#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer import logging
from tensorlayer.layers.core import Layer
__all__ = [
'Concat',
'Elementwise',
]
[文档]class Concat(Layer):
"""A layer that concats multiple tensors according to given axis.
Parameters
----------
concat_dim : int
The dimension to concatenate.
name : None or str
A unique layer name.
Examples
----------
>>> class CustomModel(tl.models.Model):
>>> def __init__(self):
>>> super(CustomModel, self).__init__(name="custom")
>>> self.dense1 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu1_1')
>>> self.dense2 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu2_1')
>>> self.concat = tl.layers.Concat(concat_dim=1, name='concat_layer')
>>> def forward(self, inputs):
>>> d1 = self.dense1(inputs)
>>> d2 = self.dense2(inputs)
>>> outputs = self.concat([d1, d2])
>>> return outputs
"""
def __init__(
self,
concat_dim=-1,
name=None, #'concat',
):
super(Concat, self).__init__(name)
self.concat_dim = concat_dim
self.build(None)
self._built = True
logging.info("Concat %s: concat_dim: %d" % (self.name, concat_dim))
def __repr__(self):
s = ('{classname}(concat_dim={concat_dim})')
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape):
pass
# @tf.function
def forward(self, inputs):
"""
prev_layer : list of :class:`Layer`
List of layers to concatenate.
"""
outputs = tf.concat(inputs, self.concat_dim, name=self.name)
return outputs
[文档]class Elementwise(Layer):
"""A layer that combines multiple :class:`Layer` that have the same output shapes
according to an element-wise operation.
If the element-wise operation is complicated, please consider to use :class:`ElementwiseLambda`.
Parameters
----------
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>`__ .
If the combine function is more complicated, please consider to use :class:`ElementwiseLambda`.
act : activation function
The activation function of this layer.
name : None or str
A unique layer name.
Examples
--------
>>> class CustomModel(tl.models.Model):
>>> def __init__(self):
>>> super(CustomModel, self).__init__(name="custom")
>>> self.dense1 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu1_1')
>>> self.dense2 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu2_1')
>>> self.element = tl.layers.Elementwise(combine_fn=tf.minimum, name='minimum', act=tf.identity)
>>> def forward(self, inputs):
>>> d1 = self.dense1(inputs)
>>> d2 = self.dense2(inputs)
>>> outputs = self.element([d1, d2])
>>> return outputs
"""
def __init__(
self,
combine_fn=tf.minimum,
act=None,
name=None, #'elementwise',
):
super(Elementwise, self).__init__(name, act=act)
self.combine_fn = combine_fn
self.build(None)
self._built = True
logging.info(
"Elementwise %s: fn: %s act: %s" %
(self.name, combine_fn.__name__, ('No Activation' if self.act is None else self.act.__name__))
)
def __repr__(self):
actstr = self.act.__name__ if self.act is not None else 'No Activation'
s = ('{classname}(combine_fn={combine_fn}, ' + actstr)
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
# @tf.function
def forward(self, inputs):
outputs = inputs[0]
for input in inputs[1:]:
outputs = self.combine_fn(outputs, input, name=self.name)
if self.act:
outputs = self.act(outputs)
return outputs