De-Noise Autoencoder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train , y_train),(X_test , y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "              '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",
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       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
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       "\n",
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       "\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",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
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       "\n",
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       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
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       "\n",
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       "        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",
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       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
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       "        mouse_event_fn(event);\n",
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       "\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",
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       "    }\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",
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       "        return false;\n",
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       "    nav_element.attr('style', 'width: 100%');\n",
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       "\n",
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       "            // 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 / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // 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>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d2684ecc88>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.imshow(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize the image\n",
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train/255\n",
    "X_test = X_test/255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Architecture of Auto encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential,Model\n",
    "from tensorflow.keras.layers import Dense,Flatten,Reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = Sequential()\n",
    "encoder.add(Flatten( input_shape=[28,28]))\n",
    "encoder.add(Dense(400 , activation ='relu'))\n",
    "encoder.add(Dense(200 , activation = 'relu'))\n",
    "encoder.add(Dense(100 , activation = 'relu'))\n",
    "encoder.add(Dense(50 , activation = 'relu'))\n",
    "encoder.add(Dense(25 , activation = 'relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder = Sequential()\n",
    "decoder.add(Dense(50 , activation ='relu' , input_shape =[25]))\n",
    "decoder.add(Dense(100 ,activation ='relu'))\n",
    "decoder.add(Dense(200 ,activation ='relu'))\n",
    "decoder.add(Dense(400 ,activation ='relu'))\n",
    "decoder.add(Dense(784 , activation ='sigmoid'))\n",
    "decoder.add(Reshape([28,28]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder = Sequential([encoder , decoder])\n",
    "autoencoder.compile(optimizer= SGD(1.5) , loss ='binary_crossentropy' , metrics =['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 29s 477us/sample - loss: 0.2505 - accuracy: 0.7914 - val_loss: 0.2001 - val_accuracy: 0.7957\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 28s 465us/sample - loss: 0.1800 - accuracy: 0.7983 - val_loss: 0.1579 - val_accuracy: 0.8009\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 24s 402us/sample - loss: 0.1547 - accuracy: 0.8030 - val_loss: 0.1471 - val_accuracy: 0.8013\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 24s 399us/sample - loss: 0.1449 - accuracy: 0.8048 - val_loss: 0.1406 - val_accuracy: 0.8058\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 25s 411us/sample - loss: 0.1398 - accuracy: 0.8058 - val_loss: 0.1366 - val_accuracy: 0.8048\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d267ead748>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.fit(X_train , X_train , epochs = 5 , validation_data = [X_test , X_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_images = autoencoder.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ORIGINAL IMAGE of Number :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d27fec79c8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1\n",
    "print('ORIGINAL IMAGE of Number :')\n",
    "plt.imshow(X_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d27fda8f08>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(pred_images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(n):\n",
    "    print('Original Image')\n",
    "    plt.imshow(X_test[n])\n",
    "    plt.show()\n",
    "    print('Decoded Image')\n",
    "    plt.imshow(pred_images[n])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Image\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded Image\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Denoising Images With Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import GaussianNoise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = GaussianNoise(0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer gaussian_noise is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "noisy = sample(X_test[0:2] , training = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d207c24e48>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(noisy[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(101) # for same distribution\n",
    "\n",
    "encoder = Sequential()\n",
    "encoder.add(Flatten(input_shape=[28,28]))\n",
    "\n",
    "#ADD NOISE\n",
    "encoder.add(GaussianNoise(0.3))\n",
    "\n",
    "encoder.add(Dense(400,activation=\"relu\"))\n",
    "encoder.add(Dense(200,activation=\"relu\"))\n",
    "encoder.add(Dense(100,activation=\"relu\"))\n",
    "encoder.add(Dense(50,activation=\"relu\"))\n",
    "encoder.add(Dense(25,activation=\"relu\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder = Sequential()\n",
    "decoder.add(Dense(50 , activation ='relu' , input_shape =[25]))\n",
    "decoder.add(Dense(100 ,activation ='relu'))\n",
    "decoder.add(Dense(200 ,activation ='relu'))\n",
    "decoder.add(Dense(400 ,activation ='relu'))\n",
    "decoder.add(Dense(784 , activation ='sigmoid'))\n",
    "decoder.add(Reshape([28,28]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_remover = Sequential([encoder , decoder])\n",
    "noise_remover.compile(optimizer =SGD(1.5) , loss ='binary_crossentropy' , metrics =['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 24s 401us/sample - loss: 0.2455 - accuracy: 0.7921 - val_loss: 0.2164 - val_accuracy: 0.7921\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 26s 431us/sample - loss: 0.1871 - accuracy: 0.7977 - val_loss: 0.1652 - val_accuracy: 0.8026\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 26s 431us/sample - loss: 0.1611 - accuracy: 0.8021 - val_loss: 0.1511 - val_accuracy: 0.8015\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 26s 433us/sample - loss: 0.1510 - accuracy: 0.8041 - val_loss: 0.1438 - val_accuracy: 0.8023\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 25s 417us/sample - loss: 0.1442 - accuracy: 0.8056 - val_loss: 0.1390 - val_accuracy: 0.8023\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 27s 449us/sample - loss: 0.1391 - accuracy: 0.8065 - val_loss: 0.1343 - val_accuracy: 0.8038\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 25s 416us/sample - loss: 0.1352 - accuracy: 0.8072 - val_loss: 0.1281 - val_accuracy: 0.8063\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 26s 428us/sample - loss: 0.1318 - accuracy: 0.8079 - val_loss: 0.1254 - val_accuracy: 0.8070\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 24s 404us/sample - loss: 0.1287 - accuracy: 0.8084 - val_loss: 0.1252 - val_accuracy: 0.8088\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 26s 430us/sample - loss: 0.1260 - accuracy: 0.8089 - val_loss: 0.1175 - val_accuracy: 0.8094\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d208135b88>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_remover.fit(X_train , X_train , epochs=10 , validation_data =[X_test , X_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "noisy_images = sample(X_test , training = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d20903c148>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(noisy_images[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d208fdc608>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_test[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_images = noise_remover.predict(noisy_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Image :\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Noisy Image :\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded Image :\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z3Qj7DklLJz0+R9LOLvQxpYjYWbvdI+kR9d5U1LuPzaBbu93T5X7e00vTeE81zbh64LXr5vTn3Qj7M5JW2F5me5akz0la14U+PsT23NoHJ7I9V9JV6r2pqNdJurF2/0ZJj3axl/fplWm8q6YZV5dfu65Pfx4RHf+SdK0mPpF/WdI/dqOHir7Ok/SL2tfz3e5N0oOaeFs3qol3RDdJOl3SBkkv1m4X9FBv35W0WdImTQRrcZd6+5Qm/jTcJGlj7evabr92hb468rpxuCyQBEfQAUkQdiAJwg4kQdiBJAg7kARhB5Ig7EAS/wcpFH6Zmu7xIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 0\n",
    "print('Original Image :')\n",
    "plt.imshow(X_test[n])\n",
    "plt.show()\n",
    "print('Noisy Image :')\n",
    "plt.imshow(noisy_images[n])\n",
    "plt.show()\n",
    "print('Encoded Image :')\n",
    "plt.imshow(pred_images[n])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Conculsion:</b> <i>Auto encoder is best for noise remover and Dimensionlity Reduction </i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}