def test_std():
"""Tests std"""
# Should std along the last dimension by default
ones = tf.ones([5, 4, 3])
val = ops.std(ones)
assert isinstance(val, tf.Tensor)
assert val.ndim == 2
assert val.shape[0] == 5
assert val.shape[1] == 4
assert np.all(val.numpy() == 0.0)
# But can change that w/ the axis kwarg
ones = tf.ones([5, 4, 3])
val = ops.std(ones, axis=1)
assert isinstance(val, tf.Tensor)
assert val.ndim == 2
assert val.shape[0] == 5
assert val.shape[1] == 3
assert np.all(val.numpy() == 0.0)
# Actually test values
val = ops.std(tf.constant([0.9, 1.9, 2.1, 3.1]))
assert is_close(val.numpy(), 0.781024968)
val = ops.std(tf.constant([1.0, 2.0, 3.0]))
assert is_close(val.numpy(), 0.816496581)
def test_std():
"""Tests std"""
pf.set_backend('pytorch')
# Should std along the last dimension by default
ones = torch.ones([5, 4, 3])
val = ops.std(ones)
assert isinstance(val, torch.Tensor)
assert val.ndim == 2
assert val.shape[0] == 5
assert val.shape[1] == 4
assert np.all(val.numpy() == 0.0)
# But can change that w/ the axis kwarg
ones = torch.ones([5, 4, 3])
val = ops.std(ones, axis=1)
assert isinstance(val, torch.Tensor)
assert val.ndim == 2
assert val.shape[0] == 5
assert val.shape[1] == 3
assert np.all(val.numpy() == 0.0)
# Actually test values
val = ops.std(torch.Tensor([0.9, 1.9, 2.1, 3.1]))
assert is_close(val.numpy(), np.std([0.9, 1.9, 2.1, 3.1], ddof=1))
val = ops.std(torch.Tensor([1.0, 2.0, 3.0]))
assert is_close(val.numpy(), np.std([1.0, 2.0, 3.0], ddof=1))
def __call__(self, x):
"""Perform the forward pass"""
mean = O.mean(x, axis=0)
std = O.std(x, axis=0)
return self.weight()*(x-mean)/std+self.bias()