Day082_HW-訓練神經網路的細節與技巧.ipynb

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    "## Work\n",
    "1. 請比較使用不同層數以及不同 Dropout rate 對訓練的效果\n",
    "2. 將 optimizer 改成使用 Adam 並加上適當的 dropout rate 檢視結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import keras\n",
    "import itertools\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,
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   "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    "
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "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": [
    "\n",
    "def build_mlp():\n",
    "    \"\"\"Code Here\n",
    "    建立你的神經網路\n",
    "    \"\"\"\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Code Here\n",
    "設定超參數\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "results = {}\n",
    "\"\"\"Code Here\n",
    "撰寫你的訓練流程並將結果用 dictionary 紀錄\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\"\"\"Code Here\n",
    "將結果繪出\n",
    "\"\"\""
   ]
  }
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