pfunctions

Module contents

class poornn.pfunctions.PReLU(input_shape, itype, leak=0.1, var_mask=[True])

Bases: poornn.core.ParamFunction

Parametric ReLU,

\begin{align} f(x)=\text{relu}(x)=\begin{cases} x, &\Re[x]>0\\ \text{leak}\cdot x,&\Re[x]<0 \end{cases} \end{align}

where leak is a trainable parameter if var_mask[0] is True.

Parameters:

• leak (float, default=0.1) – leakage,
• var_mask (1darray<bool>, default=[True]) – variable mask

leak

float – leakage,

var_mask

1darray<bool> – variable mask

set_runtime_vars(var_dict={})

Set runtime variables for layers.

Parameters:var_dict (dict) – the runtime variables dict.

class poornn.pfunctions.Poly(input_shape, itype, params, kernel='polynomial', var_mask=None, factorial_rescale=False)

Bases: poornn.core.ParamFunction

Ploynomial function layer.

e.g. for polynomial kernel, we have

• \(f(x) = \sum\limits_i \text{params}[i]x^i/i!\) (factorial_rescale is True)
• \(f(x) = \sum\limits_i \text{params}[i]x^i\) (factorial_rescale is False)

Parameters:

• kernel (str, default='polynomial') – the kind of polynomial serie expansion, see Poly.kernel_dict for detail.
• factorial_rescale (bool, default=False) – rescale high order factors to avoid overflow.
• var_mask (1darray<bool>, default=(True,True,..)) – variable mask

kernel

str – the kind of polynomial serie expansion, see Poly.kernel_dict for detail.

factorial_rescale

bool – rescale high order factors to avoid overflow.

var_mask

1darray<bool> – variable mask

kernel_dict = {'chebyshev': <class 'numpy.polynomial.chebyshev.Chebyshev'>, 'legendre': <class 'numpy.polynomial.legendre.Legendre'>, 'hermiteE': <class 'numpy.polynomial.hermite_e.HermiteE'>, 'hermite': <class 'numpy.polynomial.hermite.Hermite'>, 'laguerre': <class 'numpy.polynomial.laguerre.Laguerre'>, 'polynomial': <class 'numpy.polynomial.polynomial.Polynomial'>}

dict of available kernels, with values target functions.

max_order

int – maximum order appeared.

set_runtime_vars(var_dict={})

Set runtime variables for layers.

Parameters:var_dict (dict) – the runtime variables dict.

class poornn.pfunctions.Mobius(input_shape, itype, params, var_mask=None)

Bases: poornn.core.ParamFunction

Mobius transformation, \(f(x) = \frac{(z-a)(b-c)}{(z-c)(b-a)}\)

\(a, b, c\) map to \(0, 1, \infty\) respectively.

set_runtime_vars(var_dict={})

Set runtime variables for layers.

Parameters:var_dict (dict) – the runtime variables dict.

class poornn.pfunctions.Georgiou1992(input_shape, itype, params, var_mask=None)

Bases: poornn.core.ParamFunction

Function \(f(x) = \frac{x}{c+|x|/r}\)

set_runtime_vars(var_dict={})

Set runtime variables for layers.

Parameters:var_dict (dict) – the runtime variables dict.

class poornn.pfunctions.Gaussian(input_shape, itype, params, var_mask=None)

Bases: poornn.core.ParamFunction

Function \(f(x) = \frac{1}{\sqrt{2\pi}\sigma} \exp(-\frac{\|x-\mu\|^2}{2\sigma^2})\), where \(\mu,\sigma\) are mean and variance respectively.

set_runtime_vars(var_dict={})

Set runtime variables for layers.

Parameters:var_dict (dict) – the runtime variables dict.

class poornn.pfunctions.PMul(input_shape, itype, c=1.0, var_mask=None)

Bases: poornn.core.ParamFunction

Function \(f(x) = cx\), where c is trainable if var_mask[0] is True.

Parameters:c (number, default=1.0) – multiplier.

c

number – multiplier.

set_runtime_vars(var_dict={})

Set runtime variables for layers.

Parameters:var_dict (dict) – the runtime variables dict.