indexing-high-dimension-data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marco/.virtualenvs/ml/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import scipy.spatial\n",
    "import sklearn.pipeline\n",
    "import sklearn.decomposition\n",
    "import MulticoreTSNE\n",
    "import keras\n",
    "import keras.datasets\n",
    "import keras.backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data, label), (test_data, test_label) = keras.datasets.mnist.load_data()\n",
    "data = np.reshape(data, (data.shape[0], data.shape[1]*data.shape[2]))\n",
    "data = data / 255\n",
    "test_data = np.reshape(test_data, (test_data.shape[0], test_data.shape[1]*test_data.shape[2]))\n",
    "test_data = test_data / 255\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tsne = sklearn.pipeline.Pipeline([\n",
    "    ('pca', sklearn.decomposition.PCA(50)),\n",
    "    ('tsne', MulticoreTSNE.MulticoreTSNE(verbose=True, n_jobs=8)),\n",
    "])\n",
    "tsne_data = tsne.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 784)               3136      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 573,122\n",
      "Trainable params: 569,762\n",
      "Non-trainable params: 3,360\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "X = X_input = keras.layers.Input((data.shape[-1],))\n",
    "X = keras.layers.BatchNormalization()(X)\n",
    "X = keras.layers.Dropout(0.5)(X)\n",
    "X = keras.layers.Dense(512, kernel_initializer='he_normal')(X)\n",
    "X = keras.layers.BatchNormalization()(X)\n",
    "X = keras.layers.Activation('relu')(X)\n",
    "X = keras.layers.Dense(256, kernel_initializer='he_normal')(X)\n",
    "X = keras.layers.BatchNormalization()(X)\n",
    "X = keras.layers.Activation('relu')(X)\n",
    "X = keras.layers.Dense(128, kernel_initializer='he_normal')(X)\n",
    "X = keras.layers.BatchNormalization()(X)\n",
    "X = keras.layers.Activation('relu')(X)\n",
    "X = keras.layers.Dense(2, kernel_initializer='he_normal')(X)\n",
    "M = keras.Model(X_input, X)\n",
    "M.compile('adam', loss='mse')\n",
    "M.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "        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",
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       "            warnings.textContent = (\n",
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       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        'text-align: center; padding: 3px;\"/>');\n",
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       "\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",
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       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
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       "        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",
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       "\n",
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       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
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       "\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",
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       "\n",
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       "\n",
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       "        // Keep the size of the canvas, canvas container, and rubber band\n",
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       "\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",
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       "    // upon first draw.\n",
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       "        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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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=\"638.888905813665\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-3fd64225b503>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m M.fit(data, tsne_data, batch_size=64, epochs=300, verbose=False, callbacks=[\n\u001b[1;32m     13\u001b[0m     \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReduceLROnPlateau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatience\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLambdaCallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m ])\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1703\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1704\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1705\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1707\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1233\u001b[0m                         \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1235\u001b[0;31m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1236\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1237\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2476\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2477\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2478\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2479\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    903\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 905\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    906\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1135\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1136\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1137\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1138\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1139\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1353\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1354\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1355\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1356\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1357\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1359\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1360\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1361\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1362\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1363\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1338\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1339\u001b[0m           return tf_session.TF_Run(session, options, feed_dict, fetch_list,\n\u001b[0;32m-> 1340\u001b[0;31m                                    target_list, status, run_metadata)\n\u001b[0m\u001b[1;32m   1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1342\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "plt.ion()\n",
    "losses = []\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "fig.show()\n",
    "def plot_loss(epoch, log):\n",
    "    losses.append(log['loss'])\n",
    "    ax.clear()\n",
    "    ax.plot(losses)\n",
    "    fig.canvas.draw()\n",
    "M.fit(data, tsne_data, batch_size=64, epochs=300, verbose=False, callbacks=[\n",
    "    keras.callbacks.ReduceLROnPlateau('loss', factor=0.1, patience=10, verbose=True),\n",
    "    keras.callbacks.LambdaCallback(on_epoch_end=plot_loss)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "M_data = M.predict(data, batch_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = scipy.spatial.KDTree(M_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "4.480789873796587 4.480789873796587 15.32913136346361\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.ioff()\n",
    "def test(q):\n",
    "    q = q[np.newaxis,:]\n",
    "    q1 = M.predict(q)\n",
    "    _, i = tree.query(q1, k=256)\n",
    "    j = i[0][np.argmin(np.linalg.norm(data[i[0]] - q, axis=-1))]\n",
    "    k = np.argmin(np.linalg.norm(data - q[0], axis=-1))\n",
    "    l = np.argmax(np.linalg.norm(data - q[0], axis=-1))\n",
    "    print(k in i[0])\n",
    "    print(\n",
    "        np.linalg.norm(q[0]-data[j]),\n",
    "        np.linalg.norm(q[0]-data[k]),\n",
    "        np.linalg.norm(q[0]-data[l]))\n",
    "    plt.imshow(np.reshape(q[0], (28,28)))\n",
    "    plt.show()\n",
    "    plt.imshow(np.reshape(data[j], (28,28)))\n",
    "    plt.show()\n",
    "    plt.imshow(np.reshape(data[k], (28,28)))\n",
    "    plt.show()\n",
    "test(test_data[np.random.randint(0,test_data.shape[0])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "N = 4096\n",
    "plt.ioff()\n",
    "plt.scatter(tsne_data[:N,0], tsne_data[:N,1], c=label[:N])\n",
    "plt.show()\n",
    "plt.scatter(M_data[:N,0], M_data[:N,1], c=label[:N])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "M_test_data = M.predict(test_data)\n",
    "plt.scatter(M_test_data[:,0], M_test_data[:,1], c=test_label)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml",
   "language": "python",
   "name": "ml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}