Day082_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import keras\n",
    "import itertools\n",
    "# Disable GPU\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = keras.datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dropout\n",
    "def build_mlp(input_shape, output_units=10, num_neurons=[512, 256, 128], drp_ratio=0.2):\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 = Dropout(drp_ratio)(x)\n",
    "        else:\n",
    "            x = keras.layers.Dense(units=n_units, \n",
    "                                   activation=\"relu\", \n",
    "                                   name=\"hidden_layer\"+str(i+1))(x)\n",
    "            x = Dropout(drp_ratio)(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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Code Here\n",
    "設定超參數\n",
    "\"\"\"\n",
    "LEARNING_RATE = 1e-3\n",
    "EPOCHS = 50\n",
    "BATCH_SIZE = 256\n",
    "MOMENTUM = 0.95\n",
    "Dropout_EXP = [0.15, 0.3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Model: \"model\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_1 (InputLayer)         [(None, 3072)]            0         \n_________________________________________________________________\nhidden_layer1 (Dense)        (None, 512)               1573376   \n_________________________________________________________________\ndropout (Dropout)            (None, 512)               0         \n_________________________________________________________________\nhidden_layer2 (Dense)        (None, 256)               131328    \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 256)               0         \n_________________________________________________________________\nhidden_layer3 (Dense)        (None, 128)               32896     \n_________________________________________________________________\ndropout_2 (Dropout)          (None, 128)               0         \n_________________________________________________________________\noutput (Dense)               (None, 10)                1290      \n=================================================================\nTotal params: 1,738,890\nTrainable params: 1,738,890\nNon-trainable params: 0\n_________________________________________________________________\nEpoch 1/50\n196/196 [==============================] - 4s 22ms/step - loss: 2.0077 - accuracy: 0.2651 - val_loss: 1.7842 - val_accuracy: 0.3581\nEpoch 2/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.8053 - accuracy: 0.3494 - val_loss: 1.7022 - val_accuracy: 0.3890\nEpoch 3/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.7454 - accuracy: 0.3718 - val_loss: 1.6270 - val_accuracy: 0.4183\nEpoch 4/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.6836 - accuracy: 0.3936 - val_loss: 1.6203 - val_accuracy: 0.4136\nEpoch 5/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6529 - accuracy: 0.4088 - val_loss: 1.5489 - val_accuracy: 0.4455\nEpoch 6/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6235 - accuracy: 0.4146 - val_loss: 1.5412 - val_accuracy: 0.4528\nEpoch 7/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5958 - accuracy: 0.4261 - val_loss: 1.5112 - val_accuracy: 0.4635\nEpoch 8/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5686 - accuracy: 0.4373 - val_loss: 1.5193 - val_accuracy: 0.4585\nEpoch 9/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.5658 - accuracy: 0.4381 - val_loss: 1.4933 - val_accuracy: 0.4679\nEpoch 10/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5346 - accuracy: 0.4459 - val_loss: 1.4829 - val_accuracy: 0.4762\nEpoch 11/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5269 - accuracy: 0.4505 - val_loss: 1.4793 - val_accuracy: 0.4710\nEpoch 12/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5150 - accuracy: 0.4563 - val_loss: 1.4707 - val_accuracy: 0.4767\nEpoch 13/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.4956 - accuracy: 0.4636 - val_loss: 1.4758 - val_accuracy: 0.4874\nEpoch 14/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.4924 - accuracy: 0.4630 - val_loss: 1.4403 - val_accuracy: 0.4782\nEpoch 15/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.4745 - accuracy: 0.4682 - val_loss: 1.4339 - val_accuracy: 0.4887\nEpoch 16/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.4834 - accuracy: 0.4680 - val_loss: 1.4503 - val_accuracy: 0.4812\nEpoch 17/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.4711 - accuracy: 0.4710 - val_loss: 1.4343 - val_accuracy: 0.4893\nEpoch 18/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.4524 - accuracy: 0.4801 - val_loss: 1.4105 - val_accuracy: 0.4981\nEpoch 19/50\n196/196 [==============================] - 4s 23ms/step - loss: 1.4371 - accuracy: 0.4845 - val_loss: 1.3869 - val_accuracy: 0.5022\nEpoch 20/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.4344 - accuracy: 0.4846 - val_loss: 1.4245 - val_accuracy: 0.4907\nEpoch 21/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.4254 - accuracy: 0.4894 - val_loss: 1.3899 - val_accuracy: 0.5032\nEpoch 22/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.4130 - accuracy: 0.4918 - val_loss: 1.3959 - val_accuracy: 0.5003\nEpoch 23/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.4057 - accuracy: 0.4940 - val_loss: 1.4138 - val_accuracy: 0.4922\nEpoch 24/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.4019 - accuracy: 0.4953 - val_loss: 1.4018 - val_accuracy: 0.5023\nEpoch 25/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.3911 - accuracy: 0.4996 - val_loss: 1.3862 - val_accuracy: 0.5007\nEpoch 26/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.3816 - accuracy: 0.5037 - val_loss: 1.3882 - val_accuracy: 0.5070\nEpoch 27/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3795 - accuracy: 0.5067 - val_loss: 1.3697 - val_accuracy: 0.5146\nEpoch 28/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3756 - accuracy: 0.5057 - val_loss: 1.3889 - val_accuracy: 0.5080\nEpoch 29/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3673 - accuracy: 0.5069 - val_loss: 1.3690 - val_accuracy: 0.5122\nEpoch 30/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.3606 - accuracy: 0.5133 - val_loss: 1.3674 - val_accuracy: 0.5154\nEpoch 31/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.3534 - accuracy: 0.5148 - val_loss: 1.3822 - val_accuracy: 0.5110\nEpoch 32/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3493 - accuracy: 0.5151 - val_loss: 1.3991 - val_accuracy: 0.4993\nEpoch 33/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3472 - accuracy: 0.5149 - val_loss: 1.3758 - val_accuracy: 0.5126\nEpoch 34/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3409 - accuracy: 0.5190 - val_loss: 1.3598 - val_accuracy: 0.5093\nEpoch 35/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3359 - accuracy: 0.5184 - val_loss: 1.3579 - val_accuracy: 0.5168\nEpoch 36/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.3288 - accuracy: 0.5243 - val_loss: 1.3491 - val_accuracy: 0.5229\nEpoch 37/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.3239 - accuracy: 0.5237 - val_loss: 1.3571 - val_accuracy: 0.5210\nEpoch 38/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3184 - accuracy: 0.5250 - val_loss: 1.3477 - val_accuracy: 0.5204\nEpoch 39/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3140 - accuracy: 0.5285 - val_loss: 1.3420 - val_accuracy: 0.5192\nEpoch 40/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3092 - accuracy: 0.5295 - val_loss: 1.3618 - val_accuracy: 0.5138\nEpoch 41/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3052 - accuracy: 0.5295 - val_loss: 1.3653 - val_accuracy: 0.5137\nEpoch 42/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.3050 - accuracy: 0.5275 - val_loss: 1.3436 - val_accuracy: 0.5239\nEpoch 43/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3050 - accuracy: 0.5289 - val_loss: 1.3559 - val_accuracy: 0.5136\nEpoch 44/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2918 - accuracy: 0.5344 - val_loss: 1.3397 - val_accuracy: 0.5181\nEpoch 45/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2854 - accuracy: 0.5386 - val_loss: 1.3482 - val_accuracy: 0.5201\nEpoch 46/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2872 - accuracy: 0.5378 - val_loss: 1.3423 - val_accuracy: 0.5210\nEpoch 47/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2867 - accuracy: 0.5379 - val_loss: 1.3476 - val_accuracy: 0.5168\nEpoch 48/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2923 - accuracy: 0.5362 - val_loss: 1.3634 - val_accuracy: 0.5180\nEpoch 49/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2711 - accuracy: 0.5422 - val_loss: 1.3765 - val_accuracy: 0.5072\nEpoch 50/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2784 - accuracy: 0.5394 - val_loss: 1.3583 - val_accuracy: 0.5154\nModel: \"model_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_2 (InputLayer)         [(None, 3072)]            0         \n_________________________________________________________________\nhidden_layer1 (Dense)        (None, 512)               1573376   \n_________________________________________________________________\ndropout_3 (Dropout)          (None, 512)               0         \n_________________________________________________________________\nhidden_layer2 (Dense)        (None, 256)               131328    \n_________________________________________________________________\ndropout_4 (Dropout)          (None, 256)               0         \n_________________________________________________________________\nhidden_layer3 (Dense)        (None, 128)               32896     \n_________________________________________________________________\ndropout_5 (Dropout)          (None, 128)               0         \n_________________________________________________________________\noutput (Dense)               (None, 10)                1290      \n=================================================================\nTotal params: 1,738,890\nTrainable params: 1,738,890\nNon-trainable params: 0\n_________________________________________________________________\nEpoch 1/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.0810 - accuracy: 0.2255 - val_loss: 1.8454 - val_accuracy: 0.3317\nEpoch 2/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.9133 - accuracy: 0.2984 - val_loss: 1.7966 - val_accuracy: 0.3527\nEpoch 3/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.8730 - accuracy: 0.3166 - val_loss: 1.7615 - val_accuracy: 0.3816\nEpoch 4/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.8234 - accuracy: 0.3369 - val_loss: 1.7257 - val_accuracy: 0.3860\nEpoch 5/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.8041 - accuracy: 0.3429 - val_loss: 1.7242 - val_accuracy: 0.3989\nEpoch 6/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.7881 - accuracy: 0.3522 - val_loss: 1.6735 - val_accuracy: 0.4143\nEpoch 7/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.7560 - accuracy: 0.3611 - val_loss: 1.6562 - val_accuracy: 0.4171\nEpoch 8/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.7426 - accuracy: 0.3703 - val_loss: 1.6505 - val_accuracy: 0.4188\nEpoch 9/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.7335 - accuracy: 0.3713 - val_loss: 1.6444 - val_accuracy: 0.4225\nEpoch 10/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.7217 - accuracy: 0.3768 - val_loss: 1.6245 - val_accuracy: 0.4315\nEpoch 11/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.7041 - accuracy: 0.3852 - val_loss: 1.6166 - val_accuracy: 0.4298\nEpoch 12/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.7031 - accuracy: 0.3866 - val_loss: 1.6334 - val_accuracy: 0.4286\nEpoch 13/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.6963 - accuracy: 0.3905 - val_loss: 1.6196 - val_accuracy: 0.4327\nEpoch 14/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6936 - accuracy: 0.3895 - val_loss: 1.5905 - val_accuracy: 0.4393\nEpoch 15/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6779 - accuracy: 0.3932 - val_loss: 1.6043 - val_accuracy: 0.4381\nEpoch 16/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6680 - accuracy: 0.3978 - val_loss: 1.5909 - val_accuracy: 0.4428\nEpoch 17/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6622 - accuracy: 0.4034 - val_loss: 1.6024 - val_accuracy: 0.4336\nEpoch 18/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6565 - accuracy: 0.4050 - val_loss: 1.5603 - val_accuracy: 0.4540\nEpoch 19/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6513 - accuracy: 0.4056 - val_loss: 1.5773 - val_accuracy: 0.4448\nEpoch 20/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6580 - accuracy: 0.4029 - val_loss: 1.5594 - val_accuracy: 0.4526\nEpoch 21/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6478 - accuracy: 0.4084 - val_loss: 1.5783 - val_accuracy: 0.4395\nEpoch 22/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6342 - accuracy: 0.4135 - val_loss: 1.5650 - val_accuracy: 0.4508\nEpoch 23/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.6291 - accuracy: 0.4146 - val_loss: 1.5788 - val_accuracy: 0.4516\nEpoch 24/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6330 - accuracy: 0.4135 - val_loss: 1.5540 - val_accuracy: 0.4528\nEpoch 25/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6251 - accuracy: 0.4166 - val_loss: 1.5718 - val_accuracy: 0.4539\nEpoch 26/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.6208 - accuracy: 0.4156 - val_loss: 1.5505 - val_accuracy: 0.4548\nEpoch 27/50\n196/196 [==============================] - 6s 29ms/step - loss: 1.6225 - accuracy: 0.4148 - val_loss: 1.5788 - val_accuracy: 0.4387\nEpoch 28/50\n196/196 [==============================] - 6s 29ms/step - loss: 1.6173 - accuracy: 0.4158 - val_loss: 1.5383 - val_accuracy: 0.4601\nEpoch 29/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6096 - accuracy: 0.4215 - val_loss: 1.5547 - val_accuracy: 0.4438\nEpoch 30/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6173 - accuracy: 0.4171 - val_loss: 1.5574 - val_accuracy: 0.4579\nEpoch 31/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.6190 - accuracy: 0.4168 - val_loss: 1.5526 - val_accuracy: 0.4560\nEpoch 32/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.6116 - accuracy: 0.4212 - val_loss: 1.5471 - val_accuracy: 0.4512\nEpoch 33/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6013 - accuracy: 0.4219 - val_loss: 1.5521 - val_accuracy: 0.4514\nEpoch 34/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5995 - accuracy: 0.4247 - val_loss: 1.5476 - val_accuracy: 0.4554\nEpoch 35/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5967 - accuracy: 0.4256 - val_loss: 1.5530 - val_accuracy: 0.4584\nEpoch 36/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5845 - accuracy: 0.4307 - val_loss: 1.5032 - val_accuracy: 0.4711\nEpoch 37/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.5906 - accuracy: 0.4306 - val_loss: 1.5355 - val_accuracy: 0.4562\nEpoch 38/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5816 - accuracy: 0.4326 - val_loss: 1.5118 - val_accuracy: 0.4663\nEpoch 39/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.5773 - accuracy: 0.4344 - val_loss: 1.5397 - val_accuracy: 0.4620\nEpoch 40/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5789 - accuracy: 0.4360 - val_loss: 1.5244 - val_accuracy: 0.4608\nEpoch 41/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.5788 - accuracy: 0.4336 - val_loss: 1.5274 - val_accuracy: 0.4633\nEpoch 42/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5660 - accuracy: 0.4364 - val_loss: 1.4898 - val_accuracy: 0.4785\nEpoch 43/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5717 - accuracy: 0.4362 - val_loss: 1.5001 - val_accuracy: 0.4721\nEpoch 44/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5656 - accuracy: 0.4376 - val_loss: 1.5116 - val_accuracy: 0.4692\nEpoch 45/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5663 - accuracy: 0.4381 - val_loss: 1.5197 - val_accuracy: 0.4671\nEpoch 46/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.5570 - accuracy: 0.4417 - val_loss: 1.5093 - val_accuracy: 0.4736\nEpoch 47/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5625 - accuracy: 0.4378 - val_loss: 1.4977 - val_accuracy: 0.4703\nEpoch 48/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5552 - accuracy: 0.4437 - val_loss: 1.4996 - val_accuracy: 0.4749\nEpoch 49/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5472 - accuracy: 0.4444 - val_loss: 1.5023 - val_accuracy: 0.4754\nEpoch 50/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5458 - accuracy: 0.4479 - val_loss: 1.5108 - val_accuracy: 0.4677\n"
    }
   ],
   "source": [
    "results = {}\n",
    "\"\"\"Code Here\n",
    "撰寫你的訓練流程並將結果用 dictionary 紀錄\n",
    "\"\"\"\n",
    "for i, drop_rate in enumerate(Dropout_EXP):\n",
    "    model = build_mlp(input_shape=x_train.shape[1:], drp_ratio=drop_rate)\n",
    "    model.summary()\n",
    "    optimizer = keras.optimizers.SGD(lr=LEARNING_RATE, nesterov=True, momentum=MOMENTUM)\n",
    "    model.compile(loss=\"categorical_crossentropy\", metrics=[\"accuracy\"], optimizer='Adam')\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",
    "\n",
    "    # Collect results\n",
    "    train_loss = model.history.history[\"loss\"]\n",
    "    valid_loss = model.history.history[\"val_loss\"]\n",
    "    train_acc = model.history.history[\"accuracy\"]\n",
    "    valid_acc = model.history.history[\"val_accuracy\"]\n",
    "\n",
    "    exp_name_tag = \"exp-Drop_Rate-%.6f\" % str(drop_rate)\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": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\"\"\"Code Here\n",
    "將結果繪出\n",
    "\"\"\"\n",
    "color_bar = [\"r\", \"g\", \"b\", \"y\", \"m\", \"k\"]\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.ylim([0, 5])\n",
    "plt.legend()\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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.7 64-bit",
   "language": "python",
   "name": "python_defaultSpec_1593682153780"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}