diff --git a/lstm_word2vec_archive.ipynb b/lstm_word2vec_archive.ipynb
new file mode 100644
index 0000000..5b7ad05
--- /dev/null
+++ b/lstm_word2vec_archive.ipynb
@@ -0,0 +1,771 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The code in this notebook is based on the [Keras documentation](https://keras.io/) and [blog](https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html) as well as this [word2vec tutorial](http://adventuresinmachinelearning.com/gensim-word2vec-tutorial/).   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using CNTK backend\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "import time\n",
+    "\n",
+    "os.environ['KERAS_BACKEND']='cntk'\n",
+    "from keras.preprocessing import sequence\n",
+    "from keras.preprocessing.text import Tokenizer, text_to_word_sequence\n",
+    "from keras.models import Sequential\n",
+    "from keras import regularizers\n",
+    "from keras.optimizers import SGD\n",
+    "from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional\n",
+    "from keras.callbacks import History, CSVLogger\n",
+    "from keras.utils import to_categorical"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Download the Amazon reviews data for food from the Internet archive \n",
+    "[J. McAuley and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. RecSys, 2013]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2017-09-21 15:00:07--  https://archive.org/download/amazon-reviews-1995-2013/Gourmet_Foods.txt.gz\n",
+      "Resolving archive.org (archive.org)... 207.241.224.2\n",
+      "Connecting to archive.org (archive.org)|207.241.224.2|:443... connected.\n",
+      "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
+      "Location: https://ia801306.us.archive.org/24/items/amazon-reviews-1995-2013/Gourmet_Foods.txt.gz [following]\n",
+      "--2017-09-21 15:00:07--  https://ia801306.us.archive.org/24/items/amazon-reviews-1995-2013/Gourmet_Foods.txt.gz\n",
+      "Resolving ia801306.us.archive.org (ia801306.us.archive.org)... 207.241.228.136\n",
+      "Connecting to ia801306.us.archive.org (ia801306.us.archive.org)|207.241.228.136|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 31388180 (30M) [application/octet-stream]\n",
+      "Saving to: ‘Gourmet_Foods.txt.gz’\n",
+      "\n",
+      "Gourmet_Foods.txt.g 100%[===================>]  29.93M   186KB/s    in 77s     \n",
+      "\n",
+      "2017-09-21 15:01:25 (397 KB/s) - ‘Gourmet_Foods.txt.gz’ saved [31388180/31388180]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget \"https://archive.org/download/amazon-reviews-1995-2013/Gourmet_Foods.txt.gz\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "!gunzip -f Gourmet_Foods.txt.gz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"Gourmet_Foods.txt\", \"r\") as fp:\n",
+    "    lst = fp.readlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract scores and review texts from file "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "text_lst = lst[9:len(lst):11]\n",
+    "score_lst = lst[6:len(lst):11]\n",
+    "score_lst2 = [sc[14:17] for sc in score_lst]\n",
+    "text_lst2 = [txt[13:] for txt in text_lst]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "all_data = pd.DataFrame(data={'text': text_lst2, 'rating': score_lst2})\n",
+    "all_data.loc[:, 'rating'] = all_data['rating'].astype(float)\n",
+    "all_data.loc[:, 'rating'] = all_data['rating'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remove medium rating and convert to binary classification (high vs. low rating).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "all_data = all_data[all_data['rating'] != 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "new_data = all_data.replace({'rating': {1: '0', 2: '0', 4: '1', 5: '1'}})\n",
+    "new_data.loc[:, 'rating'] = new_data['rating'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract a balanced subsample and split into training and test sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "sample_data = pd.concat([new_data[new_data.rating == 0].sample(10000), new_data[new_data.rating == 1].sample(10000)])\n",
+    "shuffled = sample_data.iloc[np.random.permutation(20000), :]\n",
+    "train_data = shuffled.iloc[:10000, :]\n",
+    "test_data = shuffled.iloc[10000:, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    5005\n",
+       "0    4995\n",
+       "Name: rating, dtype: int64"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.rating.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    5005\n",
+       "1    4995\n",
+       "Name: rating, dtype: int64"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.rating.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Set the dimensions of the input and the embedding. \n",
+    "\n",
+    "MAX_DOC_LENGTH : the size of the input i.e. the number of words in the document. Longer documents will be truncated, shorter ones will be padded with zeros.\n",
+    "\n",
+    "VOCAB_SIZE : the size of the word encoding (number of most frequent words to keep in the vocabulary)\n",
+    "\n",
+    "EMBEDDING_DIM : the dimensionality of the word embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "MAX_DOC_LEN = 300\n",
+    "VOCAB_SIZE = 6000\n",
+    "EMBEDDING_DIM = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "TEXT_COL = 'text'\n",
+    "LABEL_COL = 'rating'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Fit a Keras tokenizer to the most frequent words using the entire training data set as the corpus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# tokenize, create seqs, pad\n",
+    "tok = Tokenizer(num_words=VOCAB_SIZE, lower=True, split=\" \")\n",
+    "tok.fit_on_texts(train_data[TEXT_COL])\n",
+    "train_seq = tok.texts_to_sequences(train_data[TEXT_COL])\n",
+    "train_seq = sequence.pad_sequences(train_seq, maxlen=MAX_DOC_LEN)\n",
+    "test_seq = tok.texts_to_sequences(test_data[TEXT_COL])\n",
+    "test_seq = sequence.pad_sequences(test_seq, maxlen=MAX_DOC_LEN)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Convert the ratings to one-hot categorical labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "labels = to_categorical(np.asarray(train_data[LABEL_COL]))\n",
+    "labels = labels.astype('float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of reviews by class in training set\n",
+      "[ 4995.  5005.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Number of reviews by class in training set')\n",
+    "print(labels.sum(axis=0))\n",
+    "n_classes = labels.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /home/anargyri/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nltk \n",
+    "\n",
+    "nltk.download('punkt')\n",
+    "\n",
+    "sent_lst = []\n",
+    "\n",
+    "for doc in train_data[TEXT_COL]:\n",
+    "    sentences = nltk.tokenize.sent_tokenize(doc)\n",
+    "    for sent in sentences:\n",
+    "        word_lst = [w for w in nltk.tokenize.word_tokenize(sent) if w.isalnum()]\n",
+    "        sent_lst.append(word_lst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2017-09-21 16:29:01,720 : INFO : collecting all words and their counts\n",
+      "2017-09-21 16:29:01,721 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
+      "2017-09-21 16:29:01,761 : INFO : PROGRESS: at sentence #10000, processed 151224 words, keeping 11866 word types\n",
+      "2017-09-21 16:29:01,800 : INFO : PROGRESS: at sentence #20000, processed 300037 words, keeping 17003 word types\n",
+      "2017-09-21 16:29:01,843 : INFO : PROGRESS: at sentence #30000, processed 453236 words, keeping 21102 word types\n",
+      "2017-09-21 16:29:01,890 : INFO : PROGRESS: at sentence #40000, processed 606597 words, keeping 24141 word types\n",
+      "2017-09-21 16:29:01,919 : INFO : collected 26181 word types from a corpus of 712241 raw words and 47144 sentences\n",
+      "2017-09-21 16:29:01,921 : INFO : Loading a fresh vocabulary\n",
+      "2017-09-21 16:29:01,952 : INFO : min_count=6 retains 6289 unique words (24% of original 26181, drops 19892)\n",
+      "2017-09-21 16:29:01,953 : INFO : min_count=6 leaves 678196 word corpus (95% of original 712241, drops 34045)\n",
+      "2017-09-21 16:29:01,975 : INFO : deleting the raw counts dictionary of 26181 items\n",
+      "2017-09-21 16:29:01,977 : INFO : sample=0.001 downsamples 54 most-common words\n",
+      "2017-09-21 16:29:01,977 : INFO : downsampling leaves estimated 500290 word corpus (73.8% of prior 678196)\n",
+      "2017-09-21 16:29:01,978 : INFO : estimated required memory for 6289 words and 100 dimensions: 8175700 bytes\n",
+      "2017-09-21 16:29:01,993 : INFO : resetting layer weights\n",
+      "2017-09-21 16:29:02,117 : INFO : training model with 24 workers on 6289 vocabulary and 100 features, using sg=1 hs=0 sample=0.001 negative=5 window=5\n",
+      "2017-09-21 16:29:03,133 : INFO : PROGRESS: at 48.01% examples, 1196686 words/s, in_qsize 46, out_qsize 1\n",
+      "2017-09-21 16:29:03,918 : INFO : worker thread finished; awaiting finish of 23 more threads\n",
+      "2017-09-21 16:29:03,923 : INFO : worker thread finished; awaiting finish of 22 more threads\n",
+      "2017-09-21 16:29:03,928 : INFO : worker thread finished; awaiting finish of 21 more threads\n",
+      "2017-09-21 16:29:03,932 : INFO : worker thread finished; awaiting finish of 20 more threads\n",
+      "2017-09-21 16:29:03,946 : INFO : worker thread finished; awaiting finish of 19 more threads\n",
+      "2017-09-21 16:29:03,949 : INFO : worker thread finished; awaiting finish of 18 more threads\n",
+      "2017-09-21 16:29:03,960 : INFO : worker thread finished; awaiting finish of 17 more threads\n",
+      "2017-09-21 16:29:03,961 : INFO : worker thread finished; awaiting finish of 16 more threads\n",
+      "2017-09-21 16:29:03,964 : INFO : worker thread finished; awaiting finish of 15 more threads\n",
+      "2017-09-21 16:29:03,971 : INFO : worker thread finished; awaiting finish of 14 more threads\n",
+      "2017-09-21 16:29:03,972 : INFO : worker thread finished; awaiting finish of 13 more threads\n",
+      "2017-09-21 16:29:03,973 : INFO : worker thread finished; awaiting finish of 12 more threads\n",
+      "2017-09-21 16:29:03,975 : INFO : worker thread finished; awaiting finish of 11 more threads\n",
+      "2017-09-21 16:29:03,976 : INFO : worker thread finished; awaiting finish of 10 more threads\n",
+      "2017-09-21 16:29:03,979 : INFO : worker thread finished; awaiting finish of 9 more threads\n",
+      "2017-09-21 16:29:03,981 : INFO : worker thread finished; awaiting finish of 8 more threads\n",
+      "2017-09-21 16:29:03,982 : INFO : worker thread finished; awaiting finish of 7 more threads\n",
+      "2017-09-21 16:29:03,983 : INFO : worker thread finished; awaiting finish of 6 more threads\n",
+      "2017-09-21 16:29:03,985 : INFO : worker thread finished; awaiting finish of 5 more threads\n",
+      "2017-09-21 16:29:03,990 : INFO : worker thread finished; awaiting finish of 4 more threads\n",
+      "2017-09-21 16:29:03,990 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
+      "2017-09-21 16:29:03,992 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
+      "2017-09-21 16:29:04,003 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
+      "2017-09-21 16:29:04,011 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
+      "2017-09-21 16:29:04,012 : INFO : training on 3561205 raw words (2500170 effective words) took 1.9s, 1328895 effective words/s\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gensim, logging\n",
+    "\n",
+    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
+    "# use skip-gram\n",
+    "word2vec_model = gensim.models.Word2Vec(sentences=sent_lst, min_count=6, size=EMBEDDING_DIM, sg=1, workers=os.cpu_count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Create the initial embedding matrix from the output of word2vec."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total 6289 word vectors.\n"
+     ]
+    }
+   ],
+   "source": [
+    "embeddings_index = {}\n",
+    "\n",
+    "for word in word2vec_model.wv.vocab:\n",
+    "    coefs = np.asarray(word2vec_model.wv[word], dtype='float32')\n",
+    "    embeddings_index[word] = coefs\n",
+    "\n",
+    "print('Total %s word vectors.' % len(embeddings_index))\n",
+    "\n",
+    "# Initial embedding\n",
+    "embedding_matrix = np.zeros((VOCAB_SIZE, EMBEDDING_DIM))\n",
+    "\n",
+    "for word, i in tok.word_index.items():\n",
+    "    embedding_vector = embeddings_index.get(word)\n",
+    "    if embedding_vector is not None and i < VOCAB_SIZE:\n",
+    "        embedding_matrix[i] = embedding_vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "BATCH_SIZE = 50\n",
+    "NUM_EPOCHS = 30\n",
+    "LSTM_DIM = 100\n",
+    "OPTIMIZER = SGD(lr=0.01, nesterov=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def lstm_create_train(reg_param):\n",
+    "    l2_reg = regularizers.l2(reg_param)\n",
+    "\n",
+    "    # model init\n",
+    "    embedding_layer = Embedding(VOCAB_SIZE,\n",
+    "                                EMBEDDING_DIM,\n",
+    "                                input_length=MAX_DOC_LEN,\n",
+    "                                trainable=True,\n",
+    "                                mask_zero=False,\n",
+    "                                embeddings_regularizer=l2_reg,\n",
+    "                                weights=[embedding_matrix])\n",
+    "\n",
+    "    lstm_layer = LSTM(units=LSTM_DIM, kernel_regularizer=l2_reg)\n",
+    "    dense_layer = Dense(n_classes, activation='softmax', kernel_regularizer=l2_reg)\n",
+    "\n",
+    "    model = Sequential()\n",
+    "    model.add(embedding_layer)\n",
+    "    model.add(Bidirectional(lstm_layer))\n",
+    "    model.add(dense_layer)\n",
+    "\n",
+    "    model.compile(loss='categorical_crossentropy',\n",
+    "                  optimizer=OPTIMIZER,\n",
+    "                  metrics=['acc'])\n",
+    "\n",
+    "    fname = \"lstm_food\"\n",
+    "    history = History()\n",
+    "    csv_logger = CSVLogger('./{0}_{1}.log'.format(fname, reg_param),\n",
+    "                           separator=',',\n",
+    "                           append=True)\n",
+    "\n",
+    "    t1 = time.time()\n",
+    "    # model fit\n",
+    "    model.fit(train_seq,\n",
+    "              labels.astype('float32'),\n",
+    "              batch_size=BATCH_SIZE,\n",
+    "              epochs=NUM_EPOCHS,\n",
+    "              callbacks=[history, csv_logger],\n",
+    "              verbose=2)\n",
+    "    t2 = time.time()\n",
+    "\n",
+    "    # save model\n",
+    "    model.save('./{0}_{1}_model.h5'.format(fname, reg_param))\n",
+    "    np.savetxt('./{0}_{1}_time.txt'.format(fname, reg_param), \n",
+    "               [reg_param, (t2-t1) / 3600])\n",
+    "    with open('./{0}_{1}_history.txt'.format(fname, reg_param), \"w\") as res_file:\n",
+    "        res_file.write(str(history.history))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/15\n",
+      "76s - loss: 0.6882 - acc: 0.5696\n",
+      "Epoch 2/15\n",
+      "75s - loss: 0.6825 - acc: 0.6040\n",
+      "Epoch 3/15\n",
+      "75s - loss: 0.6768 - acc: 0.6188\n",
+      "Epoch 4/15\n",
+      "75s - loss: 0.6705 - acc: 0.6373\n",
+      "Epoch 5/15\n",
+      "74s - loss: 0.6633 - acc: 0.6426\n",
+      "Epoch 6/15\n",
+      "75s - loss: 0.6543 - acc: 0.6563\n",
+      "Epoch 7/15\n",
+      "74s - loss: 0.6412 - acc: 0.6653\n",
+      "Epoch 8/15\n",
+      "74s - loss: 0.6125 - acc: 0.6884\n",
+      "Epoch 9/15\n",
+      "74s - loss: 0.5350 - acc: 0.7356\n",
+      "Epoch 10/15\n",
+      "75s - loss: 0.5045 - acc: 0.7573\n",
+      "Epoch 11/15\n",
+      "75s - loss: 0.4971 - acc: 0.7684\n",
+      "Epoch 12/15\n",
+      "75s - loss: 0.4850 - acc: 0.7749\n",
+      "Epoch 13/15\n",
+      "75s - loss: 0.4825 - acc: 0.7734\n",
+      "Epoch 14/15\n",
+      "74s - loss: 0.4708 - acc: 0.7800\n",
+      "Epoch 15/15\n",
+      "74s - loss: 0.4700 - acc: 0.7798\n"
+     ]
+    }
+   ],
+   "source": [
+    "lstm_create_train(1e-7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy = 0.8023 \t AUC = 0.8845375045375046\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve\n",
+    "\n",
+    "model = load_model('./lstm_food_{0}_model.h5'.format(1e-7))\n",
+    "preds = model.predict(test_seq, verbose=0)\n",
+    "print((\"Accuracy = {0} \\t AUC = {1}\".format(accuracy_score(test_data[LABEL_COL], preds.argmax(axis=1)), \n",
+    "       roc_auc_score(test_data[LABEL_COL], preds[:, 1]))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7fc621b71518>"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiAAAAF5CAYAAACm4JG+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc621cafef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fpr, tpr, _ = roc_curve(test_data[LABEL_COL], preds[:, 1])\n",
+    "plot(fpr, tpr)\n",
+    "xlabel('FPR')\n",
+    "ylabel('TPR')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lstm_word2vec_small.ipynb b/lstm_word2vec_small.ipynb
index bdbec87..b070565 100644
--- a/lstm_word2vec_small.ipynb
+++ b/lstm_word2vec_small.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -65,7 +65,6 @@
    },
    "outputs": [],
    "source": [
-    "\"\"\"\n",
     "from azureml import Workspace\n",
     "ws = Workspace(\n",
     "    workspace_id='817780d9ee0d4a878e25f8c9deb3b866',\n",
@@ -75,13 +74,12 @@
     "ds = ws.datasets['Book Reviews from Amazon']\n",
     "all_data = ds.to_dataframe()\n",
     "all_data.rename(columns={0: 'rating', 1: 'text'}, inplace=True)\n",
-    "all_data.loc[:, 'rating'] = all_data['rating'] - 1           # reindex ratings to start from 0\n",
-    "\"\"\""
+    "all_data.loc[:, 'rating'] = all_data['rating'] - 1           # reindex ratings to start from 0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -89,6 +87,7 @@
    },
    "outputs": [],
    "source": [
+    "\"\"\"\n",
     "from azureml import Workspace\n",
     "ws = Workspace(\n",
     "    workspace_id='817780d9ee0d4a878e25f8c9deb3b866',\n",
@@ -98,7 +97,8 @@
     "ds = ws.datasets['dfe_happysad_utf.csv']\n",
     "all_data = ds.to_dataframe()\n",
     "all_data.rename(columns={'features': 'text', 'label': 'rating'}, inplace=True)\n",
-    "all_data.replace({'rating': {'sadness': 0, 'happiness': 1}}, inplace=True)"
+    "all_data.replace({'rating': {'sadness': 0, 'happiness': 1}}, inplace=True)\n",
+    "\"\"\""
    ]
   },
   {
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 4,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -230,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -478,110 +478,6 @@
     "        res_file.write(str(history.history))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training model with regularization parameter = 0.0001\n",
-      "Epoch 1/10\n",
-      "33s - loss: 3.5155 - acc: 0.5719\n",
-      "Epoch 2/10\n",
-      "32s - loss: 3.3546 - acc: 0.5819\n",
-      "Epoch 3/10\n",
-      "32s - loss: 3.3257 - acc: 0.5819\n",
-      "Epoch 4/10\n",
-      "32s - loss: 3.3131 - acc: 0.5819\n",
-      "Epoch 5/10\n",
-      "32s - loss: 3.3049 - acc: 0.5819\n",
-      "Epoch 6/10\n",
-      "32s - loss: 3.2991 - acc: 0.5819\n",
-      "Epoch 7/10\n",
-      "32s - loss: 3.2944 - acc: 0.5819\n",
-      "Epoch 8/10\n",
-      "32s - loss: 3.2905 - acc: 0.5819\n",
-      "Epoch 9/10\n",
-      "32s - loss: 3.2875 - acc: 0.5819\n",
-      "Epoch 10/10\n",
-      "32s - loss: 3.2847 - acc: 0.5819\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "lstm_create_train(1e-4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "model = load_model('./lstm_wvec_{}_model.h5'.format(1e-4))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2500/2500 [==============================] - 12s     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
-     ]
-    }
-   ],
-   "source": [
-    "preds = model.predict_classes(test_seq) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.58640000000000003"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score\n",
-    "\n",
-    "accuracy_score(test_data[LABEL_COL], preds)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 58,
@@ -752,7 +648,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": []