Day079_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Work\n",
    "1. 請比較 SGD optimizer 不同的 momentum 及使用 nesterov 與否的表現"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import keras\n",
    "\n",
    "# 本作業可以不需使用 GPU, 將 GPU 設定為 \"無\" (若想使用可自行開啟)\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",
    "# 資料前處理 - X 標準化\n",
    "x_train = preproc_x(x_train)\n",
    "x_test = preproc_x(x_test)\n",
    "\n",
    "# 資料前處理 -Y 轉成 onehot\n",
    "y_train = preproc_y(y_train)\n",
    "y_test = preproc_y(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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, activation=\"relu\", name=\"hidden_layer\"+str(i+1))(input_layer)\n",
    "        else:\n",
    "            x = keras.layers.Dense(units=n_units, activation=\"relu\", name=\"hidden_layer\"+str(i+1))(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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Code Here\n",
    "設定超參數\n",
    "\"\"\"\n",
    "## 超參數設定\n",
    "LEARNING_RATE = 0.1\n",
    "EPOCHS = 50\n",
    "BATCH_SIZE = 256\n",
    "MOMENTUM = [0.95, 0.5, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "loss: 2.1066 - val_accuracy: 0.1873\nEpoch 27/50\n196/196 [==============================] - 4s 20ms/step - loss: 2.0865 - accuracy: 0.2006 - val_loss: 2.0676 - val_accuracy: 0.2132\nEpoch 28/50\n196/196 [==============================] - 4s 20ms/step - loss: 2.0902 - accuracy: 0.2017 - val_loss: 2.0682 - val_accuracy: 0.2103\nEpoch 29/50\n196/196 [==============================] - 4s 20ms/step - loss: 2.1147 - accuracy: 0.1999 - val_loss: 2.1092 - val_accuracy: 0.1888\nEpoch 30/50\n196/196 [==============================] - 4s 20ms/step - loss: 2.1120 - accuracy: 0.1993 - val_loss: 2.0821 - val_accuracy: 0.2199\nEpoch 31/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.0555 - accuracy: 0.2160 - val_loss: 2.0756 - val_accuracy: 0.2045\nEpoch 32/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.0582 - accuracy: 0.2129 - val_loss: 2.0840 - val_accuracy: 0.2233\nEpoch 33/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.0465 - accuracy: 0.2222 - val_loss: 2.0880 - val_accuracy: 0.2118\nEpoch 34/50\n196/196 [==============================] - 4s 23ms/step - loss: 2.1364 - accuracy: 0.1843 - val_loss: 2.1236 - val_accuracy: 0.1906\nEpoch 35/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.0737 - accuracy: 0.2127 - val_loss: 2.1102 - val_accuracy: 0.1829\nEpoch 36/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.0692 - accuracy: 0.2074 - val_loss: 2.0571 - val_accuracy: 0.2087\nEpoch 37/50\n196/196 [==============================] - 4s 20ms/step - loss: 2.0817 - accuracy: 0.2125 - val_loss: 2.1234 - val_accuracy: 0.1885\nEpoch 38/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.1350 - accuracy: 0.1892 - val_loss: 2.1215 - val_accuracy: 0.1720\nEpoch 39/50\n196/196 [==============================] - 4s 20ms/step - loss: 2.1607 - accuracy: 0.1606 - val_loss: 2.1980 - val_accuracy: 0.1725\nEpoch 40/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.1357 - accuracy: 0.1693 - val_loss: 2.1263 - val_accuracy: 0.1754\nEpoch 41/50\n196/196 [==============================] - 4s 20ms/step - loss: 2.1657 - accuracy: 0.1613 - val_loss: 2.1615 - val_accuracy: 0.1638\nEpoch 42/50\n196/196 [==============================] - 4s 20ms/step - loss: 2.2682 - accuracy: 0.1197 - val_loss: 2.3034 - val_accuracy: 0.1000\nEpoch 43/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.3045 - accuracy: 0.0987 - val_loss: 2.3038 - val_accuracy: 0.1000\nEpoch 44/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.3045 - accuracy: 0.0991 - val_loss: 2.3046 - val_accuracy: 0.1000\nEpoch 45/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.3046 - accuracy: 0.1009 - val_loss: 2.3057 - val_accuracy: 0.1000\nEpoch 46/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.3049 - accuracy: 0.0993 - val_loss: 2.3045 - val_accuracy: 0.1000\nEpoch 47/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.3048 - accuracy: 0.0979 - val_loss: 2.3034 - val_accuracy: 0.1000\nEpoch 48/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.3043 - accuracy: 0.1001 - val_loss: 2.3038 - val_accuracy: 0.1000\nEpoch 49/50\n196/196 [==============================] - 4s 21ms/step - loss: 2.3041 - accuracy: 0.0998 - val_loss: 2.3050 - val_accuracy: 0.1000\nEpoch 50/50\n196/196 [==============================] - 4s 20ms/step - loss: 2.3039 - accuracy: 0.1000 - val_loss: 2.3035 - val_accuracy: 0.1000\nExperiment with mom = 0.500000\nModel: \"model\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_1 (InputLayer)         [(None, 3072)]            0         \n_________________________________________________________________\nhidden_layer1 (Dense)        (None, 512)               1573376   \n_________________________________________________________________\nhidden_layer2 (Dense)        (None, 256)               131328    \n_________________________________________________________________\nhidden_layer3 (Dense)        (None, 128)               32896     \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: 1.9945 - accuracy: 0.2728 - val_loss: 1.8957 - val_accuracy: 0.3165\nEpoch 2/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.7823 - accuracy: 0.3590 - val_loss: 1.6836 - val_accuracy: 0.4045\nEpoch 3/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6842 - accuracy: 0.3972 - val_loss: 1.7744 - val_accuracy: 0.3644\nEpoch 4/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6147 - accuracy: 0.4225 - val_loss: 1.7076 - val_accuracy: 0.3923\nEpoch 5/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5601 - accuracy: 0.4436 - val_loss: 1.5986 - val_accuracy: 0.4247\nEpoch 6/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.5185 - accuracy: 0.4542 - val_loss: 1.6023 - val_accuracy: 0.4076\nEpoch 7/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.4868 - accuracy: 0.4692 - val_loss: 1.5529 - val_accuracy: 0.4515\nEpoch 8/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.4503 - accuracy: 0.4839 - val_loss: 1.5610 - val_accuracy: 0.4539\nEpoch 9/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.4600 - accuracy: 0.4802 - val_loss: 1.5749 - val_accuracy: 0.4397\nEpoch 10/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3916 - accuracy: 0.5037 - val_loss: 1.4257 - val_accuracy: 0.4930\nEpoch 11/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.3617 - accuracy: 0.5150 - val_loss: 1.4765 - val_accuracy: 0.4717\nEpoch 12/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3433 - accuracy: 0.5222 - val_loss: 1.4309 - val_accuracy: 0.4871\nEpoch 13/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3103 - accuracy: 0.5318 - val_loss: 1.4611 - val_accuracy: 0.4911\nEpoch 14/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2857 - accuracy: 0.5424 - val_loss: 1.4538 - val_accuracy: 0.4923\nEpoch 15/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2623 - accuracy: 0.5490 - val_loss: 1.4158 - val_accuracy: 0.4939\nEpoch 16/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2427 - accuracy: 0.5564 - val_loss: 1.4652 - val_accuracy: 0.4868\nEpoch 17/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2201 - accuracy: 0.5642 - val_loss: 1.4665 - val_accuracy: 0.4886\nEpoch 18/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.1926 - accuracy: 0.5724 - val_loss: 1.3803 - val_accuracy: 0.5098\nEpoch 19/50\n196/196 [==============================] - 4s 19ms/step - loss: 1.1744 - accuracy: 0.5815 - val_loss: 1.4384 - val_accuracy: 0.5012\nEpoch 20/50\n196/196 [==============================] - 4s 18ms/step - loss: 1.1509 - accuracy: 0.5862 - val_loss: 1.4422 - val_accuracy: 0.4985\nEpoch 21/50\n196/196 [==============================] - 3s 17ms/step - loss: 1.1322 - accuracy: 0.5969 - val_loss: 1.5051 - val_accuracy: 0.4713\nEpoch 22/50\n196/196 [==============================] - 3s 17ms/step - loss: 1.1083 - accuracy: 0.6048 - val_loss: 1.4569 - val_accuracy: 0.4983\nEpoch 23/50\n196/196 [==============================] - 3s 18ms/step - loss: 1.0896 - accuracy: 0.6091 - val_loss: 1.4310 - val_accuracy: 0.5068\nEpoch 24/50\n196/196 [==============================] - 3s 17ms/step - loss: 1.0732 - accuracy: 0.6164 - val_loss: 1.5440 - val_accuracy: 0.4766\nEpoch 25/50\n196/196 [==============================] - 3s 17ms/step - loss: 1.0516 - accuracy: 0.6225 - val_loss: 1.4755 - val_accuracy: 0.5015\nEpoch 26/50\n196/196 [==============================] - 3s 17ms/step - loss: 1.0369 - accuracy: 0.6289 - val_loss: 1.4463 - val_accuracy: 0.5071\nEpoch 27/50\n196/196 [==============================] - 3s 17ms/step - loss: 1.0123 - accuracy: 0.6402 - val_loss: 1.5479 - val_accuracy: 0.4903\nEpoch 28/50\n196/196 [==============================] - 3s 17ms/step - loss: 0.9905 - accuracy: 0.6445 - val_loss: 1.5122 - val_accuracy: 0.5050\nEpoch 29/50\n196/196 [==============================] - 4s 18ms/step - loss: 0.9679 - accuracy: 0.6538 - val_loss: 1.5273 - val_accuracy: 0.4945\nEpoch 30/50\n196/196 [==============================] - 3s 17ms/step - loss: 0.9491 - accuracy: 0.6602 - val_loss: 1.5795 - val_accuracy: 0.4952\nEpoch 31/50\n196/196 [==============================] - 3s 17ms/step - loss: 0.9381 - accuracy: 0.6644 - val_loss: 1.4877 - val_accuracy: 0.5108\nEpoch 32/50\n196/196 [==============================] - 3s 18ms/step - loss: 0.9165 - accuracy: 0.6729 - val_loss: 1.6526 - val_accuracy: 0.4860\nEpoch 33/50\n196/196 [==============================] - 3s 17ms/step - loss: 0.8947 - accuracy: 0.6779 - val_loss: 1.5500 - val_accuracy: 0.5159\nEpoch 34/50\n196/196 [==============================] - 3s 17ms/step - loss: 0.8740 - accuracy: 0.6852 - val_loss: 1.5775 - val_accuracy: 0.5099\nEpoch 35/50\n196/196 [==============================] - 3s 17ms/step - loss: 0.8611 - accuracy: 0.6890 - val_loss: 1.6673 - val_accuracy: 0.4962\nEpoch 36/50\n196/196 [==============================] - 3s 17ms/step - loss: 0.8394 - accuracy: 0.6975 - val_loss: 1.6675 - val_accuracy: 0.4985\nEpoch 37/50\n196/196 [==============================] - 3s 17ms/step - loss: 0.8100 - accuracy: 0.7086 - val_loss: 1.5838 - val_accuracy: 0.5010\nEpoch 38/50\n196/196 [==============================] - 3s 17ms/step - loss: 0.8010 - accuracy: 0.7106 - val_loss: 1.6024 - val_accuracy: 0.5111\nEpoch 39/50\n196/196 [==============================] - 3s 18ms/step - loss: 0.7906 - accuracy: 0.7167 - val_loss: 1.6990 - val_accuracy: 0.4963\nEpoch 40/50\n196/196 [==============================] - 4s 18ms/step - loss: 0.7626 - accuracy: 0.7261 - val_loss: 1.6039 - val_accuracy: 0.5185\nEpoch 41/50\n196/196 [==============================] - 3s 18ms/step - loss: 0.7542 - accuracy: 0.7315 - val_loss: 1.7254 - val_accuracy: 0.4947\nEpoch 42/50\n196/196 [==============================] - 4s 19ms/step - loss: 0.7280 - accuracy: 0.7400 - val_loss: 1.8776 - val_accuracy: 0.4544\nEpoch 43/50\n196/196 [==============================] - 4s 18ms/step - loss: 0.7242 - accuracy: 0.7405 - val_loss: 1.8912 - val_accuracy: 0.4770\nEpoch 44/50\n196/196 [==============================] - 3s 18ms/step - loss: 0.7054 - accuracy: 0.7474 - val_loss: 1.7889 - val_accuracy: 0.4939\nEpoch 45/50\n196/196 [==============================] - 4s 18ms/step - loss: 0.6874 - accuracy: 0.7533 - val_loss: 1.7927 - val_accuracy: 0.5046\nEpoch 46/50\n196/196 [==============================] - 5s 25ms/step - loss: 0.6639 - accuracy: 0.7607 - val_loss: 1.7646 - val_accuracy: 0.5028\nEpoch 47/50\n196/196 [==============================] - 4s 22ms/step - loss: 0.6466 - accuracy: 0.7665 - val_loss: 1.8556 - val_accuracy: 0.4998\nEpoch 48/50\n196/196 [==============================] - 4s 21ms/step - loss: 0.6401 - accuracy: 0.7693 - val_loss: 1.9755 - val_accuracy: 0.4989\nEpoch 49/50\n196/196 [==============================] - 4s 21ms/step - loss: 0.6349 - accuracy: 0.7705 - val_loss: 1.8620 - val_accuracy: 0.5074\nEpoch 50/50\n196/196 [==============================] - 4s 20ms/step - loss: 0.6011 - accuracy: 0.7849 - val_loss: 2.0076 - val_accuracy: 0.4913\nExperiment with mom = 0.100000\nModel: \"model\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_1 (InputLayer)         [(None, 3072)]            0         \n_________________________________________________________________\nhidden_layer1 (Dense)        (None, 512)               1573376   \n_________________________________________________________________\nhidden_layer2 (Dense)        (None, 256)               131328    \n_________________________________________________________________\nhidden_layer3 (Dense)        (None, 128)               32896     \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: 2.0377 - accuracy: 0.2577 - val_loss: 2.0213 - val_accuracy: 0.2782\nEpoch 2/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.8128 - accuracy: 0.3487 - val_loss: 1.7448 - val_accuracy: 0.3726\nEpoch 3/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.7264 - accuracy: 0.3822 - val_loss: 1.9552 - val_accuracy: 0.3373\nEpoch 4/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.6546 - accuracy: 0.4103 - val_loss: 1.6964 - val_accuracy: 0.3986\nEpoch 5/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.6100 - accuracy: 0.4249 - val_loss: 1.5783 - val_accuracy: 0.4321\nEpoch 6/50\n196/196 [==============================] - 5s 25ms/step - loss: 1.5686 - accuracy: 0.4404 - val_loss: 1.7011 - val_accuracy: 0.3796\nEpoch 7/50\n196/196 [==============================] - 4s 23ms/step - loss: 1.5312 - accuracy: 0.4536 - val_loss: 1.7978 - val_accuracy: 0.3645\nEpoch 8/50\n196/196 [==============================] - 5s 24ms/step - loss: 1.4998 - accuracy: 0.4672 - val_loss: 1.6170 - val_accuracy: 0.4140\nEpoch 9/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.4748 - accuracy: 0.4765 - val_loss: 1.6441 - val_accuracy: 0.4084\nEpoch 10/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.4499 - accuracy: 0.4832 - val_loss: 1.5992 - val_accuracy: 0.4408\nEpoch 11/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.4101 - accuracy: 0.4998 - val_loss: 1.4905 - val_accuracy: 0.4662\nEpoch 12/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3949 - accuracy: 0.5041 - val_loss: 1.4971 - val_accuracy: 0.4611\nEpoch 13/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3679 - accuracy: 0.5122 - val_loss: 1.5434 - val_accuracy: 0.4503\nEpoch 14/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.3416 - accuracy: 0.5219 - val_loss: 1.8352 - val_accuracy: 0.3740\nEpoch 15/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3269 - accuracy: 0.5277 - val_loss: 1.4636 - val_accuracy: 0.4804\nEpoch 16/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.3033 - accuracy: 0.5358 - val_loss: 1.6322 - val_accuracy: 0.4436\nEpoch 17/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2830 - accuracy: 0.5422 - val_loss: 1.5827 - val_accuracy: 0.4572\nEpoch 18/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2600 - accuracy: 0.5516 - val_loss: 1.5650 - val_accuracy: 0.4582\nEpoch 19/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2481 - accuracy: 0.5562 - val_loss: 1.7106 - val_accuracy: 0.4207\nEpoch 20/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.2277 - accuracy: 0.5616 - val_loss: 1.4593 - val_accuracy: 0.4761\nEpoch 21/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.1980 - accuracy: 0.5729 - val_loss: 1.4015 - val_accuracy: 0.5125\nEpoch 22/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.1851 - accuracy: 0.5793 - val_loss: 1.6278 - val_accuracy: 0.4381\nEpoch 23/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.1732 - accuracy: 0.5811 - val_loss: 1.4486 - val_accuracy: 0.4994\nEpoch 24/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.1459 - accuracy: 0.5913 - val_loss: 1.4137 - val_accuracy: 0.5027\nEpoch 25/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.1357 - accuracy: 0.5945 - val_loss: 1.5773 - val_accuracy: 0.4614\nEpoch 26/50\n196/196 [==============================] - 4s 22ms/step - loss: 1.1050 - accuracy: 0.6061 - val_loss: 1.4577 - val_accuracy: 0.5020\nEpoch 27/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.1007 - accuracy: 0.6075 - val_loss: 1.5926 - val_accuracy: 0.4717\nEpoch 28/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.0839 - accuracy: 0.6121 - val_loss: 1.4991 - val_accuracy: 0.4924\nEpoch 29/50\n196/196 [==============================] - 4s 21ms/step - loss: 1.0565 - accuracy: 0.6242 - val_loss: 1.5270 - val_accuracy: 0.4826\nEpoch 30/50\n196/196 [==============================] - 4s 20ms/step - loss: 1.0344 - accuracy: 0.6299 - val_loss: 1.5221 - val_accuracy: 0.4875\nEpoch 31/50\n196/196 [==============================] - 5s 26ms/step - loss: 1.0206 - accuracy: 0.6388 - val_loss: 1.4249 - val_accuracy: 0.5115\nEpoch 32/50\n196/196 [==============================] - 5s 26ms/step - loss: 0.9969 - accuracy: 0.6443 - val_loss: 1.6030 - val_accuracy: 0.4589\nEpoch 33/50\n196/196 [==============================] - 6s 29ms/step - loss: 0.9936 - accuracy: 0.6452 - val_loss: 1.6973 - val_accuracy: 0.4439\nEpoch 34/50\n196/196 [==============================] - 5s 27ms/step - loss: 0.9694 - accuracy: 0.6552 - val_loss: 1.5364 - val_accuracy: 0.4975\nEpoch 35/50\n196/196 [==============================] - 5s 27ms/step - loss: 0.9511 - accuracy: 0.6609 - val_loss: 1.5975 - val_accuracy: 0.4792\nEpoch 36/50\n196/196 [==============================] - 5s 25ms/step - loss: 0.9381 - accuracy: 0.6654 - val_loss: 1.6054 - val_accuracy: 0.4680\nEpoch 37/50\n196/196 [==============================] - 5s 25ms/step - loss: 0.9073 - accuracy: 0.6748 - val_loss: 1.4425 - val_accuracy: 0.5199\nEpoch 38/50\n196/196 [==============================] - 5s 26ms/step - loss: 0.9040 - accuracy: 0.6783 - val_loss: 1.5717 - val_accuracy: 0.4840\nEpoch 39/50\n196/196 [==============================] - 5s 24ms/step - loss: 0.8901 - accuracy: 0.6832 - val_loss: 1.7157 - val_accuracy: 0.4686\nEpoch 40/50\n196/196 [==============================] - 5s 25ms/step - loss: 0.8751 - accuracy: 0.6890 - val_loss: 1.5375 - val_accuracy: 0.4964\nEpoch 41/50\n196/196 [==============================] - 5s 24ms/step - loss: 0.8486 - accuracy: 0.6967 - val_loss: 1.7153 - val_accuracy: 0.4800\nEpoch 42/50\n196/196 [==============================] - 5s 24ms/step - loss: 0.8311 - accuracy: 0.7042 - val_loss: 1.6397 - val_accuracy: 0.4908\nEpoch 43/50\n196/196 [==============================] - 5s 25ms/step - loss: 0.8191 - accuracy: 0.7097 - val_loss: 1.7598 - val_accuracy: 0.4455\nEpoch 44/50\n196/196 [==============================] - 5s 24ms/step - loss: 0.8029 - accuracy: 0.7116 - val_loss: 1.9222 - val_accuracy: 0.4349\nEpoch 45/50\n196/196 [==============================] - 5s 24ms/step - loss: 0.7793 - accuracy: 0.7234 - val_loss: 1.5941 - val_accuracy: 0.5072\nEpoch 46/50\n196/196 [==============================] - 5s 23ms/step - loss: 0.7601 - accuracy: 0.7269 - val_loss: 1.6242 - val_accuracy: 0.4964\nEpoch 47/50\n196/196 [==============================] - 5s 24ms/step - loss: 0.7515 - accuracy: 0.7333 - val_loss: 1.6051 - val_accuracy: 0.5058\nEpoch 48/50\n196/196 [==============================] - 5s 25ms/step - loss: 0.7331 - accuracy: 0.7358 - val_loss: 1.9220 - val_accuracy: 0.4792\nEpoch 49/50\n196/196 [==============================] - 5s 24ms/step - loss: 0.7351 - accuracy: 0.7387 - val_loss: 1.6527 - val_accuracy: 0.5109\nEpoch 50/50\n196/196 [==============================] - 5s 26ms/step - loss: 0.7010 - accuracy: 0.7501 - val_loss: 1.7315 - val_accuracy: 0.5037\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 mom in MOMENTUM:\n",
    "    keras.backend.clear_session() # 把舊的 Graph 清掉\n",
    "    print(\"Experiment with mom = %.6f\" % (mom))\n",
    "    model = build_mlp(input_shape=x_train.shape[1:])\n",
    "    model.summary()\n",
    "    optimizer = keras.optimizers.SGD(learning_rate=LEARNING_RATE,momentum=mom, nesterov=False)\n",
    "    model.compile(loss=\"categorical_crossentropy\", metrics=[\"accuracy\"], optimizer=optimizer)\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-mom-%s\" % str(mom)\n",
    "    results[exp_name_tag] = {'train-loss': train_loss,\n",
    "                             'valid-loss': valid_loss,\n",
    "                             'train-acc': train_acc,\n",
    "                             'valid-acc': valid_acc}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x432 with 1 Axes>",
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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.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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.7 64-bit",
   "language": "python",
   "name": "python_defaultSpec_1593530717681"
  },
  "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
}