From 57b475792c5b47e4758783e509f4ce069087fc03 Mon Sep 17 00:00:00 2001
From: Ubuntu
 <anargyri@anargyrigpu2.4jaf42zu4v0erje5lkulqf23gh.bx.internal.cloudapp.net>
Date: Mon, 11 Sep 2017 18:36:38 +0000
Subject: [PATCH] Update LSTM notebook

---
 lstm_word2vec.ipynb | 67 ++++++++++++++++++++++++++++++++++++---------
 1 file changed, 54 insertions(+), 13 deletions(-)

diff --git a/lstm_word2vec.ipynb b/lstm_word2vec.ipynb
index 938072f..d839e7e 100644
--- a/lstm_word2vec.ipynb
+++ b/lstm_word2vec.ipynb
@@ -29,7 +29,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Download the Amazon reviews training data from a public Azure blob"
    ]
@@ -38,7 +41,9 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -62,7 +67,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Set the dimensions of the input and the embedding. \n",
     "\n",
@@ -77,7 +85,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -102,7 +112,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Fit a Keras tokenizer to the most frequent words using the entire training data set as the corpus."
    ]
@@ -126,9 +139,24 @@
     "test_seq = sequence.pad_sequences(test_seq, maxlen=MAX_DOC_LEN)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "np.save('train_seq', train_seq)\n",
+    "np.save('test_seq', test_seq)"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Convert the ratings to one-hot categorical labels."
    ]
@@ -137,7 +165,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -172,7 +202,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Train word2vec on all the documents in order to initialize the word embedding. Ignore rare words (min_count=6). Use skip-gram as the training algorithm (sg=1)."
    ]
@@ -181,7 +214,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1261,7 +1296,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Create the initial embedding matrix from the output of word2vec."
    ]
@@ -1303,7 +1341,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "LSTM_DIM is the dimensionality of each LSTM output (the number of LSTM units).\n",
     "The mask_zero option determines whether masking is performed, i.e. whether the layers ignore the padded zeros in shorter documents. CNTK / Keras does not support masking yet."
@@ -1366,8 +1407,8 @@
     "\n",
     "    t1 = time.time()\n",
     "    # model fit\n",
-    "    model.fit(train_seq[ind,:],\n",
-    "              y_train[ind,:].astype('float32'),\n",
+    "    model.fit(train_seq,\n",
+    "              labels.astype('float32'),\n",
     "              batch_size=BATCH_SIZE,\n",
     "              epochs=NUM_EPOCHS,\n",
     "              callbacks=[history, csv_logger],\n",