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pytensor
提交 7dcaea7e authored 作者: Frederic Bastien's avatar Frederic Bastien
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added test for argmax

上级 da45efb4
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正在显示 包含 114 行增加 和 93 行删除
theano/tensor/tests/test_basic.py
浏览文件 @ 7dcaea7e
...@@ -930,17 +930,18 @@ class T_max_and_argmax(unittest.TestCase): ...@@ -930,17 +930,18 @@ class T_max_and_argmax(unittest.TestCase):
utt.verify_grad(lambda v: max_and_argmax(v.flatten())[1], [data]) utt.verify_grad(lambda v: max_and_argmax(v.flatten())[1], [data])
check_grad_max(data,eval_outputs(grad(max_and_argmax(n.flatten())[0],n))) check_grad_max(data,eval_outputs(grad(max_and_argmax(n.flatten())[0],n)))
class T_argmin(unittest.TestCase): class T_argmin_argmax(unittest.TestCase):
def setUp(self): def setUp(self):
utt.seed_rng() utt.seed_rng()
MaxAndArgmax.debug = 0 MaxAndArgmax.debug = 0
def test0(self): def test0(self):
n = as_tensor_variable(5.0) for fct in [argmin,argmax]:
i = eval_outputs(argmin(n)) n = as_tensor_variable(5.0)
self.failUnless(i == 0) i = eval_outputs(fct(n))
v = eval_outputs(argmin(n).shape) self.failUnless(i == 0)
assert len(v)==0 v = eval_outputs(fct(n).shape)
assert len(v)==0
def test1(self): def test1(self):
n = as_tensor_variable([1,2,3,2,-6]) n = as_tensor_variable([1,2,3,2,-6])
...@@ -949,117 +950,137 @@ class T_argmin(unittest.TestCase): ...@@ -949,117 +950,137 @@ class T_argmin(unittest.TestCase):
v = eval_outputs(argmin(n).shape) v = eval_outputs(argmin(n).shape)
assert len(v)==0 assert len(v)==0
n = as_tensor_variable([1,2,3,2,-6])
i = eval_outputs(argmax(n))
self.failUnless(i == 2)
v = eval_outputs(argmax(n).shape)
assert len(v)==0
def test2(self): def test2(self):
data = numpy.random.rand(2,3) for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
n = as_tensor_variable(data) data = numpy.random.rand(2,3)
i = eval_outputs(argmin(n)) n = as_tensor_variable(data)
self.failUnless(numpy.all(i == numpy.argmin(data,-1))) i = eval_outputs(fct(n))
v = eval_outputs(argmin(n).shape) self.failUnless(numpy.all(i == nfct(data,-1)))
assert v==(2) v = eval_outputs(fct(n).shape)
assert v==(2)
def test2b(self): def test2b(self):
data = numpy.random.rand(2,3) for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
n = as_tensor_variable(data) data = numpy.random.rand(2,3)
i = eval_outputs(argmin(n,0)) n = as_tensor_variable(data)
self.failUnless(numpy.all(i == numpy.argmin(data,0))) i = eval_outputs(fct(n,0))
v = eval_outputs(argmin(n,0).shape) self.failUnless(numpy.all(i == nfct(data,0)))
assert v==(3) v = eval_outputs(fct(n,0).shape)
v = eval_outputs(argmin(n,1).shape) assert v==(3)
assert v==(2) v = eval_outputs(fct(n,1).shape)
#currently not supported assert v==(2)
#v = eval_outputs(argmin(n,[0,1]).shape) #currently not supported
#assert v.size==0 #v = eval_outputs(fct(n,[0,1]).shape)
#assert v.size==0
def test2_invalid(self): def test2_invalid(self):
n = as_tensor_variable(numpy.random.rand(2,3)) for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
# Silence expected error messages n = as_tensor_variable(numpy.random.rand(2,3))
_logger = logging.getLogger('theano.gof.opt') # Silence expected error messages
oldlevel = _logger.getEffectiveLevel() _logger = logging.getLogger('theano.gof.opt')
_logger.setLevel(logging.CRITICAL) oldlevel = _logger.getEffectiveLevel()
try: _logger.setLevel(logging.CRITICAL)
try: try:
eval_outputs(argmin(n,3)) try:
assert False eval_outputs(fct(n,3))
except ValueError, e: assert False
pass except ValueError, e:
finally: pass
_logger.setLevel(oldlevel) finally:
_logger.setLevel(oldlevel)
def test2_invalid_neg(self): def test2_invalid_neg(self):
n = as_tensor_variable(numpy.random.rand(2,3)) for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
old_stderr = sys.stderr n = as_tensor_variable(numpy.random.rand(2,3))
sys.stderr = StringIO.StringIO() old_stderr = sys.stderr
try: sys.stderr = StringIO.StringIO()
try: try:
eval_outputs(argmin(n,-3)) try:
assert False eval_outputs(fct(n,-3))
except ValueError, e: assert False
pass except ValueError, e:
finally: pass
sys.stderr = old_stderr finally:
sys.stderr = old_stderr
def test2_valid_neg(self): def test2_valid_neg(self):
n = as_tensor_variable(numpy.random.rand(2,3)) for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
i = eval_outputs(argmin(n,-1)) n = as_tensor_variable(numpy.random.rand(2,3))
self.failUnless(i.shape == (2,)) i = eval_outputs(fct(n,-1))
self.failUnless(numpy.all(i == numpy.argmin(n.value,-1))) self.failUnless(i.shape == (2,))
i = eval_outputs(argmin(n,-2)) self.failUnless(numpy.all(i == nfct(n.value,-1)))
self.failUnless(i.shape == (3,)) i = eval_outputs(fct(n,-2))
self.failUnless(numpy.all(i == numpy.argmin(n.value,-2))) self.failUnless(i.shape == (3,))
self.failUnless(numpy.all(i == nfct(n.value,-2)))
v = eval_outputs(argmin(n,-1).shape) v = eval_outputs(fct(n,-1).shape)
assert v==(2) assert v==(2)
v = eval_outputs(argmin(n,-2).shape) v = eval_outputs(fct(n,-2).shape)
assert v==(3) assert v==(3)
def test3(self): def test3(self):
n = as_tensor_variable(numpy.random.rand(2,3,4)) for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
i = eval_outputs(argmin(n,0)) n = as_tensor_variable(numpy.random.rand(2,3,4))
self.failUnless(i.shape == (3,4)) i = eval_outputs(fct(n,0))
self.failUnless(numpy.all(i == numpy.argmin(n.value,0))) self.failUnless(i.shape == (3,4))
i = eval_outputs(argmin(n,1)) self.failUnless(numpy.all(i == nfct(n.value,0)))
self.failUnless(i.shape == (2,4)) i = eval_outputs(fct(n,1))
self.failUnless(numpy.all(i == numpy.argmin(n.value,1))) self.failUnless(i.shape == (2,4))
i = eval_outputs(argmin(n,2)) self.failUnless(numpy.all(i == nfct(n.value,1)))
self.failUnless(i.shape == (2,3)) i = eval_outputs(fct(n,2))
self.failUnless(numpy.all(i == numpy.argmin(n.value,2))) self.failUnless(i.shape == (2,3))
self.failUnless(numpy.all(i == nfct(n.value,2)))
v = eval_outputs(argmin(n,0).shape) v = eval_outputs(fct(n,0).shape)
assert tuple(v)==(3,4) assert tuple(v)==(3,4)
v = eval_outputs(argmin(n,1).shape) v = eval_outputs(fct(n,1).shape)
assert tuple(v)==(2,4) assert tuple(v)==(2,4)
v = eval_outputs(argmin(n,2).shape) v = eval_outputs(fct(n,2).shape)
assert tuple(v)==(2,3) assert tuple(v)==(2,3)
def test_grad(self): def test_grad_argmin(self):
data = numpy.random.rand(2,3) data = numpy.random.rand(2,3)
n = as_tensor_variable(data) n = as_tensor_variable(data)
def check_grad_min(data, min_grad_data, axis=None): #test grad of argmin
#This work only for axis in [0,None]
assert axis in [0,None]
z = numpy.zeros_like(data)
z = z.flatten()
argmin=numpy.argmin(data,axis=axis)
if argmin.ndim==0:
z[numpy.argmin(data,axis=axis)]+=1
else:
for id,v in enumerate(argmin):
z[v*numpy.prod(data.shape[data.ndim-1:axis:-1])+id]+=1
z = z.reshape(data.shape)
assert numpy.all(min_grad_data == z)
#test grad of min
#axis is the last one
utt.verify_grad(lambda v: argmin(v), [data]) utt.verify_grad(lambda v: argmin(v), [data])
utt.verify_grad(lambda v: argmin(v,axis=[0]), [data]) utt.verify_grad(lambda v: argmin(v,axis=[0]), [data])
#check_grad_min(data,eval_outputs(grad(argmin(n,axis=0),n)),axis=0)
utt.verify_grad(lambda v: argmin(v,axis=[1]), [data]) utt.verify_grad(lambda v: argmin(v,axis=[1]), [data])
#check_grad_min(data,eval_outputs(grad(argmin(n,axis=1),n)),axis=1)
utt.verify_grad(lambda v: argmin(v.flatten()), [data]) utt.verify_grad(lambda v: argmin(v.flatten()), [data])
#check_grad_min(data,eval_outputs(grad(argmin(n.flatten()),n)))
try:
grad(argmin(n),n)
raise Exception('Expected an error')
except TypeError:
pass
def test_grad_argmax(self):
data = numpy.random.rand(2,3)
n = as_tensor_variable(data)
#test grad of argmax
utt.verify_grad(lambda v: argmax(v), [data])
utt.verify_grad(lambda v: argmax(v,axis=[0]), [data])
utt.verify_grad(lambda v: argmax(v,axis=[1]), [data])
utt.verify_grad(lambda v: argmax(v.flatten()), [data])
try:
grad(argmax(n),n)
raise Exception('Expected an error')
except TypeError:
pass
class T_min_max(unittest.TestCase): class T_min_max(unittest.TestCase):
def setUp(self): def setUp(self):
......
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