nets¶

Module contents¶

ABC of neural network.

class poornn.nets.ANN(layers=None, labels=None)¶

Bases: poornn.core.Container

Sequential Artificial Neural network.

add_layer(cls, label=None, **kwargs)¶

Add a new layer, comparing with self.layers.append()

input_shape of new layer is infered from output_shape of last layer.

itype of new layer is infered from otype of last layer.

Parameters:	cls (class) – create a layer instance, take input_shape and itype as first and second parameters. label (str\|None, default=None) – label to index this layer, leave None if indexing is not needed. **kwargs – keyword arguments used by `cls.__init__()`, excluding `input_shape` and `itype`.

Note

if num_layers is 0, this function will raise an error, because it fails to infer input_shape and itype.

Returns:	newly generated object.
Return type:	Layer

backward(xy, dy=array(1), data_cache=None, do_shape_check=False)¶

Compute gradients.

Parameters:	xy (tuple) – input and output dy (ndarray) – gradient of output defined as \(\partial J/\partial y\). data_cache (dict) – a dict with collected datas. do_shape_check (bool) – check shape of data flow if True.
Returns:	gradients for vairables in layers.
Return type:	list

dtype¶: variable data shape, which is infered from layers.

forward(x, data_cache=None, do_shape_check=False, **kwargs)¶

Feed input to this feed forward network.

Parameters:	x (ndarray) – input in ‘F’ order. data_cache (dict\|None, default=None) – a dict used to collect datas. do_shape_check (bool) – check shape of data flow if True.

Note

data_cache should be pass to this method if you are about to call a subsequent backward() method, because backward need data_cache.

'%d-ys'%id(self) is used as the key to store run-time output of layers in this network. data_cache['%d-ys'%id(self)] is a list with contents outputs in each layers generate in this forward run.

Returns:	output in each layer.
Return type:	list

get_runtimes()¶: show runtime variables used in this Container.

get_variables()¶: Dump values to an array.

num_layers¶: number of layers.

num_variables¶: int – number of variables.

set_runtime_vars(var_dict)¶

Set runtime variables.

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

set_variables(v)¶

Load data from an array.

Parameters:	v (1darray) – variables.

tags¶: tags for this Container, which is infered from self.layers.

class poornn.nets.ParallelNN(axis=0, layers=None, labels=None)¶

Bases: poornn.core.Container

Parallel Artificial Neural network.

Parameters:	axis (int, default=0) – specify the additional axis on which outputs are packed.

axis¶: int – specify the additional axis on which outputs are packed.

add_layer(cls, **kwargs)¶

add a new layer, comparing with self.layers.append()

input_shape of new layer is infered from input_shape of first layer.

itype of new layer is infered from itype of first layer.

otype of new layer is infered from otype of first layer.

Parameters:	cls (class) – create a layer instance, take input_shape and itype as first and second parameters. **kwargs – keyword arguments used by cls.__init__, excluding input_shape and itype.

Note

if self.num_layers is 0, this function will raise an error, because it fails to infer input_shape, itype and otype.

Returns:	newly generated object.
Return type:	Layer

backward(xy, dy=array(1), do_shape_check=False, **kwargs)¶

Compute gradients.

Parameters:	xy (tuple) – input and output dy (ndarray) – gradient of output defined as \(\partial J/\partial y\). do_shape_check (bool) – check shape of data flow if True.
Returns:	gradients for vairables in layers.
Return type:	list

dtype¶: variable data shape, which is infered from layers.

forward(x, do_shape_check=False, **kwargs)¶

Feed input, it will generate a new axis,and storge the outputs of layers parallel along this axis.

Parameters:	x (ndarray) – input in ‘F’ order. do_shape_check (bool) – check shape of data flow if True.
Returns:	output,
Return type:	ndarray

get_runtimes()¶: show runtime variables used in this Container.

get_variables()¶: Dump values to an array.

num_layers¶: number of layers.

num_variables¶: int – number of variables.

set_runtime_vars(var_dict)¶

Set runtime variables.

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

set_variables(v)¶

Load data from an array.

Parameters:	v (1darray) – variables.

tags¶: tags for this Container, which is infered from self.layers.

class poornn.nets.JointComplex(real, imag)¶

Bases: poornn.core.Container

Function \(f(z) = h(x) + ig(y)\), where \(h\) and \(g\) are real functions. This Container can be used to generate complex layers, but its non-holomophic (analytical type 3).

Parameters:	real (Layer) – layer for real part. imag (Layer) – layer for imaginary part.

dtype¶: variable data shape, which is infered from layers.

get_runtimes()¶: show runtime variables used in this Container.

get_variables()¶: Dump values to an array.

imag¶: the imaginary part layer.

num_layers¶: number of layers.

num_variables¶: int – number of variables.

real¶: the real part layer.

set_runtime_vars(var_dict)¶

Set runtime variables.

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

set_variables(v)¶

Load data from an array.

Parameters:	v (1darray) – variables.

class poornn.nets.KeepSignFunc(h, is_real=False)¶

Bases: poornn.core.Container

Function \(f(z) = h(|z|)\sign(z)\), where \(h\) is a real function. This Container inherit sign from input, so it must have same input and ouput dimension. It can also be used to generate complex layers, but its non-holomophic (analytical type 3).

Parameters:	is_real (bool, default=False) – input is real if True.

is_real¶: bool – input is real if True.

dtype¶: variable data shape, which is infered from layers.

get_runtimes()¶: show runtime variables used in this Container.

get_variables()¶: Dump values to an array.

h¶: layer applied on amplitude.

num_layers¶: number of layers.

num_variables¶: int – number of variables.

set_runtime_vars(var_dict)¶

Set runtime variables.

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

set_variables(v)¶

Load data from an array.

Parameters:	v (1darray) – variables.