update TL to latest version

Author: zsdonghao

README.md

@@ -64,6 +64,9 @@ python main.py --mode=evaluate
 * [1] [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802)
 * [2] [Is the deconvolution layer the same as a convolutional layer ?](https://arxiv.org/abs/1609.07009)
 
+### Author
+- [zsdonghao](https://github.com/zsdonghao)
+
 ### License
 
 - For academic and non-commercial use only.

main.py

@@ -278,8 +278,8 @@ def evaluate():
     # print(valid_lr_img.min(), valid_lr_img.max())
 
     size = valid_lr_img.shape
-    t_image = tf.placeholder('float32', [None, size[0], size[1], size[2]], name='input_image')
-    # t_image = tf.placeholder('float32', [1, None, None, 3], name='input_image')
+    # t_image = tf.placeholder('float32', [None, size[0], size[1], size[2]], name='input_image') # the old version of TL need to specify the image size
+    t_image = tf.placeholder('float32', [1, None, None, 3], name='input_image')
 
     net_g = SRGAN_g(t_image, is_train=False, reuse=False)
 

tensorlayer/__init__.py

@@ -21,12 +21,13 @@
 from . import prepro
 from . import nlp
 from . import rein
+from . import distributed
 
 # alias
 act = activation
 vis = visualize
 
-__version__ = "1.5.0"
+__version__ = "1.7.3"
 
 global_flag = {}
 global_dict = {}

tensorlayer/activation.py

@@ -1,7 +1,5 @@
 #! /usr/bin/python
-# -*- coding: utf8 -*-
-
-
+# -*- coding: utf-8 -*-
 
 import tensorflow as tf
 
@@ -13,7 +11,6 @@ def identity(x, name=None):
     x : a tensor input
         input(s)
 
-
     Returns
     --------
     A `Tensor` with the same type as `x`.
@@ -37,14 +34,13 @@ def ramp(x=None, v_min=0, v_max=1, name=None):
     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)
 
-def leaky_relu(x=None, alpha=0.1, name="LeakyReLU"):
+def leaky_relu(x=None, alpha=0.1, name="lrelu"):
     """The LeakyReLU, Shortcut is ``lrelu``.
 
     Modified version of ReLU, introducing a nonzero gradient for negative
@@ -67,16 +63,33 @@ def leaky_relu(x=None, alpha=0.1, name="LeakyReLU"):
     ------------
     - `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:
+    # 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)
+    x = tf.maximum(x, alpha * x, name=name)
     return x
 
 #Shortcut
 lrelu = leaky_relu
 
+
+def swish(x, name='swish'):
+    """The Swish function, see `Swish: a Self-Gated Activation Function <https://arxiv.org/abs/1710.05941>`_.
+
+    Parameters
+    ----------
+    x : a tensor input
+        input(s)
+
+    Returns
+    --------
+    A `Tensor` with the same type as `x`.
+    """
+    with tf.name_scope(name) as scope:
+        x =  tf.nn.sigmoid(x) * x
+    return x
+
 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.