroadcast(unittest.TestCase):
def test_raise_error(self):
x = tensor.tensor3()
initial_x = tensor.constant(numpy.zeros((1, 10)))
initial_x = tensor.constant(np.zeros((1, 10)))
y, updates = theano.scan(fn=lambda x, prev_x: x + prev_x,
sequences=x,
outputs_info=[dict(initial=initial_x)])
......
theano/scan_module/tests/test_scan_checkpoints.py
浏览文件 @ bea31470
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import unittest
import theano
......@@ -39,14 +39,14 @@ class TestScanCheckpoint(unittest.TestCase):
f = theano.function(inputs=[self.A, self.k],
outputs=[self.result, self.result_check])
out, out_check = f(range(10), 101)
assert numpy.allclose(out, out_check)
assert np.allclose(out, out_check)
def test_backward_pass(self):
"""Test gradient computation of A**k."""
f = theano.function(inputs=[self.A, self.k],
outputs=[self.grad_A, self.grad_A_check])
out, out_check = f(range(10), 101)
assert numpy.allclose(out, out_check)
assert np.allclose(out, out_check)
@unittest.skipUnless(PYGPU_AVAILABLE, 'Requires pygpu.')
def test_memory(self):
......@@ -59,7 +59,7 @@ class TestScanCheckpoint(unittest.TestCase):
f_check = theano.function(inputs=[self.A, self.k],
outputs=self.grad_A_check, mode=mode_with_gpu)
free_gmem = theano.gpuarray.type._context_reg[None].free_gmem
data = numpy.ones(free_gmem // 3000, dtype=numpy.float32)
data = np.ones(free_gmem // 3000, dtype=np.float32)
# Check that it works with the checkpoints
f_check(data, 1000)
# Check that the basic scan fails in that case
......
theano/scan_module/tests/test_scan_opt.py
浏览文件 @ bea31470
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import unittest
import theano
......@@ -18,7 +18,7 @@ class TestGaussNewton(unittest.TestCase):
This test case is based on code by Sigurd Spieckermann.
"""
def setUp(self):
self.rng = numpy.random.RandomState(utt.fetch_seed())
self.rng = np.random.RandomState(utt.fetch_seed())
def _run(self, num_features, num_timesteps, batch_size, mode):
# determine shapes of inputs and targets depending on the batch size
......@@ -58,8 +58,8 @@ class TestGaussNewton(unittest.TestCase):
W_hy = theano.shared(
(0.01 * self.rng.uniform(size=(10, 1))).astype(config.floatX),
borrow=True)
b_h = theano.shared(numpy.zeros(10).astype(config.floatX), borrow=True)
b_y = theano.shared(numpy.zeros(1).astype(config.floatX), borrow=True)
b_h = theano.shared(np.zeros(10).astype(config.floatX), borrow=True)
b_y = theano.shared(np.zeros(1).astype(config.floatX), borrow=True)
params = [W_xh, W_hh, W_hy, b_h, b_y]
......@@ -171,8 +171,8 @@ class TestPushOutScanOutputDot(object):
# Ensure that the function compiled with the optimization produces
# the same results as the function compiled without
v_value = numpy.random.random((4)).astype(config.floatX)
m_value = numpy.random.random((4, 5)).astype(config.floatX)
v_value = np.random.random((4)).astype(config.floatX)
m_value = np.random.random((4, 5)).astype(config.floatX)
output_opt = f_opt(v_value, m_value)
output_no_opt = f_no_opt(v_value, m_value)
......@@ -217,8 +217,8 @@ class TestPushOutScanOutputDot(object):
# Ensure that the function compiled with the optimization produces
# the same results as the function compiled without
a_value = numpy.random.random((3, 4)).astype(config.floatX)
b_value = numpy.random.random((4, 5)).astype(config.floatX)
a_value = np.random.random((3, 4)).astype(config.floatX)
b_value = np.random.random((4, 5)).astype(config.floatX)
output_opt = f_opt(a_value, b_value)
output_no_opt = f_no_opt(a_value, b_value)
......@@ -263,8 +263,8 @@ class TestPushOutScanOutputDot(object):
# Ensure that the function compiled with the optimization produces
# the same results as the function compiled without
a_value = numpy.random.random((3, 4)).astype(config.floatX)
b_value = numpy.random.random((4, 5)).astype(config.floatX)
a_value = np.random.random((3, 4)).astype(config.floatX)
b_value = np.random.random((4, 5)).astype(config.floatX)
output_opt = f_opt(a_value, b_value)
output_no_opt = f_no_opt(a_value, b_value)
......@@ -296,7 +296,7 @@ class TestPushOutSumOfDot():
dim = 5
# Weight matrices
U = theano.shared(numpy.random.normal(size=(dim, dim),
U = theano.shared(np.random.normal(size=(dim, dim),
scale=0.0001).astype(config.floatX))
U.name = 'U'
V = theano.shared(U.get_value())
......@@ -306,7 +306,7 @@ class TestPushOutSumOfDot():
# Variables and their values
x = T.tensor3('x')
x_value = numpy.random.normal(size=(seq_len, batch_size, dim),
x_value = np.random.normal(size=(seq_len, batch_size, dim),
scale=0.0001).astype(config.floatX)
ri = T.tensor3('ri')
......@@ -315,7 +315,7 @@ class TestPushOutSumOfDot():
zi = T.tensor3('zi')
zi_value = x_value
init = T.alloc(numpy.cast[config.floatX](0), batch_size, dim)
init = T.alloc(np.cast[config.floatX](0), batch_size, dim)
def rnn_step1(
# sequences
x, ri, zi,
......@@ -375,8 +375,8 @@ class TestPushOutSumOfDot():
input2 = T.tensor3()
input3 = T.tensor3()
W = theano.shared(numpy.random.normal(size=(4, 5))).astype(config.floatX)
U = theano.shared(numpy.random.normal(size=(6, 7))).astype(config.floatX)
W = theano.shared(np.random.normal(size=(4, 5))).astype(config.floatX)
U = theano.shared(np.random.normal(size=(6, 7))).astype(config.floatX)
def inner_fct(seq1, seq2, seq3, previous_output):
temp1 = T.dot(seq1, W) + seq3
......@@ -384,7 +384,7 @@ class TestPushOutSumOfDot():
dot_output = T.dot(temp1, temp2)
return previous_output + dot_output
init = T.as_tensor_variable(numpy.random.normal(size=(3, 7)))
init = T.as_tensor_variable(np.random.normal(size=(3, 7)))
# Compile the function twice, once with the optimization and once
# without
......@@ -410,9 +410,9 @@ class TestPushOutSumOfDot():
# TODO
# Compare the outputs of the 2 functions
input1_value = numpy.random.random((2, 3, 4)).astype(config.floatX)
input2_value = numpy.random.random((2, 5, 6)).astype(config.floatX)
input3_value = numpy.random.random((2, 3, 5)).astype(config.floatX)
input1_value = np.random.random((2, 3, 4)).astype(config.floatX)
input2_value = np.random.random((2, 5, 6)).astype(config.floatX)
input3_value = np.random.random((2, 3, 5)).astype(config.floatX)
output_opt = f_opt(input1_value, input2_value, input3_value)
output_no_opt = f_no_opt(input1_value, input2_value, input3_value)
......
theano/scan_module/tests/test_scan_utils.py
浏览文件 @ bea31470
from __future__ import absolute_import, print_function, division
import itertools
import unittest
import numpy
import numpy as np
import theano
from theano import tensor
from theano.scan_module.scan_utils import equal_computations, map_variables
......@@ -51,8 +51,8 @@ class TestMapVariables(unittest.TestCase):
s2, = map_variables(self.replacer, [s])
f = theano.function([x, y, z], [s, s2])
rval = f(x=numpy.array([1, 2, 3], dtype=numpy.float32), y=1, z=2)
assert numpy.array_equal(rval, [[1, 2, 3], [2, 4, 6]])
rval = f(x=np.array([1, 2, 3], dtype=np.float32), y=1, z=2)
assert np.array_equal(rval, [[1, 2, 3], [2, 4, 6]])
def test_scan(self):
x = tensor.vector('x')
......@@ -64,7 +64,7 @@ class TestMapVariables(unittest.TestCase):
# should do this as well.
outer = tensor.scalar("outer")
shared = theano.shared(
numpy.array(1., dtype=theano.config.floatX),
np.array(1., dtype=theano.config.floatX),
name="shared")
constant = tensor.constant(1, name="constant")
......@@ -77,7 +77,7 @@ class TestMapVariables(unittest.TestCase):
return r
s, _ = theano.scan(step, sequences=x,
outputs_info=[numpy.array(0.)])
outputs_info=[np.array(0.)])
# ensure z is owned by the outer graph so map_variables() will need to
# jump through additional hoops to placate FunctionGraph.
t = z * s
......@@ -85,8 +85,8 @@ class TestMapVariables(unittest.TestCase):
t2 = z * s2
f = theano.function([x, outer], [t, t2])
rval = f(x=numpy.array([1, 2, 3], dtype=numpy.float32), outer=0.5)
assert numpy.array_equal(rval, [[1, 3, 6], [-1, -3, -6]])
rval = f(x=np.array([1, 2, 3], dtype=np.float32), outer=0.5)
assert np.array_equal(rval, [[1, 3, 6], [-1, -3, -6]])
def test_scan_with_shared_update(self):
x = tensor.vector('x')
......@@ -104,7 +104,7 @@ class TestMapVariables(unittest.TestCase):
return r
s, _ = theano.scan(step, sequences=x,
outputs_info=[numpy.array(0.)])
outputs_info=[np.array(0.)])
self.assertRaises(NotImplementedError,
map_variables, self.replacer, [s])
......@@ -128,7 +128,7 @@ class TestMapVariables(unittest.TestCase):
return r + counter
s, _ = theano.scan(step, sequences=x,
outputs_info=[numpy.array(0.)])
outputs_info=[np.array(0.)])
self.assertRaises(NotImplementedError,
map_variables, self.replacer, [s])
......@@ -137,7 +137,7 @@ class TestMapVariables(unittest.TestCase):
# inner graph.
outer = tensor.scalar("outer")
shared = theano.shared(
numpy.array(1., dtype=theano.config.floatX),
np.array(1., dtype=theano.config.floatX),
name="shared")
constant = tensor.constant(1., name="constant")
z = outer * (shared + constant)
......
theano/tensor/blas.py
浏览文件 @ bea31470
......@@ -130,10 +130,10 @@ import logging
import os
import time
import numpy
import numpy as np
import numpy.distutils
try:
import numpy.distutils.__config__
import numpy.distutils.__config__ # noqa
except ImportError:
pass
......@@ -166,10 +166,10 @@ try:
# `scipy.linalg.blas.fblas` with `scipy.linalg.blas`.
# See http://github.com/scipy/scipy/pull/358
fblas = scipy.linalg.blas
_blas_gemv_fns = {numpy.dtype('float32'): fblas.sgemv,
numpy.dtype('float64'): fblas.dgemv,
numpy.dtype('complex64'): fblas.cgemv,
numpy.dtype('complex128'): fblas.zgemv}
_blas_gemv_fns = {np.dtype('float32'): fblas.sgemv,
np.dtype('float64'): fblas.dgemv,
np.dtype('complex64'): fblas.cgemv,
np.dtype('complex128'): fblas.zgemv}
except ImportError as e:
have_fblas = False
# This is used in Gemv and ScipyGer. We use CGemv and CGer
......@@ -190,12 +190,12 @@ def check_init_y():
if not have_fblas:
check_init_y._result = False
y = float('NaN') * numpy.ones((2,))
x = numpy.ones((2,))
A = numpy.ones((2, 2))
y = float('NaN') * np.ones((2,))
x = np.ones((2,))
A = np.ones((2, 2))
gemv = _blas_gemv_fns[y.dtype]
gemv(1.0, A.T, x, 0.0, y, overwrite_y=True, trans=True)
check_init_y._result = numpy.isnan(y).any()
check_init_y._result = np.isnan(y).any()
return check_init_y._result
......@@ -269,7 +269,7 @@ class Gemv(Op):
out_storage[0][0] = gemv(alpha, A.T, x, beta, y,
overwrite_y=self.inplace, trans=True)
else:
out = numpy.dot(A, x)
out = np.dot(A, x)
if alpha != 1:
out *= alpha
if beta != 0:
......@@ -277,7 +277,7 @@ class Gemv(Op):
out += beta * y
else:
out += y
out_storage[0][0] = numpy.asarray(out, dtype=y.dtype)
out_storage[0][0] = np.asarray(out, dtype=y.dtype)
def infer_shape(self, node, input_shapes):
return [input_shapes[0]]
......@@ -341,9 +341,9 @@ class Ger(Op):
else:
A = cA.copy()
if calpha != 1:
A += calpha * numpy.outer(cx, cy)
A += calpha * np.outer(cx, cy)
else:
A += numpy.outer(cx, cy)
A += np.outer(cx, cy)
cZ[0] = A
def infer_shape(self, node, input_shapes):
......@@ -900,26 +900,26 @@ class Gemm(GemmRelated):
if not self.inplace:
z = z.copy() # the original z will not be changed
if z.shape == ():
z.itemset(z * a + b * numpy.dot(x, y))
z.itemset(z * a + b * np.dot(x, y))
zout[0] = z
else:
if b == 0.0:
if a == 1.0:
z[:] = numpy.dot(x, y)
z[:] = np.dot(x, y)
elif a == -1.0:
z[:] = -numpy.dot(x, y)
z[:] = -np.dot(x, y)
else:
z[:] = a * numpy.dot(x, y)
z[:] = a * np.dot(x, y)
elif b == 1.0:
if a == 1.0:
z += numpy.dot(x, y)
z += np.dot(x, y)
elif a == -1.0:
z -= numpy.dot(x, y)
z -= np.dot(x, y)
else:
z += a * numpy.dot(x, y)
z += a * np.dot(x, y)
else:
z *= b
z += a * numpy.dot(x, y)
z += a * np.dot(x, y)
zout[0] = z
def infer_shape(self, node, input_shapes):
......@@ -1066,7 +1066,7 @@ def _as_scalar(res, dtype=None):
"""Return None or a TensorVariable whose type is in T.float_scalar_types"""
if dtype is None:
dtype = config.floatX
if numpy.all(res.type.broadcastable):
if np.all(res.type.broadcastable):
while res.owner and isinstance(res.owner.op, T.DimShuffle):
res = res.owner.inputs[0]
# may still have some number of True's
......@@ -1216,7 +1216,7 @@ def _gemm_canonicalize(r, scale, rval, maxclients):
vectors = []
matrices = []
for i in r.owner.inputs:
if numpy.all(i.type.broadcastable):
if np.all(i.type.broadcastable):
while i.owner and isinstance(i.owner.op, T.DimShuffle):
i = i.owner.inputs[0]
if i.type.broadcastable:
......@@ -1539,7 +1539,7 @@ class Dot22(GemmRelated):
x, y = inp
z, = out
try:
z[0] = numpy.asarray(numpy.dot(x, y))
z[0] = np.asarray(np.dot(x, y))
except ValueError as e:
# The error raised by numpy has no shape information, we mean to
# add that
......@@ -1704,8 +1704,8 @@ def local_dot22_to_ger_or_gemv(node):
x, y = node.inputs
xb = x.broadcastable
yb = y.broadcastable
one = T.as_tensor_variable(numpy.asarray(1, dtype=x.dtype))
zero = T.as_tensor_variable(numpy.asarray(0, dtype=x.dtype))
one = T.as_tensor_variable(np.asarray(1, dtype=x.dtype))
zero = T.as_tensor_variable(np.asarray(0, dtype=x.dtype))
if xb[1] and yb[0]:
# x and y are both vectors so this might qualifies for a GER
xv = x.dimshuffle(0)
......@@ -1810,7 +1810,7 @@ class Dot22Scalar(GemmRelated):
x, y, scalar = inp
z, = out
try:
z[0] = numpy.asarray(scalar * numpy.dot(x, y))
z[0] = np.asarray(scalar * np.dot(x, y))
except ValueError as e:
# The error raised by numpy has no shape information, we
# mean to add that
......@@ -2034,9 +2034,9 @@ class BatchedDot(Op):
shape = self.infer_shape(node, [i.shape for i in inp])[0]
dtype = node.outputs[0].dtype
z0 = z[0] = numpy.empty(shape, dtype=dtype)
z0 = z[0] = np.empty(shape, dtype=dtype)
for i in xrange(z0.shape[0]):
z0[i] = numpy.dot(x[i], y[i])
z0[i] = np.dot(x[i], y[i])
def c_support_code(self):
batch_gemm_defn = """
......
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