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#! /usr/bin/python

# -*- coding: utf-8 -*-

import tensorflow as tf

from tensorlayer import logging

from tensorlayer.decorators import deprecated_alias

from tensorlayer.layers.core import Layer

__all__ = [

'Stack',

'UnStack',

]

class Stack(Layer):

"""

The :class:`Stack` class is a layer for stacking a list of rank-R tensors into one rank-(R+1) tensor, see `tf.stack() <https://www.tensorflow.org/api_docs/python/tf/stack>`__.

Parameters

----------

axis : int

New dimension along which to stack.

name : str

A unique layer name.

Examples

---------

>>> import tensorflow as tf

>>> import tensorlayer as tl

>>> ni = tl.layers.Input([None, 784], name='input')

>>> net1 = tl.layers.Dense(10, name='dense1')(ni)

>>> net2 = tl.layers.Dense(10, name='dense2')(ni)

>>> net3 = tl.layers.Dense(10, name='dense3')(ni)

>>> net = tl.layers.Stack(axis=1, name='stack')([net1, net2, net3])

(?, 3, 10)

"""

def __init__(

self,

axis=1,

name=None, #'stack',

):

super().__init__(name)

self.axis = axis

self.build(None)

self._built = True

logging.info("Stack %s: axis: %d" % (self.name, self.axis))

def __repr__(self):

s = '{classname}(axis={axis}'

if self.name is not None:

s += ', name=\'{name}\''

s += ')'

return s.format(classname=self.__class__.__name__, **self.__dict__)

def build(self, inputs_shape):

pass

def forward(self, inputs):

outputs = tf.stack(inputs, axis=self.axis, name=self.name)

return outputs

class UnStack(Layer):

"""

The :class:`UnStack` class is a layer for unstacking the given dimension of a rank-R tensor into rank-(R-1) tensors., see `tf.unstack() <https://www.tensorflow.org/api_docs/python/tf/unstack>`__.

Parameters

----------

num : int or None

The length of the dimension axis. Automatically inferred if None (the default).

axis : int

Dimension along which axis to concatenate.

name : str

A unique layer name.

Returns

-------

list of :class:`Layer`

The list of layer objects unstacked from the input.

Examples

--------

>>> ni = Input([4, 10], name='input')

>>> nn = Dense(n_units=5)(ni)

>>> nn = UnStack(axis=1)(nn) # unstack in channel axis

>>> len(nn) # 5

>>> nn[0].shape # (4,)

"""

def __init__(self, num=None, axis=0, name=None): #'unstack'):

super().__init__(name)

self.num = num

self.axis = axis

self.build(None)

self._built = True

logging.info("UnStack %s: num: %s axis: %d" % (self.name, self.num, self.axis))

def __repr__(self):

s = '{classname}(num={num}, axis={axis}'

if self.name is not None:

s += ', name=\'{name}\''

s += ')'

return s.format(classname=self.__class__.__name__, **self.__dict__)

def build(self, inputs_shape):

pass

def forward(self, inputs):

outputs = tf.unstack(inputs, num=self.num, axis=self.axis, name=self.name)

return outputs