# -*- coding: utf-8 -*-
from .core import *
from .. import _logging as logging
import tensorflow as tf
__all__ = [
'StackLayer',
'UnStackLayer',
]
[docs]class StackLayer(Layer):
"""
The :class:`StackLayer` class is 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
----------
layers : list of :class:`Layer`
Previous layers to stack.
axis : int
Dimension along which to concatenate.
name : str
A unique layer name.
Examples
---------
>>> x = tf.placeholder(tf.float32, shape=[None, 30])
>>> net = tl.layers.InputLayer(x, name='input')
>>> net1 = tl.layers.DenseLayer(net, 10, name='dense1')
>>> net2 = tl.layers.DenseLayer(net, 10, name='dense2')
>>> net3 = tl.layers.DenseLayer(net, 10, name='dense3')
>>> net = tl.layers.StackLayer([net1, net2, net3], axis=1, name='stack')
... (?, 3, 10)
"""
def __init__(
self,
layers,
axis=1,
name='stack',
):
Layer.__init__(self, prev_layer=layers, name=name)
self.inputs = []
for l in layers:
self.inputs.append(l.outputs)
self.outputs = tf.stack(self.inputs, axis=axis, name=name)
logging.info("StackLayer %s: axis: %d" % (self.name, axis))
# self.all_layers = list(layers[0].all_layers)
# self.all_params = list(layers[0].all_params)
# self.all_drop = dict(layers[0].all_drop)
#
# for i in range(1, len(layers)):
# self.all_layers.extend(list(layers[i].all_layers))
# self.all_params.extend(list(layers[i].all_params))
# self.all_drop.update(dict(layers[i].all_drop))
#
# self.all_layers = list_remove_repeat(self.all_layers)
# self.all_params = list_remove_repeat(self.all_params)
self.all_layers.append(self.outputs)
def unstack_layer(layer, num=None, axis=0, name='unstack'):
"""
It is 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
----------
layer : :class:`Layer`
Previous layer
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.
"""
inputs = layer.outputs
with tf.variable_scope(name):
outputs = tf.unstack(inputs, num=num, axis=axis)
logging.info("UnStackLayer %s: num: %s axis: %d, n_outputs: %d" % (name, num, axis, len(outputs)))
net_new = []
scope_name = tf.get_variable_scope().name
if scope_name:
full_name = scope_name + '/' + name
else:
full_name = name
for i, _v in enumerate(outputs):
n = Layer(prev_layer=layer, name=full_name + str(i))
n.outputs = outputs[i]
# n.all_layers = list(layer.all_layers)
# n.all_params = list(layer.all_params)
# n.all_drop = dict(layer.all_drop)
# n.all_layers.append(inputs)
net_new.append(n)
return net_new
# Alias
UnStackLayer = unstack_layer