#! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
[docs]def identity(x, name=None):
"""The identity activation function, Shortcut is ``linear``.
Parameters
----------
x : a tensor input
input(s)
Returns
--------
A `Tensor` with the same type as `x`.
"""
return x
# Shortcut
linear = identity
[docs]def ramp(x=None, v_min=0, v_max=1, name=None):
"""The ramp activation function.
Parameters
----------
x : a tensor input
input(s)
v_min : float
if input(s) smaller than v_min, change inputs to v_min
v_max : float
if input(s) greater than v_max, change inputs to v_max
name : a string or None
An optional name to attach to this activation function.
Returns
--------
A `Tensor` with the same type as `x`.
"""
return tf.clip_by_value(x, clip_value_min=v_min, clip_value_max=v_max, name=name)
[docs]def leaky_relu(x=None, alpha=0.1, name="LeakyReLU"):
"""The LeakyReLU, Shortcut is ``lrelu``.
Modified version of ReLU, introducing a nonzero gradient for negative
input.
Parameters
----------
x : A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
`int16`, or `int8`.
alpha : `float`. slope.
name : a string or None
An optional name to attach to this activation function.
Examples
---------
>>> network = tl.layers.DenseLayer(network, n_units=100, name = 'dense_lrelu',
... act= lambda x : tl.act.lrelu(x, 0.2))
References
------------
- `Rectifier Nonlinearities Improve Neural Network Acoustic Models, Maas et al. (2013) <http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf>`_
"""
with tf.name_scope(name) as scope:
# x = tf.nn.relu(x)
# m_x = tf.nn.relu(-x)
# x -= alpha * m_x
x = tf.maximum(x, alpha * x)
return x
#Shortcut
lrelu = leaky_relu
[docs]def pixel_wise_softmax(output, name='pixel_wise_softmax'):
"""Return the softmax outputs of images, every pixels have multiple label, the sum of a pixel is 1.
Usually be used for image segmentation.
Parameters
------------
output : tensor
- For 2d image, 4D tensor [batch_size, height, weight, channel], channel >= 2.
- For 3d image, 5D tensor [batch_size, depth, height, weight, channel], channel >= 2.
Examples
---------
>>> outputs = pixel_wise_softmax(network.outputs)
>>> dice_loss = 1 - dice_coe(outputs, y_, epsilon=1e-5)
References
-----------
- `tf.reverse <https://www.tensorflow.org/versions/master/api_docs/python/array_ops.html#reverse>`_
"""
with tf.name_scope(name) as scope:
return tf.nn.softmax(output)
## old implementation
# exp_map = tf.exp(output)
# if output.get_shape().ndims == 4: # 2d image
# evidence = tf.add(exp_map, tf.reverse(exp_map, [False, False, False, True]))
# elif output.get_shape().ndims == 5: # 3d image
# evidence = tf.add(exp_map, tf.reverse(exp_map, [False, False, False, False, True]))
# else:
# raise Exception("output parameters should be 2d or 3d image, not %s" % str(output._shape))
# return tf.div(exp_map, evidence)