Keras_sentiment_analysis.ipynb

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   "cell_type": "code",
   "execution_count": 142,
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   "source": [
    "import numpy as np\n",
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "import re\n",
    "from collections import Counter\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import add\n",
    "from keras.layers import concatenate\n",
    "from keras.layers import Concatenate\n",
    "from keras.layers import Input\n",
    "from keras.layers import Conv1D,Convolution1D\n",
    "from keras.layers import MaxPool1D,MaxPooling1D\n",
    "from keras.layers import SpatialDropout1D\n",
    "from keras.optimizers import Adam\n",
    "from keras.objectives import binary_crossentropy\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import Merge\n",
    "from keras.layers.recurrent import LSTM\n",
    "import numpy as Math\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "\n",
    "import sklearn\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "def read_files(foldername):\n",
    "    import os\n",
    "    sentiments = []\n",
    "    filenames = os.listdir(os.curdir+ \"/\"+foldername)\n",
    "    for file in filenames:\n",
    "        with open(foldername+\"/\"+file,\"r\", encoding=\"utf8\") as pos_file:\n",
    "            data=pos_file.read().replace('\\n', '')\n",
    "            sentiments.append(data)\n",
    "    return sentiments\n",
    "    \n",
    "    \n",
    "foldername = \"easy/pos\"\n",
    "postive_sentiment = read_files(foldername)\n",
    "\n",
    "foldername = \"easy/neg\"\n",
    "negative_sentiment = read_files(foldername)\n",
    "\n",
    "positive_labels = [1 for _ in postive_sentiment]\n",
    "negative_labels = [0 for _ in negative_sentiment]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
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   "source": [
    "def clean_str(string):\n",
    "    \"\"\"\n",
    "    Tokenization/string cleaning for all datasets except for SST.\n",
    "    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py\n",
    "    \"\"\"\n",
    "    string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\n",
    "    string = re.sub(r\"\\'s\", \" \\'s\", string)\n",
    "    string = re.sub(r\"\\'ve\", \" \\'ve\", string)\n",
    "    string = re.sub(r\"n\\'t\", \" n\\'t\", string)\n",
    "    string = re.sub(r\"\\'re\", \" \\'re\", string)\n",
    "    string = re.sub(r\"\\'d\", \" \\'d\", string)\n",
    "    string = re.sub(r\"\\'ll\", \" \\'ll\", string)\n",
    "    string = re.sub(r\",\", \" \", string)\n",
    "    string = re.sub(r\"!\", \" ! \", string)\n",
    "    string = re.sub(r\"\\(\", \" \", string)\n",
    "    string = re.sub(r\"\\)\", \" \", string)\n",
    "    string = re.sub(r\"\\?\", \" \\? \", string)\n",
    "    string = re.sub(r\"\\s{2,}\", \" \", string)\n",
    "    string = re.sub(r\",\", \" \", string)\n",
    "    string = re.sub(r\"'\", \" \", string)\n",
    "    string = re.sub(r\"\\[\", \" \", string)\n",
    "    string = re.sub(r\"\\]\", \" \", string)\n",
    "    return string.strip().lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# word_dict = {}\n",
    "# word_counter = Counter()\n",
    "# def create_count(sentiments):\n",
    "#     idx = 0\n",
    "#     for line in sentiments:\n",
    "#         for word in (clean_str(line)).split():\n",
    "#             if word not in word_dict.keys():               \n",
    "#                 word_counter[word] = 1\n",
    "#                 word_dict[word] = idx\n",
    "#                 idx+=1\n",
    "#             else:\n",
    "#                 word_counter[word] += 1\n",
    "\n",
    "\n",
    "# model_path = \"../models/\"\n",
    "# all_sentiments = postive_sentiment + negative_sentiment\n",
    "# create_count(all_sentiments)\n",
    "# idx2word = {v: k for k, v in word_dict.items()}\n",
    "\n",
    "word_counter = Counter()\n",
    "def create_count(sentiments):\n",
    "    idx = 0\n",
    "    for line in sentiments:\n",
    "        for word in (clean_str(line)).split():\n",
    "            if word not in word_counter.keys():               \n",
    "                word_counter[word] = 1\n",
    "            else:\n",
    "                word_counter[word] += 1\n",
    "\n",
    "def create_word_index():\n",
    "    idx = 0\n",
    "    word_dict = {}\n",
    "    for word in word_counter.most_common():\n",
    "        word_dict[word[0]] = idx\n",
    "        idx+=1\n",
    "    return word_dict\n",
    "    \n",
    "\n",
    "\n",
    "model_path = \"../models/\"\n",
    "all_sentiments = postive_sentiment + negative_sentiment\n",
    "all_labels = positive_labels + negative_labels\n",
    "create_count(all_sentiments)\n",
    "word_dict = create_word_index()\n",
    "idx2word = {v: k for k, v in word_dict.items()}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
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   "outputs": [],
   "source": [
    "def encoded_sentences(input_file,word_dict):\n",
    "    output_string = []\n",
    "    for line in input_file:\n",
    "        output_line = []\n",
    "        print(line)\n",
    "        for word in (clean_str(line)).split():\n",
    "            output_line.append(word_dict[word])\n",
    "        output_string.append(output_line)\n",
    "    return output_string\n",
    "\n",
    "def decode_sentences(input_file,word_dict):\n",
    "    output_string = []\n",
    "    for line in input_file:\n",
    "        output_line = ''\n",
    "        for idx in line:\n",
    "            output_line += idx2word[idx] + ' '\n",
    "        output_string.append(output_line)\n",
    "    return output_string\n",
    "\n",
    "def pad_sequences(sentences,maxlen=500,value=0):\n",
    "    \"\"\"\n",
    "    Pads all sentences to the same length. The length is defined by the longest sentence.\n",
    "    Returns padded sentences.\n",
    "    \"\"\"\n",
    "    padded_sentences = []\n",
    "    for sen in sentences:\n",
    "        new_sentence = []\n",
    "        if(len(sen) > maxlen):\n",
    "            new_sentence = sen[:maxlen]\n",
    "            padded_sentences.append(new_sentence)\n",
    "        else:\n",
    "            num_padding = maxlen - len(sen)\n",
    "            new_sentence = np.append(sen,[value] * num_padding)\n",
    "            padded_sentences.append(new_sentence)\n",
    "    return padded_sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "positive_encoded = encoded_sentences(postive_sentiment,word_dict)\n",
    "negative_encoded = encoded_sentences(negative_sentiment,word_dict)\n",
    "\n",
    "all_encoded = positive_encoded + negative_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vocab_size = 5000\n",
    "seq_len = 500\n",
    "embedding_dim = 50\n",
    "\n",
    "#trn = [np.array([i if i<vocab_size-1 else vocab_size-1 for i in s]) for s in train_set]\n",
    "#test = [np.array([i if i<vocab_size-1 else vocab_size-1 for i in s]) for s in test_set]\n",
    "t_data = [np.array([i if i<vocab_size-1 else vocab_size-1 for i in s]) for s in all_encoded]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_val, X_test, y_train_val, y_test = train_test_split(t_data, all_labels, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the minimum length is: 10\n",
      "the maximum length is: 2564\n",
      "the average length is: 245.28632\n"
     ]
    }
   ],
   "source": [
    "min_len = min(map(len, t_data))\n",
    "max_len = max(map(len,t_data))\n",
    "avg_len = sum(map(len,t_data)) / len(t_data)\n",
    "print(\"the minimum length is:\",min_len)\n",
    "print(\"the maximum length is:\",max_len)\n",
    "print(\"the average length is:\",avg_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trn = sequence.pad_sequences(X_train, maxlen=seq_len, value=0)\n",
    "# val = sequence.pad_sequences(X_val, maxlen=seq_len, value=0)\n",
    "# test = sequence.pad_sequences(X_test, maxlen=seq_len, value=0)\n",
    "\n",
    "trn = np.array(pad_sequences(X_train, maxlen=seq_len, value=0))\n",
    "val = np.array(pad_sequences(X_val, maxlen=seq_len, value=0))\n",
    "test = np.array(pad_sequences(X_test, maxlen=seq_len, value=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dense_model = Sequential([\n",
    "    Embedding(vocab_size, embedding_dim, input_length=seq_len),\n",
    "    Flatten(),\n",
    "    Dense(1, activation='relu')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_4 (Embedding)      (None, 500, 50)           250000    \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 25000)             0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 25001     \n",
      "=================================================================\n",
      "Total params: 275,001\n",
      "Trainable params: 275,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "dense_model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
    "dense_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 12250 samples, validate on 5250 samples\n",
      "Epoch 1/5\n",
      "12250/12250 [==============================] - 1s - loss: 0.7631 - acc: 0.5695 - val_loss: 0.6206 - val_acc: 0.7050\n",
      "Epoch 2/5\n",
      "12250/12250 [==============================] - 1s - loss: 0.4576 - acc: 0.8310 - val_loss: 0.4269 - val_acc: 0.8371\n",
      "Epoch 3/5\n",
      "12250/12250 [==============================] - 1s - loss: 0.2501 - acc: 0.9021 - val_loss: 0.4569 - val_acc: 0.8446\n",
      "Epoch 4/5\n",
      "12250/12250 [==============================] - 1s - loss: 0.1457 - acc: 0.9173 - val_loss: 0.4834 - val_acc: 0.8240\n",
      "Epoch 5/5\n",
      "12250/12250 [==============================] - 1s - loss: 0.0830 - acc: 0.9078 - val_loss: 0.5377 - val_acc: 0.7897\n"
     ]
    }
   ],
   "source": [
    "dense_model.fit(trn, y_train, validation_data=(val, y_val), epochs=5, batch_size=64)\n",
    "dense_model.save_weights(\"model/dense.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dense_model.load_weights(\"model/dense.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n",
      "[[-0.00239288 -0.01767137 -0.02746839 ..., -0.02318721 -0.00642159\n",
      "  -0.02290398]\n",
      " [-0.00600406  0.00443663 -0.03258764 ...,  0.01854093  0.02275047\n",
      "   0.03073082]\n",
      " [-0.01933826  0.0009991   0.02818683 ..., -0.00352166  0.01050212\n",
      "   0.03621733]\n",
      " ..., \n",
      " [ 0.05159397 -0.09803655 -0.02393755 ...,  0.00204469  0.02954685\n",
      "   0.05481454]\n",
      " [-0.03628892  0.0141897  -0.04213191 ...,  0.0391539  -0.02609709\n",
      "   0.13599665]\n",
      " [-0.01367764 -0.00889422  0.02013562 ..., -0.04143726  0.03607545\n",
      "  -0.00659699]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'and'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = dense_model.get_weights()\n",
    "#print(weights[1][1])\n",
    "dense_model.get_config()\n",
    "print(len(weights[0]))\n",
    "print((weights[0]))\n",
    "idx2word[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
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       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"638.888905813665\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne = TSNE(n_components=2, random_state=0)\n",
    "size = 1000\n",
    "Y = tsne.fit_transform(weights[0][:size,:])\n",
    "plt.scatter(Y[:, 0], Y[:, 1])\n",
    "for idx, (x, y) in enumerate(zip(Y[:, 0], Y[:, 1])):\n",
    "    plt.annotate(idx2word[idx], xy=(x, y), xytext=(0, 0), textcoords='offset points')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7424/7500 [============================>.] - ETA: 0sthe accuracy on test set is: 0.794666666667\n"
     ]
    }
   ],
   "source": [
    "loss_and_metrics = dense_model.evaluate(test, y_test, batch_size=64);\n",
    "print(\"the accuracy on test set is:\",loss_and_metrics[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv1 = Sequential([\n",
    "    Embedding(vocab_size, embedding_dim, input_length=seq_len),\n",
    "    SpatialDropout1D(0.4),\n",
    "    Convolution1D(64, 5, padding='same', activation='relu'),\n",
    "    Dropout(0.4),\n",
    "    MaxPooling1D(),\n",
    "    Flatten(),\n",
    "    Dense(200, activation='relu'),\n",
    "    Dropout(0.3),\n",
    "    Dense(1, activation='sigmoid')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv1.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 12250 samples, validate on 5250 samples\n",
      "Epoch 1/10\n",
      "12250/12250 [==============================] - 7s - loss: 0.5856 - acc: 0.6527 - val_loss: 0.3425 - val_acc: 0.8533\n",
      "Epoch 2/10\n",
      "12250/12250 [==============================] - 7s - loss: 0.2839 - acc: 0.8904 - val_loss: 0.2919 - val_acc: 0.8827\n",
      "Epoch 3/10\n",
      "12250/12250 [==============================] - 7s - loss: 0.1948 - acc: 0.9270 - val_loss: 0.2881 - val_acc: 0.8802\n",
      "Epoch 4/10\n",
      "12250/12250 [==============================] - 7s - loss: 0.1530 - acc: 0.9451 - val_loss: 0.3433 - val_acc: 0.8630\n",
      "Epoch 5/10\n",
      "12250/12250 [==============================] - 7s - loss: 0.1083 - acc: 0.9607 - val_loss: 0.3911 - val_acc: 0.8636\n",
      "Epoch 6/10\n",
      "12250/12250 [==============================] - 7s - loss: 0.0763 - acc: 0.9736 - val_loss: 0.4551 - val_acc: 0.8629\n",
      "Epoch 7/10\n",
      "12250/12250 [==============================] - 7s - loss: 0.0675 - acc: 0.9749 - val_loss: 0.4508 - val_acc: 0.8693\n",
      "Epoch 8/10\n",
      "12250/12250 [==============================] - 7s - loss: 0.0528 - acc: 0.9806 - val_loss: 0.5131 - val_acc: 0.8613\n",
      "Epoch 9/10\n",
      "12250/12250 [==============================] - 7s - loss: 0.0501 - acc: 0.9821 - val_loss: 0.5265 - val_acc: 0.8691\n",
      "Epoch 10/10\n",
      "12250/12250 [==============================] - 7s - loss: 0.0437 - acc: 0.9845 - val_loss: 0.5361 - val_acc: 0.8667\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x243417e9f60>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv1.fit(trn, y_train, validation_data=(val, y_val), epochs=10, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7488/7500 [============================>.] - ETA: 0sthe accuracy on test set is: 0.870266666667\n"
     ]
    }
   ],
   "source": [
    "loss_and_metrics = conv1.evaluate(test, y_test, batch_size=64);\n",
    "print(\"the accuracy on test set is:\",loss_and_metrics[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_7 (Embedding)      (None, 500, 50)           250000    \n",
      "_________________________________________________________________\n",
      "spatial_dropout1d_3 (Spatial (None, 500, 50)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_6 (Conv1D)            (None, 500, 64)           16064     \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 500, 64)           0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_6 (MaxPooling1 (None, 250, 64)           0         \n",
      "_________________________________________________________________\n",
      "flatten_10 (Flatten)         (None, 16000)             0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 200)               3200200   \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 1)                 201       \n",
      "=================================================================\n",
      "Total params: 3,466,465\n",
      "Trainable params: 3,466,465\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "graph_in = Input((vocab_size, embedding_dim))\n",
    "convs = [ ] \n",
    "for fsz in range (3, 6): \n",
    "    x = Convolution1D(64, fsz, padding='same', activation=\"relu\")(graph_in)\n",
    "    x = MaxPooling1D()(x) \n",
    "    x = Flatten()(x) \n",
    "    convs.append(x)\n",
    "out = concatenate(convs) \n",
    "graph = Model(graph_in, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv1_multiple = Sequential ([\n",
    "    Embedding(vocab_size, embedding_dim, input_length=seq_len),\n",
    "    SpatialDropout1D(0.2),\n",
    "    graph,\n",
    "    Dropout (0.5),\n",
    "    Dense (100, activation=\"relu\"),\n",
    "    Dropout (0.3),\n",
    "    Dense (1, activation='sigmoid')\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'conv1_multiple' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-107-8872a6c585d8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mconv1_multiple\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'binary_crossentropy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mAdam\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mconv1_multiple\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'conv1_multiple' is not defined"
     ]
    }
   ],
   "source": [
    "conv1_multiple.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
    "conv1_multiple.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv1_multiple.fit(trn, y_train, validation_data=(val, y_val), epochs=10, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss_and_metrics = conv1_multiple.evaluate(test, y_test, batch_size=64);\n",
    "print(\"the accuracy on test set is:\",loss_and_metrics[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_glove_index(loc):\n",
    "    f = open(loc,encoding=\"utf8\")\n",
    "    embeddings_index = {}\n",
    "    for line in f:\n",
    "        values = line.split()\n",
    "        word = values[0]\n",
    "        coefs = np.asarray(values[1:], dtype='float32')\n",
    "        embeddings_index[word] = coefs\n",
    "    f.close()\n",
    "    return embeddings_index\n",
    "\n",
    "def create_emb():\n",
    "    embedding_matrix = np.zeros((vocab_size, embedding_dim))\n",
    "    for word, i in word_dict.items():\n",
    "        if i >= vocab_size:\n",
    "            continue\n",
    "        embedding_vector = embeddings_index.get(word)\n",
    "        if embedding_vector is not None:\n",
    "            # words not found in embedding index will be all-zeros.\n",
    "            embedding_matrix[i] = embedding_vector\n",
    "    return embedding_matrix\n",
    "embeddings_index = load_glove_index(\"glove/\" + 'glove.6B.50d.txt')\n",
    "embedding_matrix = create_emb();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv1_multiple_embedding = Sequential ([\n",
    "    Embedding(vocab_size, embedding_dim, input_length=seq_len,weights=[embedding_matrix],trainable=False),\n",
    "    SpatialDropout1D(0.2),\n",
    "    graph,\n",
    "    Dropout (0.3),\n",
    "    Dense (200, activation=\"relu\"),\n",
    "    Dropout (0.3),\n",
    "    Dense (1, activation='sigmoid')\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv1_multiple_embedding.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
    "conv1_multiple_embedding.fit(trn, y_train, validation_data=(val, y_val), epochs=14, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss_and_metrics = conv1_multiple_embedding.evaluate(test, y_test, batch_size=64);\n",
    "print(\"the accuracy on test set is:\",loss_and_metrics[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv1_embed = Sequential([\n",
    "    Embedding(vocab_size, embedding_dim, input_length=seq_len,weights=[embedding_matrix]),\n",
    "    SpatialDropout1D(0.4),\n",
    "    Convolution1D(64, 5, padding='same', activation='relu'),\n",
    "    Dropout(0.4),\n",
    "    MaxPooling1D(),\n",
    "    Flatten(),\n",
    "    Dense(100, activation='relu'),\n",
    "    Dropout(0.3),\n",
    "    Dense(1, activation='sigmoid')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv1_embed.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
    "conv1_embed.fit(trn, y_train, validation_data=(val, y_val), epochs=12, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss_and_metrics = conv1_embed.evaluate(test, y_test, batch_size=64);\n",
    "print(\"the accuracy on test set is:\",loss_and_metrics[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"710.0000188085772\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne = TSNE(n_components=2, random_state=0)\n",
    "size = 1000\n",
    "Y = tsne.fit_transform(embedding_matrix[:size,:])\n",
    "plt.scatter(Y[:, 0], Y[:, 1])\n",
    "for idx, (x, y) in enumerate(zip(Y[:, 0], Y[:, 1])):\n",
    "    plt.annotate(idx2word[idx], xy=(x, y), xytext=(0, 0), textcoords='offset points')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.plot()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def encoded_sentences2(input_file,word_dict):\n",
    "    output_string = []\n",
    "    for line in input_file:\n",
    "        output_line = []\n",
    "        for word in (clean_str(line)).split():\n",
    "            output_line.append(word_dict[word])\n",
    "        output_string.append(output_line)\n",
    "    return output_string\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the movie was awesome. Loved it\n",
      "I hate this movie\n"
     ]
    }
   ],
   "source": [
    "my_sen =[\"the movie was awesome. Loved it\",\"I hate this movie\"]\n",
    "my_sen_encoded = encoded_sentences2(my_sen,word_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sklearn' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-137-db22eb6f271f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msklearn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'sklearn' is not defined"
     ]
    }
   ],
   "source": [
    "sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}