def test_dot():
print >>sys.stdout, 'starting test_dot'
utt.seed_rng()
rng = numpy.random.RandomState(utt.fetch_seed())
a0 = theano._asarray(rng.randn(4, 7), dtype='float32')
a1 = theano._asarray(rng.randn(7, 6), dtype='float32')
b0 = cuda_ndarray.CudaNdarray(a0)
b1 = cuda_ndarray.CudaNdarray(a1)
assert _allclose(numpy.dot(a0, a1), cuda_ndarray.dot(b0, b1))
a1 = theano._asarray(rng.randn(6, 7), dtype='float32')
b1 = cuda_ndarray.CudaNdarray(a1)
numpy_version = numpy.dot(a0, a1.T)
transposed = cuda_ndarray.dimshuffle(b1,(1,0))
cuda_version = cuda_ndarray.dot(b0, transposed)
assert _allclose(numpy_version, cuda_version)
a1 = theano._asarray(rng.randn(7, 6), dtype='float32')
b1 = cuda_ndarray.CudaNdarray(a1)
a0 = theano._asarray(rng.randn(7, 4), dtype='float32')
b0 = cuda_ndarray.CudaNdarray(a0)
assert _allclose(numpy.dot(a0.T, a1),
cuda_ndarray.dot(cuda_ndarray.dimshuffle(b0,(1,0)), b1))
a1 = theano._asarray(rng.randn(6, 7), dtype='float32')
b1 = cuda_ndarray.CudaNdarray(a1)
assert _allclose(numpy.dot(a0.T, a1.T),
cuda_ndarray.dot(cuda_ndarray.dimshuffle(b0,(1,0)),
cuda_ndarray.dimshuffle(b1,(1,0))))
def test_dimshuffle(self):
utt.seed_rng()
rng = numpy.random.RandomState(utt.fetch_seed())
# 2d -> 0d
a = theano._asarray(rng.randn(1,1), dtype='float32')
b = cuda_ndarray.CudaNdarray(a)
assert numpy.allclose(numpy.transpose(a), cuda_ndarray.dimshuffle(b,()))
# Test when we drop a axis that don't have shape 1
a = theano._asarray(rng.randn(2,1), dtype='float32')
b = cuda_ndarray.CudaNdarray(a)
self.assertRaises(ValueError, cuda_ndarray.dimshuffle, b,())
# Test that we can't take a dimensions multiple time
a = theano._asarray(rng.randn(2,1), dtype='float32')
b = cuda_ndarray.CudaNdarray(a)
self.assertRaises(ValueError, cuda_ndarray.dimshuffle, b,(1,1))
# 1d
a = theano._asarray(rng.randn(3,), dtype='float32')
b = cuda_ndarray.CudaNdarray(a)
assert numpy.allclose(numpy.transpose(a), cuda_ndarray.dimshuffle(b,(0,)))
assert numpy.allclose(a[None,:,None], cuda_ndarray.dimshuffle(b,(-1,0,-1)))
# 2d
a = theano._asarray(rng.randn(3,11), dtype='float32')
b = cuda_ndarray.CudaNdarray(a)
assert numpy.allclose(numpy.transpose(a), cuda_ndarray.dimshuffle(b,(1,0)))
assert numpy.allclose(numpy.transpose(a)[None,:,None,:,None], cuda_ndarray.dimshuffle(b,(-1,1,-1,0,-1)))
# 2d -> 1d
a = theano._asarray(rng.randn(1,11), dtype='float32')
b = cuda_ndarray.CudaNdarray(a)
assert numpy.allclose(a[:,], cuda_ndarray.dimshuffle(b,(1,)))
a = theano._asarray(rng.randn(11,1), dtype='float32')
b = cuda_ndarray.CudaNdarray(a)
assert numpy.allclose(a.reshape((11,)), cuda_ndarray.dimshuffle(b,(0,)))
# 3d
a = theano._asarray(rng.randn(3,4,5), dtype='float32')
b = cuda_ndarray.CudaNdarray(a)
assert numpy.allclose(a, cuda_ndarray.dimshuffle(b,(0,1,2)))
assert numpy.allclose(numpy.swapaxes(a,0,1), cuda_ndarray.dimshuffle(b,(1,0,2)))
assert numpy.allclose(numpy.swapaxes(a,0,2), cuda_ndarray.dimshuffle(b,(2,1,0)))
assert numpy.allclose(numpy.swapaxes(a,1,2), cuda_ndarray.dimshuffle(b,(0,2,1)))
assert numpy.allclose(numpy.swapaxes(a,1,2)[None,:,None,:,:,None], cuda_ndarray.dimshuffle(b,(-1,0,-1,2,1,-1)))
# 4d
a = theano._asarray(rng.randn(3,11,4,5), dtype='float32')
b = cuda_ndarray.CudaNdarray(a)
assert numpy.allclose(numpy.swapaxes(a,0,1), cuda_ndarray.dimshuffle(b,(1,0,2,3)))
assert numpy.allclose(numpy.swapaxes(a,0,2), cuda_ndarray.dimshuffle(b,(2,1,0,3)))
assert numpy.allclose(numpy.swapaxes(a,0,3), cuda_ndarray.dimshuffle(b,(3,1,2,0)))
assert numpy.allclose(numpy.swapaxes(a,0,3), cuda_ndarray.dimshuffle(b,(3,1,2,0)))
assert numpy.allclose(numpy.swapaxes(a,0,3)[None,:,None,:,:,:], cuda_ndarray.dimshuffle(b,(-1,3,-1,1,2,0)))
def transpose(cuda_mat):
# The easiest way to transpose a cuda matrix for now
return tcn.dimshuffle(cuda_mat, [1, 0])
