def run_gpu_svd(self, A_val, full_matrices=True, compute_uv=True):
A = theano.tensor.fmatrix("A")
f = theano.function(
[A],
gpu_svd(A, full_matrices=full_matrices, compute_uv=compute_uv),
mode=mode_with_gpu,
)
return f(A_val)
def test_gpu_singular_values(self):
A = theano.tensor.fmatrix("A")
f_cpu = theano.function(
[A], theano.tensor.nlinalg.svd(A, compute_uv=False), mode=mode_without_gpu
)
f_gpu = theano.function([A], gpu_svd(A, compute_uv=False), mode=mode_with_gpu)
A_val = rand(50, 100).astype("float32")
utt.assert_allclose(f_cpu(A_val), f_gpu(A_val))
A_val = rand(100, 50).astype("float32")
utt.assert_allclose(f_cpu(A_val), f_gpu(A_val))
def test_gpu_singular_values(self):
A = theano.tensor.fmatrix("A")
f_cpu = theano.function(
[A], theano.tensor.nlinalg.svd(A, compute_uv=False),
mode=mode_without_gpu)
f_gpu = theano.function(
[A], gpu_svd(A, compute_uv=False), mode=mode_with_gpu)
A_val = rand(50, 100).astype('float32')
utt.assert_allclose(f_cpu(A_val), f_gpu(A_val))
A_val = rand(100, 50).astype('float32')
utt.assert_allclose(f_cpu(A_val), f_gpu(A_val))
def run_gpu_svd(self, A_val, full_matrices=True, compute_uv=True):
A = theano.tensor.fmatrix("A")
f = theano.function(
[A], gpu_svd(A, full_matrices=full_matrices, compute_uv=compute_uv),
mode=mode_with_gpu)
return f(A_val)