[Tensorflow] Several nn & example (dmlc#1191)

* several nn example

* appnp

* fix lint

* lint

* add dgi

* fix

* fix

* fix

* fff

* docs

* 111

* fix

* change init

* change result

* tiaocan+1

* fix

* fix lint

* fix

* fix

docs/source/api/python/nn.rst

@@ -9,3 +9,4 @@ NN Modules
 
    nn.pytorch
    nn.mxnet
+   nn.tensorflow

docs/source/api/python/nn.tensorflow.rst

@@ -0,0 +1,118 @@
+.. _apinn-tensorflow:
+
+NN Modules (Tensorflow)
+====================
+
+.. contents:: Contents
+    :local:
+
+We welcome your contribution! If you want a model to be implemented in DGL as a NN module,
+please `create an issue <https://github.com/dmlc/dgl/issues>`_ started with "[Feature Request] NN Module XXXModel".
+
+If you want to contribute a NN module, please `create a pull request <https://github.com/dmlc/dgl/pulls>`_ started
+with "[NN] XXXModel in tensorflow NN Modules" and our team member would review this PR.
+
+Conv Layers 
+----------------------------------------
+
+.. automodule:: dgl.nn.tensorflow.conv
+
+GraphConv
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.conv.GraphConv
+    :members: weight, bias, forward, reset_parameters
+    :show-inheritance:
+
+RelGraphConv
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.conv.RelGraphConv
+    :members: forward
+    :show-inheritance:
+
+GATConv
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.conv.GATConv
+    :members: forward
+    :show-inheritance:
+
+SAGEConv
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.conv.SAGEConv
+    :members: forward
+    :show-inheritance:
+
+SGConv
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.conv.SGConv
+    :members: forward
+    :show-inheritance:
+
+APPNPConv
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.conv.APPNPConv
+    :members: forward
+    :show-inheritance:
+
+GINConv
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.conv.GINConv
+    :members: forward
+    :show-inheritance:
+
+
+Global Pooling Layers 
+----------------------------------------
+
+.. automodule:: dgl.nn.tensorflow.glob
+
+SumPooling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.glob.SumPooling
+    :members:
+    :show-inheritance:
+
+AvgPooling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.glob.AvgPooling
+    :members:
+    :show-inheritance:
+
+MaxPooling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.glob.MaxPooling
+    :members:
+    :show-inheritance:
+
+SortPooling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.glob.SortPooling
+    :members:
+    :show-inheritance:
+
+GlobalAttentionPooling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: dgl.nn.tensorflow.glob.GlobalAttentionPooling
+    :members:
+    :show-inheritance:
+
+
+Utility Modules
+----------------------------------------
+
+Edge Softmax
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. automodule:: dgl.nn.tensorflow.softmax
+    :members: edge_softmax

examples/pytorch/dgi/gcn.py

@@ -31,5 +31,5 @@ def forward(self, features):
         for i, layer in enumerate(self.layers):
             if i != 0:
                 h = self.dropout(h)
-            h = layer(h, self.g)
+            h = layer(self.g, h)
         return h

examples/pytorch/gat/README.md

@@ -41,11 +41,11 @@ python3 train_ppi.py --gpu=0
 Results
 -------
 
-| Dataset | Test Accuracy | Time(s) | Baseline#1 times(s) | Baseline#2 times(s) |
-| ------- | ------------- | ------- | ------------------- | ------------------- |
-| Cora | 84.02(0.40) | 0.0113 | 0.0982 (**8.7x**) | 0.0424 (**3.8x**) |
-| Citeseer | 70.91(0.79) | 0.0111 | n/a | n/a |
-| Pubmed | 78.57(0.75) | 0.0115 | n/a | n/a |
+| Dataset  | Test Accuracy | Time(s) | Baseline#1 times(s) | Baseline#2 times(s) |
+| -------- | ------------- | ------- | ------------------- | ------------------- |
+| Cora     | 84.02(0.40)   | 0.0113  | 0.0982 (**8.7x**)   | 0.0424 (**3.8x**)   |
+| Citeseer | 70.91(0.79)   | 0.0111  | n/a                 | n/a                 |
+| Pubmed   | 78.57(0.75)   | 0.0115  | n/a                 | n/a                 |
 
 * All the accuracy numbers are obtained after 300 epochs.
 * The time measures how long it takes to train one epoch.

examples/tensorflow/dgi/README.md

@@ -0,0 +1,38 @@
+Deep Graph Infomax (DGI)
+========================
+
+- Paper link: [https://arxiv.org/abs/1809.10341](https://arxiv.org/abs/1809.10341)
+- Author's code repo (in Pytorch):
+  [https://github.com/PetarV-/DGI](https://github.com/PetarV-/DGI)
+
+Dependencies
+------------
+- tensorflow 2.1+
+- requests
+
+```bash
+pip install tensorflow requests
+```
+
+How to run
+----------
+
+Run with following:
+
+```bash
+python3 train.py --dataset=cora --gpu=0 --self-loop
+```
+
+```bash
+python3 train.py --dataset=citeseer --gpu=0
+```
+
+```bash
+python3 train.py --dataset=pubmed --gpu=0
+```
+
+Results
+-------
+* cora: ~81.6 (80.9-82.9) (paper: 82.3)
+* citeseer: ~70.2 (paper: 71.8)
+* pubmed: ~77.2 (paper: 76.8)

examples/tensorflow/dgi/dgi.py

@@ -0,0 +1,75 @@
+"""
+Deep Graph Infomax in DGL
+
+References
+----------
+Papers: https://arxiv.org/abs/1809.10341
+Author's code: https://github.com/PetarV-/DGI
+"""
+
+import tensorflow as tf
+from tensorflow.keras import layers
+import numpy as np
+import math
+from gcn import GCN
+
+
+class Encoder(layers.Layer):
+    def __init__(self, g, in_feats, n_hidden, n_layers, activation, dropout):
+        super(Encoder, self).__init__()
+        self.g = g
+        self.conv = GCN(g, in_feats, n_hidden, n_hidden,
+                        n_layers, activation, dropout)
+
+    def call(self, features, corrupt=False):
+        if corrupt:
+            perm = np.random.permutation(self.g.number_of_nodes())
+            features = tf.gather(features, perm)
+        features = self.conv(features)
+        return features
+
+
+class Discriminator(layers.Layer):
+    def __init__(self, n_hidden):
+        super(Discriminator, self).__init__()
+        uinit = tf.keras.initializers.RandomUniform(
+            -1.0/math.sqrt(n_hidden), 1.0/math.sqrt(n_hidden))
+        self.weight = tf.Variable(initial_value=uinit(
+            shape=(n_hidden, n_hidden), dtype='float32'), trainable=True)
+
+    def call(self, features, summary):
+        features = tf.matmul(features, tf.matmul(
+            self.weight, tf.expand_dims(summary, -1)))
+        return features
+
+
+class DGI(tf.keras.Model):
+    def __init__(self, g, in_feats, n_hidden, n_layers, activation, dropout):
+        super(DGI, self).__init__()
+        self.encoder = Encoder(g, in_feats, n_hidden,
+                               n_layers, activation, dropout)
+        self.discriminator = Discriminator(n_hidden)
+        self.loss = tf.nn.sigmoid_cross_entropy_with_logits
+
+    def call(self, features):
+        positive = self.encoder(features, corrupt=False)
+        negative = self.encoder(features, corrupt=True)
+        summary = tf.nn.sigmoid(tf.reduce_mean(positive, axis=0))
+
+        positive = self.discriminator(positive, summary)
+        negative = self.discriminator(negative, summary)
+
+        l1 = self.loss(tf.ones(positive.shape),positive)
+        l2 = self.loss(tf.zeros(negative.shape), negative)
+
+        return tf.reduce_mean(l1) + tf.reduce_mean(l2)
+
+
+class Classifier(layers.Layer):
+    def __init__(self, n_hidden, n_classes):
+        super(Classifier, self).__init__()
+        self.fc = layers.Dense(n_classes)
+
+    def call(self, features):
+        features = self.fc(features)
+        return features

examples/tensorflow/dgi/gcn.py

@@ -0,0 +1,36 @@
+"""
+This code was copied from the GCN implementation in DGL examples.
+"""
+import tensorflow as tf
+from tensorflow.keras import layers
+
+from dgl.nn.tensorflow import GraphConv
+
+class GCN(layers.Layer):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_hidden,
+                 n_classes,
+                 n_layers,
+                 activation,
+                 dropout):
+        super(GCN, self).__init__()
+        self.g = g
+        self.layers =[]
+        # input layer
+        self.layers.append(GraphConv(in_feats, n_hidden, activation=activation))
+        # hidden layers
+        for i in range(n_layers - 1):
+            self.layers.append(GraphConv(n_hidden, n_hidden, activation=activation))
+        # output layer
+        self.layers.append(GraphConv(n_hidden, n_classes))
+        self.dropout = layers.Dropout(dropout)
+
+    def call(self, features):
+        h = features
+        for i, layer in enumerate(self.layers):
+            if i != 0:
+                h = self.dropout(h)
+            h = layer(self.g, h)
+        return h