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提交 1f833c24 authored 作者: Frederic's avatar Frederic
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theano/tensor/tests/test_basic.py
浏览文件 @ 1f833c24
...@@ -1449,6 +1449,7 @@ class T_Shape(unittest.TestCase): ...@@ -1449,6 +1449,7 @@ class T_Shape(unittest.TestCase):
s = shape(numpy.ones((5, 3, 10))) s = shape(numpy.ones((5, 3, 10)))
self.assertTrue((eval_outputs([s]) == [5, 3, 10]).all()) self.assertTrue((eval_outputs([s]) == [5, 3, 10]).all())
class T_max_and_argmax(unittest.TestCase): class T_max_and_argmax(unittest.TestCase):
def setUp(self): def setUp(self):
utt.seed_rng() utt.seed_rng()
...@@ -1456,108 +1457,110 @@ class T_max_and_argmax(unittest.TestCase): ...@@ -1456,108 +1457,110 @@ class T_max_and_argmax(unittest.TestCase):
def test0(self): def test0(self):
n = as_tensor_variable(5.0) n = as_tensor_variable(5.0)
v,i = eval_outputs(max_and_argmax(n)) v, i = eval_outputs(max_and_argmax(n))
self.assertTrue(v == 5.0) self.assertTrue(v == 5.0)
self.assertTrue(i == 0) self.assertTrue(i == 0)
v = eval_outputs(max_and_argmax(n)[0].shape) v = eval_outputs(max_and_argmax(n)[0].shape)
assert len(v)==0 assert len(v) == 0
v = eval_outputs(max_and_argmax(n)[1].shape) v = eval_outputs(max_and_argmax(n)[1].shape)
assert len(v)==0 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])
v,i = eval_outputs(max_and_argmax(n)) v, i = eval_outputs(max_and_argmax(n))
self.assertTrue(v == 3) self.assertTrue(v == 3)
self.assertTrue(i == 2) self.assertTrue(i == 2)
v = eval_outputs(max_and_argmax(n)[0].shape) v = eval_outputs(max_and_argmax(n)[0].shape)
assert len(v)==0 assert len(v) == 0
def test2(self): def test2(self):
data = numpy.random.rand(2,3) data = numpy.random.rand(2, 3)
n = as_tensor_variable(data) n = as_tensor_variable(data)
v,i = eval_outputs(max_and_argmax(n,-1)) v, i = eval_outputs(max_and_argmax(n, -1))
self.assertTrue(numpy.all(v == numpy.max(data,-1))) self.assertTrue(numpy.all(v == numpy.max(data, -1)))
self.assertTrue(numpy.all(i == numpy.argmax(data,-1))) self.assertTrue(numpy.all(i == numpy.argmax(data, -1)))
v = eval_outputs(max_and_argmax(n,-1)[0].shape) v = eval_outputs(max_and_argmax(n, -1)[0].shape)
assert v==(2) assert v == (2)
def test2b(self): def test2b(self):
data = numpy.random.rand(2,3) data = numpy.random.rand(2, 3)
n = as_tensor_variable(data) n = as_tensor_variable(data)
v,i = eval_outputs(max_and_argmax(n,0)) v, i = eval_outputs(max_and_argmax(n, 0))
self.assertTrue(numpy.all(v == numpy.max(data,0))) self.assertTrue(numpy.all(v == numpy.max(data, 0)))
self.assertTrue(numpy.all(i == numpy.argmax(data,0))) self.assertTrue(numpy.all(i == numpy.argmax(data, 0)))
v = eval_outputs(max_and_argmax(n,0)[0].shape) v = eval_outputs(max_and_argmax(n, 0)[0].shape)
assert v==(3) assert v == (3)
v = eval_outputs(max_and_argmax(n,1)[0].shape) v = eval_outputs(max_and_argmax(n, 1)[0].shape)
assert v==(2) assert v == (2)
#currently not supported #currently not supported
#v = eval_outputs(max_and_argmax(n,[0,1])[0].shape) #v = eval_outputs(max_and_argmax(n,[0,1])[0].shape)
#assert v.size==0 #assert v.size==0
def test2_invalid(self): def test2_invalid(self):
n = as_tensor_variable(numpy.random.rand(2,3)) n = as_tensor_variable(numpy.random.rand(2, 3))
# Silence expected error messages # Silence expected error messages
_logger = logging.getLogger('theano.gof.opt') _logger = logging.getLogger('theano.gof.opt')
oldlevel = _logger.level oldlevel = _logger.level
_logger.setLevel(logging.CRITICAL) _logger.setLevel(logging.CRITICAL)
try: try:
try: try:
eval_outputs(max_and_argmax(n,3)) eval_outputs(max_and_argmax(n, 3))
assert False assert False
except ValueError, e: except ValueError, e:
pass pass
finally: finally:
_logger.setLevel(oldlevel) _logger.setLevel(oldlevel)
def test2_invalid_neg(self): def test2_invalid_neg(self):
n = as_tensor_variable(numpy.random.rand(2,3)) n = as_tensor_variable(numpy.random.rand(2, 3))
old_stderr = sys.stderr old_stderr = sys.stderr
sys.stderr = StringIO.StringIO() sys.stderr = StringIO.StringIO()
try: try:
try: try:
eval_outputs(max_and_argmax(n,-3)) eval_outputs(max_and_argmax(n, -3))
assert False assert False
except ValueError, e: except ValueError, e:
pass pass
finally: finally:
sys.stderr = old_stderr sys.stderr = old_stderr
def test2_valid_neg(self): def test2_valid_neg(self):
n = as_tensor_variable(numpy.random.rand(2,3)) n = as_tensor_variable(numpy.random.rand(2, 3))
v,i = eval_outputs(max_and_argmax(n,-1)) v, i = eval_outputs(max_and_argmax(n, -1))
self.assertTrue(v.shape == (2,)) self.assertTrue(v.shape == (2,))
self.assertTrue(i.shape == (2,)) self.assertTrue(i.shape == (2,))
self.assertTrue(numpy.all(v == numpy.max(n.value,-1))) self.assertTrue(numpy.all(v == numpy.max(n.value, -1)))
self.assertTrue(numpy.all(i == numpy.argmax(n.value,-1))) self.assertTrue(numpy.all(i == numpy.argmax(n.value, -1)))
v,i = eval_outputs(max_and_argmax(n,-2)) v, i = eval_outputs(max_and_argmax(n, -2))
self.assertTrue(v.shape == (3,)) self.assertTrue(v.shape == (3,))
self.assertTrue(i.shape == (3,)) self.assertTrue(i.shape == (3,))
self.assertTrue(numpy.all(v == numpy.max(n.value,-2))) self.assertTrue(numpy.all(v == numpy.max(n.value, -2)))
self.assertTrue(numpy.all(i == numpy.argmax(n.value,-2))) self.assertTrue(numpy.all(i == numpy.argmax(n.value, -2)))
v = eval_outputs(max_and_argmax(n,-1)[0].shape) v = eval_outputs(max_and_argmax(n, -1)[0].shape)
assert v==(2) assert v == (2)
v = eval_outputs(max_and_argmax(n,-2)[0].shape) v = eval_outputs(max_and_argmax(n, -2)[0].shape)
assert v==(3) assert v == (3)
def test3(self): def test3(self):
n = as_tensor_variable(numpy.random.rand(2,3,4)) n = as_tensor_variable(numpy.random.rand(2, 3, 4))
v,i = eval_outputs(max_and_argmax(n,0)) v, i = eval_outputs(max_and_argmax(n, 0))
self.assertTrue(v.shape == (3,4)) self.assertTrue(v.shape == (3, 4))
self.assertTrue(i.shape == (3,4)) self.assertTrue(i.shape == (3, 4))
v,i = eval_outputs(max_and_argmax(n,1)) v, i = eval_outputs(max_and_argmax(n, 1))
self.assertTrue(v.shape == (2,4)) self.assertTrue(v.shape == (2, 4))
self.assertTrue(i.shape == (2,4)) self.assertTrue(i.shape == (2, 4))
v,i = eval_outputs(max_and_argmax(n,2)) v, i = eval_outputs(max_and_argmax(n, 2))
self.assertTrue(v.shape == (2,3)) self.assertTrue(v.shape == (2, 3))
self.assertTrue(i.shape == (2,3)) self.assertTrue(i.shape == (2, 3))
v = eval_outputs(max_and_argmax(n,0)[0].shape) v = eval_outputs(max_and_argmax(n, 0)[0].shape)
assert tuple(v)==(3,4) assert tuple(v) == (3, 4)
v = eval_outputs(max_and_argmax(n,1)[0].shape) v = eval_outputs(max_and_argmax(n, 1)[0].shape)
assert tuple(v)==(2,4) assert tuple(v) == (2, 4)
v = eval_outputs(max_and_argmax(n,2)[0].shape) v = eval_outputs(max_and_argmax(n, 2)[0].shape)
assert tuple(v)==(2,3) assert tuple(v) == (2, 3)
def test_grad(self): def test_grad(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_max(data, max_grad_data, axis=None): def check_grad_max(data, max_grad_data, axis=None):
...@@ -1565,35 +1568,39 @@ class T_max_and_argmax(unittest.TestCase): ...@@ -1565,35 +1568,39 @@ class T_max_and_argmax(unittest.TestCase):
Why this is needed? verify_grad is not enought? Why this is needed? verify_grad is not enought?
""" """
#This work only for axis in [0,None] #This work only for axis in [0,None]
assert axis in [0,None] assert axis in [0, None]
z = numpy.zeros_like(data) z = numpy.zeros_like(data)
z = z.flatten() z = z.flatten()
argmax=numpy.argmax(data,axis=axis) argmax = numpy.argmax(data, axis=axis)
if argmax.ndim==0: if argmax.ndim == 0:
z[argmax]+=1 z[argmax] += 1
else: else:
for id,v in enumerate(argmax): for id, v in enumerate(argmax):
z[v*numpy.prod(data.shape[data.ndim-1:axis:-1])+id]+=1 z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1])
+ id] += 1
z = z.reshape(data.shape) z = z.reshape(data.shape)
assert numpy.all(max_grad_data == z) assert numpy.all(max_grad_data == z)
#test grad of max #test grad of max
#axis is the last one #axis is the last one
utt.verify_grad(lambda v: max_and_argmax(v,axis=-1)[0], [data]) utt.verify_grad(lambda v: max_and_argmax(v, axis=-1)[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v,axis=-1)[1], [data]) utt.verify_grad(lambda v: max_and_argmax(v, axis=-1)[1], [data])
utt.verify_grad(lambda v: max_and_argmax(v,axis=[0])[0], [data]) utt.verify_grad(lambda v: max_and_argmax(v, axis=[0])[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v,axis=[0])[1], [data]) utt.verify_grad(lambda v: max_and_argmax(v, axis=[0])[1], [data])
check_grad_max(data,eval_outputs(grad(max_and_argmax(n,axis=0)[0].sum(),n)),axis=0) check_grad_max(data, eval_outputs(grad(
max_and_argmax(n, axis=0)[0].sum(), n)), axis=0)
utt.verify_grad(lambda v: max_and_argmax(v,axis=[1])[0], [data]) utt.verify_grad(lambda v: max_and_argmax(v, axis=[1])[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v,axis=[1])[1], [data]) utt.verify_grad(lambda v: max_and_argmax(v, axis=[1])[1], [data])
#check_grad_max(data,eval_outputs(grad(max_and_argmax(n,axis=1)[0],n)),axis=1) #check_grad_max(data,eval_outputs(grad(
# max_and_argmax(n,axis=1)[0],n)),axis=1)
utt.verify_grad(lambda v: max_and_argmax(v.flatten())[0], [data]) utt.verify_grad(lambda v: max_and_argmax(v.flatten())[0], [data])
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)))
# Test 4d inner dimensions # Test 4d inner dimensions
data = numpy.random.rand(2, 3, 4, 5) data = numpy.random.rand(2, 3, 4, 5)
...@@ -1608,60 +1615,60 @@ class T_argmin_argmax(unittest.TestCase): ...@@ -1608,60 +1615,60 @@ class T_argmin_argmax(unittest.TestCase):
utt.seed_rng() utt.seed_rng()
MaxAndArgmax.debug = 0 MaxAndArgmax.debug = 0
def test0(self): def test_scalar(self):
for fct in [argmin,argmax]: for fct in [argmin, argmax]:
n = as_tensor_variable(5.0) n = as_tensor_variable(5.0)
i = eval_outputs(fct(n)) i = eval_outputs(fct(n))
self.assertTrue(i == 0) self.assertTrue(i == 0)
v = eval_outputs(fct(n).shape) v = eval_outputs(fct(n).shape)
assert len(v)==0 assert len(v) == 0
def test1(self): def test_list(self):
n = as_tensor_variable([1,2,3,2,-6]) n = as_tensor_variable([1, 2, 3, 2, -6])
i = eval_outputs(argmin(n)) i = eval_outputs(argmin(n))
self.assertTrue(i == 4) self.assertTrue(i == 4)
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]) n = as_tensor_variable([1, 2, 3, 2, -6])
i = eval_outputs(argmax(n)) i = eval_outputs(argmax(n))
self.assertTrue(i == 2) self.assertTrue(i == 2)
v = eval_outputs(argmax(n).shape) v = eval_outputs(argmax(n).shape)
assert len(v)==0 assert len(v) == 0
def test2(self): def test2(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]: for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
data = numpy.random.rand(2,3) data = numpy.random.rand(2, 3)
n = as_tensor_variable(data) n = as_tensor_variable(data)
i = eval_outputs(fct(n,-1)) i = eval_outputs(fct(n, -1))
self.assertTrue(numpy.all(i == nfct(data,-1))) self.assertTrue(numpy.all(i == nfct(data, -1)))
v = eval_outputs(fct(n,-1).shape) v = eval_outputs(fct(n, -1).shape)
assert v==(2) assert v == (2)
def test2b(self): def test2b(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]: for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
data = numpy.random.rand(2,3) data = numpy.random.rand(2, 3)
n = as_tensor_variable(data) n = as_tensor_variable(data)
i = eval_outputs(fct(n,0)) i = eval_outputs(fct(n, 0))
self.assertTrue(numpy.all(i == nfct(data,0))) self.assertTrue(numpy.all(i == nfct(data, 0)))
v = eval_outputs(fct(n,0).shape) v = eval_outputs(fct(n, 0).shape)
assert v==(3) assert v == (3)
v = eval_outputs(fct(n,1).shape) v = eval_outputs(fct(n, 1).shape)
assert v==(2) assert v == (2)
#currently not supported #currently not supported
#v = eval_outputs(fct(n,[0,1]).shape) #v = eval_outputs(fct(n,[0,1]).shape)
#assert v.size==0 #assert v.size==0
def test2_invalid(self): def test2_invalid(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]: for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2,3)) n = as_tensor_variable(numpy.random.rand(2, 3))
# Silence expected error messages # Silence expected error messages
_logger = logging.getLogger('theano.gof.opt') _logger = logging.getLogger('theano.gof.opt')
oldlevel = _logger.level oldlevel = _logger.level
_logger.setLevel(logging.CRITICAL) _logger.setLevel(logging.CRITICAL)
try: try:
try: try:
eval_outputs(fct(n,3)) eval_outputs(fct(n, 3))
assert False assert False
except ValueError, e: except ValueError, e:
pass pass
...@@ -1669,13 +1676,13 @@ class T_argmin_argmax(unittest.TestCase): ...@@ -1669,13 +1676,13 @@ class T_argmin_argmax(unittest.TestCase):
_logger.setLevel(oldlevel) _logger.setLevel(oldlevel)
def test2_invalid_neg(self): def test2_invalid_neg(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]: for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2,3)) n = as_tensor_variable(numpy.random.rand(2, 3))
old_stderr = sys.stderr old_stderr = sys.stderr
sys.stderr = StringIO.StringIO() sys.stderr = StringIO.StringIO()
try: try:
try: try:
eval_outputs(fct(n,-3)) eval_outputs(fct(n, -3))
assert False assert False
except ValueError, e: except ValueError, e:
pass pass
...@@ -1683,286 +1690,297 @@ class T_argmin_argmax(unittest.TestCase): ...@@ -1683,286 +1690,297 @@ class T_argmin_argmax(unittest.TestCase):
sys.stderr = old_stderr sys.stderr = old_stderr
def test2_valid_neg(self): def test2_valid_neg(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]: for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2,3)) n = as_tensor_variable(numpy.random.rand(2, 3))
i = eval_outputs(fct(n,-1)) i = eval_outputs(fct(n, -1))
self.assertTrue(i.shape == (2,)) self.assertTrue(i.shape == (2,))
self.assertTrue(numpy.all(i == nfct(n.value,-1))) self.assertTrue(numpy.all(i == nfct(n.value, -1)))
i = eval_outputs(fct(n,-2)) i = eval_outputs(fct(n, -2))
self.assertTrue(i.shape == (3,)) self.assertTrue(i.shape == (3,))
self.assertTrue(numpy.all(i == nfct(n.value,-2))) self.assertTrue(numpy.all(i == nfct(n.value, -2)))
v = eval_outputs(fct(n,-1).shape) v = eval_outputs(fct(n, -1).shape)
assert v==(2) assert v == (2)
v = eval_outputs(fct(n,-2).shape) v = eval_outputs(fct(n, -2).shape)
assert v==(3) assert v == (3)
def test3(self): def test3(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]: for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2,3,4)) n = as_tensor_variable(numpy.random.rand(2, 3, 4))
i = eval_outputs(fct(n,0)) i = eval_outputs(fct(n, 0))
self.assertTrue(i.shape == (3,4)) self.assertTrue(i.shape == (3, 4))
self.assertTrue(numpy.all(i == nfct(n.value,0))) self.assertTrue(numpy.all(i == nfct(n.value, 0)))
i = eval_outputs(fct(n,1)) i = eval_outputs(fct(n, 1))
self.assertTrue(i.shape == (2,4)) self.assertTrue(i.shape == (2, 4))
self.assertTrue(numpy.all(i == nfct(n.value,1))) self.assertTrue(numpy.all(i == nfct(n.value, 1)))
i = eval_outputs(fct(n,2)) i = eval_outputs(fct(n, 2))
self.assertTrue(i.shape == (2,3)) self.assertTrue(i.shape == (2, 3))
self.assertTrue(numpy.all(i == nfct(n.value,2))) self.assertTrue(numpy.all(i == nfct(n.value, 2)))
v = eval_outputs(fct(n,0).shape) v = eval_outputs(fct(n, 0).shape)
assert tuple(v)==(3,4) assert tuple(v) == (3, 4)
v = eval_outputs(fct(n,1).shape) v = eval_outputs(fct(n, 1).shape)
assert tuple(v)==(2,4) assert tuple(v) == (2, 4)
v = eval_outputs(fct(n,2).shape) v = eval_outputs(fct(n, 2).shape)
assert tuple(v)==(2,3) assert tuple(v) == (2, 3)
def test_grad_argmin(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)
#test grad of argmin #test grad of argmin
utt.verify_grad(lambda v: argmin(v,axis=-1), [data]) utt.verify_grad(lambda v: argmin(v, axis=-1), [data])
utt.verify_grad(lambda v: argmin(v,axis=[0]), [data]) utt.verify_grad(lambda v: argmin(v, axis=[0]), [data])
utt.verify_grad(lambda v: argmin(v,axis=[1]), [data]) utt.verify_grad(lambda v: argmin(v, axis=[1]), [data])
utt.verify_grad(lambda v: argmin(v.flatten()), [data]) utt.verify_grad(lambda v: argmin(v.flatten()), [data])
try: try:
grad(argmin(n,axis=-1),n) grad(argmin(n, axis=-1), n)
raise Exception('Expected an error') raise Exception('Expected an error')
except TypeError: except TypeError:
pass pass
def test_grad_argmax(self): def test_grad_argmax(self):
data = numpy.random.rand(2,3) data = numpy.random.rand(2, 3)
n = as_tensor_variable(data) n = as_tensor_variable(data)
#test grad of argmax #test grad of argmax
utt.verify_grad(lambda v: argmax(v, axis=-1), [data]) utt.verify_grad(lambda v: argmax(v, axis=-1), [data])
utt.verify_grad(lambda v: argmax(v,axis=[0]), [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, axis=[1]), [data])
utt.verify_grad(lambda v: argmax(v.flatten()), [data]) utt.verify_grad(lambda v: argmax(v.flatten()), [data])
try: try:
grad(argmax(n, axis=-1),n) grad(argmax(n, axis=-1), n)
raise Exception('Expected an error') raise Exception('Expected an error')
except TypeError: except TypeError:
pass pass
class T_min_max(unittest.TestCase): class T_min_max(unittest.TestCase):
def setUp(self): def setUp(self):
utt.seed_rng() utt.seed_rng()
MaxAndArgmax.debug = 0 MaxAndArgmax.debug = 0
def test0(self): def test_scalar(self):
for fct in [max,min]: for fct in [max, min]:
n = as_tensor_variable(5.0) n = as_tensor_variable(5.0)
v = eval_outputs(fct(n)) v = eval_outputs(fct(n))
self.assertTrue(v == 5.0) self.assertTrue(v == 5.0)
v = eval_outputs(fct(n).shape) v = eval_outputs(fct(n).shape)
assert len(v)==0 assert len(v) == 0
def test1(self): def test_list(self):
for fct,nfct in [(max,numpy.max),(min,numpy.min)]: for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
n = as_tensor_variable([1,2,3,2,-6]) n = as_tensor_variable([1, 2, 3, 2, -6])
v = eval_outputs([fct(n)]) v = eval_outputs([fct(n)])
self.assertTrue(v == nfct(n.value)) self.assertTrue(v == nfct(n.value))
v = eval_outputs(fct(n).shape) v = eval_outputs(fct(n).shape)
assert len(v)==0 assert len(v) == 0
def test2(self): def test2(self):
for fct,nfct in [(max,numpy.max),(min,numpy.min)]: for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
data = numpy.random.rand(2,3) data = numpy.random.rand(2, 3)
n = as_tensor_variable(data) n = as_tensor_variable(data)
v = eval_outputs(fct(n,-1)) v = eval_outputs(fct(n, -1))
self.assertTrue(numpy.all(v == nfct(data,-1))) self.assertTrue(numpy.all(v == nfct(data, -1)))
v = eval_outputs(fct(n,-1).shape) v = eval_outputs(fct(n, -1).shape)
assert v==(2) assert v == (2)
def test2b(self): def test2b(self):
for fct,nfct in [(max,numpy.max),(min,numpy.min)]: for fct, nfct in [(max, numpy.max),(min, numpy.min)]:
data = numpy.random.rand(2,3) data = numpy.random.rand(2, 3)
n = as_tensor_variable(data) n = as_tensor_variable(data)
v = eval_outputs(fct(n,0)) v = eval_outputs(fct(n, 0))
self.assertTrue(numpy.all(v == nfct(data,0))) self.assertTrue(numpy.all(v == nfct(data, 0)))
v = eval_outputs(fct(n,0).shape) v = eval_outputs(fct(n, 0).shape)
assert v==(3) assert v == (3)
v = eval_outputs(fct(n,1).shape) v = eval_outputs(fct(n, 1).shape)
assert v==(2) assert v == (2)
v = eval_outputs(fct(n,[0,1]).shape) v = eval_outputs(fct(n, [0, 1]).shape)
assert v.size==0 assert v.size == 0
def test2_invalid(self): def test2_invalid(self):
for fct in [max,min]: for fct in [max, min]:
n = as_tensor_variable(numpy.random.rand(2,3)) n = as_tensor_variable(numpy.random.rand(2, 3))
# Silence expected error messages # Silence expected error messages
_logger = logging.getLogger('theano.gof.opt') _logger = logging.getLogger('theano.gof.opt')
oldlevel = _logger.level oldlevel = _logger.level
_logger.setLevel(logging.CRITICAL) _logger.setLevel(logging.CRITICAL)
try: try:
try: try:
eval_outputs(fct(n,3)) eval_outputs(fct(n, 3))
assert False assert False
except ValueError, e: except ValueError, e:
pass pass
finally: finally:
_logger.setLevel(oldlevel) _logger.setLevel(oldlevel)
def test2_invalid_neg(self): def test2_invalid_neg(self):
for fct in [max,min]: for fct in [max, min]:
n = as_tensor_variable(numpy.random.rand(2,3)) n = as_tensor_variable(numpy.random.rand(2, 3))
old_stderr = sys.stderr old_stderr = sys.stderr
sys.stderr = StringIO.StringIO() sys.stderr = StringIO.StringIO()
try: try:
try: try:
eval_outputs(fct(n,-3)) eval_outputs(fct(n, -3))
assert False assert False
except ValueError, e: except ValueError, e:
pass pass
finally: finally:
sys.stderr = old_stderr sys.stderr = old_stderr
def test2_valid_neg(self): def test2_valid_neg(self):
for fct,nfct in [(max,numpy.max),(min,numpy.min)]: for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
n = as_tensor_variable(numpy.random.rand(2,3)) n = as_tensor_variable(numpy.random.rand(2, 3))
v = eval_outputs(fct(n,-1)) v = eval_outputs(fct(n, -1))
self.assertTrue(v.shape == (2,)) self.assertTrue(v.shape == (2,))
self.assertTrue(numpy.all(v == nfct(n.value,-1))) self.assertTrue(numpy.all(v == nfct(n.value, -1)))
v = eval_outputs(fct(n,-2)) v = eval_outputs(fct(n, -2))
self.assertTrue(v.shape == (3,)) self.assertTrue(v.shape == (3,))
self.assertTrue(numpy.all(v == nfct(n.value,-2))) self.assertTrue(numpy.all(v == nfct(n.value, -2)))
v = eval_outputs(fct(n,-1).shape) v = eval_outputs(fct(n, -1).shape)
assert v==(2) assert v == (2)
v = eval_outputs(fct(n,-2).shape) v = eval_outputs(fct(n, -2).shape)
assert v==(3) assert v == (3)
def test3(self): def test3(self):
for fct,nfct in [(max,numpy.max),(min,numpy.min)]: for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
n = as_tensor_variable(numpy.random.rand(2,3,4)) n = as_tensor_variable(numpy.random.rand(2, 3, 4))
v = eval_outputs(fct(n,0)) v = eval_outputs(fct(n, 0))
self.assertTrue(v.shape == (3,4)) self.assertTrue(v.shape == (3, 4))
self.assertTrue(numpy.all(v == nfct(n.value,0))) self.assertTrue(numpy.all(v == nfct(n.value, 0)))
v = eval_outputs(fct(n,1)) v = eval_outputs(fct(n, 1))
self.assertTrue(v.shape == (2,4)) self.assertTrue(v.shape == (2, 4))
self.assertTrue(numpy.all(v == nfct(n.value,1))) self.assertTrue(numpy.all(v == nfct(n.value, 1)))
v = eval_outputs(fct(n,2)) v = eval_outputs(fct(n, 2))
self.assertTrue(v.shape == (2,3)) self.assertTrue(v.shape == (2, 3))
self.assertTrue(numpy.all(v == nfct(n.value,2))) self.assertTrue(numpy.all(v == nfct(n.value, 2)))
v = eval_outputs(fct(n,[0,1])) v = eval_outputs(fct(n, [0, 1]))
self.assertTrue(v.shape == (4,)) self.assertTrue(v.shape == (4,))
self.assertTrue(numpy.all(v == nfct(nfct(n.value,1),0))) self.assertTrue(numpy.all(v == nfct(nfct(n.value, 1), 0)))
v = eval_outputs(fct(n,[0,2])) v = eval_outputs(fct(n, [0, 2]))
self.assertTrue(v.shape == (3,)) self.assertTrue(v.shape == (3,))
self.assertTrue(numpy.all(v == nfct(nfct(n.value,2),0))) self.assertTrue(numpy.all(v == nfct(nfct(n.value, 2), 0)))
v = eval_outputs(fct(n,[1,2])) v = eval_outputs(fct(n, [1, 2]))
self.assertTrue(v.shape == (2,)) self.assertTrue(v.shape == (2,))
self.assertTrue(numpy.all(v == nfct(nfct(n.value,2),1))) self.assertTrue(numpy.all(v == nfct(nfct(n.value, 2), 1)))
v = eval_outputs(fct(n,[0,1,2])) v = eval_outputs(fct(n, [0, 1, 2]))
self.assertTrue(v.shape == ()) self.assertTrue(v.shape == ())
v = eval_outputs(fct(n,0).shape) v = eval_outputs(fct(n, 0).shape)
assert tuple(v)==(3,4) assert tuple(v) == (3, 4)
v = eval_outputs(fct(n,1).shape) v = eval_outputs(fct(n, 1).shape)
assert tuple(v)==(2,4) assert tuple(v) == (2, 4)
v = eval_outputs(fct(n,2).shape) v = eval_outputs(fct(n, 2).shape)
assert tuple(v)==(2,3) assert tuple(v) == (2, 3)
v = eval_outputs(fct(n,[0,1]).shape) v = eval_outputs(fct(n, [0, 1]).shape)
self.assertTrue(v == (4,)) self.assertTrue(v == (4,))
v = eval_outputs(fct(n,[0,2]).shape) v = eval_outputs(fct(n, [0, 2]).shape)
self.assertTrue(v == (3,)) self.assertTrue(v == (3,))
v = eval_outputs(fct(n,[1,2]).shape) v = eval_outputs(fct(n, [1, 2]).shape)
self.assertTrue(v == (2,)) self.assertTrue(v == (2,))
v = eval_outputs(fct(n,[0,1,2]).shape) v = eval_outputs(fct(n, [0, 1, 2]).shape)
self.assertTrue(v.size == 0) self.assertTrue(v.size == 0)
def test_grad_max(self): def test_grad_max(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_max(data, max_grad_data, axis=None): def check_grad_max(data, max_grad_data, axis=None):
#This work only for axis in [0,None] #This work only for axis in [0,None]
assert axis in [0,None] assert axis in [0, None]
z = numpy.zeros_like(data) z = numpy.zeros_like(data)
z = z.flatten() z = z.flatten()
argmax=numpy.argmax(data,axis=axis) argmax = numpy.argmax(data, axis=axis)
if argmax.ndim==0: if argmax.ndim == 0:
z[numpy.argmax(data,axis=axis)]+=1 z[numpy.argmax(data, axis=axis)] += 1
else: else:
for id,v in enumerate(argmax): for id, v in enumerate(argmax):
z[v*numpy.prod(data.shape[data.ndim-1:axis:-1])+id]+=1 z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1])
+ id] += 1
z = z.reshape(data.shape) z = z.reshape(data.shape)
assert numpy.all(max_grad_data == z) assert numpy.all(max_grad_data == z)
#test grad of max #test grad of max
#axis is the last one #axis is the last one
utt.verify_grad(lambda v: max(v,axis=-1), [data]) utt.verify_grad(lambda v: max(v, axis=-1), [data])
utt.verify_grad(lambda v: max(v,axis=[0]), [data]) utt.verify_grad(lambda v: max(v, axis=[0]), [data])
check_grad_max(data,eval_outputs(grad(max(n,axis=0).sum(),n)),axis=0) check_grad_max(data, eval_outputs(grad(max(n, axis=0).sum(), n)),
axis=0)
utt.verify_grad(lambda v: max(v,axis=[1]), [data]) utt.verify_grad(lambda v: max(v, axis=[1]), [data])
#check_grad_max(data,eval_outputs(grad(max(n,axis=1),n)),axis=1) #check_grad_max(data,eval_outputs(grad(max(n,axis=1),n)),axis=1)
utt.verify_grad(lambda v: max(v.flatten()), [data]) utt.verify_grad(lambda v: max(v.flatten()), [data])
check_grad_max(data,eval_outputs(grad(max(n.flatten()),n))) check_grad_max(data, eval_outputs(grad(max(n.flatten()), n)))
def test_grad_min(self): def test_grad_min(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): def check_grad_min(data, min_grad_data, axis=None):
#This work only for axis in [0,None] #This work only for axis in [0, None]
assert axis in [0,None] assert axis in [0, None]
z = numpy.zeros_like(data) z = numpy.zeros_like(data)
z = z.flatten() z = z.flatten()
argmin=numpy.argmin(data,axis=axis) argmin = numpy.argmin(data, axis=axis)
if argmin.ndim==0: if argmin.ndim == 0:
z[numpy.argmin(data,axis=axis)]+=1 z[numpy.argmin(data, axis=axis)] += 1
else: else:
for id,v in enumerate(argmin): for id, v in enumerate(argmin):
z[v*numpy.prod(data.shape[data.ndim-1:axis:-1])+id]+=1 z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1])
+ id] += 1
z = z.reshape(data.shape) z = z.reshape(data.shape)
assert numpy.all(min_grad_data == z) assert numpy.all(min_grad_data == z)
#test grad of min #test grad of min
#axis is the last one #axis is the last one
utt.verify_grad(lambda v: min(v,axis=-1), [data]) utt.verify_grad(lambda v: min(v, axis=-1), [data])
utt.verify_grad(lambda v: min(v,axis=[0]), [data]) utt.verify_grad(lambda v: min(v, axis=[0]), [data])
check_grad_min(data,eval_outputs(grad(min(n,axis=0).sum(),n)),axis=0) check_grad_min(data, eval_outputs(grad(min(n, axis=0).sum(), n)),
axis=0)
utt.verify_grad(lambda v: min(v,axis=[1]), [data]) utt.verify_grad(lambda v: min(v, axis=[1]), [data])
#check_grad_min(data,eval_outputs(grad(min(n,axis=1),n)),axis=1) #check_grad_min(data,eval_outputs(grad(min(n,axis=1),n)),axis=1)
utt.verify_grad(lambda v: min(v.flatten()), [data]) utt.verify_grad(lambda v: min(v.flatten()), [data])
check_grad_min(data,eval_outputs(grad(min(n.flatten()),n))) check_grad_min(data, eval_outputs(grad(min(n.flatten()), n)))
def _grad_list(self): def _grad_list(self):
""" """
Test the gradient when we have multiple axis at the same time. Test the gradient when we have multiple axis at the same time.
This not implemented, so we disable the test. See ticket: http://trac-hg.assembla.com/theano/ticket/511 This not implemented, so we disable the test. See ticket:
http://trac-hg.assembla.com/theano/ticket/511
""" """
data = numpy.random.rand(2,3) data = numpy.random.rand(2, 3)
n = as_tensor_variable(data) n = as_tensor_variable(data)
for fct in [max_and_argmax,max,min]: for fct in [max_and_argmax, max, min]:
utt.verify_grad(lambda v: fct(v,axis=[0,1]), [data]) utt.verify_grad(lambda v: fct(v, axis=[0, 1]), [data])
#check_grad_max(data,eval_outputs(grad(max_and_argmax(n,axis=1)[0],n)),axis=1) #check_grad_max(data, eval_outputs(grad(max_and_argmax(n,
#axis=1)[0], n)),axis=1)
class T_subtensor(unittest.TestCase): class T_subtensor(unittest.TestCase):
""" """
This is build in a way that allow to reuse it to test the equivalent gpu op. This is build in a way that allow to reuse it to test the
equivalent gpu op.
""" """
def __init__(self, name, shared=_shared, def __init__(self, name, shared=_shared,
sub=tensor.Subtensor, sub=tensor.Subtensor,
......
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