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_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_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_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_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_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_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),
                         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_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_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_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 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_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_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_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 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_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]))