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Minor cosmetic changes

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This commit is contained in:
blackfly 2020-04-11 17:35:32 +02:00
parent e54bf07030
commit f80d9648c3
4 changed files with 29 additions and 25 deletions
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4
RELEASE.md
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@ -3,8 +3,8 @@
## Release 0.1.1-dev0 ## Release 0.1.1-dev0
### Includes ### Includes
- Bugfixes. - Minor bugfixes.
- 100% test coverage. - 100% line coverage.
## Release 0.1.0-dev0 ## Release 0.1.0-dev0

24
prototorch/functions/activations.py
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@ -16,18 +16,24 @@ def register_activation(f):
@register_activation @register_activation
# @torch.jit.script # @torch.jit.script
def identity(x, beta=torch.tensor([0])): def identity(x, beta=torch.tensor([0])):
""":math:`f(x) = x`.""" """Identity activation function.
Definition:
:math:`f(x) = x`
"""
return x return x
@register_activation @register_activation
# @torch.jit.script # @torch.jit.script
def sigmoid_beta(x, beta=torch.tensor([10])): def sigmoid_beta(x, beta=torch.tensor([10])):
r""":math:`f(x) = \\frac{1}{1 + e^{-\\beta x}}`. r"""Sigmoid activation function with scaling.
Definition:
:math:`f(x) = \frac{1}{1 + e^{-\beta x}}`
Keyword Arguments: Keyword Arguments:
__________________ beta (`torch.tensor`): Scaling parameter :math:`\beta`
beta (float): Parameter :math:`\\beta`
""" """
out = torch.reciprocal(1.0 + torch.exp(-int(beta.item()) * x)) out = torch.reciprocal(1.0 + torch.exp(-int(beta.item()) * x))
return out return out
@ -36,11 +42,13 @@ def sigmoid_beta(x, beta=torch.tensor([10])):
@register_activation @register_activation
# @torch.jit.script # @torch.jit.script
def swish_beta(x, beta=torch.tensor([10])): def swish_beta(x, beta=torch.tensor([10])):
r""":math:`f(x) = \\frac{x}{1 + e^{-\\beta x}}`. r"""Swish activation function with scaling.
Definition:
:math:`f(x) = \frac{x}{1 + e^{-\beta x}}`
Keyword Arguments: Keyword Arguments:
__________________ beta (`torch.tensor`): Scaling parameter :math:`\beta`
beta (float): Parameter :math:`\\beta`
""" """
out = x * sigmoid_beta(x, beta=beta) out = x * sigmoid_beta(x, beta=beta)
return out return out
@ -49,8 +57,6 @@ def swish_beta(x, beta=torch.tensor([10])):
def get_activation(funcname): def get_activation(funcname):
if callable(funcname): if callable(funcname):
return funcname return funcname
else:
if funcname in ACTIVATIONS: if funcname in ACTIVATIONS:
return ACTIVATIONS.get(funcname) return ACTIVATIONS.get(funcname)
else:
raise NameError(f'Activation {funcname} was not found.') raise NameError(f'Activation {funcname} was not found.')

12
prototorch/functions/distances.py
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@ -30,8 +30,8 @@ def euclidean_distance(x, y):
def lpnorm_distance(x, y, p): def lpnorm_distance(x, y, p):
""" r"""
Compute :math:`{\\langle x, y \\rangle}_p`. Compute :math:`{\langle x, y \rangle}_p`.
Expected dimension of x is 2. Expected dimension of x is 2.
Expected dimension of y is 2. Expected dimension of y is 2.
@ -41,10 +41,10 @@ def lpnorm_distance(x, y, p):
def omega_distance(x, y, omega): def omega_distance(x, y, omega):
""" r"""
Omega distance. Omega distance.
Compute :math:`{\\langle \\Omega x, \\Omega y \\rangle}_p` Compute :math:`{\langle \Omega x, \Omega y \rangle}_p`
Expected dimension of x is 2. Expected dimension of x is 2.
Expected dimension of y is 2. Expected dimension of y is 2.
@ -57,10 +57,10 @@ def omega_distance(x, y, omega):
def lomega_distance(x, y, omegas): def lomega_distance(x, y, omegas):
""" r"""
Localized Omega distance. Localized Omega distance.
Compute :math:`{\\langle \\Omega_k x, \\Omega_k y_k \\rangle}_p` Compute :math:`{\langle \Omega_k x, \Omega_k y_k \rangle}_p`
Expected dimension of x is 2. Expected dimension of x is 2.
Expected dimension of y is 2. Expected dimension of y is 2.

2
prototorch/functions/initializers.py
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@ -86,8 +86,6 @@ def stratified_random(x_train, y_train, prototype_distribution):
def get_initializer(funcname): def get_initializer(funcname):
if callable(funcname): if callable(funcname):
return funcname return funcname
else:
if funcname in INITIALIZERS: if funcname in INITIALIZERS:
return INITIALIZERS.get(funcname) return INITIALIZERS.get(funcname)
else:
raise NameError(f'Initializer {funcname} was not found.') raise NameError(f'Initializer {funcname} was not found.')