homework/Day_085_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Work\n",
    "1. 試改變 monitor \"Validation Accuracy\" 並比較結果\n",
    "2. 調整 earlystop 的等待次數至 10, 25 並比較結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# Disable GPU\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = keras.datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 資料前處理\n",
    "def preproc_x(x, flatten=True):\n",
    "    x = x / 255.\n",
    "    if flatten:\n",
    "        x = x.reshape((len(x), -1))\n",
    "    return x\n",
    "\n",
    "def preproc_y(y, num_classes = 10):\n",
    "    if y.shape[-1] == 1:\n",
    "        y = keras.utils.to_categorical(y, num_classes)\n",
    "    return y    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train = train\n",
    "x_test, y_test = test\n",
    "\n",
    "# Preproc the inputs\n",
    "x_train = preproc_x(x_train)\n",
    "x_test = preproc_x(x_test)\n",
    "\n",
    "# Preprc the outputs\n",
    "y_train = preproc_y(y_train)\n",
    "y_test = preproc_y(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_mlp(input_shape, output_units = 10, num_neurons = [512, 256, 128]):\n",
    "    \"\"\"Code Here\n",
    "    建立你的神經網路\n",
    "    \"\"\"\n",
    "    input_layer = keras.layers.Input(input_shape)\n",
    "    \n",
    "    for i, n_units in enumerate(num_neurons):\n",
    "        if i == 0:\n",
    "            x = keras.layers.Dense(units = n_units, \n",
    "                                   activation = \"relu\", \n",
    "                                   name = \"hidden_layer\"+str(i + 1))(input_layer)\n",
    "            x = BatchNormalization()(x)\n",
    "        else:\n",
    "            x = keras.layers.Dense(units = n_units, \n",
    "                                   activation = \"relu\", \n",
    "                                   name = \"hidden_layer\" + str(i + 1))(x)\n",
    "            x = BatchNormalization()(x)\n",
    "    \n",
    "    out = keras.layers.Dense(units = output_units, activation = \"softmax\", name = \"output\")(x)\n",
    "    \n",
    "    model = keras.models.Model(inputs = [input_layer], outputs = [out])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Code Here\n",
    "設定超參數\n",
    "\"\"\"\n",
    "LEARNING_RATE = 1e-3\n",
    "EPOCHS = 50\n",
    "BATCH_SIZE = 1024\n",
    "MOMENTUM = 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 載入 Callbacks\n",
    "\"\"\"Code Here\n",
    "設定 callbacks: Earlystop\n",
    "\"\"\"\n",
    "\n",
    "results = {}\n",
    "\"\"\"Code Here\n",
    "撰寫你的訓練流程並將結果用 dictionary 紀錄\n",
    "\"\"\"\n",
    "PATIENCE = [10, 25]\n",
    "\n",
    "for pt in PATIENCE:\n",
    "    keras.backend.clear_session()\n",
    "    model = build_mlp(input_shape=x_train.shape[1:])\n",
    "    model.summary()\n",
    "    optimiz = keras.optimizers.SGD(lr = LEARNING_RATE, nesterov = True, momentum = MOMENTUM)\n",
    "    \n",
    "    model.compile(loss = \"categorical_crossentropy\", metrics = [\"accuracy\"], optimizer = optimiz)\n",
    "    earlystop = EarlyStopping(monitor = \"Validation Accuracy\", \n",
    "                          patience = PATIENCE, \n",
    "                          verbose = 1\n",
    "                          )\n",
    "    model.fit(x_train, y_train, \n",
    "              epochs = EPOCHS, \n",
    "              batch_size = BATCH_SIZE, \n",
    "              validation_data = (x_test, y_test), \n",
    "              shuffle = True,\n",
    "              callbacks = [earlystop]\n",
    "             )\n",
    "\n",
    "    # Collect results\n",
    "    train_loss = model.history.history[\"loss\"]\n",
    "    valid_loss = model.history.history[\"val_loss\"]\n",
    "    train_acc = model.history.history[\"acc\"]\n",
    "    valid_acc = model.history.history[\"val_acc\"]\n",
    "\n",
    "    exp_name_tag = \"earlystop-%s\" % (str(pt))\n",
    "    results[exp_name_tag] = {'train-loss': train_loss,\n",
    "                             'valid-loss': valid_loss,\n",
    "                             'train-acc': train_acc,\n",
    "                             'valid-acc': valid_acc}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as mplcm\n",
    "import matplotlib.colors as colors\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Code Here\n",
    "將結果繪出\n",
    "\"\"\"\n",
    "NUM_COLORS = 20\n",
    "\n",
    "cm = plt.get_cmap('gist_rainbow')\n",
    "cNorm  = colors.Normalize(vmin = 0, vmax = NUM_COLORS-1)\n",
    "scalarMap = mplcm.ScalarMappable(norm = cNorm, cmap = cm)\n",
    "color_bar = [scalarMap.to_rgba(i) for i in range(NUM_COLORS)]\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "for i, cond in enumerate(results.keys()):\n",
    "    plt.plot(range(len(results[cond]['train-loss'])), results[cond]['train-loss'], '-', label=cond, color = color_bar[i])\n",
    "    plt.plot(range(len(results[cond]['valid-loss'])), results[cond]['valid-loss'], '--', label=cond, color = color_bar[i])\n",
    "plt.title(\"Loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "for i, cond in enumerate(results.keys()):\n",
    "    plt.plot(range(len(results[cond]['train-acc'])), results[cond]['train-acc'], '-', label = cond, color=color_bar[i])\n",
    "    plt.plot(range(len(results[cond]['valid-acc'])), results[cond]['valid-acc'], '--', label = cond, color=color_bar[i])\n",
    "plt.title(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}