Skip to content

P
pytensor
提交 95e24883 authored 作者: nouiz's avatar nouiz
浏览文件
操作

Merge pull request #104 from delallea/import_fix

Import fix
上级 965afb68 897516c1
隐藏空白字符变更
内嵌 并排
正在显示 包含 130 行增加 和 127 行删除
theano/tensor/tests/test_basic.py
浏览文件 @ 95e24883
import itertools
import logging
import operator
import StringIO
import sys
import unittest
import warnings
from copy import copy
from nose.plugins.skip import SkipTest
import numpy
from numpy.testing import dec
from numpy.testing.noseclasses import KnownFailureTest
from theano.tensor import *
from theano.tensor import _shared
from theano.tensor import basic as tensor # for hidden symbols
from theano.tensor import inplace
from copy import copy
from theano import compile, config
from theano import gof
from theano.gof.python25 import any, all, combinations
import theano
from theano import compile, config, function, gof, tensor
from theano.compile.mode import get_default_mode
from theano import function
from theano.gof.python25 import any, all, combinations
from theano.tensor import (_shared, wvector, bvector, autocast_float_as,
argmin, max_and_argmax, cscalar, Subtensor, ctensor3, join,
horizontal_stack, vertical_stack, argmax, get_vector_length,
fscalar, zeros_like, sum, tensor3, vector, izip, add, addbroadcast,
alloc, as_tensor_variable, tensor_from_scalar, ARange, autocast_float,
basic, clip, constant, default, dot, inc_subtensor, set_subtensor,
dmatrix, dscalar, dvector, eq, eye, fill, flatten, inverse_permutation,
tensor4, permute_row_elements, Flatten, fmatrix, fscalars, grad,
inplace, iscalar, matrix, minimum, matrices, maximum, mul, neq,
Reshape, row, scalar, scalars, second, smallest, stack, sub, Tensor,
tensor_copy, tensordot, tensordot_grad, TensorType, unbroadcast,
var, value, Join, shape, MaxAndArgmax, lscalar, zvector, exp,
get_constant_value, ivector, reshape, scalar_from_tensor, scal,
iscalars, arange, dscalars, fvector, imatrix, numeric_grad,
opt, ComplexError, TensorDot, lvector, true_div, max, min)
from theano.tests import unittest_tools as utt
import theano.tensor as T
imported_scipy_special = False
......@@ -526,7 +535,7 @@ if config.floatX=='float32':
# float32.
# This is probably caused by our way of computing the gradient error.
div_grad_rtol=0.025
TrueDivTester = makeBroadcastTester(op = true_div,
TrueDivTester = makeBroadcastTester(op = tensor.true_div,
expected = lambda x, y: check_floatX((x, y), x / y),
good = _good_broadcast_div_mod_normal_float,
# integers = (randint(2, 3), randint_nonzero(2, 3)),
......@@ -542,7 +551,7 @@ TrueDivInplaceTester = makeBroadcastTester(op = inplace.true_div_inplace,
grad_rtol=div_grad_rtol,
inplace = True)
ModTester = makeBroadcastTester(op = mod,
ModTester = makeBroadcastTester(op = tensor.mod,
expected = lambda x, y: numpy.asarray(x % y, dtype=theano.scalar.basic.upcast(x.dtype, y.dtype)),
good = _good_broadcast_div_mod_normal_float_no_complex,
# integers = (randint(2, 3), randint_nonzero(2, 3)),
......@@ -638,7 +647,7 @@ AbsInplaceTester = makeBroadcastTester(op = inplace.abs__inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
NegTester = makeBroadcastTester(op = neg,
NegTester = makeBroadcastTester(op = tensor.neg,
expected = lambda x: -x,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -648,7 +657,7 @@ NegInplaceTester = makeBroadcastTester(op = inplace.neg_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
SgnTester = makeBroadcastTester(op = sgn,
SgnTester = makeBroadcastTester(op = tensor.sgn,
expected = numpy.sign,
good = _good_broadcast_unary_normal_no_complex,
grad = _grad_broadcast_unary_normal,)
......@@ -657,7 +666,7 @@ SgnInplaceTester = makeBroadcastTester(op = inplace.sgn_inplace,
good = _good_broadcast_unary_normal_no_complex,
grad = _grad_broadcast_unary_normal,
inplace = True)
CeilTester = makeBroadcastTester(op = ceil,
CeilTester = makeBroadcastTester(op = tensor.ceil,
expected = lambda a: numpy.asarray(numpy.ceil(a),a.dtype),
good = _good_broadcast_unary_normal_no_complex,
grad = _grad_broadcast_unary_normal)
......@@ -667,7 +676,7 @@ CeilInplaceTester = makeBroadcastTester(op = inplace.ceil_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
FloorTester = makeBroadcastTester(op = floor,
FloorTester = makeBroadcastTester(op = tensor.floor,
expected = lambda a: numpy.asarray(numpy.floor(a),a.dtype),
good = _good_broadcast_unary_normal_no_complex,
grad = _grad_broadcast_unary_normal)
......@@ -677,7 +686,7 @@ FloorInplaceTester = makeBroadcastTester(op = inplace.floor_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
RoundHalfToEvenTester = makeBroadcastTester(op = round_half_to_even,
RoundHalfToEvenTester = makeBroadcastTester(op = tensor.round_half_to_even,
expected = numpy.round,
good = _good_broadcast_unary_normal_float_no_complex)
# TODO: Why complex are accepted in the next one?
......@@ -689,7 +698,7 @@ RoundHalfToEvenInplaceTester = makeBroadcastTester(op = inplace.round_half_to_ev
#numpy.vectorize don't handle correctly empty ndarray.
#see in their file numpy/lib/function_base.py in class vectorize.__call__
#This happen in float32 mode.
RoundHalfAwayFromZeroTester = makeBroadcastTester(op = round_half_away_from_zero,
RoundHalfAwayFromZeroTester = makeBroadcastTester(op = tensor.round_half_away_from_zero,
expected = theano.scalar.basic.round_half_away_from_zero_vec,
good = _good_broadcast_unary_normal_float_no_empty_no_complex)#_good_broadcast_unary_normal_float)
RoundHalfAwayFromZeroInplaceTester = makeBroadcastTester(op = inplace.round_half_away_from_zero_inplace,
......@@ -697,7 +706,7 @@ RoundHalfAwayFromZeroInplaceTester = makeBroadcastTester(op = inplace.round_half
good = _good_broadcast_unary_normal_float_no_empty_no_complex,
inplace = True)
SqrTester = makeBroadcastTester(op = sqr,
SqrTester = makeBroadcastTester(op = tensor.sqr,
expected = numpy.square,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -707,7 +716,7 @@ SqrInplaceTester = makeBroadcastTester(op = inplace.sqr_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
ExpTester = makeBroadcastTester(op = exp,
ExpTester = makeBroadcastTester(op = tensor.exp,
expected = numpy.exp,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -729,7 +738,7 @@ _grad_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),),
#empty = (numpy.asarray([]),),
)
LogTester = makeBroadcastTester(op = log,
LogTester = makeBroadcastTester(op = tensor.log,
expected = numpy.log,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
......@@ -739,7 +748,7 @@ LogInplaceTester = makeBroadcastTester(op = inplace.log_inplace,
grad = _grad_broadcast_unary_positive,
inplace = True)
Log2Tester = makeBroadcastTester(op = log2,
Log2Tester = makeBroadcastTester(op = tensor.log2,
expected = numpy.log2,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
......@@ -749,7 +758,7 @@ Log2InplaceTester = makeBroadcastTester(op = inplace.log2_inplace,
grad = _grad_broadcast_unary_positive,
inplace = True)
Log10Tester = makeBroadcastTester(op = log10,
Log10Tester = makeBroadcastTester(op = tensor.log10,
expected = numpy.log10,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
......@@ -759,7 +768,7 @@ Log10InplaceTester = makeBroadcastTester(op = inplace.log10_inplace,
grad = _grad_broadcast_unary_positive,
inplace = True)
Log1pTester = makeBroadcastTester(op = log1p,
Log1pTester = makeBroadcastTester(op = tensor.log1p,
expected = numpy.log1p,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
......@@ -770,7 +779,7 @@ Log1pInplaceTester = makeBroadcastTester(op = inplace.log1p_inplace,
inplace = True)
SqrtTester = makeBroadcastTester(op = sqrt,
SqrtTester = makeBroadcastTester(op = tensor.sqrt,
expected = numpy.sqrt,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
......@@ -803,7 +812,7 @@ _grad_broadcast_unary_arccos = dict(normal = (rand_ranged(-1.+1e-7, 1-1e-7, (2,
)
SinTester = makeBroadcastTester(op = sin,
SinTester = makeBroadcastTester(op = tensor.sin,
expected = numpy.sin,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide)
......@@ -813,7 +822,7 @@ SinInplaceTester = makeBroadcastTester(op = inplace.sin_inplace,
grad = _grad_broadcast_unary_wide,
inplace = True)
CosTester = makeBroadcastTester(op = cos,
CosTester = makeBroadcastTester(op = tensor.cos,
expected = numpy.cos,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide)
......@@ -822,7 +831,7 @@ CosInplaceTester = makeBroadcastTester(op = inplace.cos_inplace,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide,
inplace = True)
ArccosTester = makeBroadcastTester(op = arccos,
ArccosTester = makeBroadcastTester(op = tensor.arccos,
expected = numpy.arccos,
good = _good_broadcast_unary_arccos,
grad = _grad_broadcast_unary_arccos)
......@@ -837,7 +846,7 @@ if config.floatX=='float32':
#We raise the relative tolerence for the grad as their is error in float32
#This is probably caused by our way of computing the gradient error.
tan_grad_rtol = 0.052
TanTester = makeBroadcastTester(op = tan,
TanTester = makeBroadcastTester(op = tensor.tan,
expected = numpy.tan,
good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)),
......@@ -854,7 +863,7 @@ TanInplaceTester = makeBroadcastTester(op = inplace.tan_inplace,
inplace = True)
CoshTester = makeBroadcastTester(op = cosh,
CoshTester = makeBroadcastTester(op = tensor.cosh,
expected = numpy.cosh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -864,7 +873,7 @@ CoshInplaceTester = makeBroadcastTester(op = inplace.cosh_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
SinhTester = makeBroadcastTester(op = sinh,
SinhTester = makeBroadcastTester(op = tensor.sinh,
expected = numpy.sinh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -874,7 +883,7 @@ SinhInplaceTester = makeBroadcastTester(op = inplace.sinh_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
TanhTester = makeBroadcastTester(op = tanh,
TanhTester = makeBroadcastTester(op = tensor.tanh,
expected = numpy.tanh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -904,7 +913,7 @@ else:
expected_erfc = []
skip_scipy = "scipy is not present"
ErfTester = makeBroadcastTester(op = erf,
ErfTester = makeBroadcastTester(op = tensor.erf,
expected = expected_erf,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
......@@ -920,7 +929,7 @@ ErfInplaceTester = makeBroadcastTester(op = inplace.erf_inplace,
inplace = True,
skip = skip_scipy)
ErfcTester = makeBroadcastTester(op = erfc,
ErfcTester = makeBroadcastTester(op = tensor.erfc,
expected = expected_erfc,
good = _good_broadcast_unary_normal_no_int_no_complex,
grad = _grad_broadcast_unary_normal,
......@@ -936,12 +945,12 @@ ErfcInplaceTester = makeBroadcastTester(op = inplace.erfc_inplace,
inplace = True,
skip = skip_scipy)
ZerosLikeTester = makeBroadcastTester(op = zeros_like,
ZerosLikeTester = makeBroadcastTester(op = tensor.zeros_like,
expected = numpy.zeros_like,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
OnesLikeTester = makeBroadcastTester(op = ones_like,
OnesLikeTester = makeBroadcastTester(op = tensor.ones_like,
expected = numpy.ones_like,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -1818,10 +1827,10 @@ class T_subtensor(unittest.TestCase):
This is build in a way that allow to reuse it to test the equivalent gpu op.
"""
def __init__(self, name, shared=_shared,
sub=theano.tensor.basic.Subtensor,
inc_sub=theano.tensor.basic.IncSubtensor,
adv_sub1=theano.tensor.basic.AdvancedSubtensor1,
adv_incsub1=theano.tensor.basic.AdvancedIncSubtensor1,
sub=tensor.Subtensor,
inc_sub=tensor.IncSubtensor,
adv_sub1=tensor.AdvancedSubtensor1,
adv_incsub1=tensor.AdvancedIncSubtensor1,
mode=None,
dtype=theano.config.floatX,
ignore_topo=(theano.compile.function_module.DeepCopyOp)):
......@@ -2114,7 +2123,7 @@ class T_subtensor(unittest.TestCase):
t = n[idx]
# We test again AdvancedSubtensor1 as we transfer data to the cpu.
self.assertTrue(isinstance(t.owner.op, theano.tensor.basic.AdvancedSubtensor1))
self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))
val = self.eval_output_and_check(t, list=True)
if isinstance(idx, list):
......@@ -2151,7 +2160,7 @@ class T_subtensor(unittest.TestCase):
l = lvector()
t = n[l]
# We test again AdvancedSubtensor1 as we transfer data to the cpu.
self.assertTrue(isinstance(t.owner.op, theano.tensor.basic.AdvancedSubtensor1))
self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))
f = function([l], t, mode=self.mode)
topo = f.maker.env.toposort()
......@@ -2166,7 +2175,7 @@ class T_subtensor(unittest.TestCase):
n = self.shared(ones*5, broadcastable=(True, False))
idx = tensor.lvector()
t = n[idx]
self.assertTrue(isinstance(t.owner.op, theano.tensor.basic.AdvancedSubtensor1))
self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))
f = function([idx], t, mode=self.mode)
topo = f.maker.env.toposort()
......@@ -2196,7 +2205,7 @@ class T_subtensor(unittest.TestCase):
t_shapes = f()
for t_shape, shape in zip(t_shapes,shapes):
assert numpy.all(t_shape == shape)
assert theano.tensor.Subtensor not in [ x.op for x in
assert tensor.Subtensor not in [ x.op for x in
f.maker.env.toposort() ]
def test_shape_i_scalar(self):
......@@ -2208,13 +2217,12 @@ class T_subtensor(unittest.TestCase):
mode_opt = compile.mode.get_mode(mode_opt)
v_data = numpy.array(numpy.arange(5), dtype=self.dtype)
t_data = self.shared(v_data)
start = theano.tensor.iscalar('b')
stop = theano.tensor.iscalar('e')
step = theano.tensor.iscalar('s')
start = tensor.iscalar('b')
stop = tensor.iscalar('e')
step = tensor.iscalar('s')
f = function([start,stop,step], t_data[start:stop:step].shape, mode = mode_opt)
f2 = function([start,stop,step],t_data[start:stop:step])
assert theano.tensor.Subtensor not in [x.op for x in
f.maker.env.toposort() ]
assert tensor.Subtensor not in [x.op for x in f.maker.env.toposort()]
for start in [-8,-5,-4,-1,0,1,4,5,8]:
for stop in [-8,-5,-4,-1,0,1,4,5,8]:
for step in [-3,-1,2,5]:
......@@ -2223,17 +2231,16 @@ class T_subtensor(unittest.TestCase):
def test_slice_canonical_form_0(self):
start = theano.tensor.iscalar('b')
stop = theano.tensor.iscalar('e')
step = theano.tensor.iscalar('s')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(start,stop,step),
length)
start = tensor.iscalar('b')
stop = tensor.iscalar('e')
step = tensor.iscalar('s')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(start,stop,step), length)
f = function([start,stop,step, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2248,16 +2255,15 @@ class T_subtensor(unittest.TestCase):
def test_slice_canonical_form_1(self):
stop = theano.tensor.iscalar('e')
step = theano.tensor.iscalar('s')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(None,stop,step),
length)
stop = tensor.iscalar('e')
step = tensor.iscalar('s')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(None,stop,step), length)
f = function([stop,step, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2271,16 +2277,15 @@ class T_subtensor(unittest.TestCase):
def test_slice_canonical_form_2(self):
start = theano.tensor.iscalar('b')
step = theano.tensor.iscalar('s')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(start,None,step),
length)
start = tensor.iscalar('b')
step = tensor.iscalar('s')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(start,None,step), length)
f = function([start,step, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2294,16 +2299,15 @@ class T_subtensor(unittest.TestCase):
def test_slice_canonical_form_3(self):
start = theano.tensor.iscalar('b')
stop = theano.tensor.iscalar('e')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(start,stop,None),
length)
start = tensor.iscalar('b')
stop = tensor.iscalar('e')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(start,stop,None), length)
f = function([start,stop, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2316,15 +2320,14 @@ class T_subtensor(unittest.TestCase):
assert numpy.all(t_out.shape == v_out.shape)
def test_slice_canonical_form_4(self):
step = theano.tensor.iscalar('s')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(None,None,step),
length)
step = tensor.iscalar('s')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(None,None,step), length)
f = function([step, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2337,15 +2340,14 @@ class T_subtensor(unittest.TestCase):
def test_slice_canonical_form_5(self):
start = theano.tensor.iscalar('b')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(start,None,None),
length)
start = tensor.iscalar('b')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(start,None,None), length)
f = function([start, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2357,15 +2359,14 @@ class T_subtensor(unittest.TestCase):
assert numpy.all(t_out.shape == v_out.shape)
def test_slice_canonical_form_6(self):
stop = theano.tensor.iscalar('e')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(None,stop,None),
length)
stop = tensor.iscalar('e')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(None,stop,None), length)
f = function([stop, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2710,12 +2711,12 @@ class T_Join_and_Split(unittest.TestCase):
assert not c.type.broadcastable[1]
# Opt can remplace the int by a Theano constant
c = join(theano.tensor.constant(1), a, b)
c = join(tensor.constant(1), a, b)
assert c.type.broadcastable[0] and c.type.broadcastable[2]
assert not c.type.broadcastable[1]
# In case futur opt insert other useless stuff
c = join(theano.tensor.cast(theano.tensor.constant(1), dtype="int32"),
c = join(tensor.cast(tensor.constant(1), dtype="int32"),
a, b)
assert c.type.broadcastable[0] and c.type.broadcastable[2]
assert not c.type.broadcastable[1]
......@@ -3020,7 +3021,7 @@ class T_add(unittest.TestCase):
class T_ceil(unittest.TestCase):
def test_complex(self):
self.assertRaises(TypeError, ceil, zvector())
self.assertRaises(TypeError, tensor.ceil, tensor.zvector())
class T_exp(unittest.TestCase):
def test_grad_0(self):
......@@ -3074,14 +3075,14 @@ class T_divimpl(unittest.TestCase):
class T_mean(unittest.TestCase):
def test_regression_mean_of_ndarray_failure(self):
try:
theano.tensor.mean(numpy.zeros(1))
tensor.mean(numpy.zeros(1))
except AttributeError:
self.fail()
def test0(self):
#Simple test...
x = theano.tensor.vector()
f = theano.function([x],theano.tensor.mean(x))
x = tensor.vector()
f = theano.function([x],tensor.mean(x))
data = numpy.asarray(numpy.random.rand(50), dtype=config.floatX)
assert numpy.allclose(f(data), numpy.mean(data))
......@@ -3653,7 +3654,7 @@ class test_grad(unittest.TestCase):
"""grad: Test passing a single variable param"""
o = test_grad.O()
a1 = o.make_node()
self.assertTrue(o.gval0 is grad(a1.outputs[0], a1.inputs[0]))
self.assertTrue(o.gval0 is tensor.grad(a1.outputs[0], a1.inputs[0]))
def test_Nparam(self):
"""grad: Test passing multiple variable params"""
......@@ -3667,17 +3668,19 @@ class test_grad(unittest.TestCase):
def test_grad_keep_type(self):
"""Tests that the theano grad method returns a list if it is passed a list
and a single variable if it is passed a single variable.
pylearn2 depends on theano behaving this way but theano developers have
repeatedly changed it """
pylearn2 depends on theano behaving this way. This functionality has been
added three times and erroneously removed twice. If you do anything that
requires changing this test or making it fail you are almost certainly
making a common mistake, NOT fixing something. """
X = T.matrix()
X = tensor.matrix()
y = X.sum()
G = T.grad(y, [X])
G = tensor.grad(y, [X])
assert isinstance(G,list)
G = T.grad(y, X)
G = tensor.grad(y, X)
assert not isinstance(G,list)
......@@ -3838,7 +3841,7 @@ class T_reshape(unittest.TestCase):
def test_make_column_matrix_broadcastable():
# The goal of the operation made by `b` is to ensure the second dimension
# of the column matrix is broadcastable.
a = dmatrix()
a = tensor.dmatrix()
b = a.reshape((a.shape[0], )).dimshuffle(0, 'x')
f = function([a], b)
assert (f(numpy.zeros((3, 1))) + numpy.ones(2) == numpy.ones((3, 2))).all()
......@@ -5115,12 +5118,12 @@ def test_unalign():
raise Exception("Theano raised an exception when none was expected")
def test_dimshuffle_duplicate():
x = theano.tensor.vector()
x = tensor.vector()
success = False
try:
y = theano.tensor.DimShuffle((False, ), (0, 0))(x)
y = tensor.DimShuffle((False, ), (0, 0))(x)
except ValueError, e:
assert str(e).find("may not appear twice") != -1
success = True
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论