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提交 200babca authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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Merge pull request #4443 from nouiz/grad_abs

Change the grad of abs to prevent relu from generating nan in fast_compile
上级 46fbfeb6 3974f9b4
隐藏空白字符变更
内嵌 并排
正在显示 包含 90 行增加 和 64 行删除
doc/faq.txt
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......@@ -25,6 +25,20 @@ Python requires that *__len__* returns an integer, yet it cannot be done as Thea
This error message cannot be made more explicit because the relevant aspects of Python's
internals cannot be modified.
Output slight numerical difference
----------------------------------
Sometimes when you compare the output of Theano using different
Theano flags, Theano versions, CPU and GPU or with other software like
NumPy, you will see small numerical differences.
This is normal. Floating point numbers are approximations of real
numbers. This is why doing a+(b+c) vs (a+b)+c can give small
differences of value. This is normal. For more details, see: `What
Every Computer Scientist Should Know About Floating-Point Arithmetic
<https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html>`_.
Faster gcc optimization
-----------------------
......
theano/scalar/basic.py
浏览文件 @ 200babca
......@@ -2150,6 +2150,8 @@ class Abs(UnaryScalarOp):
else:
return [x.zeros_like()]
if x.type in float_types:
return gz * sgn(x),
return gz * x / abs(x), # formula works for complex and real
def c_code(self, node, name, inputs, outputs, sub):
......
theano/scalar/tests/test_basic.py
浏览文件 @ 200babca
......@@ -453,6 +453,16 @@ def test_grad_inrange():
utt.assert_allclose(f(7, 1, 5), [0, 0, 0])
def test_grad_abs():
a = theano.tensor.fscalar("a")
b = theano.tensor.nnet.relu(a)
c = theano.grad(b, a)
f = theano.function([a], c, mode=theano.Mode(optimizer=None))
# Currently Theano return 0.5, but it isn't sure it won't change
# in the futur.
ret = f(0.)
assert ret == 0.5, ret
# Testing of Composite is done in tensor/tests/test_opt.py
# in test_fusion, TestCompositeCodegen
......
theano/tensor/tests/test_opt.py
浏览文件 @ 200babca
......@@ -564,7 +564,7 @@ class test_canonize(unittest.TestCase):
mode=mode)
out = f(*val_inputs)
assert(out_dtype == out.dtype)
assert numpy.allclose(out, val_inputs[1])
utt.assert_allclose(out, val_inputs[1])
topo = f.maker.fgraph.toposort()
if topo and not(len(topo) == 1 and topo[0].op == deep_copy_op):
for node in topo[:-1]:
......@@ -587,7 +587,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, (1 / val_inputs[1]))
utt.assert_allclose(out, (1 / val_inputs[1]))
topo = f.maker.fgraph.toposort()
elem = [t for t in topo if isinstance(t.op, T.Elemwise)]
assert len(elem) == nb_elemwise
......@@ -612,7 +612,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, (val_inputs[0] / val_inputs[3]))
utt.assert_allclose(out, (val_inputs[0] / val_inputs[3]))
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert isinstance(topo[0].op, (T.Elemwise, ))
......@@ -636,7 +636,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, (0.5 *
utt.assert_allclose(out, (0.5 *
val_inputs[0] / val_inputs[1]))
topo = f.maker.fgraph.toposort()
assert len(topo) == 2
......@@ -662,7 +662,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, val_inputs[0])
utt.assert_allclose(out, val_inputs[0])
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
topo[0].op == deep_copy_op
......@@ -681,7 +681,7 @@ class test_canonize(unittest.TestCase):
mode=mode)
out = f(*val_inputs)
assert numpy.all(numpy.isfinite(out))
assert numpy.allclose(out, numpy.sign(val_inputs[0]))
utt.assert_allclose(out, numpy.sign(val_inputs[0]))
assert(out_dtype == out.dtype)
assert len(f.maker.fgraph.toposort()) == 1
......@@ -705,7 +705,7 @@ class test_canonize(unittest.TestCase):
topo = f.maker.fgraph.toposort()
out = f(*val_inputs)
assert numpy.all(numpy.isfinite(out))
assert numpy.allclose(out, numpy.sign(val_inputs[0]) * 2 / 3)
utt.assert_allclose(out, numpy.sign(val_inputs[0]) * 2 / 3)
assert(out_dtype == out.dtype)
def test_abs_mul_div(self):
......@@ -781,7 +781,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, val_inputs[0] /
utt.assert_allclose(out, val_inputs[0] /
val_inputs[1] / val_inputs[2])
topo = f.maker.fgraph.toposort()
assert len(topo) == 2
......@@ -799,7 +799,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, val_inputs[0] / (
utt.assert_allclose(out, val_inputs[0] / (
val_inputs[1] / val_inputs[2]))
topo = f.maker.fgraph.toposort()
assert len(topo) == 2
......@@ -905,7 +905,7 @@ def test_const_type_in_mul_canonizer():
betaval = numpy.random.rand(5)
aval = numpy.random.rand(5)
assert numpy.allclose(
utt.assert_allclose(
f2(ival, wval, visbval, hidbval, betaval, aval),
f1(ival, wval, visbval, hidbval, betaval, aval))
......@@ -1608,11 +1608,11 @@ def test_log_add():
f([10000], [10000]) # causes overflow if handled incorrectly
assert numpy.isfinite(f([10000], [10000]))
assert numpy.allclose(f([10000], [10000]), 10000 + numpy.log1p(1))
utt.assert_allclose(f([10000], [10000]), 10000 + numpy.log1p(1))
# test that it give the same result when it don't overflow
f([10], [10]) # don't causes overflow
assert numpy.allclose(f([10], [10]), 10 + numpy.log1p(1))
utt.assert_allclose(f([10], [10]), 10 + numpy.log1p(1))
# test that it also works with more than two args, (this currently fails)
x = dvector()
......@@ -1622,8 +1622,8 @@ def test_log_add():
try:
f([10000], [10000]) # causes overflow if handled incorrectly
assert numpy.allclose(f([10000], [10000]), 20000)
except AssertionError:
utt.assert_allclose(f([10000], [10000]), 20000)
except utt.WrongValue:
raise SkipTest("log(add(exp)) is not stabilized when adding "
"more than 2 elements, see #623")
......@@ -2734,7 +2734,7 @@ class test_local_adv_sub1_adv_inc_sub1(unittest.TestCase):
f = theano.function([x, y, idx], o, self.mode_no_assert)
res = f(dx, dy, didx)
assert numpy.allclose(dy, res)
utt.assert_allclose(dy, res)
topo = f.maker.fgraph.toposort()
if opt:
assert len(topo) == 1
......@@ -2748,7 +2748,7 @@ class test_local_adv_sub1_adv_inc_sub1(unittest.TestCase):
f = theano.function([x, y, idx], o, self.mode_no_assert)
res = f(dx, dy, didx)
assert numpy.allclose((dx[didx] + dy), res)
utt.assert_allclose((dx[didx] + dy), res)
topo = f.maker.fgraph.toposort()
len(topo) == 2
......@@ -2758,7 +2758,7 @@ class test_local_adv_sub1_adv_inc_sub1(unittest.TestCase):
f = theano.function([x, y, idx], o, self.mode_no_assert)
res = f(dx, dy, didx)
assert numpy.allclose(dy, res)
utt.assert_allclose(dy, res)
topo = f.maker.fgraph.toposort()
if opt:
assert len(topo) == 1
......@@ -4178,22 +4178,22 @@ def test_local_pow_specialize():
f = function([v], v ** 0, mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
assert nodes == [Shape_i(0), T.alloc]
assert numpy.allclose(f(val), val ** 0)
utt.assert_allclose(f(val), val ** 0)
f = function([v], v ** 1, mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
nodes == [deep_copy_op]
assert numpy.allclose(f(val), val ** 1)
utt.assert_allclose(f(val), val ** 1)
f = function([v], v ** (-1), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
assert nodes == [T.inv]
assert numpy.allclose(f(val_no0), val_no0 ** (-1))
utt.assert_allclose(f(val_no0), val_no0 ** (-1))
f = function([v], v ** 2, mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
assert nodes == [T.sqr]
assert numpy.allclose(f(val), val ** 2)
utt.assert_allclose(f(val), val ** 2)
f = function([v], v ** (-2), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
......@@ -4201,12 +4201,12 @@ def test_local_pow_specialize():
assert nodes[0] == T.sqr
assert isinstance(nodes[1].scalar_op, theano.scalar.basic.Inv)
# assert nodes == [T.sqr,T.inv]#Why this don't work?
assert numpy.allclose(f(val_no0), val_no0 ** (-2))
utt.assert_allclose(f(val_no0), val_no0 ** (-2))
f = function([v], v ** (.5), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
assert nodes == [T.sqrt]
assert numpy.allclose(f(val), val ** (.5))
utt.assert_allclose(f(val), val ** (.5))
f = function([v], v ** (-.5), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
......@@ -4214,7 +4214,7 @@ def test_local_pow_specialize():
assert nodes[0] == T.sqrt
assert isinstance(nodes[1].scalar_op, theano.scalar.basic.Inv)
# assert nodes == [T.sqrt,T.inv]#Why this don't work?
assert numpy.allclose(f(val_no0), val_no0 ** (-.5))
utt.assert_allclose(f(val_no0), val_no0 ** (-.5))
def test_local_pow_specialize_device_more_aggressive_on_cpu():
......@@ -4232,7 +4232,7 @@ def test_local_pow_specialize_device_more_aggressive_on_cpu():
assert len(nodes) == 1
assert len(f.maker.fgraph.toposort()[0].op.scalar_op.fgraph.apply_nodes) == 6
assert isinstance(nodes[0].scalar_op, theano.scalar.Composite)
assert numpy.allclose(f(val), val ** 15)
utt.assert_allclose(f(val), val ** 15)
f = function([v], v ** (-15), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
......@@ -4240,14 +4240,14 @@ def test_local_pow_specialize_device_more_aggressive_on_cpu():
assert len(f.maker.fgraph.toposort()[0].op.scalar_op.fgraph.apply_nodes) == 6
assert isinstance(nodes[0].scalar_op, theano.scalar.Composite)
assert isinstance(nodes[-1].scalar_op, theano.scalar.basic.Inv)
assert numpy.allclose(f(val_no0), val_no0 ** (-15))
utt.assert_allclose(f(val_no0), val_no0 ** (-15))
f = function([v], v ** (16), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
assert len(nodes) == 1
assert len(f.maker.fgraph.toposort()[0].op.scalar_op.fgraph.apply_nodes) == 4
assert isinstance(nodes[0].scalar_op, theano.scalar.Composite)
assert numpy.allclose(f(val), val ** 16)
utt.assert_allclose(f(val), val ** 16)
f = function([v], v ** (-16), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
......@@ -4255,7 +4255,7 @@ def test_local_pow_specialize_device_more_aggressive_on_cpu():
assert len(f.maker.fgraph.toposort()[0].op.scalar_op.fgraph.apply_nodes) == 4
assert isinstance(nodes[0].scalar_op, theano.scalar.Composite)
assert isinstance(nodes[-1].scalar_op, theano.scalar.basic.Inv)
assert numpy.allclose(f(val_no0), val_no0 ** (-16))
utt.assert_allclose(f(val_no0), val_no0 ** (-16))
class T_Rebroadcast(unittest.TestCase):
......@@ -5146,26 +5146,26 @@ class T_local_sum_prod(unittest.TestCase):
# test sum
f = theano.function([a], a.sum(), mode=self.mode)
assert len(f.maker.fgraph.apply_nodes) == 1
assert numpy.allclose(f(input), input.sum())
utt.assert_allclose(f(input), input.sum())
# test prod
f = theano.function([a], a.prod(), mode=self.mode)
assert len(f.maker.fgraph.apply_nodes) == 1
assert numpy.allclose(f(input), input.prod())
utt.assert_allclose(f(input), input.prod())
# test sum
f = theano.function([a], a.sum([0, 1, 2]), mode=self.mode)
assert len(f.maker.fgraph.apply_nodes) == 1
assert numpy.allclose(f(input), input.sum())
utt.assert_allclose(f(input), input.sum())
# test prod
f = theano.function([a], a.prod([0, 1, 2]), mode=self.mode)
assert len(f.maker.fgraph.apply_nodes) == 1
assert numpy.allclose(f(input), input.prod())
utt.assert_allclose(f(input), input.prod())
backup = config.warn.sum_sum_bug
config.warn.sum_sum_bug = False
try:
f = theano.function([a], a.sum(0).sum(0).sum(0), mode=self.mode)
assert len(f.maker.fgraph.apply_nodes) == 1
assert numpy.allclose(f(input), input.sum())
utt.assert_allclose(f(input), input.sum())
finally:
config.warn.sum_sum_bug = backup
......@@ -5216,19 +5216,19 @@ class T_local_sum_prod(unittest.TestCase):
for d, dd in dims:
expected = my_sum(input, d, dd)
f = theano.function([a], a.sum(d).sum(dd), mode=self.mode)
assert numpy.allclose(f(input), expected)
utt.assert_allclose(f(input), expected)
assert len(f.maker.fgraph.apply_nodes) == 1
for d, dd in dims[:6]:
f = theano.function([a], a.sum(d).sum(dd).
sum(0), mode=self.mode)
assert numpy.allclose(f(input), input.sum(d).sum(dd).sum(0))
utt.assert_allclose(f(input), input.sum(d).sum(dd).sum(0))
assert len(f.maker.fgraph.apply_nodes) == 1
for d in [0, 1, 2]:
f = theano.function([a], a.sum(d).sum(None), mode=self.mode)
assert numpy.allclose(f(input), input.sum(d).sum())
utt.assert_allclose(f(input), input.sum(d).sum())
assert len(f.maker.fgraph.apply_nodes) == 1
f = theano.function([a], a.sum(None).sum(), mode=self.mode)
assert numpy.allclose(f(input), input.sum())
utt.assert_allclose(f(input), input.sum())
assert len(f.maker.fgraph.apply_nodes) == 1
finally:
config.warn.sum_sum_bug = backup
......@@ -5237,38 +5237,38 @@ class T_local_sum_prod(unittest.TestCase):
for d, dd in dims:
expected = my_prod(input, d, dd)
f = theano.function([a], a.prod(d).prod(dd), mode=self.mode)
assert numpy.allclose(f(input), expected)
utt.assert_allclose(f(input), expected)
assert len(f.maker.fgraph.apply_nodes) == 1
for d, dd in dims[:6]:
f = theano.function([a], a.prod(d).prod(dd).
prod(0), mode=self.mode)
assert numpy.allclose(f(input), input.prod(d).prod(dd).prod(0))
utt.assert_allclose(f(input), input.prod(d).prod(dd).prod(0))
assert len(f.maker.fgraph.apply_nodes) == 1
for d in [0, 1, 2]:
f = theano.function([a], a.prod(d).prod(None), mode=self.mode)
assert numpy.allclose(f(input), input.prod(d).prod())
utt.assert_allclose(f(input), input.prod(d).prod())
assert len(f.maker.fgraph.apply_nodes) == 1
f = theano.function([a], a.prod(None).prod(), mode=self.mode)
assert numpy.allclose(f(input), input.prod())
utt.assert_allclose(f(input), input.prod())
assert len(f.maker.fgraph.apply_nodes) == 1
# test sum prod don't get opt.
for d, dd in dims:
expected = my_sum_prod(input, d, dd)
f = theano.function([a], a.sum(d).prod(dd), mode=self.mode)
assert numpy.allclose(f(input), expected)
utt.assert_allclose(f(input), expected)
assert len(f.maker.fgraph.apply_nodes) == 2
for d, dd in dims[:6]:
f = theano.function([a], a.sum(d).prod(dd).
prod(0), mode=self.mode)
assert numpy.allclose(f(input), input.sum(d).prod(dd).prod(0))
utt.assert_allclose(f(input), input.sum(d).prod(dd).prod(0))
assert len(f.maker.fgraph.apply_nodes) == 2
for d in [0, 1, 2]:
f = theano.function([a], a.sum(d).prod(None), mode=self.mode)
assert numpy.allclose(f(input), input.sum(d).prod())
utt.assert_allclose(f(input), input.sum(d).prod())
assert len(f.maker.fgraph.apply_nodes) == 2
f = theano.function([a], a.sum(None).prod(), mode=self.mode)
assert numpy.allclose(f(input), input.sum())
utt.assert_allclose(f(input), input.sum())
assert len(f.maker.fgraph.apply_nodes) == 1
def test_local_sum_prod_alloc(self):
......@@ -5283,23 +5283,23 @@ class T_local_sum_prod(unittest.TestCase):
# test sum
f = theano.function([a], t_like(a).sum(None), mode=mode)
assert numpy.allclose(f(input), n_like(input).sum())
utt.assert_allclose(f(input), n_like(input).sum())
assert len(f.maker.fgraph.apply_nodes) == nb_nodes[0]
f = theano.function([a], t_like(a).sum([0, 1, 2]), mode=mode)
assert numpy.allclose(f(input), n_like(input).sum())
utt.assert_allclose(f(input), n_like(input).sum())
assert len(f.maker.fgraph.apply_nodes) == nb_nodes[0]
for d in xrange(3):
f = theano.function([a], t_like(a).sum(d), mode=mode)
assert numpy.allclose(f(input), n_like(input).sum(d))
utt.assert_allclose(f(input), n_like(input).sum(d))
assert len(f.maker.fgraph.apply_nodes) == nb_nodes[1]
topo = f.maker.fgraph.toposort()
assert topo[-1].op == T.alloc
assert not any([isinstance(node.op, T.Sum) for node in topo])
for i in xrange(3):
f = theano.function([a], t_like(a).sum(i), mode=mode)
assert numpy.allclose(f(input), n_like(input).sum(i))
utt.assert_allclose(f(input), n_like(input).sum(i))
assert len(f.maker.fgraph.apply_nodes) == nb_nodes[2]
topo = f.maker.fgraph.toposort()
assert topo[-1].op == T.alloc
......@@ -5307,23 +5307,23 @@ class T_local_sum_prod(unittest.TestCase):
# test prod
f = theano.function([a], t_like(a).prod(None), mode=mode)
assert numpy.allclose(f(input), n_like(input).prod())
utt.assert_allclose(f(input), n_like(input).prod())
#assert len(f.maker.fgraph.apply_nodes) == nb_nodes[0]
f = theano.function([a], t_like(a).prod([0, 1, 2]), mode=mode)
assert numpy.allclose(f(input), n_like(input).prod())
utt.assert_allclose(f(input), n_like(input).prod())
#assert len(f.maker.fgraph.apply_nodes) == nb_nodes[0]
for d in range(3):
f = theano.function([a], t_like(a).prod(d), mode=mode)
assert numpy.allclose(f(input), n_like(input).prod(d))
utt.assert_allclose(f(input), n_like(input).prod(d))
#assert len(f.maker.fgraph.apply_nodes) == nb_nodes[1]
topo = f.maker.fgraph.toposort()
assert topo[-1].op == T.alloc
assert not any([isinstance(node.op, T.elemwise.Prod) for node in topo])
for i in range(3):
f = theano.function([a], t_like(a).prod(i), mode=mode)
assert numpy.allclose(f(input), n_like(input).prod(i))
utt.assert_allclose(f(input), n_like(input).prod(i))
#assert len(f.maker.fgraph.apply_nodes) == nb_nodes[2]
topo = f.maker.fgraph.toposort()
assert topo[-1].op == T.alloc
......@@ -5335,7 +5335,7 @@ class T_local_sum_prod(unittest.TestCase):
for d, dd in [(0, 0), (1, 0), (2, 0), (0, 1), (1, 1), (2, 1)]:
f = theano.function([a], t_like(a).
sum(d).sum(dd), mode=mode)
assert numpy.allclose(f(input),
utt.assert_allclose(f(input),
n_like(input).sum(d).sum(dd))
assert len(f.maker.fgraph.apply_nodes) == nb_nodes[3]
topo = f.maker.fgraph.toposort()
......@@ -5483,7 +5483,7 @@ class T_local_reduce(unittest.TestCase):
A = theano.shared(numpy.array([1, 2, 3, 4, 5], dtype='int64'))
f = theano.function([], T.sum(T.stack([A, A]), axis=0), mode=self.mode)
assert numpy.allclose(f(), [2, 4, 6, 8, 10])
utt.assert_allclose(f(), [2, 4, 6, 8, 10])
topo = f.maker.fgraph.toposort()
assert isinstance(topo[-1].op, T.Elemwise)
......@@ -5495,7 +5495,7 @@ class T_local_reduce(unittest.TestCase):
mode=self.mode)
finally:
theano.config.warn.reduce_join = old
assert numpy.allclose(f(), [15, 15])
utt.assert_allclose(f(), [15, 15])
topo = f.maker.fgraph.toposort()
assert not isinstance(topo[-1].op, T.Elemwise)
......@@ -5503,14 +5503,14 @@ class T_local_reduce(unittest.TestCase):
A = theano.shared(numpy.array([1, 2, 3, 4, 5]).reshape(5, 1))
f = theano.function([], T.sum(T.concatenate((A, A), axis=1), axis=1),
mode=self.mode)
assert numpy.allclose(f(), [2, 4, 6, 8, 10])
utt.assert_allclose(f(), [2, 4, 6, 8, 10])
topo = f.maker.fgraph.toposort()
assert not isinstance(topo[-1].op, T.Elemwise)
A = theano.shared(numpy.array([1, 2, 3, 4, 5]).reshape(5, 1))
f = theano.function([], T.sum(T.concatenate((A, A), axis=1), axis=0),
mode=self.mode)
assert numpy.allclose(f(), [15, 15])
utt.assert_allclose(f(), [15, 15])
topo = f.maker.fgraph.toposort()
assert not isinstance(topo[-1].op, T.Elemwise)
......@@ -5742,7 +5742,7 @@ class TestMakeVector(utt.InferShapeTester):
if dtype.startswith('int'):
# The gradient should be 0
assert numpy.allclose(g_val, 0)
utt.assert_allclose(g_val, 0)
else:
for var, grval in zip((b, i, d), g_val):
float_inputs = []
......@@ -6001,7 +6001,7 @@ def test_local_div_to_inv():
f = theano.function([num_len_s, denom_s], out)
out_val = f(3, 2.)
assert out_val.shape == (1, 3)
assert numpy.allclose(out_val, 0.5)
utt.assert_allclose(out_val, 0.5)
def test_local_useless_split():
......@@ -6153,14 +6153,14 @@ class TestShape_i(utt.InferShapeTester):
advec_val = numpy.random.rand(3).astype(config.floatX)
f = function([advec], Shape_i(0)(advec))
out = f(advec_val)
assert numpy.allclose(out, advec_val.shape[0])
utt.assert_allclose(out, advec_val.shape[0])
admat = matrix()
admat_val = numpy.random.rand(4, 3).astype(config.floatX)
for i in xrange(2):
f = function([admat], Shape_i(i)(admat))
out = f(admat_val)
assert numpy.allclose(out, admat_val.shape[i])
utt.assert_allclose(out, admat_val.shape[i])
def test_infer_shape(self):
admat = matrix()
......@@ -6313,7 +6313,7 @@ def test_local_sumsqr2dot():
f_val = f(w_val, g_val)
f_test = numpy.dot(numpy.square(g_val), numpy.square(w_val).sum(axis=0))
assert numpy.allclose(f_val, f_test)
utt.assert_allclose(f_val, f_test)
assert any(isinstance(n.op, (tensor.basic.Dot, tensor.blas.Dot22,
tensor.blas.Gemv, tensor.blas_c.CGemv))
for n in f.maker.fgraph.toposort())
......@@ -6336,7 +6336,7 @@ def test_local_expm1():
f_val = f(x_val)
f_test = function([x], T.expm1(x), mode=MODE)
assert numpy.allclose(f_val, f_test(x_val))
utt.assert_allclose(f_val, f_test(x_val))
assert any(isinstance(n.op, T.Elemwise) and isinstance(n.op.scalar_op, theano.scalar.basic.Expm1)
for n in f.maker.fgraph.toposort())
......
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