def test_less_equal(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.less_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 1], [1, 0]]))
def test_some_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.array([[0, 1], [0, 1]])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 0], [1, 0]]))
def test_greater(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.greater(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 0], [0, 1]]))
def test_some_equal(self):
left = self.be.ones([2, 2])
right = self.be.array([[0, 1], [0, 1]])
out = self.be.empty([2, 2])
self.be.equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 1], [0, 1]]))
def test_less_equal(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.less_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 1], [1, 0]]))
def test_some_equal(self):
left = self.be.ones([2, 2])
right = self.be.array([[0, 1], [0, 1]])
out = self.be.empty([2, 2])
self.be.equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, CPUTensor([[0, 1], [0, 1]]))
def test_all_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.zeros([2, 2])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, CPUTensor([[1, 1], [1, 1]]))
def test_some_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.array([[0, 1], [0, 1]])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, CPUTensor([[1, 0], [1, 0]]))
def test_none_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, CPUTensor([[0, 0], [0, 0]]))
def test_greater(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.greater(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, CPUTensor([[0, 0], [0, 1]]))
def test_all_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.zeros([2, 2])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 1], [1, 1]]))
def test_none_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 0], [0, 0]]))
def test_0norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum(tsr != 0, axis=0) -> [2, 1]
assert_tensor_equal(self.be.norm(tsr, order=0, axis=0),
CPUTensor([[2, 1]]))
# -> sum(tsr != 0, axis=1) -> [1, 2]
assert_tensor_equal(self.be.norm(tsr, order=0, axis=1),
CPUTensor([1, 2]))
def test_rectleaky_slope_zero_rectlin_equiv():
be = CPU()
inputs = be.uniform(low=-5.0, high=10.0, size=(10, 10))
lin_buf = be.empty(inputs.shape)
leaky_buf = be.empty(inputs.shape)
be.rectlin(inputs, out=lin_buf)
be.rectleaky(inputs, slope=0.0, out=leaky_buf)
assert_tensor_equal(lin_buf, leaky_buf)
def test_1norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum([[1, 0], [1, 3]], axis=0)**1 -> [2, 3]
assert_tensor_equal(self.be.norm(tsr, order=1, axis=0),
CPUTensor([[2, 3]]))
# -> sum([[1, 0], [1, 3]], axis=1)**1 -> [1, 4]
assert_tensor_equal(self.be.norm(tsr, order=1, axis=1),
CPUTensor([1, 4]))
def test_1norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum([[1, 0], [1, 3]], axis=0)**1 -> [2, 3]
assert_tensor_equal(self.be.norm(tsr, order=1, axis=0),
CPUTensor([[2, 3]]))
# -> sum([[1, 0], [1, 3]], axis=1)**1 -> [1, 4]
assert_tensor_equal(self.be.norm(tsr, order=1, axis=1),
CPUTensor([1, 4]))
def test_infnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> max(abs(tsr), axis=0) -> [1, 3]
assert_tensor_equal(self.be.norm(tsr, order=float('inf'), axis=0),
CPUTensor([[1, 3]]))
# -> max(abs(tsr), axis=1) -> [1, 3]
assert_tensor_equal(self.be.norm(tsr, order=float('inf'), axis=1),
CPUTensor([1, 3]))
def test_neginfnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> min(abs(tsr), axis=0) -> [1, 0]
assert_tensor_equal(self.be.norm(tsr, order=float('-inf'), axis=0),
self.gpt([[1, 0]]))
# -> min(abs(tsr), axis=1) -> [0, 1]
assert_tensor_equal(self.be.norm(tsr, order=float('-inf'), axis=1),
self.gpt([0, 1]))
def test_neginfnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> min(abs(tsr), axis=0) -> [1, 0]
assert_tensor_equal(self.be.norm(tsr, order=float('-inf'), axis=0),
CPUTensor([[1, 0]]))
# -> min(abs(tsr), axis=1) -> [0, 1]
assert_tensor_equal(self.be.norm(tsr, order=float('-inf'), axis=1),
CPUTensor([0, 1]))
def test_range_slicing(self):
tns = CPUTensor([[1, 2], [3, 4]])
res = tns[0:2, 0]
expected_shape = (2, )
while len(expected_shape) < res._min_dims:
expected_shape += (1, )
assert res.shape == expected_shape
assert_tensor_equal(res, CPUTensor([1, 3]))
def test_infnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> max(abs(tsr), axis=0) -> [1, 3]
assert_tensor_equal(self.be.norm(tsr, order=float('inf'), axis=0),
self.gpt([[1, 3]]))
# -> max(abs(tsr), axis=1) -> [1, 3]
assert_tensor_equal(self.be.norm(tsr, order=float('inf'), axis=1),
self.gpt([1, 3]))
def test_0norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum(tsr != 0, axis=0) -> [2, 1]
assert_tensor_equal(self.be.norm(tsr, order=0, axis=0),
CPUTensor([[2, 1]]))
# -> sum(tsr != 0, axis=1) -> [1, 2]
assert_tensor_equal(self.be.norm(tsr, order=0, axis=1),
CPUTensor([1, 2]))
def test_2norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 2
# -> sum([[1, 0], [1, 9]], axis=0)**.5 -> sqrt([2, 9])
assert_tensor_equal(self.be.norm(tsr, order=2, axis=0),
CPUTensor([[2**rpow, 9**rpow]]))
# -> sum([[1, 0], [1, 9]], axis=1)**.5 -> sqrt([1, 10])
assert_tensor_equal(self.be.norm(tsr, order=2, axis=1),
CPUTensor([1**rpow, 10**rpow]))
def test_cc2_rectleaky_derivative_slope_zero_rectlin_equiv():
from neon.backends.cc2 import GPU
be = GPU()
inputs = be.uniform(low=-5.0, high=10.0, size=(10, 10))
lin_buf = be.empty(inputs.shape)
leaky_buf = be.empty(inputs.shape)
be.rectlin_derivative(inputs, out=lin_buf)
be.rectleaky_derivative(inputs, slope=0.0, out=leaky_buf)
assert_tensor_equal(lin_buf, leaky_buf)
def test_2norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 2
# -> sum([[1, 0], [1, 9]], axis=0)**.5 -> sqrt([2, 9])
assert_tensor_equal(self.be.norm(tsr, order=2, axis=0),
CPUTensor([[2**rpow, 9**rpow]]))
# -> sum([[1, 0], [1, 9]], axis=1)**.5 -> sqrt([1, 10])
assert_tensor_equal(self.be.norm(tsr, order=2, axis=1),
CPUTensor([1**rpow, 10**rpow]))
def test_1norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum([[1, 0], [1, 3]], axis=0)**1 -> [2, 3]
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=1, axis=0, out=out),
self.gpt([[2, 3]]))
# -> sum([[1, 0], [1, 3]], axis=1)**1 -> [1, 4]
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=1, axis=1, out=out),
self.gpt([1, 4]))
def test_0norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum(tsr != 0, axis=0) -> [2, 1]
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=0, axis=0, out=out),
self.gpt([[2, 1]]))
# -> sum(tsr != 0, axis=1) -> [1, 2]
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=0, axis=1, out=out),
self.gpt([1, 2]))
def test_0norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum(tsr != 0, axis=0) -> [2, 1]
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=0, axis=0, out=out),
self.gpt([[2, 1]]))
# -> sum(tsr != 0, axis=1) -> [1, 2]
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=0, axis=1, out=out),
self.gpt([1, 2]))
def test_1norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum([[1, 0], [1, 3]], axis=0)**1 -> [2, 3]
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=1, axis=0, out=out),
self.gpt([[2, 3]]))
# -> sum([[1, 0], [1, 3]], axis=1)**1 -> [1, 4]
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=1, axis=1, out=out),
self.gpt([1, 4]))
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 compare_cpu_tensors(inputs, outputs, deriv=False):
rlin = RectLeaky()
be = CPU()
temp = be.zeros(inputs.shape)
if deriv is True:
rlin.apply_derivative(be, CPUTensor(inputs), temp)
else:
rlin.apply_function(be, CPUTensor(inputs), temp)
be.subtract(temp, CPUTensor(outputs), temp)
assert_tensor_equal(temp, be.zeros(inputs.shape))
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_2norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 2
# -> sum([[1, 0], [1, 9]], axis=0)**.5 -> sqrt([2, 9])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=2, axis=0, out=out),
self.gpt([[2**rpow, 9**rpow]]))
# -> sum([[1, 0], [1, 9]], axis=1)**.5 -> sqrt([1, 10])
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=2, axis=1, out=out),
self.gpt([1**rpow, 10**rpow]))
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_2norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 2
# -> sum([[1, 0], [1, 9]], axis=0)**.5 -> sqrt([2, 9])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=2, axis=0, out=out),
self.gpt([[2**rpow, 9**rpow]]))
# -> sum([[1, 0], [1, 9]], axis=1)**.5 -> sqrt([1, 10])
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=2, axis=1, out=out),
self.gpt([1**rpow, 10**rpow]))
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 compare_cc2_tensors(inputs, outputs, deriv=False):
from neon.backends.cc2 import GPU, GPUTensor
rlin = RectLeaky()
be = GPU()
temp = be.zeros(inputs.shape)
if deriv is True:
rlin.apply_derivative(be, GPUTensor(inputs), temp)
else:
rlin.apply_function(be, GPUTensor(inputs), temp)
be.subtract(temp, GPUTensor(outputs), temp)
assert_tensor_equal(temp, be.zeros(inputs.shape))
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_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_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_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 check_bprop(layer, backend):
errors = backend.ones((nout, batch_size))
deltas = backend.zeros((nin, batch_size))
deltas[:2] = backend.ones((nout, batch_size))
# initialize deltas since they are not set
# by the layer initialize method.
layer.deltas = backend.ones((nin, batch_size))
# layers should be refactored to remove references
# to external layers. inputs can be cached during
# fprop.
class PreviousLayer(object):
def __init__(self):
self.is_data = True
self.output = backend.ones((nin, batch_size))
layer.prev_layer = PreviousLayer()
layer.bprop(errors)
assert_tensor_equal(layer.deltas, deltas)
def check_bprop(layer, backend):
errors = backend.ones((nout, batch_size))
deltas = backend.zeros((nin, batch_size))
deltas[:2] = backend.ones((nout, batch_size))
# initialize deltas since they are not set
# by the layer initialize method.
layer.deltas = backend.ones((nin, batch_size))
# layers should be refactored to remove references
# to external layers. inputs can be cached during
# fprop.
class PreviousLayer(object):
def __init__(self):
self.is_data = True
self.output = backend.ones((nin, batch_size))
layer.prev_layer = PreviousLayer()
layer.bprop(errors)
assert_tensor_equal(layer.deltas, deltas)
def m_assert_tensor_equal(t1, t2):
for _t1, _t2, ctx in zip(t1._tensorlist, t2._tensorlist, t1._ctxs):
ctx.push()
assert_tensor_equal(_t1, _t2)
ctx.pop()
def test_fill(self):
tns = self.gpt([[1, 2], [3, 4]])
tns.fill(-9.5)
assert_tensor_equal(tns, self.gpt([[-9.5, -9.5], [-9.5, -9.5]]))
def test_fill(self):
tns = CPUTensor([[1, 2], [3, 4]])
tns.fill(-9.5)
assert_tensor_equal(tns, CPUTensor([[-9.5, -9.5], [-9.5, -9.5]]))
def test_transpose(self):
tns = CPUTensor([[1, 2], [3, 4]])
res = tns.transpose()
assert_tensor_equal(res, CPUTensor([[1, 3], [2, 4]]))
def test_asnumpyarray(self):
tns = CPUTensor([[1, 2], [3, 4]])
res = tns.asnumpyarray()
assert isinstance(res, np.ndarray)
assert_tensor_equal(res, np.array([[1, 2], [3, 4]]))
def test_scalar_slice_assignment(self):
tns = CPUTensor([[1, 2], [3, 4]])
tns[1, 0] = 9
assert_tensor_equal(tns, CPUTensor([[1, 2], [9, 4]]))
def check_fprop(layer, backend):
inputs = backend.ones((nin, batch_size))
output = backend.ones((nout, batch_size))
layer.fprop(inputs)
assert_tensor_equal(layer.output, output)