DistWidgetHPO_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras MNIST classifier with _distributed_ random-search hyper-parameter optimization and _interactive widgets_\n",
    "\n",
    "In this notebook we demonstrate distributed hyper-parameter optimization (HPO) with an _interactive widget_ which shows a live table of model results and training objective plots for a selected model row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/cscratch1/sd/sfarrell/conda/isc-ihpc/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": [
    "# System imports\n",
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "from __future__ import absolute_import\n",
    "import os\n",
    "from functools import partial\n",
    "\n",
    "# External imports\n",
    "import ipyparallel as ipp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "\n",
    "# Local imports\n",
    "from mnist import load_data\n",
    "from hpo_widgets import ModelPlot, ParamSpanWidget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connect to cluster\n",
    "\n",
    "Here we use a shell command to query the slurm job ID running our cluster, then we connect using the corresponding cluster ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)\n",
      "          13958933 interacti       sh sfarrell  R      37:17      8 nid00[047-052,124,153]\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "squeue -u sfarrell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Worker IDs: [0, 1, 2, 3, 4, 5, 6, 7]\n"
     ]
    }
   ],
   "source": [
    "# Cluster ID taken from job ID above\n",
    "job_id = 13958933\n",
    "cluster_id = 'cori_{}'.format(job_id)\n",
    "\n",
    "# Use default profile\n",
    "c = ipp.Client(timeout=60, cluster_id=cluster_id)\n",
    "print('Worker IDs:', c.ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "x_test shape: (10000, 28, 28, 1)\n",
      "y_train shape: (60000, 10)\n",
      "y_test shape: (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "x_train, y_train, x_test, y_test = load_data()\n",
    "\n",
    "print('x_train shape:', x_train.shape)\n",
    "print('x_test shape:', x_test.shape)\n",
    "print('y_train shape:', y_train.shape)\n",
    "print('y_test shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure the tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Temporarily making things reproducible for development\n",
    "np.random.seed(0)\n",
    "\n",
    "# Define the hyper-parameter search points\n",
    "n_hpo_trials = 8 #64\n",
    "grid_h1 = np.random.choice([4, 8, 16, 32, 64], size=n_hpo_trials)\n",
    "grid_h2 = np.random.choice([4, 8, 16, 32, 64], size=n_hpo_trials)\n",
    "grid_h3 = np.random.choice([8, 16, 32, 64, 128], size=n_hpo_trials)\n",
    "grid_dropout = np.random.rand(n_hpo_trials)\n",
    "grid_optimizer = np.random.choice(['Adadelta', 'Adam', 'Nadam'], size=n_hpo_trials)\n",
    "\n",
    "# Training config\n",
    "batch_size = 128\n",
    "n_epochs = 16\n",
    "valid_frac = 0.17\n",
    "checkpoint_dir = '/global/cscratch1/sd/sfarrell/cori-interactive-dl/mnist_hpo_%i' % job_id\n",
    "os.makedirs(checkpoint_dir, exist_ok=True)\n",
    "\n",
    "checkpoint_dir = os.path.join(os.environ['SCRATCH'],\n",
    "                              'cori-interactive-dl/mnist_hpo_%i' % job_id)\n",
    "os.makedirs(checkpoint_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the trainings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_and_train(valid_frac, batch_size, n_epochs,\n",
    "                    h1, h2, h3, dropout, optimizer, verbose=0,\n",
    "                    checkpoint_file=None):\n",
    "    \"\"\"Run training for one set of hyper-parameters\"\"\"\n",
    "    import keras\n",
    "    from mnist import build_model, load_data\n",
    "    from mlextras import configure_session, IPyParallelLogger\n",
    "    # Load the data\n",
    "    x_train, y_train, _, _ = load_data()\n",
    "    # Thread settings\n",
    "    keras.backend.set_session(configure_session())\n",
    "    # Build the model\n",
    "    model = build_model(h1=h1, h2=h2, h3=h3,\n",
    "                        dropout=dropout, optimizer=optimizer)\n",
    "    callbacks = []\n",
    "    if checkpoint_file is not None:\n",
    "        callbacks.append(keras.callbacks.ModelCheckpoint(checkpoint_file))\n",
    "    callbacks.append(IPyParallelLogger())\n",
    "    # Train the model\n",
    "    history = model.fit(x_train, y_train,\n",
    "                        validation_split=valid_frac,\n",
    "                        batch_size=batch_size, epochs=n_epochs,\n",
    "                        verbose=verbose, callbacks=callbacks)\n",
    "    return history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f2e0db8dc2d46c08cfceeaa91036988",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ParamSpanWidget(children=(Output(layout=Layout(border='1px solid', height='600px', overflow_x='scroll', overfl…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_func = partial(build_and_train, valid_frac=valid_frac,\n",
    "                     batch_size=batch_size, n_epochs=n_epochs,\n",
    "                     verbose=2)\n",
    "\n",
    "plot_func = partial(\n",
    "    ModelPlot,\n",
    "    y=['loss', 'acc', 'val_loss', 'val_acc'],\n",
    "    xlim=[0, n_epochs],\n",
    "    xlabel='epochs',\n",
    "    ylabel='training metrics'\n",
    ")\n",
    "\n",
    "hpo_params = dict(\n",
    "    h1=grid_h1,\n",
    "    h2=grid_h2,\n",
    "    h3=grid_h3,\n",
    "    dropout=grid_dropout,\n",
    "    optimizer=grid_optimizer\n",
    ")\n",
    "psw = ParamSpanWidget(\n",
    "    compute_func=train_func, \n",
    "    vis_func=plot_func, \n",
    "    params=hpo_params,\n",
    "    ipp_cluster_id=cluster_id\n",
    ")\n",
    "\n",
    "psw.submit_computations()\n",
    "psw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broken from here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'ParamSpanWidget' object has no attribute 'model_runs'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-37b5e426b7b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpsw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_runs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'ParamSpanWidget' object has no attribute 'model_runs'"
     ]
    }
   ],
   "source": [
    "results = psw.model_runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Tasks completed: %i / %i' % (np.sum([ar.ready() for ar in results]), len(results)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ar = results[0]\n",
    "ar.metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "histories = [ar.get() for ar in results]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model selection and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_scores = np.array([h['val_acc'][-1] for h in histories])\n",
    "best_scores = np.array([max(h['val_acc']) for h in histories])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hyperparameters: trial 4 hidden 32-16-64 dropout 0.118 opt Adadelta\n",
      "  Last validation accuracy 0.9895\n",
      "  Best validation accuracy 0.9905\n"
     ]
    },
    {
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       "        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=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find the best in terms of final validation set accuracy\n",
    "i = best_scores.argmax()\n",
    "h = histories[i]\n",
    "\n",
    "print('Hyperparameters: trial %i hidden %i-%i-%i dropout %.3f opt %s' %\n",
    "      (i, grid_h1[i], grid_h2[i], grid_h3[i], grid_dropout[i], grid_optimizer[i]))\n",
    "print('  Last validation accuracy %.4f' % last_scores[i])\n",
    "print('  Best validation accuracy %.4f' % best_scores[i])\n",
    "\n",
    "plt.figure(figsize=(9,4))\n",
    "\n",
    "# Loss\n",
    "plt.subplot(121)\n",
    "plt.plot(h['loss'], label='Training')\n",
    "plt.plot(h['val_loss'], label='Validation')\n",
    "plt.xlim(xmin=0, xmax=len(h['loss'])-1)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(loc=0)\n",
    "\n",
    "# Accuracy\n",
    "plt.subplot(122)\n",
    "plt.plot(h['acc'], label='Training')\n",
    "plt.plot(h['val_acc'], label='Validation')\n",
    "plt.xlim(xmin=0, xmax=len(h['loss'])-1)\n",
    "plt.ylim((0, 1))\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc=0)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Worst model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hyperparameters: trial 0 hidden 64-64-16 dropout 0.979 opt Nadam\n",
      "  Last validation accuracy 0.1066\n",
      "  Best validation accuracy 0.1066\n"
     ]
    },
    {
     "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",
       "        fig.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 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>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find the best in terms of final validation set accuracy\n",
    "i = best_scores.argmin()\n",
    "h = histories[i]\n",
    "\n",
    "print('Hyperparameters: trial %i hidden %i-%i-%i dropout %.3f opt %s' %\n",
    "      (i, grid_h1[i], grid_h2[i], grid_h3[i], grid_dropout[i], grid_optimizer[i]))\n",
    "print('  Last validation accuracy %.4f' % last_scores[i])\n",
    "print('  Best validation accuracy %.4f' % best_scores[i])\n",
    "\n",
    "plt.figure(figsize=(9,4))\n",
    "\n",
    "# Loss\n",
    "plt.subplot(121)\n",
    "plt.plot(h['loss'], label='Training')\n",
    "plt.plot(h['val_loss'], label='Validation')\n",
    "plt.xlim(xmin=0, xmax=len(h['loss'])-1)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(loc=0)\n",
    "\n",
    "# Accuracy\n",
    "plt.subplot(122)\n",
    "plt.plot(h['acc'], label='Training')\n",
    "plt.plot(h['val_acc'], label='Validation')\n",
    "plt.xlim(xmin=0, xmax=len(h['loss'])-1)\n",
    "plt.ylim((0, 1))\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc=0)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test set evaluation\n",
    "\n",
    "Here we load the best selected model and evaluate final performance on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = best_scores.argmax()\n",
    "model_file = os.path.join(checkpoint_dir, 'model_%i.h5' % i)\n",
    "model = keras.models.load_model(model_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Test loss: %.4f' % test_loss)\n",
    "print('Test accuracy: %.4f' % test_acc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "isc-ihpc",
   "language": "python",
   "name": "isc-ihpc"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}