wildflower_classifier3-season2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wild Flower Classification Project 3 - season2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wild Flower Classification using Transfer Learning 3 season2\n",
    "\n",
    "This is taken from `wildflower_classifier3.ipynb` for season 2 (Refined data version)\n",
    "\n",
    "- Using InceptionV3 + `tf.keras.applications.inception_v3.preprocess_input` instead of `rescale = 1./255`\n",
    "- weight decay : 0.0001 -> 0.00001  ===> Same weight decay as classifier2. (= 0.0001)\n",
    "- **Fine tune last 2 Inception Modules**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "#os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"   # see issue #152\n",
    "#os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.2.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "%load_ext tensorboard\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "#from tensorflow.keras.preprocessing import image_dataset_from_directory # cant import in Tensorflow v2.2\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras import losses\n",
    "from tensorflow.keras import optimizers\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "from numpy.random import default_rng\n",
    "\n",
    "print('TensorFlow version:', tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from centerLoss import prelu, zero_loss, my_model\n",
    "#from centerLoss import CenterLossLayer\n",
    "#from utils import tbProjector, PCAPlotter, create_testdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tf.test.is_gpu_available()\n",
    "tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.list_physical_devices('CPU')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counting Training Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wf_data2/train\n",
      "items = 108\n"
     ]
    }
   ],
   "source": [
    "image_dir = \"wf_data2\"\n",
    "train_dir = os.path.join(image_dir, \"train\")\n",
    "print(train_dir)\n",
    "\n",
    "train_classnum = len(os.listdir(train_dir))\n",
    "print(\"items = {}\".format(len(os.listdir(train_dir))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All image files = 4853\n",
      "class_list[:10] = ['Actinotus_helianthi', 'Alyogyne_huegelii', 'Anthocercis_littorea', 'Anthocercis_viscosa', 'Banksia', 'Beaufortia_squarrosa', 'Blandfordia_grandiflora', 'Bossiaea_aquifolium_', 'Bossiaea_eriocarpa', 'Burchardia_congesta']\n"
     ]
    }
   ],
   "source": [
    "all_images = 0\n",
    "class_list = sorted(os.listdir(train_dir))\n",
    "for i in class_list:\n",
    "    #print(i)\n",
    "    all_images += len(os.listdir(os.path.join(train_dir, i)))\n",
    "print(\"All image files = {}\".format(all_images))\n",
    "print(\"class_list[:10] = {}\".format(class_list[:10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "_BATCH_SIZE = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dataset(image_samples, name_list):\n",
    "\n",
    "    #plt.figure(figsize=(5, 2))\n",
    "    fig, axs = plt.subplots(len(image_samples)//5, 5, figsize=(9, 5) )\n",
    "                            \n",
    "    count = 0\n",
    "    for i in range(len(image_samples)//5):\n",
    "        for j in range(5):\n",
    " \n",
    "            img = tf.keras.preprocessing.image.load_img(image_samples[count])\n",
    "            img = tf.keras.preprocessing.image.img_to_array(img)\n",
    "            img /= 255.\n",
    "            axs[i, j].imshow(img)\n",
    "            axs[i, j].set_xticks([])\n",
    "            axs[i, j].set_yticks([])\n",
    "            axs[i, j].set_title(name_list[count], fontdict={'fontsize' : 'small'})\n",
    "            count+=1\n",
    "        #plt.title(examples[1][i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "        return MozWebSocket;\n",
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       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
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       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
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       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
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       "        'style',\n",
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       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
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       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
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       "        for (var i = 0; i < nentries; i++) {\n",
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       "                    // Firefox implements old version of spec.\n",
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       "                // Chrome <84 implements even older version of spec.\n",
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       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
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       "                // Chrome 84 implements new version of spec.\n",
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       "                canvas.setAttribute('width', width * mpl.ratio);\n",
       "                canvas.setAttribute('height', height * mpl.ratio);\n",
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       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
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       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
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       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
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       "        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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
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       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\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,\n",
       "        0,\n",
       "        fig.canvas.width / mpl.ratio,\n",
       "        fig.canvas.height / mpl.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        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, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / mpl.ratio;\n",
       "    fig.root.removeEventListener('remove', this._remove_fig_handler);\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.innerHTML =\n",
       "        '<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'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function () {\n",
       "    this.close_ws(this, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "    el.addEventListener('remove', this._remove_fig_handler);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\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": [
    "show_plot_num = 10\n",
    "\n",
    "plot_list = []\n",
    "for i, cls in enumerate(class_list):\n",
    "    if i >= show_plot_num:\n",
    "        break\n",
    "        \n",
    "    cls_path = os.path.join(train_dir, cls)\n",
    "    imlist = os.listdir(cls_path)\n",
    "    plot_list.append(os.path.join(cls_path, imlist[0]))\n",
    "    \n",
    "#print(plot_list)\n",
    "#print(class_list[:show_plot_num])\n",
    "plot_dataset(plot_list, class_list[:show_plot_num])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup train data generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4853 images belonging to 108 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "    shear_range=0.1,\n",
    "    #zoom_range=0.1,\n",
    "    rotation_range=20,\n",
    "    horizontal_flip=True,   \n",
    "    #rescale=1./255\n",
    "    preprocessing_function=keras.applications.inception_v3.preprocess_input)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=_BATCH_SIZE,\n",
    "    class_mode='categorical'\n",
    "    ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup validation data generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All image files in validation = 605\n"
     ]
    }
   ],
   "source": [
    "valid_dir = os.path.join(image_dir, \"valid\")\n",
    "\n",
    "all_images_valid = 0\n",
    "\n",
    "for cl in class_list:\n",
    "    all_images_valid += len(os.listdir(os.path.join(valid_dir, cl)))\n",
    "\n",
    "print(\"All image files in validation = {}\".format(all_images_valid))\n",
    "#print(\"class_list[:10] = {}\".format(class_list[:10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 605 images belonging to 108 classes.\n"
     ]
    }
   ],
   "source": [
    "#valid_datagen = ImageDataGenerator(rescale=1./255)\n",
    "valid_datagen = ImageDataGenerator(preprocessing_function=keras.applications.inception_v3.preprocess_input)\n",
    "\n",
    "valid_generator = valid_datagen.flow_from_directory(\n",
    "    os.path.join(image_dir, \"valid\"),\n",
    "    target_size=(224, 224),\n",
    "    batch_size=_BATCH_SIZE,\n",
    "    class_mode='categorical'\n",
    "    ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a pre-trained model and attach some fully-connected layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "inception_v3 (Model)         (None, 5, 5, 2048)        21802784  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               1049088   \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 108)               27756     \n",
      "=================================================================\n",
      "Total params: 23,010,956\n",
      "Trainable params: 1,208,172\n",
      "Non-trainable params: 21,802,784\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "weight_decay = 0.0001\n",
    "\n",
    "img_input = keras.Input(shape=(224, 224, 3))\n",
    "train_labels = keras.Input(shape=(train_classnum,))\n",
    "\n",
    "base_model = keras.applications.InceptionV3(include_top=False, weights='imagenet', input_shape=(224, 224, 3))\n",
    "base_model.trainable = False\n",
    "\n",
    "#x = base_model(img_input, training=False)\n",
    "x = base_model(img_input)\n",
    "#x = keras.layers.Flatten()(x)\n",
    "x = keras.layers.GlobalAveragePooling2D()(x)\n",
    "#x = keras.layers.Dropout(0.5)(x)\n",
    "x = keras.layers.Dense(512, activation='relu')(x)\n",
    "x = keras.layers.Dropout(0.5)(x)\n",
    "x = keras.layers.Dense(256, activation='relu')(x)\n",
    "x = keras.layers.Dropout(0.5)(x)\n",
    "#out = keras.layers.Dense(train_classnum, activation='softmax', name='output', kernel_regularizer=l2(weight_decay))(x)\n",
    "out = keras.layers.Dense(train_classnum, activation='softmax', name='output')(x)\n",
    "\n",
    "model = keras.Model(inputs=img_input, outputs=out)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## compile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initial_learning_rate = 0.001\n",
    "\n",
    "optim = optimizers.Adam(learning_rate=0.001)\n",
    "model.compile(optimizer=optim,\n",
    "                  loss=losses.categorical_crossentropy,\n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "151/151 [==============================] - 49s 327ms/step - loss: 4.7080 - accuracy: 0.0217 - val_loss: 4.3511 - val_accuracy: 0.0781\n",
      "Epoch 2/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 4.2101 - accuracy: 0.0585 - val_loss: 3.6238 - val_accuracy: 0.1562\n",
      "Epoch 3/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 3.8574 - accuracy: 0.0871 - val_loss: 3.3170 - val_accuracy: 0.2049\n",
      "Epoch 4/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 3.6287 - accuracy: 0.1278 - val_loss: 3.0614 - val_accuracy: 0.2622\n",
      "Epoch 5/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 3.4438 - accuracy: 0.1421 - val_loss: 2.9016 - val_accuracy: 0.3021\n",
      "Epoch 6/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 3.2955 - accuracy: 0.1691 - val_loss: 2.7580 - val_accuracy: 0.3299\n",
      "Epoch 7/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 3.1560 - accuracy: 0.1917 - val_loss: 2.5885 - val_accuracy: 0.3559\n",
      "Epoch 8/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 3.0274 - accuracy: 0.2145 - val_loss: 2.5487 - val_accuracy: 0.3802\n",
      "Epoch 9/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.9910 - accuracy: 0.2222 - val_loss: 2.4172 - val_accuracy: 0.3854\n",
      "Epoch 10/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.9283 - accuracy: 0.2425 - val_loss: 2.4522 - val_accuracy: 0.3872\n",
      "Epoch 11/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.8949 - accuracy: 0.2402 - val_loss: 2.3544 - val_accuracy: 0.4097\n",
      "Epoch 12/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 2.7975 - accuracy: 0.2593 - val_loss: 2.3322 - val_accuracy: 0.4062\n",
      "Epoch 13/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.7318 - accuracy: 0.2618 - val_loss: 2.2545 - val_accuracy: 0.4132\n",
      "Epoch 14/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 2.7526 - accuracy: 0.2769 - val_loss: 2.2204 - val_accuracy: 0.4497\n",
      "Epoch 15/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 2.6433 - accuracy: 0.2912 - val_loss: 2.2071 - val_accuracy: 0.4271\n",
      "Epoch 16/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 2.6425 - accuracy: 0.2910 - val_loss: 2.1992 - val_accuracy: 0.4132\n",
      "Epoch 17/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 2.6293 - accuracy: 0.2956 - val_loss: 2.1882 - val_accuracy: 0.4462\n",
      "Epoch 18/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.5809 - accuracy: 0.3035 - val_loss: 2.1714 - val_accuracy: 0.4288\n",
      "Epoch 19/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.5420 - accuracy: 0.3130 - val_loss: 2.1179 - val_accuracy: 0.4531\n",
      "Epoch 20/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.5797 - accuracy: 0.2937 - val_loss: 2.1597 - val_accuracy: 0.4479\n",
      "Epoch 21/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.4733 - accuracy: 0.3209 - val_loss: 2.1301 - val_accuracy: 0.4497\n",
      "Epoch 22/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.4887 - accuracy: 0.3215 - val_loss: 2.0827 - val_accuracy: 0.4740\n",
      "Epoch 23/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.4628 - accuracy: 0.3333 - val_loss: 2.0410 - val_accuracy: 0.4444\n",
      "Epoch 24/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 2.4434 - accuracy: 0.3263 - val_loss: 2.0727 - val_accuracy: 0.4722\n",
      "Epoch 25/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.3951 - accuracy: 0.3464 - val_loss: 2.0401 - val_accuracy: 0.4705\n",
      "Epoch 26/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.3986 - accuracy: 0.3398 - val_loss: 2.0212 - val_accuracy: 0.4601\n",
      "Epoch 27/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.4090 - accuracy: 0.3360 - val_loss: 2.0861 - val_accuracy: 0.4705\n",
      "Epoch 28/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.3671 - accuracy: 0.3439 - val_loss: 1.9787 - val_accuracy: 0.4913\n",
      "Epoch 29/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 2.3608 - accuracy: 0.3487 - val_loss: 1.9840 - val_accuracy: 0.4635\n",
      "Epoch 30/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.2924 - accuracy: 0.3709 - val_loss: 2.0505 - val_accuracy: 0.4688\n",
      "Epoch 31/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.3499 - accuracy: 0.3510 - val_loss: 1.9997 - val_accuracy: 0.4635\n",
      "Epoch 32/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.2452 - accuracy: 0.3736 - val_loss: 1.9771 - val_accuracy: 0.4792\n",
      "Epoch 33/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.2919 - accuracy: 0.3713 - val_loss: 1.9504 - val_accuracy: 0.4896\n",
      "Epoch 34/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.3210 - accuracy: 0.3653 - val_loss: 2.0189 - val_accuracy: 0.4670\n",
      "Epoch 35/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.2602 - accuracy: 0.3744 - val_loss: 1.9462 - val_accuracy: 0.4601\n",
      "Epoch 36/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.2582 - accuracy: 0.3786 - val_loss: 2.0346 - val_accuracy: 0.4757\n",
      "Epoch 37/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.2348 - accuracy: 0.3837 - val_loss: 1.9773 - val_accuracy: 0.4809\n",
      "Epoch 38/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.2437 - accuracy: 0.3937 - val_loss: 1.9500 - val_accuracy: 0.4757\n",
      "Epoch 39/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.2109 - accuracy: 0.3794 - val_loss: 1.9433 - val_accuracy: 0.4792\n",
      "Epoch 40/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.2126 - accuracy: 0.3862 - val_loss: 1.8950 - val_accuracy: 0.5000\n",
      "Epoch 41/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 2.2227 - accuracy: 0.3783 - val_loss: 1.9303 - val_accuracy: 0.4931\n",
      "Epoch 42/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.1938 - accuracy: 0.3943 - val_loss: 1.8787 - val_accuracy: 0.4896\n",
      "Epoch 43/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 2.1533 - accuracy: 0.4043 - val_loss: 1.9083 - val_accuracy: 0.4931\n",
      "Epoch 44/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 2.1524 - accuracy: 0.3912 - val_loss: 1.9552 - val_accuracy: 0.4931\n",
      "Epoch 45/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.2251 - accuracy: 0.3875 - val_loss: 1.9273 - val_accuracy: 0.4844\n",
      "Epoch 46/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.1528 - accuracy: 0.4030 - val_loss: 1.9265 - val_accuracy: 0.4948\n",
      "Epoch 47/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.1273 - accuracy: 0.3978 - val_loss: 1.8959 - val_accuracy: 0.5278\n",
      "Epoch 48/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.1311 - accuracy: 0.3976 - val_loss: 1.8882 - val_accuracy: 0.5000\n",
      "Epoch 49/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.1548 - accuracy: 0.4014 - val_loss: 1.8835 - val_accuracy: 0.5052\n",
      "Epoch 50/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.1113 - accuracy: 0.4103 - val_loss: 1.8807 - val_accuracy: 0.5035\n",
      "Epoch 51/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.1527 - accuracy: 0.4039 - val_loss: 1.8758 - val_accuracy: 0.5156\n",
      "Epoch 52/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.1105 - accuracy: 0.4132 - val_loss: 1.8871 - val_accuracy: 0.5000\n",
      "Epoch 53/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.1402 - accuracy: 0.4032 - val_loss: 1.8838 - val_accuracy: 0.4792\n",
      "Epoch 54/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.1341 - accuracy: 0.4051 - val_loss: 1.8382 - val_accuracy: 0.5017\n",
      "Epoch 55/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 2.0718 - accuracy: 0.4238 - val_loss: 1.8838 - val_accuracy: 0.5069\n",
      "Epoch 56/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0590 - accuracy: 0.4279 - val_loss: 1.8583 - val_accuracy: 0.4965\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0845 - accuracy: 0.4219 - val_loss: 1.8976 - val_accuracy: 0.5035\n",
      "Epoch 58/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0836 - accuracy: 0.4132 - val_loss: 1.8724 - val_accuracy: 0.5087\n",
      "Epoch 59/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.0633 - accuracy: 0.4165 - val_loss: 1.8521 - val_accuracy: 0.5052\n",
      "Epoch 60/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.1056 - accuracy: 0.4151 - val_loss: 1.8940 - val_accuracy: 0.5017\n",
      "Epoch 61/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0622 - accuracy: 0.4277 - val_loss: 1.8388 - val_accuracy: 0.5104\n",
      "Epoch 62/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0527 - accuracy: 0.4314 - val_loss: 1.8961 - val_accuracy: 0.5017\n",
      "Epoch 63/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0819 - accuracy: 0.4171 - val_loss: 1.8825 - val_accuracy: 0.5122\n",
      "Epoch 64/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0543 - accuracy: 0.4161 - val_loss: 1.8492 - val_accuracy: 0.5000\n",
      "Epoch 65/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.0555 - accuracy: 0.4271 - val_loss: 1.8805 - val_accuracy: 0.4844\n",
      "Epoch 66/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0641 - accuracy: 0.4192 - val_loss: 1.8661 - val_accuracy: 0.4913\n",
      "Epoch 67/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0484 - accuracy: 0.4225 - val_loss: 1.8399 - val_accuracy: 0.4878\n",
      "Epoch 68/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.0024 - accuracy: 0.4285 - val_loss: 1.8393 - val_accuracy: 0.5069\n",
      "Epoch 69/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.0063 - accuracy: 0.4354 - val_loss: 1.8180 - val_accuracy: 0.5156\n",
      "Epoch 70/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 1.9912 - accuracy: 0.4426 - val_loss: 1.8759 - val_accuracy: 0.5035\n",
      "Epoch 71/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.0218 - accuracy: 0.4327 - val_loss: 1.8557 - val_accuracy: 0.5017\n",
      "Epoch 72/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0241 - accuracy: 0.4393 - val_loss: 1.8488 - val_accuracy: 0.4931\n",
      "Epoch 73/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0136 - accuracy: 0.4215 - val_loss: 1.8656 - val_accuracy: 0.4878\n",
      "Epoch 74/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0041 - accuracy: 0.4393 - val_loss: 1.8653 - val_accuracy: 0.5017\n",
      "Epoch 75/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0292 - accuracy: 0.4283 - val_loss: 1.8335 - val_accuracy: 0.5104\n",
      "Epoch 76/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 1.9833 - accuracy: 0.4410 - val_loss: 1.8446 - val_accuracy: 0.4913\n",
      "Epoch 77/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 2.0577 - accuracy: 0.4348 - val_loss: 1.8532 - val_accuracy: 0.5365\n",
      "Epoch 78/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9936 - accuracy: 0.4325 - val_loss: 1.8297 - val_accuracy: 0.5191\n",
      "Epoch 79/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0029 - accuracy: 0.4420 - val_loss: 1.8489 - val_accuracy: 0.5174\n",
      "Epoch 80/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0131 - accuracy: 0.4333 - val_loss: 1.7957 - val_accuracy: 0.5243\n",
      "Epoch 81/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9571 - accuracy: 0.4470 - val_loss: 1.8644 - val_accuracy: 0.5069\n",
      "Epoch 82/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0057 - accuracy: 0.4337 - val_loss: 1.8344 - val_accuracy: 0.5208\n",
      "Epoch 83/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 2.0243 - accuracy: 0.4414 - val_loss: 1.8733 - val_accuracy: 0.5174\n",
      "Epoch 84/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9471 - accuracy: 0.4489 - val_loss: 1.8443 - val_accuracy: 0.5260\n",
      "Epoch 85/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 1.9489 - accuracy: 0.4514 - val_loss: 1.8513 - val_accuracy: 0.5104\n",
      "Epoch 86/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 1.9911 - accuracy: 0.4466 - val_loss: 1.8470 - val_accuracy: 0.5139\n",
      "Epoch 87/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 1.9688 - accuracy: 0.4499 - val_loss: 1.8995 - val_accuracy: 0.5122\n",
      "Epoch 88/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9426 - accuracy: 0.4528 - val_loss: 1.7942 - val_accuracy: 0.5278\n",
      "Epoch 89/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9542 - accuracy: 0.4611 - val_loss: 1.7576 - val_accuracy: 0.5104\n",
      "Epoch 90/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 1.9527 - accuracy: 0.4491 - val_loss: 1.8062 - val_accuracy: 0.5208\n",
      "Epoch 91/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9884 - accuracy: 0.4416 - val_loss: 1.7892 - val_accuracy: 0.5365\n",
      "Epoch 92/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9605 - accuracy: 0.4495 - val_loss: 1.8082 - val_accuracy: 0.5226\n",
      "Epoch 93/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 1.9844 - accuracy: 0.4470 - val_loss: 1.8061 - val_accuracy: 0.5174\n",
      "Epoch 94/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9588 - accuracy: 0.4491 - val_loss: 1.8530 - val_accuracy: 0.4965\n",
      "Epoch 95/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 1.9488 - accuracy: 0.4487 - val_loss: 1.8102 - val_accuracy: 0.5069\n",
      "Epoch 96/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9543 - accuracy: 0.4487 - val_loss: 1.7886 - val_accuracy: 0.5104\n",
      "Epoch 97/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9245 - accuracy: 0.4532 - val_loss: 1.8281 - val_accuracy: 0.5104\n",
      "Epoch 98/100\n",
      "151/151 [==============================] - 48s 320ms/step - loss: 1.9457 - accuracy: 0.4592 - val_loss: 1.7637 - val_accuracy: 0.5174\n",
      "Epoch 99/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9219 - accuracy: 0.4673 - val_loss: 1.8184 - val_accuracy: 0.5122\n",
      "Epoch 100/100\n",
      "151/151 [==============================] - 48s 319ms/step - loss: 1.9091 - accuracy: 0.4667 - val_loss: 1.7975 - val_accuracy: 0.5122\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "steps_per_epoch = int(all_images/_BATCH_SIZE)\n",
    "val_steps = int(all_images_valid/_BATCH_SIZE)\n",
    "\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    epochs=epochs,\n",
    "    validation_data=valid_generator,\n",
    "    validation_steps=val_steps\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trained model was saved into wfmodel3_s2_noft.h5.\n"
     ]
    }
   ],
   "source": [
    "#model_file = \"wfmodel_noft_\" + str(epoch_time) + \".h5\"\n",
    "model_file = \"wfmodel3_s2_noft\" + \".h5\"\n",
    "model.save(model_file)\n",
    "print(\"Trained model was saved into {}.\".format(model_file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 input_3 False\n",
      "1 conv2d False\n",
      "2 batch_normalization False\n",
      "3 activation False\n",
      "4 conv2d_1 False\n",
      "5 batch_normalization_1 False\n",
      "6 activation_1 False\n",
      "7 conv2d_2 False\n",
      "8 batch_normalization_2 False\n",
      "9 activation_2 False\n",
      "10 max_pooling2d False\n",
      "11 conv2d_3 False\n",
      "12 batch_normalization_3 False\n",
      "13 activation_3 False\n",
      "14 conv2d_4 False\n",
      "15 batch_normalization_4 False\n",
      "16 activation_4 False\n",
      "17 max_pooling2d_1 False\n",
      "18 conv2d_8 False\n",
      "19 batch_normalization_8 False\n",
      "20 activation_8 False\n",
      "21 conv2d_6 False\n",
      "22 conv2d_9 False\n",
      "23 batch_normalization_6 False\n",
      "24 batch_normalization_9 False\n",
      "25 activation_6 False\n",
      "26 activation_9 False\n",
      "27 average_pooling2d False\n",
      "28 conv2d_5 False\n",
      "29 conv2d_7 False\n",
      "30 conv2d_10 False\n",
      "31 conv2d_11 False\n",
      "32 batch_normalization_5 False\n",
      "33 batch_normalization_7 False\n",
      "34 batch_normalization_10 False\n",
      "35 batch_normalization_11 False\n",
      "36 activation_5 False\n",
      "37 activation_7 False\n",
      "38 activation_10 False\n",
      "39 activation_11 False\n",
      "40 mixed0 False\n",
      "41 conv2d_15 False\n",
      "42 batch_normalization_15 False\n",
      "43 activation_15 False\n",
      "44 conv2d_13 False\n",
      "45 conv2d_16 False\n",
      "46 batch_normalization_13 False\n",
      "47 batch_normalization_16 False\n",
      "48 activation_13 False\n",
      "49 activation_16 False\n",
      "50 average_pooling2d_1 False\n",
      "51 conv2d_12 False\n",
      "52 conv2d_14 False\n",
      "53 conv2d_17 False\n",
      "54 conv2d_18 False\n",
      "55 batch_normalization_12 False\n",
      "56 batch_normalization_14 False\n",
      "57 batch_normalization_17 False\n",
      "58 batch_normalization_18 False\n",
      "59 activation_12 False\n",
      "60 activation_14 False\n",
      "61 activation_17 False\n",
      "62 activation_18 False\n",
      "63 mixed1 False\n",
      "64 conv2d_22 False\n",
      "65 batch_normalization_22 False\n",
      "66 activation_22 False\n",
      "67 conv2d_20 False\n",
      "68 conv2d_23 False\n",
      "69 batch_normalization_20 False\n",
      "70 batch_normalization_23 False\n",
      "71 activation_20 False\n",
      "72 activation_23 False\n",
      "73 average_pooling2d_2 False\n",
      "74 conv2d_19 False\n",
      "75 conv2d_21 False\n",
      "76 conv2d_24 False\n",
      "77 conv2d_25 False\n",
      "78 batch_normalization_19 False\n",
      "79 batch_normalization_21 False\n",
      "80 batch_normalization_24 False\n",
      "81 batch_normalization_25 False\n",
      "82 activation_19 False\n",
      "83 activation_21 False\n",
      "84 activation_24 False\n",
      "85 activation_25 False\n",
      "86 mixed2 False\n",
      "87 conv2d_27 False\n",
      "88 batch_normalization_27 False\n",
      "89 activation_27 False\n",
      "90 conv2d_28 False\n",
      "91 batch_normalization_28 False\n",
      "92 activation_28 False\n",
      "93 conv2d_26 False\n",
      "94 conv2d_29 False\n",
      "95 batch_normalization_26 False\n",
      "96 batch_normalization_29 False\n",
      "97 activation_26 False\n",
      "98 activation_29 False\n",
      "99 max_pooling2d_2 False\n",
      "100 mixed3 False\n",
      "101 conv2d_34 False\n",
      "102 batch_normalization_34 False\n",
      "103 activation_34 False\n",
      "104 conv2d_35 False\n",
      "105 batch_normalization_35 False\n",
      "106 activation_35 False\n",
      "107 conv2d_31 False\n",
      "108 conv2d_36 False\n",
      "109 batch_normalization_31 False\n",
      "110 batch_normalization_36 False\n",
      "111 activation_31 False\n",
      "112 activation_36 False\n",
      "113 conv2d_32 False\n",
      "114 conv2d_37 False\n",
      "115 batch_normalization_32 False\n",
      "116 batch_normalization_37 False\n",
      "117 activation_32 False\n",
      "118 activation_37 False\n",
      "119 average_pooling2d_3 False\n",
      "120 conv2d_30 False\n",
      "121 conv2d_33 False\n",
      "122 conv2d_38 False\n",
      "123 conv2d_39 False\n",
      "124 batch_normalization_30 False\n",
      "125 batch_normalization_33 False\n",
      "126 batch_normalization_38 False\n",
      "127 batch_normalization_39 False\n",
      "128 activation_30 False\n",
      "129 activation_33 False\n",
      "130 activation_38 False\n",
      "131 activation_39 False\n",
      "132 mixed4 False\n",
      "133 conv2d_44 False\n",
      "134 batch_normalization_44 False\n",
      "135 activation_44 False\n",
      "136 conv2d_45 False\n",
      "137 batch_normalization_45 False\n",
      "138 activation_45 False\n",
      "139 conv2d_41 False\n",
      "140 conv2d_46 False\n",
      "141 batch_normalization_41 False\n",
      "142 batch_normalization_46 False\n",
      "143 activation_41 False\n",
      "144 activation_46 False\n",
      "145 conv2d_42 False\n",
      "146 conv2d_47 False\n",
      "147 batch_normalization_42 False\n",
      "148 batch_normalization_47 False\n",
      "149 activation_42 False\n",
      "150 activation_47 False\n",
      "151 average_pooling2d_4 False\n",
      "152 conv2d_40 False\n",
      "153 conv2d_43 False\n",
      "154 conv2d_48 False\n",
      "155 conv2d_49 False\n",
      "156 batch_normalization_40 False\n",
      "157 batch_normalization_43 False\n",
      "158 batch_normalization_48 False\n",
      "159 batch_normalization_49 False\n",
      "160 activation_40 False\n",
      "161 activation_43 False\n",
      "162 activation_48 False\n",
      "163 activation_49 False\n",
      "164 mixed5 False\n",
      "165 conv2d_54 False\n",
      "166 batch_normalization_54 False\n",
      "167 activation_54 False\n",
      "168 conv2d_55 False\n",
      "169 batch_normalization_55 False\n",
      "170 activation_55 False\n",
      "171 conv2d_51 False\n",
      "172 conv2d_56 False\n",
      "173 batch_normalization_51 False\n",
      "174 batch_normalization_56 False\n",
      "175 activation_51 False\n",
      "176 activation_56 False\n",
      "177 conv2d_52 False\n",
      "178 conv2d_57 False\n",
      "179 batch_normalization_52 False\n",
      "180 batch_normalization_57 False\n",
      "181 activation_52 False\n",
      "182 activation_57 False\n",
      "183 average_pooling2d_5 False\n",
      "184 conv2d_50 False\n",
      "185 conv2d_53 False\n",
      "186 conv2d_58 False\n",
      "187 conv2d_59 False\n",
      "188 batch_normalization_50 False\n",
      "189 batch_normalization_53 False\n",
      "190 batch_normalization_58 False\n",
      "191 batch_normalization_59 False\n",
      "192 activation_50 False\n",
      "193 activation_53 False\n",
      "194 activation_58 False\n",
      "195 activation_59 False\n",
      "196 mixed6 False\n",
      "197 conv2d_64 False\n",
      "198 batch_normalization_64 False\n",
      "199 activation_64 False\n",
      "200 conv2d_65 False\n",
      "201 batch_normalization_65 False\n",
      "202 activation_65 False\n",
      "203 conv2d_61 False\n",
      "204 conv2d_66 False\n",
      "205 batch_normalization_61 False\n",
      "206 batch_normalization_66 False\n",
      "207 activation_61 False\n",
      "208 activation_66 False\n",
      "209 conv2d_62 False\n",
      "210 conv2d_67 False\n",
      "211 batch_normalization_62 False\n",
      "212 batch_normalization_67 False\n",
      "213 activation_62 False\n",
      "214 activation_67 False\n",
      "215 average_pooling2d_6 False\n",
      "216 conv2d_60 False\n",
      "217 conv2d_63 False\n",
      "218 conv2d_68 False\n",
      "219 conv2d_69 False\n",
      "220 batch_normalization_60 False\n",
      "221 batch_normalization_63 False\n",
      "222 batch_normalization_68 False\n",
      "223 batch_normalization_69 False\n",
      "224 activation_60 False\n",
      "225 activation_63 False\n",
      "226 activation_68 False\n",
      "227 activation_69 False\n",
      "228 mixed7 False\n",
      "229 conv2d_72 False\n",
      "230 batch_normalization_72 False\n",
      "231 activation_72 False\n",
      "232 conv2d_73 False\n",
      "233 batch_normalization_73 False\n",
      "234 activation_73 False\n",
      "235 conv2d_70 False\n",
      "236 conv2d_74 False\n",
      "237 batch_normalization_70 False\n",
      "238 batch_normalization_74 False\n",
      "239 activation_70 False\n",
      "240 activation_74 False\n",
      "241 conv2d_71 False\n",
      "242 conv2d_75 False\n",
      "243 batch_normalization_71 False\n",
      "244 batch_normalization_75 False\n",
      "245 activation_71 False\n",
      "246 activation_75 False\n",
      "247 max_pooling2d_3 False\n",
      "248 mixed8 False\n",
      "249 conv2d_80 False\n",
      "250 batch_normalization_80 False\n",
      "251 activation_80 False\n",
      "252 conv2d_77 False\n",
      "253 conv2d_81 False\n",
      "254 batch_normalization_77 False\n",
      "255 batch_normalization_81 False\n",
      "256 activation_77 False\n",
      "257 activation_81 False\n",
      "258 conv2d_78 False\n",
      "259 conv2d_79 False\n",
      "260 conv2d_82 False\n",
      "261 conv2d_83 False\n",
      "262 average_pooling2d_7 False\n",
      "263 conv2d_76 False\n",
      "264 batch_normalization_78 False\n",
      "265 batch_normalization_79 False\n",
      "266 batch_normalization_82 False\n",
      "267 batch_normalization_83 False\n",
      "268 conv2d_84 False\n",
      "269 batch_normalization_76 False\n",
      "270 activation_78 False\n",
      "271 activation_79 False\n",
      "272 activation_82 False\n",
      "273 activation_83 False\n",
      "274 batch_normalization_84 False\n",
      "275 activation_76 False\n",
      "276 mixed9_0 False\n",
      "277 concatenate False\n",
      "278 activation_84 False\n",
      "279 mixed9 False\n",
      "280 conv2d_89 False\n",
      "281 batch_normalization_89 False\n",
      "282 activation_89 False\n",
      "283 conv2d_86 False\n",
      "284 conv2d_90 False\n",
      "285 batch_normalization_86 False\n",
      "286 batch_normalization_90 False\n",
      "287 activation_86 False\n",
      "288 activation_90 False\n",
      "289 conv2d_87 False\n",
      "290 conv2d_88 False\n",
      "291 conv2d_91 False\n",
      "292 conv2d_92 False\n",
      "293 average_pooling2d_8 False\n",
      "294 conv2d_85 False\n",
      "295 batch_normalization_87 False\n",
      "296 batch_normalization_88 False\n",
      "297 batch_normalization_91 False\n",
      "298 batch_normalization_92 False\n",
      "299 conv2d_93 False\n",
      "300 batch_normalization_85 False\n",
      "301 activation_87 False\n",
      "302 activation_88 False\n",
      "303 activation_91 False\n",
      "304 activation_92 False\n",
      "305 batch_normalization_93 False\n",
      "306 activation_85 False\n",
      "307 mixed9_1 False\n",
      "308 concatenate_1 False\n",
      "309 activation_93 False\n",
      "310 mixed10 False\n"
     ]
    }
   ],
   "source": [
    "for i, layer in enumerate(base_model.layers):\n",
    "    print(i, layer.name, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train last 2 inception blocks (from 249 - )\n",
    "for layer in base_model.layers[:249]:\n",
    "    layer.trainable = False\n",
    "    \n",
    "for layer in base_model.layers[249:]:\n",
    "    layer.trainable = True\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "inception_v3 (Model)         (None, 5, 5, 2048)        21802784  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               1049088   \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 108)               27756     \n",
      "=================================================================\n",
      "Total params: 23,010,956\n",
      "Trainable params: 12,323,052\n",
      "Non-trainable params: 10,687,904\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#base_model.trainable = True\n",
    "\n",
    "optim_ft = optimizers.Adam(learning_rate=1e-5)\n",
    "#optim_ft = optimizers.SGD(learning_rate=0.0001, momentum=0.9)\n",
    "model.compile(optimizer=optim_ft,\n",
    "                  loss=losses.categorical_crossentropy,\n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "151/151 [==============================] - 50s 330ms/step - loss: 3.0970 - accuracy: 0.2433 - val_loss: 2.1972 - val_accuracy: 0.4323\n",
      "Epoch 2/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 2.6488 - accuracy: 0.3122 - val_loss: 2.2020 - val_accuracy: 0.4479\n",
      "Epoch 3/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 2.4964 - accuracy: 0.3437 - val_loss: 2.1424 - val_accuracy: 0.4601\n",
      "Epoch 4/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 2.3830 - accuracy: 0.3620 - val_loss: 2.0685 - val_accuracy: 0.4566\n",
      "Epoch 5/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 2.2995 - accuracy: 0.3792 - val_loss: 2.0392 - val_accuracy: 0.4583\n",
      "Epoch 6/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 2.1994 - accuracy: 0.4016 - val_loss: 1.9593 - val_accuracy: 0.4722\n",
      "Epoch 7/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 2.1681 - accuracy: 0.4047 - val_loss: 1.9336 - val_accuracy: 0.4635\n",
      "Epoch 8/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 2.0494 - accuracy: 0.4277 - val_loss: 1.8828 - val_accuracy: 0.5000\n",
      "Epoch 9/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 2.0798 - accuracy: 0.4231 - val_loss: 1.8510 - val_accuracy: 0.5122\n",
      "Epoch 10/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 2.0540 - accuracy: 0.4281 - val_loss: 1.8015 - val_accuracy: 0.5243\n",
      "Epoch 11/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.9410 - accuracy: 0.4568 - val_loss: 1.8091 - val_accuracy: 0.5104\n",
      "Epoch 12/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.9455 - accuracy: 0.4572 - val_loss: 1.7853 - val_accuracy: 0.5191\n",
      "Epoch 13/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.9068 - accuracy: 0.4611 - val_loss: 1.7485 - val_accuracy: 0.5226\n",
      "Epoch 14/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 1.8777 - accuracy: 0.4717 - val_loss: 1.7263 - val_accuracy: 0.5365\n",
      "Epoch 15/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.8827 - accuracy: 0.4810 - val_loss: 1.7332 - val_accuracy: 0.5434\n",
      "Epoch 16/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 1.8494 - accuracy: 0.4910 - val_loss: 1.7181 - val_accuracy: 0.5451\n",
      "Epoch 17/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.7684 - accuracy: 0.4966 - val_loss: 1.7206 - val_accuracy: 0.5434\n",
      "Epoch 18/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 1.7725 - accuracy: 0.4935 - val_loss: 1.6903 - val_accuracy: 0.5521\n",
      "Epoch 19/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.7631 - accuracy: 0.4980 - val_loss: 1.6658 - val_accuracy: 0.5608\n",
      "Epoch 20/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 1.7165 - accuracy: 0.5121 - val_loss: 1.6435 - val_accuracy: 0.5677\n",
      "Epoch 21/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 1.7165 - accuracy: 0.5076 - val_loss: 1.6386 - val_accuracy: 0.5625\n",
      "Epoch 22/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.7183 - accuracy: 0.5053 - val_loss: 1.6200 - val_accuracy: 0.5625\n",
      "Epoch 23/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.6601 - accuracy: 0.5233 - val_loss: 1.6216 - val_accuracy: 0.5833\n",
      "Epoch 24/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.6625 - accuracy: 0.5221 - val_loss: 1.5915 - val_accuracy: 0.5694\n",
      "Epoch 25/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.6236 - accuracy: 0.5362 - val_loss: 1.5708 - val_accuracy: 0.5642\n",
      "Epoch 26/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.6386 - accuracy: 0.5337 - val_loss: 1.5581 - val_accuracy: 0.5920\n",
      "Epoch 27/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.6124 - accuracy: 0.5333 - val_loss: 1.5509 - val_accuracy: 0.5833\n",
      "Epoch 28/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 1.5820 - accuracy: 0.5356 - val_loss: 1.5267 - val_accuracy: 0.5920\n",
      "Epoch 29/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.5607 - accuracy: 0.5379 - val_loss: 1.5307 - val_accuracy: 0.5816\n",
      "Epoch 30/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.6113 - accuracy: 0.5323 - val_loss: 1.5061 - val_accuracy: 0.5868\n",
      "Epoch 31/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.5321 - accuracy: 0.5518 - val_loss: 1.5134 - val_accuracy: 0.5990\n",
      "Epoch 32/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.5380 - accuracy: 0.5505 - val_loss: 1.4806 - val_accuracy: 0.5920\n",
      "Epoch 33/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.5360 - accuracy: 0.5551 - val_loss: 1.4817 - val_accuracy: 0.5938\n",
      "Epoch 34/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.4993 - accuracy: 0.5544 - val_loss: 1.4840 - val_accuracy: 0.5938\n",
      "Epoch 35/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.4941 - accuracy: 0.5592 - val_loss: 1.4911 - val_accuracy: 0.5868\n",
      "Epoch 36/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.4949 - accuracy: 0.5623 - val_loss: 1.4463 - val_accuracy: 0.6024\n",
      "Epoch 37/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.4441 - accuracy: 0.5675 - val_loss: 1.4737 - val_accuracy: 0.5903\n",
      "Epoch 38/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.4375 - accuracy: 0.5731 - val_loss: 1.4573 - val_accuracy: 0.6076\n",
      "Epoch 39/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.4594 - accuracy: 0.5696 - val_loss: 1.4746 - val_accuracy: 0.6024\n",
      "Epoch 40/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.4326 - accuracy: 0.5742 - val_loss: 1.4389 - val_accuracy: 0.5938\n",
      "Epoch 41/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 1.4070 - accuracy: 0.5822 - val_loss: 1.4435 - val_accuracy: 0.5972\n",
      "Epoch 42/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.4105 - accuracy: 0.5847 - val_loss: 1.4133 - val_accuracy: 0.6163\n",
      "Epoch 43/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.3678 - accuracy: 0.5970 - val_loss: 1.4034 - val_accuracy: 0.6163\n",
      "Epoch 44/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.4245 - accuracy: 0.5750 - val_loss: 1.4302 - val_accuracy: 0.6059\n",
      "Epoch 45/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.3414 - accuracy: 0.6034 - val_loss: 1.4225 - val_accuracy: 0.6233\n",
      "Epoch 46/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.4080 - accuracy: 0.5814 - val_loss: 1.4282 - val_accuracy: 0.6250\n",
      "Epoch 47/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.3817 - accuracy: 0.5955 - val_loss: 1.4109 - val_accuracy: 0.6181\n",
      "Epoch 48/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.3287 - accuracy: 0.6017 - val_loss: 1.3904 - val_accuracy: 0.6233\n",
      "Epoch 49/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.3206 - accuracy: 0.6096 - val_loss: 1.4026 - val_accuracy: 0.6233\n",
      "Epoch 50/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 1.3532 - accuracy: 0.5974 - val_loss: 1.4026 - val_accuracy: 0.6181\n",
      "Epoch 51/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.3290 - accuracy: 0.6080 - val_loss: 1.4142 - val_accuracy: 0.6024\n",
      "Epoch 52/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.3171 - accuracy: 0.6100 - val_loss: 1.3898 - val_accuracy: 0.6181\n",
      "Epoch 53/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 1.3177 - accuracy: 0.6028 - val_loss: 1.4031 - val_accuracy: 0.6128\n",
      "Epoch 54/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 1.2885 - accuracy: 0.6120 - val_loss: 1.3904 - val_accuracy: 0.6181\n",
      "Epoch 55/100\n",
      "151/151 [==============================] - 49s 324ms/step - loss: 1.2877 - accuracy: 0.6094 - val_loss: 1.4015 - val_accuracy: 0.6128\n",
      "Epoch 56/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 1.2775 - accuracy: 0.6158 - val_loss: 1.4074 - val_accuracy: 0.6215\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 1.2561 - accuracy: 0.6268 - val_loss: 1.3964 - val_accuracy: 0.6198\n",
      "Epoch 58/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.2693 - accuracy: 0.6181 - val_loss: 1.3920 - val_accuracy: 0.6181\n",
      "Epoch 59/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.2443 - accuracy: 0.6277 - val_loss: 1.3743 - val_accuracy: 0.6250\n",
      "Epoch 60/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.2302 - accuracy: 0.6356 - val_loss: 1.3460 - val_accuracy: 0.6267\n",
      "Epoch 61/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.2600 - accuracy: 0.6210 - val_loss: 1.3628 - val_accuracy: 0.6285\n",
      "Epoch 62/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.2201 - accuracy: 0.6382 - val_loss: 1.3539 - val_accuracy: 0.6233\n",
      "Epoch 63/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.2037 - accuracy: 0.6331 - val_loss: 1.3361 - val_accuracy: 0.6233\n",
      "Epoch 64/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.2128 - accuracy: 0.6416 - val_loss: 1.3410 - val_accuracy: 0.6354\n",
      "Epoch 65/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.2112 - accuracy: 0.6345 - val_loss: 1.3166 - val_accuracy: 0.6424\n",
      "Epoch 66/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.1897 - accuracy: 0.6399 - val_loss: 1.3654 - val_accuracy: 0.6267\n",
      "Epoch 67/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.1519 - accuracy: 0.6501 - val_loss: 1.3517 - val_accuracy: 0.6250\n",
      "Epoch 68/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.1644 - accuracy: 0.6480 - val_loss: 1.3373 - val_accuracy: 0.6337\n",
      "Epoch 69/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 1.1571 - accuracy: 0.6459 - val_loss: 1.3471 - val_accuracy: 0.6354\n",
      "Epoch 70/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.1626 - accuracy: 0.6470 - val_loss: 1.3557 - val_accuracy: 0.6285\n",
      "Epoch 71/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.1225 - accuracy: 0.6526 - val_loss: 1.3542 - val_accuracy: 0.6285\n",
      "Epoch 72/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.1661 - accuracy: 0.6441 - val_loss: 1.3288 - val_accuracy: 0.6406\n",
      "Epoch 73/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.1032 - accuracy: 0.6652 - val_loss: 1.3185 - val_accuracy: 0.6493\n",
      "Epoch 74/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 1.1394 - accuracy: 0.6530 - val_loss: 1.3410 - val_accuracy: 0.6354\n",
      "Epoch 75/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 1.1410 - accuracy: 0.6551 - val_loss: 1.3093 - val_accuracy: 0.6389\n",
      "Epoch 76/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.0911 - accuracy: 0.6706 - val_loss: 1.3369 - val_accuracy: 0.6233\n",
      "Epoch 77/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.1037 - accuracy: 0.6696 - val_loss: 1.2885 - val_accuracy: 0.6372\n",
      "Epoch 78/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.1087 - accuracy: 0.6663 - val_loss: 1.3202 - val_accuracy: 0.6372\n",
      "Epoch 79/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.0913 - accuracy: 0.6704 - val_loss: 1.3374 - val_accuracy: 0.6441\n",
      "Epoch 80/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.0823 - accuracy: 0.6646 - val_loss: 1.2919 - val_accuracy: 0.6562\n",
      "Epoch 81/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 1.1054 - accuracy: 0.6559 - val_loss: 1.3233 - val_accuracy: 0.6597\n",
      "Epoch 82/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.0974 - accuracy: 0.6692 - val_loss: 1.3372 - val_accuracy: 0.6389\n",
      "Epoch 83/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.0862 - accuracy: 0.6604 - val_loss: 1.2969 - val_accuracy: 0.6597\n",
      "Epoch 84/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.0640 - accuracy: 0.6760 - val_loss: 1.2716 - val_accuracy: 0.6493\n",
      "Epoch 85/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.0930 - accuracy: 0.6650 - val_loss: 1.2996 - val_accuracy: 0.6406\n",
      "Epoch 86/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.0659 - accuracy: 0.6785 - val_loss: 1.2729 - val_accuracy: 0.6424\n",
      "Epoch 87/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 1.0621 - accuracy: 0.6731 - val_loss: 1.2824 - val_accuracy: 0.6441\n",
      "Epoch 88/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.0427 - accuracy: 0.6843 - val_loss: 1.2900 - val_accuracy: 0.6406\n",
      "Epoch 89/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.0366 - accuracy: 0.6772 - val_loss: 1.3009 - val_accuracy: 0.6493\n",
      "Epoch 90/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.0270 - accuracy: 0.6872 - val_loss: 1.3118 - val_accuracy: 0.6528\n",
      "Epoch 91/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.0428 - accuracy: 0.6748 - val_loss: 1.3048 - val_accuracy: 0.6493\n",
      "Epoch 92/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 1.0003 - accuracy: 0.6835 - val_loss: 1.2936 - val_accuracy: 0.6510\n",
      "Epoch 93/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.0332 - accuracy: 0.6831 - val_loss: 1.2860 - val_accuracy: 0.6528\n",
      "Epoch 94/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 1.0236 - accuracy: 0.6839 - val_loss: 1.2918 - val_accuracy: 0.6458\n",
      "Epoch 95/100\n",
      "151/151 [==============================] - 49s 321ms/step - loss: 1.0148 - accuracy: 0.6864 - val_loss: 1.2959 - val_accuracy: 0.6424\n",
      "Epoch 96/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 0.9887 - accuracy: 0.6895 - val_loss: 1.3050 - val_accuracy: 0.6389\n",
      "Epoch 97/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.0183 - accuracy: 0.6880 - val_loss: 1.2783 - val_accuracy: 0.6476\n",
      "Epoch 98/100\n",
      "151/151 [==============================] - 48s 321ms/step - loss: 0.9885 - accuracy: 0.6947 - val_loss: 1.2713 - val_accuracy: 0.6493\n",
      "Epoch 99/100\n",
      "151/151 [==============================] - 49s 322ms/step - loss: 1.0227 - accuracy: 0.6889 - val_loss: 1.3062 - val_accuracy: 0.6441\n",
      "Epoch 100/100\n",
      "151/151 [==============================] - 49s 323ms/step - loss: 0.9843 - accuracy: 0.6949 - val_loss: 1.3028 - val_accuracy: 0.6406\n"
     ]
    }
   ],
   "source": [
    "ft_epochs = 100\n",
    "#ft_steps_per_epoch = int(all_images/_BATCH_SIZE)\n",
    "#ft_val_steps = int(all_images_valid/_BATCH_SIZE)\n",
    "\n",
    "history_ft = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    epochs=ft_epochs,\n",
    "    validation_data=valid_generator,\n",
    "    validation_steps=val_steps\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nhistory_ft_again = model.fit(\\n    train_generator,\\n    steps_per_epoch=steps_per_epoch,\\n    epochs=50,\\n    validation_data=valid_generator,\\n    validation_steps=val_steps\\n)\\n'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "history_ft_again = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    epochs=50,\n",
    "    validation_data=valid_generator,\n",
    "    validation_steps=val_steps\n",
    ")\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nhistory_ft_again2 = model.fit(\\n    train_generator,\\n    steps_per_epoch=steps_per_epoch,\\n    epochs=50,\\n    validation_data=valid_generator,\\n    validation_steps=val_steps\\n)\\n'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "history_ft_again2 = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    epochs=50,\n",
    "    validation_data=valid_generator,\n",
    "    validation_steps=val_steps\n",
    ")\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trained model was saved into wfmodel3_s2_ft.h5.\n"
     ]
    }
   ],
   "source": [
    "#model_file = \"wfmodel_noft_\" + str(epoch_time) + \".h5\"\n",
    "model_file_ft = \"wfmodel3_s2_ft\" + \".h5\"\n",
    "model.save(model_file_ft)\n",
    "print(\"Trained model was saved into {}.\".format(model_file_ft))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('history3_s2.pkl', 'wb') as file_pi:\n",
    "    pickle.dump(history.history, file_pi)\n",
    "    \n",
    "with open('history3_s2_ft.pkl', 'wb') as file_pi_ft:\n",
    "    pickle.dump(history_ft.history, file_pi_ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#with open('history3_ft_again.pkl', 'wb') as file_pi:\n",
    "#    pickle.dump(history_ft_again.history, file_pi)\n",
    "#    \n",
    "#with open('history3_ft_again2.pkl', 'wb') as file_pi:\n",
    "#    pickle.dump(history_ft_again2.history, file_pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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