update

docs/source/conf.py

@@ -73,13 +73,12 @@ lanaguage = "zh_cn"
 html_theme = "sphinx_rtd_theme"
 html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 html_show_sourcelink = False
-#html_logo = 'pgl_logo.png'
+html_logo = '_static/logo.png'
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = ['_static']
-'''
 html_theme_options = {
     'canonical_url': '',
     'analytics_id': 'UA-XXXXXXX-1',  #  Provided by Google in your dashboard
@@ -96,4 +95,3 @@ html_theme_options = {
     'includehidden': True,
     'titles_only': False
 }
-'''

docs/source/md/introduction.md

 # Paddle Graph Learning (PGL)
 
-<div />
-<div align=left><img src="_static/logo.png" width="300"></div>
-<div />
 Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle).
 
 

examples/metapath2vec/README.md

@@ -20,7 +20,7 @@ Some important hyper parameters in config.yaml:
 - **use_cuda**: use GPU to train model
 - **data_path**: the directory of dataset that you want to load
 - **lr**: learning rate
-- **neg_num**: number of negatie samples.
+- **neg_num**: number of negative samples.
 - **num_walks**: number of walks started from each node
 - **walk_length**: walk length
 - **metapath**: meta path scheme

pgl/layers/__init__.py

@@ -16,6 +16,7 @@
 
 from pgl.layers import conv
 from pgl.layers.conv import *
+from pgl.layers import set2set
 from pgl.layers.set2set import *
 
 __all__ = []

pgl/layers/set2set.py

@@ -23,6 +23,8 @@ import paddle.fluid.layers as L
 
 import pgl
 
+__all__ = ['Set2Set']
+
 
 class Set2Set(object):
     """Implementation of set2set pooling operator.

pgl/utils/paddle_helper.py

@@ -223,9 +223,10 @@ def scatter_add(input, index, updates):
         Same type and shape as input.
     """
 
-    output = fluid.layers.scatter(input, index, updates, mode='add')
+    output = fluid.layers.scatter(input, index, updates, overwrite=False)
     return output
 
+
 def scatter_max(input, index, updates):
     """Scatter max updates to input by given index.
 
@@ -244,4 +245,3 @@ def scatter_max(input, index, updates):
 
     output = fluid.layers.scatter(input, index, updates, mode='max')
     return output
-