Update tree lstm example

example/gluon/tree_lstm/README.md

@@ -0,0 +1,29 @@
+
+# Tree-Structured Long Short-Term Memory Networks
+This is a [MXNet Gluon](https://mxnet.io/) implementation of Tree-LSTM as described in the paper [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](http://arxiv.org/abs/1503.00075) by Kai Sheng Tai, Richard Socher, and Christopher Manning. 
+
+### Requirements
+- Python (tested on **3.6.5**, should work on **>=2.7**)
+- Java >= 8 (for Stanford CoreNLP utilities)
+- Other dependencies are in `requirements.txt`
+Note: Currently works with MXNet 1.3.0.
+
+### Usage
+Before delving into how to run the code, here is a quick overview of the contents:
+ - Use the script `fetch_and_preprocess.sh` to download the [SICK dataset](http://alt.qcri.org/semeval2014/task1/index.php?id=data-and-tools), [Stanford Parser](http://nlp.stanford.edu/software/lex-parser.shtml) and [Stanford POS Tagger](http://nlp.stanford.edu/software/tagger.shtml), and [Glove word vectors](http://nlp.stanford.edu/projects/glove/) (Common Crawl 840) -- **Warning:** this is a 2GB download!), and additionally preprocess the data, i.e. generate dependency parses using [Stanford Neural Network Dependency Parser](http://nlp.stanford.edu/software/nndep.shtml).
+- `main.py`does the actual heavy lifting of training the model and testing it on the SICK dataset. For a list of all command-line arguments, have a look at `python main.py -h`.
+- The first run caches GLOVE embeddings for words in the SICK vocabulary. In later runs, only the cache is read in during later runs.
+
+Next, these are the different ways to run the code here to train a TreeLSTM model.
+#### Local Python Environment
+If you have a working Python3 environment, simply run the following sequence of steps:
+
+```
+- bash fetch_and_preprocess.sh
+- python main.py
+```
+
+
+### Acknowledgments
+- The Gluon version is ported from this implementation [dasguptar/treelstm.pytorch](https://github.com/dasguptar/treelstm.pytorch)
+- Shout-out to [Kai Sheng Tai](https://github.com/kaishengtai/) for the [original LuaTorch implementation](https://github.com/stanfordnlp/treelstm), and to the [Pytorch team](https://github.com/pytorch/pytorch#the-team) for the fun library.

example/gluon/tree_lstm/dataset.py

@@ -115,7 +115,7 @@ def load_embedding(self, f, reset=[]):
                 self.add(word)
             if word in self.tok2idx:
                 vectors[word] = [float(x) for x in tokens[1:]]
-        dim = len(vectors.values()[0])
+        dim = len(list(vectors.values())[0])
         def to_vector(tok):
             if tok in vectors and tok not in reset:
                 return vectors[tok]
@@ -154,7 +154,7 @@ def __init__(self, path, vocab, num_classes, shuffle=True):
 
     def reset(self):
         if self.shuffle:
-            mask = range(self.size)
+            mask = list(range(self.size))
             random.shuffle(mask)
             self.l_sentences = [self.l_sentences[i] for i in mask]
             self.r_sentences = [self.r_sentences[i] for i in mask]

example/gluon/tree_lstm/fetch_and_preprocess.sh

@@ -18,8 +18,8 @@
 # under the License.
 
 set -e
-python2.7 scripts/download.py
+python scripts/download.py
 
 CLASSPATH="lib:lib/stanford-parser/stanford-parser.jar:lib/stanford-parser/stanford-parser-3.5.1-models.jar"
 javac -cp $CLASSPATH lib/*.java
-python2.7 scripts/preprocess-sick.py
+python scripts/preprocess-sick.py

example/gluon/tree_lstm/main.py

@@ -152,7 +152,6 @@ def train(epoch, ctx, train_data, dev_data):
     net.embed.weight.set_data(vocab.embed.as_in_context(ctx[0]))
     train_data.set_context(ctx[0])
     dev_data.set_context(ctx[0])
-
     # set up trainer for optimizing the network.
     trainer = gluon.Trainer(net.collect_params(), optimizer, {'learning_rate': opt.lr, 'wd': opt.wd})
 

example/gluon/tree_lstm/scripts/download.py

@@ -24,7 +24,6 @@
 """
 
 from __future__ import print_function
-import urllib2
 import sys
 import os
 import shutil