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提交 31661605 authored 作者: Frederic's avatar Frederic
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fix and make test run in floatX.

上级 e751d4f3
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正在显示 包含 31 行增加 和 31 行删除
theano/tensor/tests/test_extra_ops.py
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......@@ -5,7 +5,7 @@ import theano
from theano.tests import unittest_tools as utt
from theano.tensor.extra_ops import *
from theano import tensor as T
from theano import tensor, function, scalar
from theano import config, tensor, function, scalar
class TestBinCountOp(utt.InferShapeTester):
......@@ -16,9 +16,9 @@ class TestBinCountOp(utt.InferShapeTester):
def test_bincountOp(self):
x = T.lvector('x')
w = T.dvector('w')
w = T.vector('w')
a = np.random.random_integers(50, size=(25))
weights = np.random.random((25,))
weights = np.random.random((25,)).astype(config.floatX)
f1 = theano.function([x], bincount(x))
f2 = theano.function([x, w], bincount(x, weights=w))
......@@ -38,7 +38,7 @@ class TestBinCountOp(utt.InferShapeTester):
[np.random.random_integers(50, size=(25,))],
self.op_class)
weights = np.random.random((25,))
weights = np.random.random((25,)).astype(config.floatX)
self._compile_and_check([x],
[bincount(x, weights=weights)],
[np.random.random_integers(50, size=(25,))],
......@@ -64,8 +64,8 @@ class TestDiffOp(utt.InferShapeTester):
self.op = DiffOp()
def test_diffOp(self):
x = T.dmatrix('x')
a = np.random.random((30, 50))
x = T.matrix('x')
a = np.random.random((30, 50)).astype(config.floatX)
f = theano.function([x], diff(x))
assert np.allclose(np.diff(a), f(a))
......@@ -76,8 +76,8 @@ class TestDiffOp(utt.InferShapeTester):
assert np.allclose(np.diff(a, n=k, axis=axis), g(a))
def test_infer_shape(self):
x = T.dmatrix('x')
a = np.random.random((30, 50))
x = T.matrix('x')
a = np.random.random((30, 50)).astype(config.floatX)
self._compile_and_check([x],
[self.op(x)],
......@@ -93,14 +93,14 @@ class TestDiffOp(utt.InferShapeTester):
def test_grad(self):
x = T.vector('x')
a = np.random.random(50)
a = np.random.random(50).astype(config.floatX)
gf = theano.function([x], T.grad(T.sum(diff(x)), x))
utt.verify_grad(self.op, [a])
for k in range(TestDiffOp.nb):
dg = theano.function([x], T.grad(T.sum(diff(x, n=k)), x))
utt.verify_grad(DiffOp(n=k), [a])
utt.verify_grad(DiffOp(n=k), [a], eps=7e-3)
class TestSqueezeOp(utt.InferShapeTester):
......@@ -110,27 +110,27 @@ class TestSqueezeOp(utt.InferShapeTester):
self.op = SqueezeOp(out_nd=1)
def test_squeezeOp(self):
x = T.dmatrix('x')
a = np.random.random((1, 50))
x = T.matrix('x')
a = np.random.random((1, 50)).astype(config.floatX)
f = theano.function([x], squeeze(x, out_nd=1))
assert np.allclose(np.squeeze(a), f(a))
x = T.dtensor4('x')
x = T.tensor4('x')
f = theano.function([x], squeeze(x, out_nd=2))
a = np.random.random((1, 1, 2, 3))
a = np.random.random((1, 1, 2, 3)).astype(config.floatX)
assert np.allclose(np.squeeze(a), f(a))
a = np.random.random((1, 2, 2, 1))
a = np.random.random((1, 2, 2, 1)).astype(config.floatX)
assert np.allclose(np.squeeze(a), f(a))
a = np.random.random((4, 1, 2, 1))
a = np.random.random((4, 1, 2, 1)).astype(config.floatX)
assert np.allclose(np.squeeze(a), f(a))
def test_grad(self):
x = T.dtensor4('x')
a = np.random.random((1, 1, 3, 4))
x = T.tensor4('x')
a = np.random.random((1, 1, 3, 4)).astype(config.floatX)
gf = theano.function([x], T.grad(T.sum(squeeze(x, out_nd=1)), x))
utt.verify_grad(SqueezeOp(out_nd=2), [a])
......@@ -147,8 +147,8 @@ class TestRepeatOp(utt.InferShapeTester):
def test_repeatOp(self):
for ndim in range(3):
x = T.TensorType(theano.config.floatX, [False] * ndim)()
a = np.random.random((10, ) * ndim)
x = T.TensorType(config.floatX, [False] * ndim)()
a = np.random.random((10, ) * ndim).astype(config.floatX)
for axis in self._possible_axis(ndim):
r_var = T.lscalar()
......@@ -167,8 +167,8 @@ class TestRepeatOp(utt.InferShapeTester):
def test_infer_shape(self):
for ndim in range(4):
x = T.TensorType(theano.config.floatX, [False] * ndim)()
a = np.random.random((10, ) * ndim)
x = T.TensorType(config.floatX, [False] * ndim)()
a = np.random.random((10, ) * ndim).astype(config.floatX)
for axis in self._possible_axis(ndim):
r_var = T.lscalar()
......@@ -191,7 +191,7 @@ class TestRepeatOp(utt.InferShapeTester):
def test_grad(self):
for ndim in range(3):
a = np.random.random((10, ) * ndim)
a = np.random.random((10, ) * ndim).astype(config.floatX)
for axis in self._possible_axis(ndim):
utt.verify_grad(lambda x: RepeatOp(axis=axis)(x, 3), [a])
......@@ -240,23 +240,23 @@ class TestFillDiagonal(utt.InferShapeTester):
self.op = fill_diagonal
def test_perform(self):
x = tensor.dmatrix()
y = tensor.dscalar()
x = tensor.matrix()
y = tensor.scalar()
f = function([x, y], fill_diagonal(x, y))
for shp in [(8, 8), (5, 8), (8, 5)]:
a = numpy.random.rand(*shp)
val = numpy.random.rand()
a = numpy.random.rand(*shp).astype(config.floatX)
val = numpy.cast[config.floatX](numpy.random.rand())
out = f(a, val)
# We can't use numpy.fill_diagonal as it is bugged.
assert numpy.allclose(numpy.diag(out), val)
assert (out == val).sum() == min(a.shape)
# test for 3d tensor
a = numpy.random.rand(3, 3, 3)
x = tensor.dtensor3()
y = tensor.dscalar()
a = numpy.random.rand(3, 3, 3).astype(config.floatX)
x = tensor.tensor3()
y = tensor.scalar()
f = function([x, y], fill_diagonal(x, y))
val = numpy.random.rand() + 10
val = numpy.cast[config.floatX](numpy.random.rand() + 10)
out = f(a, val)
# We can't use numpy.fill_diagonal as it is bugged.
assert out[0, 0, 0] == val
......
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