def test_xcov_derivative_cc2tensor():
from neon.backends.cc2 import GPU, GPUTensor
be = GPU(rng_seed=0)
np.random.seed(0)
n = 10
k = 8
(k1, k2) = (3, 5)
a = np.array(np.random.randn(k, n), dtype='float32', order='C')
s = np.zeros_like(a)
acc = xcc(a[:k1], a[k1:]) # k1 x k2
c1 = a[k1:] - a[k1:].mean(1, keepdims=True) # k2 x n
c2 = a[:k1] - a[:k1].mean(1, keepdims=True) # k1 x n
s[:k1] = acc.dot(c1) / n
s[k1:] = acc.T.dot(c2) / n
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_derivative(be, outputs, [], temp, k1)
expected_result = GPUTensor(s)
assert_tensor_near_equal(expected_result, my_result)
def test_cudanet_negative(self):
self.layer.positive(self.inputs)
self.layer.negative(self.inputs)
target = np.array([0.50274211, 0.50407821],
dtype='float32')
assert_tensor_near_equal(self.layer.p_hid_minus.asnumpyarray()[:, 0],
target)
def test_xcov_derivative_cc2tensor():
from neon.backends.cc2 import GPU, GPUTensor
be = GPU(rng_seed=0)
np.random.seed(0)
n = 10
k = 8
(k1, k2) = (3, 5)
a = np.array(np.random.randn(k, n), dtype='float32', order='C')
s = np.zeros_like(a)
acc = xcc(a[:k1], a[k1:]) # k1 x k2
c1 = a[k1:] - a[k1:].mean(1, keepdims=True) # k2 x n
c2 = a[:k1] - a[:k1].mean(1, keepdims=True) # k1 x n
s[:k1] = acc.dot(c1)/n
s[k1:] = acc.T.dot(c2)/n
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_derivative(be, outputs, [], temp, k1)
expected_result = GPUTensor(s)
assert_tensor_near_equal(expected_result, my_result)
def test_softmax_cputensor():
sftmx = Softmax()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
temp = be.zeros((3, 1))
outputs = np.exp(inputs-1) / np.sum(np.exp(inputs-1))
sftmx.apply_function(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_softmax_cputensor():
sftmx = Softmax()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
temp = be.zeros((3, 1))
outputs = np.exp(inputs - 1) / np.sum(np.exp(inputs - 1))
sftmx.apply_function(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_logistic_cputensor():
lgstc = Logistic()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
temp = be.zeros((3, 1))
outputs = 1.0 / (1.0 + np.exp(-inputs))
lgstc.apply_function(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_logistic_derivative_cputensor():
lgstc = Logistic()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
outputs = 1.0 / (1.0 + np.exp(-inputs))
outputs = outputs * (1.0 - outputs)
temp = be.zeros(inputs.shape)
lgstc.apply_derivative(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_cross_entropy_derivative_cputensor():
be = CPU(rng_seed=0)
outputs = CPUTensor([0.5, 0.9, 0.1, 0.0001])
targets = CPUTensor([0.5, 0.99, 0.01, 0.2])
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = ((outputs.asnumpyarray() - targets.asnumpyarray()) /
(outputs.asnumpyarray() * (1 - outputs.asnumpyarray())))
assert_tensor_near_equal(
expected_result, cross_entropy_derivative(be, outputs, targets, temp))
def test_tanh_cc2tensor():
tntest = Tanh()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = GPUTensor([true_tanh(0), true_tanh(1), true_tanh(-2)])
be = GPU(rng_seed=0)
temp = be.zeros((3, 1))
tntest.apply_function(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(outputs, temp)
def test_tanh_cputensor():
tntest = Tanh()
be = CPU(rng_seed=0)
CPUTensor([0, 1, -2])
inputs = np.array([0, 1, -2])
temp = be.zeros(inputs.shape)
outputs = np.array([true_tanh(0), true_tanh(1), true_tanh(-2)])
tntest.apply_function(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_tanh_derivative_cputensor():
tntest = Tanh()
inputs = np.array([0, 1, -2])
be = CPU(rng_seed=0)
outputs = np.array(
[1 - true_tanh(0)**2, 1 - true_tanh(1)**2, 1 - true_tanh(-2)**2])
temp = be.zeros(inputs.shape)
tntest.apply_derivative(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_logistic_cc2tensor():
lgstc = Logistic()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = 1.0 / (1.0 + np.exp(-inputs))
be = GPU(rng_seed=0)
temp = be.zeros((3, 1))
lgstc.apply_function(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(GPUTensor(outputs), temp)
def test_softmax_cc2tensor():
sftmx = Softmax()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
be = GPU(rng_seed=0)
temp = be.zeros((3, 1))
sftmx.apply_function(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(GPUTensor(outputs), temp)
def test_tanh_cc2tensor():
tntest = Tanh()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = GPUTensor([true_tanh(0), true_tanh(1), true_tanh(-2)])
be = GPU(rng_seed=0)
temp = be.zeros((3, 1))
tntest.apply_function(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(outputs, temp)
def test_tanh_cputensor():
tntest = Tanh()
be = CPU(rng_seed=0)
CPUTensor([0, 1, -2])
inputs = np.array([0, 1, -2])
temp = be.zeros(inputs.shape)
outputs = np.array([true_tanh(0), true_tanh(1), true_tanh(-2)])
tntest.apply_function(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_softmax_cc2tensor():
sftmx = Softmax()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
be = GPU(rng_seed=0)
temp = be.zeros((3, 1))
sftmx.apply_function(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(GPUTensor(outputs), temp)
def test_tanh_derivative_cc2tensor():
tntest = Tanh()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2], dtype='float32').reshape((3, 1))
be = GPU(rng_seed=0)
outputs = GPUTensor(
[1 - true_tanh(0)**2, 1 - true_tanh(1)**2, 1 - true_tanh(-2)**2])
temp = be.zeros(inputs.shape)
tntest.apply_derivative(be, GPUTensor(inputs, dtype='float32'), temp)
assert_tensor_near_equal(outputs, temp, tolerance=1e-5)
def test_tanh_derivative_cputensor():
tntest = Tanh()
inputs = np.array([0, 1, -2])
be = CPU(rng_seed=0)
outputs = np.array([1 - true_tanh(0) ** 2,
1 - true_tanh(1) ** 2,
1 - true_tanh(-2) ** 2])
temp = be.zeros(inputs.shape)
tntest.apply_derivative(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_lrgnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 5
# -> sum([[1, 0], [1, 243]], axis=0)**rpow -> rpow([2, 243])
assert_tensor_equal(self.be.norm(tsr, order=5, axis=0),
CPUTensor([[2**rpow, 243**rpow]]))
# -> sum([[1, 0], [1, 243]], axis=1)**rpow -> rpow([1, 244])
# 244**.2 == ~3.002465 hence the near_equal test
assert_tensor_near_equal(self.be.norm(tsr, order=5, axis=1),
CPUTensor([1**rpow, 244**rpow]), 1e-6)
def test_cross_entropy_derivative_cputensor():
be = CPU(rng_seed=0)
outputs = CPUTensor([0.5, 0.9, 0.1, 0.0001])
targets = CPUTensor([0.5, 0.99, 0.01, 0.2])
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = ((outputs.asnumpyarray() - targets.asnumpyarray()) /
(outputs.asnumpyarray() * (1 - outputs.asnumpyarray())))
assert_tensor_near_equal(expected_result,
cross_entropy_derivative(be, outputs,
targets, temp))
def test_lrgnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 5
# -> sum([[1, 0], [1, 243]], axis=0)**rpow -> rpow([2, 243])
assert_tensor_equal(self.be.norm(tsr, order=5, axis=0),
CPUTensor([[2**rpow, 243**rpow]]))
# -> sum([[1, 0], [1, 243]], axis=1)**rpow -> rpow([1, 244])
# 244**.2 == ~3.002465 hence the near_equal test
assert_tensor_near_equal(self.be.norm(tsr, order=5, axis=1),
CPUTensor([1**rpow, 244**rpow]), 1e-6)
def test_negnorm(self):
tsr = self.be.array([[-1, -2], [1, 3]])
rpow = -1. / 3
# -> sum([[1, .125], [1, .037037]], axis=0)**rpow -> rpow([2, .162037])
assert_tensor_equal(self.be.norm(tsr, order=-3, axis=0),
CPUTensor([[2**rpow, .162037037037**rpow]]))
# -> sum([[1, .125], [1, .037037]], axis=1)**rpow ->
# rpow([1.125, 1.037037])
assert_tensor_near_equal(self.be.norm(tsr, order=-3, axis=1),
CPUTensor([1.125**rpow, 1.037037**rpow]),
1e-6)
def test_cross_entropy_cputensor():
be = CPU(rng_seed=0)
outputs = CPUTensor([0.5, 0.9, 0.1, 0.0001])
targets = CPUTensor([0.5, 0.99, 0.01, 0.2])
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = np.sum(
(-targets.asnumpyarray()) * np.log(outputs.asnumpyarray()) -
(1 - targets.asnumpyarray()) * np.log(1 - outputs.asnumpyarray()),
keepdims=True)
assert_tensor_near_equal(expected_result,
cross_entropy(be, outputs, targets, temp))
def test_softmax_derivative_cputensor():
sftmx = Softmax()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
errmat = np.ones(inputs.shape)
a = np.einsum('ij,ji->i', errmat.T, outputs)
outputs = outputs * (errmat - a[np.newaxis, :])
temp = be.zeros(inputs.shape)
sftmx.apply_derivative(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_tanh_derivative_cc2tensor():
tntest = Tanh()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2], dtype='float32').reshape((3, 1))
be = GPU(rng_seed=0)
outputs = GPUTensor([1 - true_tanh(0) ** 2,
1 - true_tanh(1) ** 2,
1 - true_tanh(-2) ** 2])
temp = be.zeros(inputs.shape)
tntest.apply_derivative(be, GPUTensor(inputs, dtype='float32'), temp)
assert_tensor_near_equal(outputs, temp, tolerance=1e-5)
def test_cross_entropy_cputensor():
be = CPU(rng_seed=0)
outputs = CPUTensor([0.5, 0.9, 0.1, 0.0001])
targets = CPUTensor([0.5, 0.99, 0.01, 0.2])
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = np.sum((- targets.asnumpyarray()) *
np.log(outputs.asnumpyarray()) -
(1 - targets.asnumpyarray()) *
np.log(1 - outputs.asnumpyarray()), keepdims=True)
assert_tensor_near_equal(expected_result, cross_entropy(be, outputs,
targets, temp))
def test_softmax_derivative_cputensor():
sftmx = Softmax()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
errmat = np.ones(inputs.shape)
a = np.einsum('ij,ji->i', errmat.T, outputs)
outputs = outputs * (errmat - a[np.newaxis, :])
temp = be.zeros(inputs.shape)
sftmx.apply_derivative(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_negnorm(self):
tsr = self.be.array([[-1, -2], [1, 3]])
rpow = -1. / 3
# -> sum([[1, .125], [1, .037037]], axis=0)**rpow -> rpow([2, .162037])
assert_tensor_equal(self.be.norm(tsr, order=-3, axis=0),
CPUTensor([[2**rpow, .162037037037**rpow]]))
# -> sum([[1, .125], [1, .037037]], axis=1)**rpow ->
# rpow([1.125, 1.037037])
assert_tensor_near_equal(self.be.norm(tsr, order=-3, axis=1),
CPUTensor([1.125**rpow, 1.037037**rpow]),
1e-6)
def test_softmax_derivative_cc2tensor():
sftmx = Softmax()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
errmat = np.ones(inputs.shape)
a = np.einsum('ij,ji->i', errmat.T, outputs)
outputs = outputs * (errmat - a[np.newaxis, :])
be = GPU(rng_seed=0)
temp = be.zeros(inputs.shape)
sftmx.apply_derivative(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(GPUTensor(outputs), temp)
def test_negnorm(self):
tsr = self.be.array([[-1, -2], [1, 3]])
rpow = -1. / 3
# -> sum([[1, .125], [1, .037037]], axis=0)**rpow -> rpow([2, .162037])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=-3, axis=0, out=out),
self.gpt([[2**rpow, .162037037037**rpow]]))
# -> sum([[1, .125], [1, .037037]], axis=1)**rpow ->
# rpow([1.125, 1.037037])
out = self.be.empty((2, 1))
assert_tensor_near_equal(self.be.norm(tsr, order=-3, axis=1, out=out),
self.gpt([1.125**rpow, 1.037037**rpow]), 1e-6)
def test_lrgnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 5
# -> sum([[1, 0], [1, 243]], axis=0)**rpow -> rpow([2, 243])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=5, axis=0, out=out),
self.gpt([[2**rpow, 243**rpow]]))
# -> sum([[1, 0], [1, 243]], axis=1)**rpow -> rpow([1, 244])
# 244**.2 == ~3.002465 hence the near_equal test
out = self.be.empty((2, 1))
assert_tensor_near_equal(self.be.norm(tsr, order=5, axis=1, out=out),
self.gpt([1**rpow, 244**rpow]), 1e-6)
def test_softmax_derivative_cc2tensor():
sftmx = Softmax()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
errmat = np.ones(inputs.shape)
a = np.einsum('ij,ji->i', errmat.T, outputs)
outputs = outputs * (errmat - a[np.newaxis, :])
be = GPU(rng_seed=0)
temp = be.zeros(inputs.shape)
sftmx.apply_derivative(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(GPUTensor(outputs), temp)
def test_lrgnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 5
# -> sum([[1, 0], [1, 243]], axis=0)**rpow -> rpow([2, 243])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=5, axis=0, out=out),
self.gpt([[2**rpow, 243**rpow]]))
# -> sum([[1, 0], [1, 243]], axis=1)**rpow -> rpow([1, 244])
# 244**.2 == ~3.002465 hence the near_equal test
out = self.be.empty((2, 1))
assert_tensor_near_equal(self.be.norm(tsr, order=5, axis=1, out=out),
self.gpt([1**rpow, 244**rpow]), 1e-6)
def test_cross_entropy_cc2tensor():
from neon.backends.cc2 import GPU, GPUTensor
be = GPU(rng_seed=0) # to ensure cublas_init() is called.
outputs = GPUTensor([0.5, 0.9, 0.1, 0.0001])
targets = GPUTensor([0.5, 0.99, 0.01, 0.2])
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = np.sum((- targets.asnumpyarray()) *
np.log(outputs.asnumpyarray()) -
(1 - targets.asnumpyarray()) *
np.log(1 - outputs.asnumpyarray()), keepdims=True)
assert_tensor_near_equal(expected_result, cross_entropy(be, outputs,
targets, temp),
tolerance=1e-6)
def test_cross_entropy_limits():
be = CPU(rng_seed=0)
outputs = CPUTensor([0.5, 1.0, 0.0, 0.0001])
targets = CPUTensor([0.5, 0.0, 1.0, 0.2])
eps = 2**-23
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = np.sum(
(-targets.asnumpyarray()) * np.log(outputs.asnumpyarray() + eps) -
(1 - targets.asnumpyarray()) *
np.log(1 - outputs.asnumpyarray() + eps),
keepdims=True)
assert_tensor_near_equal(expected_result,
cross_entropy(be, outputs, targets, temp, eps))
def test_cross_entropy_cc2tensor():
from neon.backends.cc2 import GPU, GPUTensor
be = GPU(rng_seed=0) # to ensure cublas_init() is called.
outputs = GPUTensor([0.5, 0.9, 0.1, 0.0001])
targets = GPUTensor([0.5, 0.99, 0.01, 0.2])
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = np.sum(
(-targets.asnumpyarray()) * np.log(outputs.asnumpyarray()) -
(1 - targets.asnumpyarray()) * np.log(1 - outputs.asnumpyarray()),
keepdims=True)
assert_tensor_near_equal(expected_result,
cross_entropy(be, outputs, targets, temp),
tolerance=1e-6)
def test_negnorm(self):
tsr = self.be.array([[-1, -2], [1, 3]])
rpow = -1. / 3
# -> sum([[1, .125], [1, .037037]], axis=0)**rpow -> rpow([2, .162037])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=-3, axis=0, out=out),
self.gpt([[2**rpow, .162037037037**rpow]]))
# -> sum([[1, .125], [1, .037037]], axis=1)**rpow ->
# rpow([1.125, 1.037037])
out = self.be.empty((2, 1))
assert_tensor_near_equal(self.be.norm(tsr, order=-3, axis=1, out=out),
self.gpt([1.125**rpow, 1.037037**rpow]),
1e-6)
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)
def test_cudanet_positive(self):
self.layer.positive(self.inputs)
target = np.array([0.50541031, 0.50804842], dtype='float32')
assert_tensor_near_equal(self.layer.p_hid_plus.asnumpyarray()[:, 0],
target)
def test_cudanet_cost(self):
self.layer.positive(self.inputs)
self.layer.negative(self.inputs)
thecost = self.cost.apply_function(self.inputs)
target = 106.588943481
assert_tensor_near_equal(thecost, target)
def test_cudanet_cost(self):
self.layer.positive(self.inputs)
self.layer.negative(self.inputs)
thecost = self.cost.apply_function(self.inputs)
target = 106.588943481
assert_tensor_near_equal(thecost, target)
def test_cudanet_positive(self):
self.layer.positive(self.inputs)
target = np.array([0.50541031, 0.50804842],
dtype='float32')
assert_tensor_near_equal(self.layer.p_hid_plus.asnumpyarray()[:, 0],
target)
def test_cudanet_negative(self):
self.layer.positive(self.inputs)
self.layer.negative(self.inputs)
target = np.array([0.50274211, 0.50407821], dtype='float32')
assert_tensor_near_equal(self.layer.p_hid_minus.asnumpyarray()[:, 0],
target)
