homework/Day_084_HW.ipynb

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    "## Work\n",
    "### 請結合前面的知識與程式碼，比較不同的 regularization 的組合對訓練的結果與影響：如 dropout, regularizers, batch-normalization 等"
   ]
  },
  {
   "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,
   "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    "
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "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": {},
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   "source": [
    "def build_mlp(nput_shape, output_units = 10, num_neurons = [512, 256, 128], drp_ratio = 0.2, l2_ratio = 1e-4):\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)\n",
    "                                   kernel_regularizer = l2(l2_ratio))(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",
    "                                   kernel_regularizer = l2(l2_ratio))(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",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Code Here\n",
    "設定超參數\n",
    "\"\"\"\n",
    "LEARNING_RATE = 0.001\n",
    "EPOCHS = 20\n",
    "BATCH_SIZE = 128\n",
    "MOMENTUM = 0.95\n",
    "Dropout_EXP = 0.2"
   ]
  },
  {
   "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",
    "\"\"\"\n",
    "model = build_mlp(input_shape = x_train.shape[1:], drp_ratio = Dropout_EXP)\n",
    "model.summary()\n",
    "\n",
    "optimiz = keras.optimizers.SGD(lr = LEARNING_RATE, momentum = MOMENTUM)\n",
    "\n",
    "model.compile(loss = \"categorical_crossentropy\", metrics = [\"accuracy\"], optimizer = optimiz)\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",
    "\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\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Code Here\n",
    "將結果繪出\n",
    "\"\"\"\n",
    "plt.plot(range(len(train_loss)), train_loss, label = \"train loss\")\n",
    "plt.plot(range(len(valid_loss)), valid_loss, label = \"valid loss\")\n",
    "plt.legend()\n",
    "plt.title(\"Loss\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(len(train_acc)), train_acc, label = \"train accuracy\")\n",
    "plt.plot(range(len(valid_acc)), valid_acc, label = \"valid accuracy\")\n",
    "plt.legend()\n",
    "plt.title(\"Accuracy\")\n",
    "\n",
    "plt.show()"
   ]
  }
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