#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer.layers.core import Layer
from tensorlayer import logging
__all__ = [
'ConcatLayer',
'ElementwiseLayer',
]
[docs]class ConcatLayer(Layer):
"""A layer that concats multiple tensors according to given axis.
Parameters
----------
prev_layer : list of :class:`Layer`
List of layers to concatenate.
concat_dim : int
The dimension to concatenate.
name : str
A unique layer name.
Examples
----------
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> sess = tf.InteractiveSession()
>>> x = tf.placeholder(tf.float32, shape=[None, 784])
>>> inputs = tl.layers.InputLayer(x, name='input_layer')
[TL] InputLayer input_layer (?, 784)
>>> net1 = tl.layers.DenseLayer(inputs, 800, act=tf.nn.relu, name='relu1_1')
[TL] DenseLayer relu1_1: 800, relu
>>> net2 = tl.layers.DenseLayer(inputs, 300, act=tf.nn.relu, name='relu2_1')
[TL] DenseLayer relu2_1: 300, relu
>>> net = tl.layers.ConcatLayer([net1, net2], 1, name ='concat_layer')
[TL] ConcatLayer concat_layer, 1100
>>> tl.layers.initialize_global_variables(sess)
>>> net.print_params()
[TL] param 0: relu1_1/W:0 (784, 800) float32_ref
[TL] param 1: relu1_1/b:0 (800,) float32_ref
[TL] param 2: relu2_1/W:0 (784, 300) float32_ref
[TL] param 3: relu2_1/b:0 (300,) float32_ref
num of params: 863500
>>> net.print_layers()
[TL] layer 0: relu1_1/Relu:0 (?, 800) float32
[TL] layer 1: relu2_1/Relu:0 (?, 300) float32
[TL] layer 2: concat_layer:0 (?, 1100) float32
"""
def __init__(
self,
prev_layer,
concat_dim=-1,
name='concat_layer',
):
super(ConcatLayer, self).__init__(prev_layer=prev_layer, name=name)
logging.info("ConcatLayer %s: axis: %d" % (self.name, concat_dim))
self.outputs = tf.concat(self.inputs, concat_dim, name=name)
self._add_layers(self.outputs)
[docs]class ElementwiseLayer(Layer):
"""A layer that combines multiple :class:`Layer` that have the same output shapes
according to an element-wise operation.
Parameters
----------
prev_layer : list of :class:`Layer`
The list of layers to combine.
combine_fn : a TensorFlow element-wise combine function
e.g. AND is ``tf.minimum`` ; OR is ``tf.maximum`` ; ADD is ``tf.add`` ; MUL is ``tf.multiply`` and so on.
See `TensorFlow Math API <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html#math>`__ .
act : activation function
The activation function of this layer.
name : str
A unique layer name.
Examples
--------
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> x = tf.placeholder(tf.float32, shape=[None, 784])
>>> inputs = tl.layers.InputLayer(x, name='input_layer')
>>> net_0 = tl.layers.DenseLayer(inputs, n_units=500, act=tf.nn.relu, name='net_0')
>>> net_1 = tl.layers.DenseLayer(inputs, n_units=500, act=tf.nn.relu, name='net_1')
>>> net = tl.layers.ElementwiseLayer([net_0, net_1], combine_fn=tf.minimum, name='minimum')
>>> net.print_params(False)
[TL] param 0: net_0/W:0 (784, 500) float32_ref
[TL] param 1: net_0/b:0 (500,) float32_ref
[TL] param 2: net_1/W:0 (784, 500) float32_ref
[TL] param 3: net_1/b:0 (500,) float32_ref
>>> net.print_layers()
[TL] layer 0: net_0/Relu:0 (?, 500) float32
[TL] layer 1: net_1/Relu:0 (?, 500) float32
[TL] layer 2: minimum:0 (?, 500) float32
"""
def __init__(
self,
prev_layer,
combine_fn=tf.minimum,
act=None,
name='elementwise_layer',
):
super(ElementwiseLayer, self).__init__(prev_layer=prev_layer, act=act, name=name)
logging.info(
"ElementwiseLayer %s: size: %s fn: %s" %
(self.name, prev_layer[0].outputs.get_shape(), combine_fn.__name__)
)
self.outputs = prev_layer[0].outputs
for l in prev_layer[1:]:
self.outputs = combine_fn(self.outputs, l.outputs, name=name)
self.outputs = self._apply_activation(self.outputs)
# for i in range(1, len(layers)):
# self._add_layers(list(layers[i].all_layers))
# self._add_params(list(layers[i].all_params))
# self.all_drop.update(dict(layers[i].all_drop))
self._add_layers(self.outputs)