Day089_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Work\n",
    "1. 請自行定義一個 loss function, 為 0.3 * focal loss + 0.7 cross-entropy，訓練並比較結果\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import keras\n",
    "\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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import BatchNormalization\n",
    "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 超參數設定\n",
    "LEARNING_RATE = 1e-3\n",
    "EPOCHS = 25\n",
    "BATCH_SIZE = 1024\n",
    "MOMENTUM = 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras.backend as K\n",
    "\n",
    "\"\"\"Code Here\n",
    "撰寫一個 loss function, 使其可以結合 focal loss 與 crossentropy loss\n",
    "\"\"\"\n",
    "def combined_loss(gamma=2., alpha=4., ce_weights =0.7, fcl_weights = 0.3):\n",
    "    \"\"\"Define the customized loss.\"\"\"\n",
    "    gamma = float(gamma)\n",
    "    alpha = float(alpha)\n",
    "    def focal_crossentropy_loss_fixed(y_true, y_pred):\n",
    "        \"\"\"Focal loss for multi-classification\n",
    "        FL(p_t)=-alpha(1-p_t)^{gamma}ln(p_t)\n",
    "        \"\"\"\n",
    "        epsilon = 1e-8\n",
    "        y_true = tf.convert_to_tensor(y_true, tf.float32)\n",
    "        y_pred = tf.convert_to_tensor(y_pred, tf.float32)\n",
    "\n",
    "        model_out = tf.add(y_pred, epsilon)\n",
    "        ce = tf.multiply(y_true, -tf.math.log(model_out))\n",
    "        weight = tf.multiply(y_true, tf.pow(tf.subtract(1., model_out), gamma))\n",
    "        fl = tf.multiply(alpha, tf.multiply(weight, ce))\n",
    "        reduced_fl = tf.reduce_max(fl, axis=1)\n",
    "        ce_loss = keras.losses.categorical_crossentropy(y_true, y_pred)\n",
    "        \n",
    "        return (fcl_weights*tf.reduce_mean(reduced_fl) + ce_weights*ce_loss)\n",
    "    \n",
    "    return focal_crossentropy_loss_fixed\n",
    "\n",
    "ce_weights_list = [0., 0.3, 0.5, 0.7, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true,
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "3206 - val_accuracy: 0.4896\nNumbers of exp: 1, ce_weight: 0.30\nModel: \"model_2\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_3 (InputLayer)         [(None, 3072)]            0         \n_________________________________________________________________\nhidden_layer1 (Dense)        (None, 512)               1573376   \n_________________________________________________________________\nbatch_normalization_6 (Batch (None, 512)               2048      \n_________________________________________________________________\nhidden_layer2 (Dense)        (None, 256)               131328    \n_________________________________________________________________\nbatch_normalization_7 (Batch (None, 256)               1024      \n_________________________________________________________________\nhidden_layer3 (Dense)        (None, 128)               32896     \n_________________________________________________________________\nbatch_normalization_8 (Batch (None, 128)               512       \n_________________________________________________________________\noutput (Dense)               (None, 10)                1290      \n=================================================================\nTotal params: 1,742,474\nTrainable params: 1,740,682\nNon-trainable params: 1,792\n_________________________________________________________________\nEpoch 1/25\n49/49 [==============================] - 3s 63ms/step - loss: 3.4073 - accuracy: 0.3023 - val_loss: 3.6007 - val_accuracy: 0.1728\nEpoch 2/25\n49/49 [==============================] - 3s 61ms/step - loss: 2.5587 - accuracy: 0.4210 - val_loss: 3.1387 - val_accuracy: 0.2686\nEpoch 3/25\n49/49 [==============================] - 3s 61ms/step - loss: 2.3490 - accuracy: 0.4599 - val_loss: 2.8174 - val_accuracy: 0.3527\nEpoch 4/25\n49/49 [==============================] - 3s 58ms/step - loss: 2.2268 - accuracy: 0.4855 - val_loss: 2.5923 - val_accuracy: 0.4069\nEpoch 5/25\n49/49 [==============================] - 3s 60ms/step - loss: 2.1282 - accuracy: 0.5051 - val_loss: 2.4658 - val_accuracy: 0.4278\nEpoch 6/25\n49/49 [==============================] - 3s 62ms/step - loss: 2.0433 - accuracy: 0.5234 - val_loss: 2.3766 - val_accuracy: 0.4424\nEpoch 7/25\n49/49 [==============================] - 3s 59ms/step - loss: 1.9666 - accuracy: 0.5379 - val_loss: 2.3531 - val_accuracy: 0.4475\nEpoch 8/25\n49/49 [==============================] - 3s 61ms/step - loss: 1.8983 - accuracy: 0.5538 - val_loss: 2.2735 - val_accuracy: 0.4653\nEpoch 9/25\n49/49 [==============================] - 3s 59ms/step - loss: 1.8287 - accuracy: 0.5681 - val_loss: 2.2362 - val_accuracy: 0.4731\nEpoch 10/25\n49/49 [==============================] - 3s 62ms/step - loss: 1.7705 - accuracy: 0.5802 - val_loss: 2.2353 - val_accuracy: 0.4752\nEpoch 11/25\n49/49 [==============================] - 3s 60ms/step - loss: 1.7137 - accuracy: 0.5932 - val_loss: 2.2419 - val_accuracy: 0.4789\nEpoch 12/25\n49/49 [==============================] - 3s 61ms/step - loss: 1.6609 - accuracy: 0.6026 - val_loss: 2.2968 - val_accuracy: 0.4722\nEpoch 13/25\n49/49 [==============================] - 3s 63ms/step - loss: 1.6090 - accuracy: 0.6167 - val_loss: 2.2383 - val_accuracy: 0.4812\nEpoch 14/25\n49/49 [==============================] - 3s 59ms/step - loss: 1.5568 - accuracy: 0.6274 - val_loss: 2.2041 - val_accuracy: 0.4845\nEpoch 15/25\n49/49 [==============================] - 3s 60ms/step - loss: 1.5076 - accuracy: 0.6378 - val_loss: 2.2235 - val_accuracy: 0.4812\nEpoch 16/25\n49/49 [==============================] - 3s 60ms/step - loss: 1.4579 - accuracy: 0.6494 - val_loss: 2.2807 - val_accuracy: 0.4768\nEpoch 17/25\n49/49 [==============================] - 3s 62ms/step - loss: 1.4096 - accuracy: 0.6602 - val_loss: 2.2371 - val_accuracy: 0.4824\nEpoch 18/25\n49/49 [==============================] - 3s 63ms/step - loss: 1.3618 - accuracy: 0.6714 - val_loss: 2.2413 - val_accuracy: 0.4865\nEpoch 19/25\n49/49 [==============================] - 3s 62ms/step - loss: 1.3164 - accuracy: 0.6825 - val_loss: 2.2776 - val_accuracy: 0.4724\nEpoch 20/25\n49/49 [==============================] - 3s 60ms/step - loss: 1.2724 - accuracy: 0.6935 - val_loss: 2.2457 - val_accuracy: 0.4812\nEpoch 21/25\n49/49 [==============================] - 3s 62ms/step - loss: 1.2273 - accuracy: 0.7039 - val_loss: 2.2897 - val_accuracy: 0.4846\nEpoch 22/25\n49/49 [==============================] - 3s 60ms/step - loss: 1.1856 - accuracy: 0.7128 - val_loss: 2.3116 - val_accuracy: 0.4794\nEpoch 23/25\n49/49 [==============================] - 3s 65ms/step - loss: 1.1435 - accuracy: 0.7201 - val_loss: 2.3309 - val_accuracy: 0.4875\nEpoch 24/25\n49/49 [==============================] - 4s 72ms/step - loss: 1.1007 - accuracy: 0.7344 - val_loss: 2.3360 - val_accuracy: 0.4828\nEpoch 25/25\n49/49 [==============================] - 3s 67ms/step - loss: 1.0573 - accuracy: 0.7450 - val_loss: 2.3649 - val_accuracy: 0.4861\nNumbers of exp: 2, ce_weight: 0.50\nModel: \"model_3\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_4 (InputLayer)         [(None, 3072)]            0         \n_________________________________________________________________\nhidden_layer1 (Dense)        (None, 512)               1573376   \n_________________________________________________________________\nbatch_normalization_9 (Batch (None, 512)               2048      \n_________________________________________________________________\nhidden_layer2 (Dense)        (None, 256)               131328    \n_________________________________________________________________\nbatch_normalization_10 (Batc (None, 256)               1024      \n_________________________________________________________________\nhidden_layer3 (Dense)        (None, 128)               32896     \n_________________________________________________________________\nbatch_normalization_11 (Batc (None, 128)               512       \n_________________________________________________________________\noutput (Dense)               (None, 10)                1290      \n=================================================================\nTotal params: 1,742,474\nTrainable params: 1,740,682\nNon-trainable params: 1,792\n_________________________________________________________________\nEpoch 1/25\n49/49 [==============================] - 3s 62ms/step - loss: 3.4557 - accuracy: 0.3021 - val_loss: 3.4627 - val_accuracy: 0.2260\nEpoch 2/25\n49/49 [==============================] - 3s 61ms/step - loss: 2.5847 - accuracy: 0.4145 - val_loss: 3.0565 - val_accuracy: 0.3204\nEpoch 3/25\n49/49 [==============================] - 4s 72ms/step - loss: 2.3681 - accuracy: 0.4568 - val_loss: 2.7531 - val_accuracy: 0.3864\nEpoch 4/25\n49/49 [==============================] - 4s 80ms/step - loss: 2.2304 - accuracy: 0.4845 - val_loss: 2.5368 - val_accuracy: 0.4348\nEpoch 5/25\n49/49 [==============================] - 3s 69ms/step - loss: 2.1274 - accuracy: 0.5065 - val_loss: 2.4375 - val_accuracy: 0.4445\nEpoch 6/25\n49/49 [==============================] - 3s 67ms/step - loss: 2.0467 - accuracy: 0.5225 - val_loss: 2.3377 - val_accuracy: 0.4577\nEpoch 7/25\n49/49 [==============================] - 3s 65ms/step - loss: 1.9658 - accuracy: 0.5405 - val_loss: 2.2868 - val_accuracy: 0.4714\nEpoch 8/25\n49/49 [==============================] - 3s 59ms/step - loss: 1.8990 - accuracy: 0.5522 - val_loss: 2.2618 - val_accuracy: 0.4680\nEpoch 9/25\n49/49 [==============================] - 3s 60ms/step - loss: 1.8368 - accuracy: 0.5653 - val_loss: 2.2352 - val_accuracy: 0.4777\nEpoch 10/25\n49/49 [==============================] - 3s 59ms/step - loss: 1.7721 - accuracy: 0.5788 - val_loss: 2.2517 - val_accuracy: 0.4769\nEpoch 11/25\n49/49 [==============================] - 3s 60ms/step - loss: 1.7217 - accuracy: 0.5915 - val_loss: 2.2121 - val_accuracy: 0.4790\nEpoch 12/25\n49/49 [==============================] - 3s 59ms/step - loss: 1.6620 - accuracy: 0.6051 - val_loss: 2.2499 - val_accuracy: 0.4699\nEpoch 13/25\n49/49 [==============================] - 3s 63ms/step - loss: 1.6053 - accuracy: 0.6171 - val_loss: 2.2077 - val_accuracy: 0.4864\nEpoch 14/25\n49/49 [==============================] - 3s 70ms/step - loss: 1.5568 - accuracy: 0.6286 - val_loss: 2.2252 - val_accuracy: 0.4895\nEpoch 15/25\n49/49 [==============================] - 3s 64ms/step - loss: 1.5061 - accuracy: 0.6396 - val_loss: 2.2083 - val_accuracy: 0.4944\nEpoch 16/25\n49/49 [==============================] - 3s 64ms/step - loss: 1.4533 - accuracy: 0.6506 - val_loss: 2.2148 - val_accuracy: 0.4927\nEpoch 17/25\n49/49 [==============================] - 3s 62ms/step - loss: 1.4044 - accuracy: 0.6632 - val_loss: 2.2071 - val_accuracy: 0.4967\nEpoch 18/25\n49/49 [==============================] - 3s 66ms/step - loss: 1.3592 - accuracy: 0.6730 - val_loss: 2.2219 - val_accuracy: 0.4938\nEpoch 19/25\n49/49 [==============================] - 4s 71ms/step - loss: 1.3113 - accuracy: 0.6838 - val_loss: 2.2313 - val_accuracy: 0.4885\nEpoch 20/25\n49/49 [==============================] - 3s 67ms/step - loss: 1.2648 - accuracy: 0.6961 - val_loss: 2.2772 - val_accuracy: 0.4879\nEpoch 21/25\n49/49 [==============================] - 3s 68ms/step - loss: 1.2218 - accuracy: 0.7069 - val_loss: 2.2711 - val_accuracy: 0.4877\nEpoch 22/25\n49/49 [==============================] - 3s 69ms/step - loss: 1.1792 - accuracy: 0.7183 - val_loss: 2.2993 - val_accuracy: 0.4843\nEpoch 23/25\n49/49 [==============================] - 3s 68ms/step - loss: 1.1375 - accuracy: 0.7257 - val_loss: 2.2957 - val_accuracy: 0.4860\nEpoch 24/25\n49/49 [==============================] - 3s 64ms/step - loss: 1.0940 - accuracy: 0.7376 - val_loss: 2.2924 - val_accuracy: 0.4904\nEpoch 25/25\n49/49 [==============================] - 3s 69ms/step - loss: 1.0483 - accuracy: 0.7492 - val_loss: 2.3028 - val_accuracy: 0.4886\nNumbers of exp: 3, ce_weight: 0.70\nModel: \"model_4\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_5 (InputLayer)         [(None, 3072)]            0         \n_________________________________________________________________\nhidden_layer1 (Dense)        (None, 512)               1573376   \n_________________________________________________________________\nbatch_normalization_12 (Batc (None, 512)               2048      \n_________________________________________________________________\nhidden_layer2 (Dense)        (None, 256)               131328    \n_________________________________________________________________\nbatch_normalization_13 (Batc (None, 256)               1024      \n_________________________________________________________________\nhidden_layer3 (Dense)        (None, 128)               32896     \n_________________________________________________________________\nbatch_normalization_14 (Batc (None, 128)               512       \n_________________________________________________________________\noutput (Dense)               (None, 10)                1290      \n=================================================================\nTotal params: 1,742,474\nTrainable params: 1,740,682\nNon-trainable params: 1,792\n_________________________________________________________________\nEpoch 1/25\n49/49 [==============================] - 3s 70ms/step - loss: 3.4542 - accuracy: 0.3028 - val_loss: 3.4723 - val_accuracy: 0.2247\nEpoch 2/25\n49/49 [==============================] - 3s 64ms/step - loss: 2.5772 - accuracy: 0.4200 - val_loss: 3.0914 - val_accuracy: 0.3143\nEpoch 3/25\n49/49 [==============================] - 3s 69ms/step - loss: 2.3598 - accuracy: 0.4612 - val_loss: 2.7828 - val_accuracy: 0.3808\nEpoch 4/25\n49/49 [==============================] - 4s 80ms/step - loss: 2.2239 - accuracy: 0.4848 - val_loss: 2.5737 - val_accuracy: 0.4219\nEpoch 5/25\n49/49 [==============================] - 5s 95ms/step - loss: 2.1247 - accuracy: 0.5044 - val_loss: 2.4390 - val_accuracy: 0.4406\nEpoch 6/25\n49/49 [==============================] - 4s 80ms/step - loss: 2.0358 - accuracy: 0.5242 - val_loss: 2.3676 - val_accuracy: 0.4544\nEpoch 7/25\n49/49 [==============================] - 4s 78ms/step - loss: 1.9581 - accuracy: 0.5423 - val_loss: 2.2814 - val_accuracy: 0.4672\nEpoch 8/25\n49/49 [==============================] - 4s 72ms/step - loss: 1.8885 - accuracy: 0.5562 - val_loss: 2.2828 - val_accuracy: 0.4658\nEpoch 9/25\n49/49 [==============================] - 4s 73ms/step - loss: 1.8247 - accuracy: 0.5707 - val_loss: 2.2579 - val_accuracy: 0.4704\nEpoch 10/25\n49/49 [==============================] - 4s 77ms/step - loss: 1.7655 - accuracy: 0.5830 - val_loss: 2.2576 - val_accuracy: 0.4710\nEpoch 11/25\n49/49 [==============================] - 4s 75ms/step - loss: 1.7052 - accuracy: 0.5961 - val_loss: 2.2156 - val_accuracy: 0.4847\nEpoch 12/25\n49/49 [==============================] - 4s 74ms/step - loss: 1.6465 - accuracy: 0.6086 - val_loss: 2.2058 - val_accuracy: 0.4861\nEpoch 13/25\n49/49 [==============================] - 3s 71ms/step - loss: 1.5930 - accuracy: 0.6210 - val_loss: 2.2173 - val_accuracy: 0.4890\nEpoch 14/25\n49/49 [==============================] - 3s 68ms/step - loss: 1.5414 - accuracy: 0.6335 - val_loss: 2.1890 - val_accuracy: 0.4959\nEpoch 15/25\n49/49 [==============================] - 3s 69ms/step - loss: 1.4931 - accuracy: 0.6430 - val_loss: 2.2552 - val_accuracy: 0.4837\nEpoch 16/25\n49/49 [==============================] - 3s 71ms/step - loss: 1.4409 - accuracy: 0.6557 - val_loss: 2.2407 - val_accuracy: 0.4884\nEpoch 17/25\n49/49 [==============================] - 3s 70ms/step - loss: 1.3921 - accuracy: 0.6663 - val_loss: 2.2254 - val_accuracy: 0.4912\nEpoch 18/25\n49/49 [==============================] - 3s 69ms/step - loss: 1.3451 - accuracy: 0.6781 - val_loss: 2.2805 - val_accuracy: 0.4877\nEpoch 19/25\n49/49 [==============================] - 3s 69ms/step - loss: 1.2980 - accuracy: 0.6871 - val_loss: 2.2465 - val_accuracy: 0.4912\nEpoch 20/25\n49/49 [==============================] - 3s 70ms/step - loss: 1.2480 - accuracy: 0.6994 - val_loss: 2.2564 - val_accuracy: 0.4898\nEpoch 21/25\n49/49 [==============================] - 3s 71ms/step - loss: 1.2033 - accuracy: 0.7130 - val_loss: 2.2937 - val_accuracy: 0.4920\nEpoch 22/25\n49/49 [==============================] - 3s 68ms/step - loss: 1.1568 - accuracy: 0.7236 - val_loss: 2.2934 - val_accuracy: 0.4914\nEpoch 23/25\n49/49 [==============================] - 3s 67ms/step - loss: 1.1173 - accuracy: 0.7331 - val_loss: 2.3293 - val_accuracy: 0.4934\nEpoch 24/25\n49/49 [==============================] - 3s 67ms/step - loss: 1.0720 - accuracy: 0.7432 - val_loss: 2.3389 - val_accuracy: 0.4832\nEpoch 25/25\n49/49 [==============================] - 3s 68ms/step - loss: 1.0309 - accuracy: 0.7540 - val_loss: 2.3233 - val_accuracy: 0.4905\nNumbers of exp: 4, ce_weight: 1.00\nModel: \"model_5\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_6 (InputLayer)         [(None, 3072)]            0         \n_________________________________________________________________\nhidden_layer1 (Dense)        (None, 512)               1573376   \n_________________________________________________________________\nbatch_normalization_15 (Batc (None, 512)               2048      \n_________________________________________________________________\nhidden_layer2 (Dense)        (None, 256)               131328    \n_________________________________________________________________\nbatch_normalization_16 (Batc (None, 256)               1024      \n_________________________________________________________________\nhidden_layer3 (Dense)        (None, 128)               32896     \n_________________________________________________________________\nbatch_normalization_17 (Batc (None, 128)               512       \n_________________________________________________________________\noutput (Dense)               (None, 10)                1290      \n=================================================================\nTotal params: 1,742,474\nTrainable params: 1,740,682\nNon-trainable params: 1,792\n_________________________________________________________________\nEpoch 1/25\n49/49 [==============================] - 4s 75ms/step - loss: 3.4408 - accuracy: 0.3032 - val_loss: 3.4844 - val_accuracy: 0.2133\nEpoch 2/25\n49/49 [==============================] - 3s 68ms/step - loss: 2.5727 - accuracy: 0.4187 - val_loss: 3.1075 - val_accuracy: 0.3076\nEpoch 3/25\n49/49 [==============================] - 3s 69ms/step - loss: 2.3589 - accuracy: 0.4578 - val_loss: 2.7688 - val_accuracy: 0.3758\nEpoch 4/25\n49/49 [==============================] - 3s 69ms/step - loss: 2.2277 - accuracy: 0.4848 - val_loss: 2.5796 - val_accuracy: 0.4152\nEpoch 5/25\n49/49 [==============================] - 3s 68ms/step - loss: 2.1263 - accuracy: 0.5046 - val_loss: 2.4555 - val_accuracy: 0.4385\nEpoch 6/25\n49/49 [==============================] - 3s 69ms/step - loss: 2.0382 - accuracy: 0.5227 - val_loss: 2.4026 - val_accuracy: 0.4433\nEpoch 7/25\n49/49 [==============================] - 3s 69ms/step - loss: 1.9637 - accuracy: 0.5370 - val_loss: 2.3120 - val_accuracy: 0.4616\nEpoch 8/25\n49/49 [==============================] - 3s 71ms/step - loss: 1.8936 - accuracy: 0.5548 - val_loss: 2.2532 - val_accuracy: 0.4683\nEpoch 9/25\n49/49 [==============================] - 3s 68ms/step - loss: 1.8290 - accuracy: 0.5665 - val_loss: 2.2512 - val_accuracy: 0.4655\nEpoch 10/25\n49/49 [==============================] - 3s 70ms/step - loss: 1.7697 - accuracy: 0.5795 - val_loss: 2.2160 - val_accuracy: 0.4800\nEpoch 11/25\n49/49 [==============================] - 3s 69ms/step - loss: 1.7135 - accuracy: 0.5927 - val_loss: 2.2148 - val_accuracy: 0.4823\nEpoch 12/25\n49/49 [==============================] - 3s 69ms/step - loss: 1.6574 - accuracy: 0.6062 - val_loss: 2.2170 - val_accuracy: 0.4834\nEpoch 13/25\n49/49 [==============================] - 3s 68ms/step - loss: 1.6046 - accuracy: 0.6168 - val_loss: 2.2175 - val_accuracy: 0.4852\nEpoch 14/25\n49/49 [==============================] - 3s 67ms/step - loss: 1.5526 - accuracy: 0.6288 - val_loss: 2.1994 - val_accuracy: 0.4856\nEpoch 15/25\n49/49 [==============================] - 3s 68ms/step - loss: 1.5033 - accuracy: 0.6403 - val_loss: 2.2426 - val_accuracy: 0.4740\nEpoch 16/25\n49/49 [==============================] - 3s 71ms/step - loss: 1.4555 - accuracy: 0.6502 - val_loss: 2.2101 - val_accuracy: 0.4923\nEpoch 17/25\n49/49 [==============================] - 4s 73ms/step - loss: 1.4066 - accuracy: 0.6611 - val_loss: 2.2066 - val_accuracy: 0.4945\nEpoch 18/25\n49/49 [==============================] - 3s 71ms/step - loss: 1.3605 - accuracy: 0.6721 - val_loss: 2.1986 - val_accuracy: 0.4935\nEpoch 19/25\n49/49 [==============================] - 3s 70ms/step - loss: 1.3165 - accuracy: 0.6833 - val_loss: 2.2240 - val_accuracy: 0.4897\nEpoch 20/25\n49/49 [==============================] - 3s 68ms/step - loss: 1.2670 - accuracy: 0.6954 - val_loss: 2.2605 - val_accuracy: 0.4847\nEpoch 21/25\n49/49 [==============================] - 3s 68ms/step - loss: 1.2229 - accuracy: 0.7052 - val_loss: 2.2677 - val_accuracy: 0.4911\nEpoch 22/25\n49/49 [==============================] - 3s 68ms/step - loss: 1.1821 - accuracy: 0.7160 - val_loss: 2.3345 - val_accuracy: 0.4799\nEpoch 23/25\n49/49 [==============================] - 3s 67ms/step - loss: 1.1351 - accuracy: 0.7253 - val_loss: 2.2802 - val_accuracy: 0.4928\nEpoch 24/25\n49/49 [==============================] - 3s 69ms/step - loss: 1.0919 - accuracy: 0.7385 - val_loss: 2.2726 - val_accuracy: 0.4990\nEpoch 25/25\n49/49 [==============================] - 3s 69ms/step - loss: 1.0482 - accuracy: 0.7483 - val_loss: 2.2743 - val_accuracy: 0.5011\n"
    }
   ],
   "source": [
    "import itertools\n",
    "results = {}\n",
    "\n",
    "for i, ce_w in enumerate(ce_weights_list):\n",
    "    print(\"Numbers of exp: %i, ce_weight: %.2f\" % (i, ce_w))\n",
    "\n",
    "    model = build_mlp(input_shape=x_train.shape[1:])\n",
    "    model.summary()\n",
    "    optimizer = keras.optimizers.SGD(lr=LEARNING_RATE, nesterov=True, momentum=MOMENTUM)\n",
    "    \"\"\"Code Here\n",
    "    將自定義的 loss function 加入模型\n",
    "    \"\"\"\n",
    "    model.compile(loss=combined_loss(), metrics=[\"accuracy\"], optimizer=optimizer)\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",
    "    \n",
    "    # Collect results\n",
    "    exp_name_tag = (\"exp-%s\" % (i))\n",
    "    results[exp_name_tag] = {'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[\"accuracy\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
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      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as mplcm\n",
    "import matplotlib.colors as colors\n",
    "%matplotlib inline\n",
    "NUM_COLORS = len(results.keys())\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(loc='center left', bbox_to_anchor=(1, 0.5))\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(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}