functions¶

Module contents¶

poornn.functions.wrapfunc(func, dfunc, classname='GeneralFunc', attrs={}, docstring='', tags={}, real_out=False)¶

wrap a function and its backward counterpart into a poornn.core.Function layer.

Parameters:	func (func) – forward function, take input (x, attrs) as parameters. dfunc (func) – derivative function, take input/output (x, y, attrs) as parameters. classname** (str) – function classname, attrs (dict) – attributes, and default input parameters. docstring (str) – the docstring of new class. tags (dict) – tags for this function, see poornn.core.TAG_LIST for detail. real_out (bool) – output data type is real for any input data type if True.
Returns:	a dynamically generated layer type.
Return type:	class

class poornn.functions.Log2cosh(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\log(2\cosh(x))\).

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Logcosh(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\log(\cosh(x))\).

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Sigmoid(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\frac{1}{1+\exp(-x)}\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Cosh(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\cosh(x)\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Sinh(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\sinh(x)\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Tan(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\tan(x)\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Tanh(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\tanh(x)\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Sum(input_shape, itype, axis, **kwargs)¶

Bases: poornn.core.Function

np.sum along axis.

Parameters:	axis (int) – the axis along which to sum over.

axis¶: int – the axis along which to sum over.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Mul(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\text{alpha}\cdot x\)

Parameters:	alpha (int) – the multiplier.

alpha¶: int – the multiplier.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Mod(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=x\%n\)

Parameters:	n (number) – the base.

n¶: number – the base.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Mean(input_shape, itype, axis, **kwargs)¶

Bases: poornn.core.Function

np.mean along axis.

Parameters:	axis (int) – the axis along which to operate.

axis¶: int – the axis along which to operate.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.FFT(input_shape, itype, axis, kernel='fft', **kwargs)¶

Bases: poornn.core.Function

scipy.fftpack.[fft|ifft|dct|idct|...] along axis.

Parameters:	axis (int) – the axis along which to operate. kernel (str, default='fft') – the kernel used.

axis¶: int – the axis along which to operate.

kernel¶: str, default=’fft’ – the kernel used. refer scipy.fftpack for available kernels.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.ReLU(input_shape, itype, leak=0.0, is_inplace=False, mode=None, **kwargs)¶

Bases: poornn.core.Function

ReLU, for mode=’ri’,

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

for mode=’r’,

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

Parameters:	leak (float, default = 0.0) – leakage, mode ('ri'\|'r', default='r' if itype is float else 'ri') – non-holomophic real-imaginary (ri) relu or holomophic real (r) relu

leak¶: float – leakage,

mode¶: ‘ri’|’r’ – non-holomophic real-imaginary (ri) relu or holomophic real (r) relu.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.ConvProd(input_shape, itype, powers, strides=None, boundary='O', **kwargs)¶

Bases: poornn.core.Function

Convolutional product layer, apply a kernel as powers to a subregion and make product over these elements.

Parameters:	powers (ndarray) – powers as a kernel. strides (tuple, default=(1, 1,..)) – stride for each dimension. boundary ('P'\|'O', default='O') – Periodic/Open boundary condition.

powers¶: ndarray – powers as a kernel.

strides¶: tuple – stride for each dimension.

boundary¶: ‘P’|’O’ – Periodic/Open boundary condition.

Note

For input array x, axes are aranged as (num_batch, nfi, img_in_dims), stored in ‘F’ order. For output array y, axes are aranged as (num_batch, nfo, img_out_dims), stored in ‘F’ order.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

img_nd¶: int – dimension of image.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Pooling(input_shape, itype, kernel_shape, mode, **kwargs)¶

Bases: poornn.core.Function

Pooling, see Pooling.mode_list for different types of kernels.

Parameters:	kernel_shape (tuple) – the shape of kernel. mode (str) – the strategy used for pooling, Pooling.mode_list for available modes. (refer) –

kernel_shape¶: tuple – the shape of kernel.

mode¶: str – the strategy used for pooling.

Note

For input array x, axes are aranged as (num_batch, nfi, img_in_dims), stored in ‘F’ order. For output array y, axes are aranged as (num_batch, nfo, img_out_dims), stored in ‘F’ order.

For complex numbers, what does max pooling looks like?

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

img_nd¶: int – dimension of image.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.DropOut(input_shape, itype, keep_rate, axis, is_inplace=False, **kwargs)¶

Bases: poornn.core.Function

DropOut, take runtime variable seed.

Parameters:	axis (int) – the axis along which to operate. keep_rate (float) – the ratio of kept data.

axis¶: int – the axis along which to operate.

keep_rate¶: float – the ratio of kept data.

Example

>>> layer = DropOut((3, 3), 'complex128', keep_rate = 0.5, axis=-1)
>>> layer.set_runtime_vars({'seed': 2})
>>> x = np.arange(9, dtype='complex128').reshape([3, 3], order='F')
>>> print(layer.forward(x))
[[  0.+0.j   6.+0.j   0.+0.j]
 [  2.+0.j   8.+0.j   0.+0.j]
 [  4.+0.j  10.+0.j   0.+0.j]]

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict)¶: Set the runtime variable seed, used to generate a random mask.

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Sin(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\sin(x)\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Cos(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\cos(x)\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.ArcTan(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\arctan(x)\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Exp(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\exp(x)\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Log(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\log(x)\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.SoftPlus(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(log(1+exp(x))\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Power(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=x^{\rm order}\)

Parameters:	order (number) – the order of power.

order¶: number – the order of power.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.SoftMax(input_shape, itype, axis, scale=1.0, **kwargs)¶

Bases: poornn.core.Function

Soft max function \(f(x)=\text{scale}\cdot\frac{\exp(x)}{\sum \exp(x)}\), with the sum performed over axis.

Parameters:	axis (int) – the axis along which to operate. scale (number) – the factor to rescale output.

axis¶: int – the axis along which to operate.

scale¶: number, default = 1.0 – the factor to rescale output.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.CrossEntropy(input_shape, itype, axis, **kwargs)¶

Bases: poornn.core.Function

Cross Entropy \(f(x)=\sum\text{y_true}\log(x)\),: with y_true the true labels, and the sum is performed over axis.

Parameters:	axis (int) – the axis along which to operate.

axis¶: int – the axis along which to operate.

forward(x, **kwargs)¶

Parameters:	x (ndarray) – satisfying \(0 < x \leq 1\). y_true (ndarray) – correct one-hot y.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.SoftMaxCrossEntropy(input_shape, itype, axis, **kwargs)¶

Bases: poornn.core.Function

Soft Max & Cross Entropy \(f(x)=\sum \text{y_true}\log(q)\), with y_true the true labels, and \(q=\frac{\exp(x)}{\sum \exp(x)}\), where the sum is performed over axis.

Parameters:	axis (int) – the axis along which to operate.

axis¶: int – the axis along which to operate.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.SquareLoss(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Square Loss \(f(x)=(x-\text{y_true})^2\). With p the true labels, requres runtime variable ‘y_true’.

y_true¶: ndarray – the ‘correct’ output.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Reshape(input_shape, output_shape, itype, dtype=None, otype=None, tags=None)¶

Bases: poornn.core.Function

Change shape of data, reshape is performed in ‘F’ order.

Note

output_shape is a mandatory parameter now.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Transpose(input_shape, itype, axes, **kwargs)¶

Bases: poornn.core.Function

Transpose data flow.

Parameters:	axes (tuple) – the target axes order.

axes¶: tuple – the target axes order.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.TypeCast(input_shape, itype, otype, **kwargs)¶

Bases: poornn.core.Function

Data type switcher.

Note

otype is a mandatory parameter now.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Filter(input_shape, itype, momentum, axes, **kwargs)¶

Bases: poornn.core.Function

Momentum Filter, single component fourier transformation. \(f(x)=\sum\limits_{n = 0}^{N-1}\exp(-i\pi k n/N)\cdot x[n]\), with index \(n\) iterate over axes.

Parameters:	momentum (1darray) – the desired momentum. axes (tuple) – lattice axes over which to filter out component with the desired momentum.

momentum¶: 1darray – the desired momentum.

axes¶: tuple – lattice axes over which to filter out component with the desired momentum.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.BatchNorm(input_shape, itype, eps=1e-08, axis=None, **kwargs)¶

Bases: poornn.core.Function

Batch normalization layer.

Parameters:	axis (int\|None, default = None) – batch axis over which we take norm. eps (float, default = 1e-8) – small number to avoid division to 0.

axis¶: int|None – batch axis over which to calculate norm, if it is None, we don’t use any axis as batch, instead, we need to set mean and variance manually.

eps¶: float – small number to avoid division to 0.

Note

shall we take mean and variance as run time variable?

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Normalize(input_shape, itype, axis, scale=1.0, **kwargs)¶

Bases: poornn.core.Function

Normalize data, \(f(x)=\text{scale}\cdot x/\|x\|\), where the norm is performed over axis.

Parameters:	axis (int) – axis over which to calculate norm. scale (number, default = 1.0) – the scaling factor.

axis¶: int – axis over which to calculate norm.

scale¶: number – the scaling factor.

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Real(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\Re[x]\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Imag(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\Im[x]\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Conj(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=x^*\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Abs(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=|x|\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Abs2(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=|x|^2\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.

class poornn.functions.Angle(input_shape, itype, **kwargs)¶

Bases: poornn.core.Function

Function \(f(x)=\text{Arg}(x)\)

get_variables()¶: Get variables, return empty (1d but with length - 0) array.

num_variables¶: number of variables, which is fixed to 0.

set_runtime_vars(var_dict={})¶

Set runtime variables for layers.

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

set_variables(*args, **kwargs)¶: passed.