From 4c388e2ba143bf82b320f3a32e3e507f4e54bbc1 Mon Sep 17 00:00:00 2001
From: Benjamin Bolte <bkbolte18@gmail.com>
Date: Wed, 31 Aug 2016 23:20:38 +0000
Subject: [PATCH] added install script for gcp

---
 .gitignore           |  4 ++++
 install.sh           | 33 +++++++++++++++++++++++++++++++++
 insurance_qa_eval.py |  8 ++++----
 3 files changed, 41 insertions(+), 4 deletions(-)
 create mode 100755 install.sh

diff --git a/.gitignore b/.gitignore
index 086c4c3..8696926 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,3 +17,7 @@ treq_eval*
 *.dict
 *.model
 
+# virtual environments
+venv/
+ENV/
+
diff --git a/install.sh b/install.sh
new file mode 100755
index 0000000..7f6666d
--- /dev/null
+++ b/install.sh
@@ -0,0 +1,33 @@
+#!/usr/bin/env bash
+
+# This script will get you up and running on a Google Compute Engine instance
+# Ubuntu 16.04 (as many CPUs as you like)
+
+# exit on failure
+set -e
+
+# install pip (not installed by default on GCE)
+sudo apt install python-pip
+
+# install virtualenv
+sudo pip install virtualenv
+
+# create and activate virtual environment
+virtualenv venv
+source venv/bin/activate
+
+# install h5py
+pip install h5py
+
+# install tensorflow
+export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl
+pip install --upgrade $TF_BINARY_URL
+
+# install keras from source in the home directory
+git clone https://github.com/fchollet/keras ~/keras/
+python ~/keras/setup.py install --home ~/keras/
+echo '{"epsilon": 1e-07, "floatx": "float32", "backend": "tensorflow"}' > ~/.keras/keras.json
+
+# alert user that we're done
+echo ">==< Successfully installed dependencies >==<"
+
diff --git a/insurance_qa_eval.py b/insurance_qa_eval.py
index 9284587..83bac7c 100644
--- a/insurance_qa_eval.py
+++ b/insurance_qa_eval.py
@@ -233,7 +233,7 @@ if __name__ == '__main__':
         'n_words': 22353,
         'question_len': 150,
         'answer_len': 150,
-        'margin': 0.2,
+        'margin': 0.05,
         'initial_embed_weights': 'word2vec_100_dim.embeddings',
 
         'training': {
@@ -243,15 +243,15 @@ if __name__ == '__main__':
         },
 
         'similarity': {
-            'mode': 'gesd',
+            'mode': 'cosine',
             'gamma': 1,
             'c': 1,
             'd': 2,
         }
     }
 
-    from keras_models import AttentionModel
-    evaluator = Evaluator(conf, model=AttentionModel, optimizer='rmsprop')
+    from keras_models import EmbeddingModel
+    evaluator = Evaluator(conf, model=EmbeddingModel, optimizer='sgd')
 
     # train the model
     best_loss = evaluator.train()