HPO_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras MNIST classifier with random-search hyper-parameter optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "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",
    "\n",
    "# Data libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Deep learning\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.losses import categorical_crossentropy\n",
    "from keras import backend as K\n",
    "import tensorflow as tf\n",
    "\n",
    "# Local imports\n",
    "from mnist import load_data, build_model\n",
    "\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concurrency configuration\n",
    "n_inter_threads = 2\n",
    "n_intra_threads = 6\n",
    "config = tf.ConfigProto(\n",
    "    inter_op_parallelism_threads=n_inter_threads,\n",
    "    intra_op_parallelism_threads=n_intra_threads,\n",
    ")\n",
    "session = tf.Session(config=config)\n",
    "K.set_session(session)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "## Define the HPO strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 = 16\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(\n",
    "    [keras.optimizers.Adadelta, keras.optimizers.Adam, keras.optimizers.Nadam],\n",
    "    size=n_hpo_trials)\n",
    "\n",
    "# Training config\n",
    "batch_size = 128\n",
    "n_epochs = 16\n",
    "valid_frac = 0.17"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the trainings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_and_train(x_train, y_train, valid_frac, batch_size, n_epochs,\n",
    "                    h1, h2, h3, dropout, optimizer, verbose=0):\n",
    "    # Build the model\n",
    "    model = build_model(h1=h1, h2=h2, h3=h3,\n",
    "                        dropout=dropout,\n",
    "                        optimizer_type=optimizer)\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)\n",
    "    return model, history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hyper-parameter trial 0\n",
      "  Hidden 64, 8, 16\n",
      "  Dropout 0.3865\n",
      "  Optimizer Adadelta\n",
      "  Final score 0.979607843231\n",
      "Hyper-parameter trial 1\n",
      "  Hidden 4, 4, 16\n",
      "  Dropout 0.9026\n",
      "  Optimizer Adadelta\n",
      "  Final score 0.685588235481\n",
      "Hyper-parameter trial 2\n",
      "  Hidden 32, 8, 16\n",
      "  Dropout 0.4499\n",
      "  Optimizer Adadelta\n",
      "  Final score 0.973137254995\n",
      "Hyper-parameter trial 3\n",
      "  Hidden 32, 64, 8\n",
      "  Dropout 0.6131\n",
      "  Optimizer Adadelta\n",
      "  Final score 0.965294117834\n",
      "Hyper-parameter trial 4\n",
      "  Hidden 32, 32, 32\n",
      "  Dropout 0.9023\n",
      "  Optimizer Adadelta\n",
      "  Final score 0.931960784173\n",
      "Hyper-parameter trial 5\n",
      "  Hidden 8, 4, 128\n",
      "  Dropout 0.0993\n",
      "  Optimizer Nadam\n",
      "  Final score 0.98803921578\n",
      "Hyper-parameter trial 6\n",
      "  Hidden 32, 32, 64\n",
      "  Dropout 0.9698\n",
      "  Optimizer Adadelta\n",
      "  Final score 0.106568627445\n",
      "Hyper-parameter trial 7\n",
      "  Hidden 16, 4, 64\n",
      "  Dropout 0.6531\n",
      "  Optimizer Nadam\n",
      "  Final score 0.979901960878\n",
      "Hyper-parameter trial 8\n",
      "  Hidden 64, 16, 32\n",
      "  Dropout 0.1709\n",
      "  Optimizer Adam\n",
      "  Final score 0.987549019701\n",
      "Hyper-parameter trial 9\n",
      "  Hidden 4, 32, 128\n",
      "  Dropout 0.3582\n",
      "  Optimizer Adam\n",
      "  Final score 0.990686274603\n",
      "Hyper-parameter trial 10\n",
      "  Hidden 4, 4, 32\n",
      "  Dropout 0.7507\n",
      "  Optimizer Adam\n",
      "  Final score 0.95372549029\n",
      "Hyper-parameter trial 11\n",
      "  Hidden 64, 8, 8\n",
      "  Dropout 0.6078\n",
      "  Optimizer Adadelta\n",
      "  Final score 0.936764705976\n",
      "Hyper-parameter trial 12\n",
      "  Hidden 16, 32, 8\n",
      "  Dropout 0.3250\n",
      "  Optimizer Adam\n",
      "  Final score 0.976470588329\n",
      "Hyper-parameter trial 13\n",
      "  Hidden 8, 32, 128\n",
      "  Dropout 0.0384\n",
      "  Optimizer Adam\n",
      "  Final score 0.989019607937\n",
      "Hyper-parameter trial 14\n",
      "  Hidden 4, 32, 8\n",
      "  Dropout 0.6343\n",
      "  Optimizer Adam\n",
      "  Final score 0.962941176237\n",
      "Hyper-parameter trial 15\n",
      "  Hidden 8, 4, 128\n",
      "  Dropout 0.9589\n",
      "  Optimizer Adadelta\n",
      "  Final score 0.233137254949\n"
     ]
    }
   ],
   "source": [
    "all_models = []\n",
    "all_histories = []\n",
    "\n",
    "# Loop over hps\n",
    "for ihp in range(n_hpo_trials):\n",
    "    print('Hyper-parameter trial', ihp)\n",
    "    print('  Hidden %i, %i, %i' % (grid_h1[ihp], grid_h2[ihp], grid_h3[ihp]))\n",
    "    print('  Dropout %.4f' % grid_dropout[ihp])\n",
    "    print('  Optimizer', grid_optimizer[ihp].__name__)\n",
    "    \n",
    "    model, history = build_and_train(x_train, y_train, valid_frac=valid_frac,\n",
    "                                     batch_size=batch_size, n_epochs=n_epochs,\n",
    "                                     h1=grid_h1[ihp], h2=grid_h2[ihp], h3=grid_h3[ihp],\n",
    "                                     dropout=grid_dropout[ihp],\n",
    "                                     optimizer=grid_optimizer[ihp])\n",
    "    \n",
    "    # Use final validation accuracy as objective metric\n",
    "    score = history.history['val_acc'][-1]\n",
    "    print('  Final score', score)\n",
    "    \n",
    "    # Save the model and history\n",
    "    all_models.append(model)\n",
    "    all_histories.append(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model selection and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_last_scores = np.array([h.history['val_acc'][-1] for h in all_histories])\n",
    "all_best_scores = np.array([max(h.history['val_acc']) for h in all_histories])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hyperparameters: trial 9 hidden 4-32-128 dropout 0.358 opt Adam\n",
      "  Last validation accuracy 0.9907\n",
      "  Best validation accuracy 0.9907\n"
     ]
    },
    {
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       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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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 = all_last_scores.argmax()\n",
    "h = all_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].__name__))\n",
    "print('  Last validation accuracy %.4f' % all_last_scores[i])\n",
    "print('  Best validation accuracy %.4f' % all_best_scores[i])\n",
    "\n",
    "plt.figure(figsize=(9,4))\n",
    "\n",
    "# Loss\n",
    "plt.subplot(121)\n",
    "plt.plot(h.epoch, h.history['loss'], label='Training')\n",
    "plt.plot(h.epoch, h.history['val_loss'], label='Validation')\n",
    "plt.xlim((min(h.epoch), max(h.epoch)))\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(loc=0)\n",
    "\n",
    "# Accuracy\n",
    "plt.subplot(122)\n",
    "plt.plot(h.history['acc'], label='Training')\n",
    "plt.plot(h.history['val_acc'], label='Validation')\n",
    "plt.xlim((min(h.epoch), max(h.epoch)))\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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hyperparameters: trial 6 hidden 32-32-64 dropout 0.970 opt Adadelta\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",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find the worst in terms of final validation set accuracy\n",
    "i = all_last_scores.argmin()\n",
    "h = all_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].__name__))\n",
    "print('  Last validation accuracy %.4f' % all_last_scores[i])\n",
    "print('  Best validation accuracy %.4f' % all_best_scores[i])\n",
    "\n",
    "plt.figure(figsize=(9,4))\n",
    "\n",
    "# Loss\n",
    "plt.subplot(121)\n",
    "plt.plot(h.epoch, h.history['loss'], label='Training')\n",
    "plt.plot(h.epoch, h.history['val_loss'], label='Validation')\n",
    "plt.xlim((min(h.epoch), max(h.epoch)))\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(loc=0)\n",
    "\n",
    "# Accuracy\n",
    "plt.subplot(122)\n",
    "plt.plot(h.history['acc'], label='Training')\n",
    "plt.plot(h.history['val_acc'], label='Validation')\n",
    "plt.xlim((min(h.epoch), max(h.epoch)))\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.0303653424745\n",
      "Test accuracy: 0.9911\n"
     ]
    }
   ],
   "source": [
    "best_model = all_models[all_last_scores.argmax()]\n",
    "score = best_model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "debug180313"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}