np_gan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from copy import deepcopy\n",
    "import numpy as np,sys,time\n",
    "import matplotlib\n",
    "import os\n",
    "import numpy as np,sys\n",
    "import matplotlib.gridspec as gridspec\n",
    "from keras.utils import np_utils\n",
    "from keras.models import Sequential\n",
    "from keras import models, layers\n",
    "import keras\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "x_train = x_train.reshape(x_train.shape[0], 784)\n",
    "\n",
    "\n",
    "def relu(x, derivation=False):\n",
    "    if(derivation == False):\n",
    "        return ((x > 0) * 1.0) * x\n",
    "    else:\n",
    "        return (x > 0) * 1.0 \n",
    "\n",
    "def arctan(x, derivation=False):\n",
    "    if(derivation == False):\n",
    "        return np.arctan(x)\n",
    "    else:\n",
    "        return 1 / (1 + x ** 2)\n",
    "\n",
    "def tanh(x, derivation=False):\n",
    "    if(derivation == False):\n",
    "        return np.tanh(x)\n",
    "    else:\n",
    "        return 1 - np.tanh(x) ** 2\n",
    "\n",
    "def log(x, derivation=False):\n",
    "    if(derivation == False):\n",
    "        return 1 / (1 + np.exp(-1*x))\n",
    "    else:\n",
    "        return log(x) * (1 - log(x))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "class layer:\n",
    "    def __init__(self, size, activation=\"relu\"):\n",
    "        self.bias = np.zeros(size[1])\n",
    "        self.weigth = np.random.normal(size=size,scale=(1. / np.sqrt(size[0] / 2.))) *0.005\n",
    "        self.size = size\n",
    "        \n",
    "        if(activation == \"relu\"):\n",
    "            self.activation = relu\n",
    "        elif(activation == \"arctan\"):\n",
    "            self.activation = arctan\n",
    "        elif(activation == \"tanh\"):\n",
    "            self.activation = tanh\n",
    "        elif(activation == \"log\"):\n",
    "            self.activation = log\n",
    "        else:\n",
    "            raise Exception(\"unkown activation function\")\n",
    "        self.activation_name = activation\n",
    "        \n",
    "        self.next_layer = None\n",
    "        self.back_layer = None\n",
    "\n",
    "        # adam\n",
    "        self.m_weigth = 0\n",
    "        self.v_weigth = 0\n",
    "        \n",
    "        self.m_bias = 0\n",
    "        self.v_bias = 0\n",
    "                \n",
    "class custom_layer:\n",
    "    def __init__(self, x):\n",
    "        self.name = \"input\"\n",
    "        self.layer_activation = x\n",
    "        \n",
    "class model:\n",
    "    def __init__(self):\n",
    "        self.root = None\n",
    "        self.linked = None\n",
    "        self.last_visit = None\n",
    "        \n",
    "        self.count = 0\n",
    "        self.layers_ref = {\n",
    "            \n",
    "        }\n",
    "        \n",
    "    def add(self, layer):\n",
    "        if(self.root == None):\n",
    "            self.root = layer\n",
    "            self.linked = layer\n",
    "            \n",
    "            self.count += 1\n",
    "        else:\n",
    "            layer.name = self.count\n",
    "            self.linked.next_layer = layer\n",
    "            old = self.linked\n",
    "            \n",
    "            self.count += 1\n",
    "            \n",
    "            self.linked = self.linked.next_layer\n",
    "            self.linked.back_layer = old\n",
    "\n",
    "        layer.name = self.count - 1\n",
    "        \n",
    "        self.layers_ref[str(self.count)] = self.linked\n",
    "            \n",
    "    def feed_forward(self, data):\n",
    "        current_layer = self.root\n",
    "        feed_forward = data\n",
    "        \n",
    "        last_point = None\n",
    "        \n",
    "        self.root.back_layer = custom_layer(data)\n",
    "        \n",
    "        while current_layer != None:\n",
    "            current_layer.plain = feed_forward.dot(current_layer.weigth) + current_layer.bias\n",
    "            current_layer.layer_activation = current_layer.activation(current_layer.plain)\n",
    "            \n",
    "            self.last_visit = current_layer\n",
    "            \n",
    "            last_point = current_layer.layer_activation\n",
    "            \n",
    "            feed_forward = current_layer.layer_activation\n",
    "            current_layer = current_layer.next_layer\n",
    "        \n",
    "        return last_point\n",
    "    \n",
    "    def backpropagation(self, type_id):\n",
    "        current_layer = self.last_visit\n",
    "        while current_layer != None and not current_layer.name == \"input\":\n",
    "            if(current_layer.activation_name == \"log\"):\n",
    "                if(type_id == \"fake\"):\n",
    "                    current_layer.error = 1/(1.0 - current_layer.layer_activation)\n",
    "                else:\n",
    "                    current_layer.error = - 1/current_layer.layer_activation             \n",
    "                    \n",
    "            elif(current_layer.activation_name == \"relu\"):\n",
    "                forward_layer = current_layer.next_layer\n",
    "                current_layer.error = (forward_layer.error).dot(forward_layer.weigth.T)\n",
    "                \n",
    "            else:\n",
    "                raise Exception(\"bug in backward\")\n",
    "            \n",
    "            current_layer.delta =  current_layer.error * current_layer.activation(current_layer.plain, derivation=True)\n",
    "            current_layer.last_activation = current_layer.back_layer.layer_activation\n",
    "            \n",
    "            current_layer.pending_weigth =  current_layer.last_activation.T.dot(current_layer.delta) \n",
    "            current_layer.pending_bias =  current_layer.delta\n",
    "            \n",
    "            current_layer = current_layer.back_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_generator(model, hint):\n",
    "    index = 0\n",
    "    current_layer = model.last_visit\n",
    "    while layer != None  and not current_layer.name == \"input\":\n",
    "        if(index == 0):\n",
    "            start = (-1/hint.last_visit.layer_activation)\n",
    "         \n",
    "            current_activation = current_layer\n",
    "            current_hint = hint.last_visit\n",
    "            \n",
    "            while current_hint.name != \"input\":\n",
    "                start = ((start) * current_activation.activation(current_hint.plain, derivation=True)).dot(current_hint.weigth.T)\n",
    "                current_hint = current_hint.back_layer\n",
    "                current_activation = current_activation.back_layer\n",
    "\n",
    "            current_layer.error = start\n",
    "        else:\n",
    "            forward_layer = current_layer.next_layer\n",
    "            current_layer.error = (forward_layer.error).dot(forward_layer.weigth.T)\n",
    "            \n",
    "        current_layer.delta = current_layer.error * current_layer.activation(current_layer.plain, derivation=True)\n",
    "        current_layer.last_activation = current_layer.back_layer.layer_activation\n",
    "\n",
    "        current_layer.new_weigth = current_layer.last_activation.T.dot(current_layer.delta) \n",
    "        current_layer.new_bias = current_layer.delta\n",
    "\n",
    "        index += 1 \n",
    "        current_layer = current_layer.back_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adam(layer,beta_1=0.9, beta_2=0.999, eps=0.00000001) :\n",
    "    layer.m_weigth = beta_1 * layer.m_weigth + (1 - beta_1) * layer.new_weigth\n",
    "    layer.v_weigth = beta_2 * layer.v_weigth + (1 - beta_2) * layer.new_weigth ** 2\n",
    "\n",
    "    layer.m_bias = beta_1 * layer.m_bias + (1 - beta_1) * layer.new_bias\n",
    "    layer.v_bias = beta_2 * layer.v_bias + (1 - beta_2) * layer.new_bias ** 2\n",
    "\n",
    "    layer.weigth = layer.weigth - (learing_rate / (np.sqrt(layer.v_weigth/(1-beta_2) ) + eps)) * (layer.m_weigth/(1-beta_1))\n",
    "    layer.bias = layer.bias - (learing_rate / (np.sqrt(layer.v_bias /(1-beta_2) ) + eps)) * (layer.m_bias/(1-beta_1)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_weigths(model, gen=\"\"):\n",
    "    current_layer = model.root\n",
    "\n",
    "    while current_layer != None:\n",
    "        old = deepcopy(current_layer.weigth)\n",
    "        adam(current_layer)\n",
    "      #  assert not np.all(old == current_layer.weigth)\n",
    "        current_layer = current_layer.next_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_models(orginal, fake, real):\n",
    "    layer_orginal = orginal.root\n",
    "    layer_fake = fake.root\n",
    "    layer_real = real.root\n",
    "\n",
    "    while layer_orginal != None:\n",
    "        layer_orginal.new_weigth = layer_fake.pending_weigth + layer_real.pending_weigth\n",
    "        layer_orginal.new_bias = layer_fake.pending_bias + layer_real.pending_bias\n",
    "\n",
    "        layer_orginal = layer_orginal.next_layer\n",
    "        layer_fake = layer_fake.next_layer\n",
    "        layer_real = layer_real.next_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
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       "        return fig.key_event(event, event['data']);\n",
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       "\n",
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       "    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",
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       "    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",
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       "\n",
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       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
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       "\n",
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       "\n",
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       "        }\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",
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       "\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",
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       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
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       "\n",
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       "    $( \".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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       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "discriminator = model()\n",
    "discriminator.add(layer((784, 128), activation='relu'))\n",
    "discriminator.add(layer((128, 1), activation='log'))\n",
    "\n",
    "generator = model()\n",
    "\n",
    "generator.add(layer((100, 164), activation=\"arctan\"))\n",
    "generator.add(layer((164, 228), activation=\"relu\"))\n",
    "generator.add(layer((228, 356), activation=\"arctan\"))\n",
    "generator.add(layer((356, 480), activation=\"relu\"))\n",
    "generator.add(layer((480, 550), activation=\"tanh\"))\n",
    "generator.add(layer((550, 690), activation=\"relu\"))\n",
    "generator.add(layer((690, 784), activation=\"log\"))\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "learing_rate = 0.0001\n",
    "\n",
    "figure, axes = plt.subplots(1)\n",
    "plt.ion()\n",
    "figure.show()\n",
    "figure.canvas.draw()\n",
    "\n",
    "\n",
    "generator_input_size = 100\n",
    "\n",
    "random_number = np.random.randint(len(x_train) - 1)\n",
    "current_image = np.expand_dims(x_train[random_number],axis=0)\n",
    "\n",
    "for i in range(100):\n",
    "    generator_input = np.random.uniform(-1., 1., size=[1, generator_input_size])\n",
    "\n",
    "    fake = generator.feed_forward(generator_input)\n",
    "\n",
    "    real_discriminator = deepcopy(discriminator)\n",
    "    fake_discriminator = deepcopy(discriminator)\n",
    "\n",
    "    real_feed_forward = real_discriminator.feed_forward(current_image)\n",
    "    fake_feed_forward = fake_discriminator.feed_forward(fake)\n",
    " \n",
    "    real_discriminator.backpropagation(\"real\")\n",
    "    fake_discriminator.backpropagation(\"fake\")\n",
    "\n",
    "    combine_models(discriminator, fake_discriminator, real_discriminator)\n",
    "    update_weigths(discriminator)\n",
    "\n",
    "    axes.set_title(i)\n",
    "    axes.imshow(fake[0].reshape(28, 28), cmap='gray')\n",
    "    figure.canvas.draw()\n",
    "\n",
    "    what_does_the_discrimantor_think = discriminator.feed_forward(fake)\n",
    "    update_generator(generator, discriminator)\n",
    "    update_weigths(generator, \"generator\")\n",
    "\n",
    "    if i < 20:\n",
    "        learing_rate *= 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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