def test_xcov_cputensor():
np.random.seed(0)
n = 10
k = 8
(k1, k2) = (3, 5)
a = np.array(np.random.randn(k, n) * 10, dtype='float32', order='C')
acc = xcc(a[:k1], a[k1:])
expected_result = 0.5 * (acc**2.).sum()
be = CPU(rng_seed=0)
outputs = CPUTensor(a.copy())
tempbuf1 = be.empty((k1, n))
tempbuf2 = be.empty((k2, n))
tempbuf3 = be.empty((k1, k2))
tempbuf4 = be.empty(outputs.shape)
temp = [tempbuf1, tempbuf2, tempbuf3, tempbuf4]
my_result = xcov_cost(be, outputs, [], temp, k1)
assert_tensor_near_equal(expected_result, my_result)
def test_xcov_cputensor():
np.random.seed(0)
n = 10
k = 8
(k1, k2) = (3, 5)
a = np.array(np.random.randn(k, n)*10, dtype='float32', order='C')
acc = xcc(a[:k1], a[k1:])
expected_result = 0.5 * (acc**2.).sum()
be = CPU(rng_seed=0)
outputs = CPUTensor(a.copy())
tempbuf1 = be.empty((k1, n))
tempbuf2 = be.empty((k2, n))
tempbuf3 = be.empty((k1, k2))
tempbuf4 = be.empty(outputs.shape)
temp = [tempbuf1, tempbuf2, tempbuf3, tempbuf4]
my_result = xcov_cost(be, outputs, [], temp, k1)
assert_tensor_near_equal(expected_result, my_result)
def test_xcov_cc2tensor():
np.random.seed(0)
n = 10
k = 8
(k1, k2) = (3, 5)
a = np.array(np.random.randn(k, n) * 10, dtype='float32', order='C')
acc = xcc(a[:k1], a[k1:])
expected_result = 0.5 * (acc**2.).sum()
from neon.backends.cc2 import GPU, GPUTensor
be = GPU(rng_seed=0) # to ensure cublas_init() is called.
outputs = GPUTensor(a.copy())
tempbuf1 = be.empty((k1, n))
tempbuf2 = be.empty((k2, n))
tempbuf3 = be.empty((k1, k2))
tempbuf4 = be.empty(outputs.shape)
temp = [tempbuf1, tempbuf2, tempbuf3, tempbuf4]
my_result = xcov_cost(be, outputs, [], temp, k1)
assert_tensor_near_equal(expected_result, my_result, tolerance=1e-3)
def test_xcov_cc2tensor():
np.random.seed(0)
n = 10
k = 8
(k1, k2) = (3, 5)
a = np.array(np.random.randn(k, n)*10, dtype='float32', order='C')
acc = xcc(a[:k1], a[k1:])
expected_result = 0.5 * (acc**2.).sum()
from neon.backends.cc2 import GPU, GPUTensor
be = GPU(rng_seed=0) # to ensure cublas_init() is called.
outputs = GPUTensor(a.copy())
tempbuf1 = be.empty((k1, n))
tempbuf2 = be.empty((k2, n))
tempbuf3 = be.empty((k1, k2))
tempbuf4 = be.empty(outputs.shape)
temp = [tempbuf1, tempbuf2, tempbuf3, tempbuf4]
my_result = xcov_cost(be, outputs, [], temp, k1)
assert_tensor_near_equal(expected_result, my_result, tolerance=1e-3)
