Day085_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Work\n",
    "1. 試改變 monitor \"Validation Accuracy\" 並比較結果\n",
    "2. 調整 earlystop 的等待次數至 10, 25 並比較結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# Disable GPU\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "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": [
    "\"\"\"Code Here\n",
    "設定超參數\n",
    "\"\"\"\n",
    "LEARNING_RATE = 1e-3\n",
    "EPOCHS = 50\n",
    "BATCH_SIZE = 1024\n",
    "MOMENTUM = 0.95\n",
    "Patience = [10, 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "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_________________________________________________________________\nbatch_normalization (BatchNo (None, 512)               2048      \n_________________________________________________________________\nhidden_layer2 (Dense)        (None, 256)               131328    \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 256)               1024      \n_________________________________________________________________\nhidden_layer3 (Dense)        (None, 128)               32896     \n_________________________________________________________________\nbatch_normalization_2 (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 1/50\n49/49 [==============================] - 5s 95ms/step - loss: 2.1873 - accuracy: 0.2783 - val_loss: 2.1360 - val_accuracy: 0.1876\nEpoch 2/50\n49/49 [==============================] - 3s 68ms/step - loss: 1.7291 - accuracy: 0.3964 - val_loss: 1.9432 - val_accuracy: 0.3137\nEpoch 3/50\n49/49 [==============================] - 3s 66ms/step - loss: 1.6023 - accuracy: 0.4402 - val_loss: 1.8050 - val_accuracy: 0.3683\nEpoch 4/50\n49/49 [==============================] - 3s 67ms/step - loss: 1.5285 - accuracy: 0.4647 - val_loss: 1.6989 - val_accuracy: 0.4121\nEpoch 5/50\n49/49 [==============================] - 3s 64ms/step - loss: 1.4741 - accuracy: 0.4844 - val_loss: 1.6221 - val_accuracy: 0.4334\nEpoch 6/50\n49/49 [==============================] - 3s 66ms/step - loss: 1.4301 - accuracy: 0.5020 - val_loss: 1.5708 - val_accuracy: 0.4439\nEpoch 7/50\n49/49 [==============================] - 3s 68ms/step - loss: 1.3913 - accuracy: 0.5155 - val_loss: 1.5434 - val_accuracy: 0.4488\nEpoch 8/50\n49/49 [==============================] - 4s 88ms/step - loss: 1.3578 - accuracy: 0.5265 - val_loss: 1.5188 - val_accuracy: 0.4591\nEpoch 9/50\n49/49 [==============================] - 4s 77ms/step - loss: 1.3260 - accuracy: 0.5388 - val_loss: 1.5077 - val_accuracy: 0.4692\nEpoch 10/50\n49/49 [==============================] - 4s 74ms/step - loss: 1.2955 - accuracy: 0.5475 - val_loss: 1.4898 - val_accuracy: 0.4692\nEpoch 11/50\n49/49 [==============================] - 4s 86ms/step - loss: 1.2684 - accuracy: 0.5582 - val_loss: 1.4841 - val_accuracy: 0.4715\nEpoch 12/50\n49/49 [==============================] - 4s 74ms/step - loss: 1.2422 - accuracy: 0.5690 - val_loss: 1.4756 - val_accuracy: 0.4814\nEpoch 13/50\n49/49 [==============================] - 4s 74ms/step - loss: 1.2162 - accuracy: 0.5783 - val_loss: 1.4747 - val_accuracy: 0.4829\nEpoch 14/50\n49/49 [==============================] - 3s 70ms/step - loss: 1.1914 - accuracy: 0.5892 - val_loss: 1.4741 - val_accuracy: 0.4856\nEpoch 15/50\n49/49 [==============================] - 3s 67ms/step - loss: 1.1677 - accuracy: 0.5975 - val_loss: 1.4703 - val_accuracy: 0.4814\nEpoch 16/50\n49/49 [==============================] - 3s 70ms/step - loss: 1.1433 - accuracy: 0.6058 - val_loss: 1.4631 - val_accuracy: 0.4852\nEpoch 17/50\n49/49 [==============================] - 3s 67ms/step - loss: 1.1206 - accuracy: 0.6151 - val_loss: 1.4690 - val_accuracy: 0.4847\nEpoch 18/50\n49/49 [==============================] - 3s 67ms/step - loss: 1.0981 - accuracy: 0.6235 - val_loss: 1.4647 - val_accuracy: 0.4830\nEpoch 19/50\n49/49 [==============================] - 3s 67ms/step - loss: 1.0759 - accuracy: 0.6331 - val_loss: 1.4674 - val_accuracy: 0.4901\nEpoch 20/50\n49/49 [==============================] - 3s 70ms/step - loss: 1.0545 - accuracy: 0.6420 - val_loss: 1.4758 - val_accuracy: 0.4851\nEpoch 21/50\n49/49 [==============================] - 3s 68ms/step - loss: 1.0330 - accuracy: 0.6501 - val_loss: 1.4622 - val_accuracy: 0.4928\nEpoch 22/50\n49/49 [==============================] - 3s 68ms/step - loss: 1.0104 - accuracy: 0.6584 - val_loss: 1.4604 - val_accuracy: 0.4923\nEpoch 23/50\n49/49 [==============================] - 3s 66ms/step - loss: 0.9912 - accuracy: 0.6648 - val_loss: 1.4623 - val_accuracy: 0.4931\nEpoch 24/50\n49/49 [==============================] - 3s 67ms/step - loss: 0.9699 - accuracy: 0.6737 - val_loss: 1.4765 - val_accuracy: 0.4934\nEpoch 25/50\n49/49 [==============================] - 3s 69ms/step - loss: 0.9489 - accuracy: 0.6837 - val_loss: 1.4655 - val_accuracy: 0.5021\nEpoch 26/50\n49/49 [==============================] - 3s 68ms/step - loss: 0.9281 - accuracy: 0.6902 - val_loss: 1.4834 - val_accuracy: 0.4903\nEpoch 27/50\n49/49 [==============================] - 3s 70ms/step - loss: 0.9087 - accuracy: 0.6981 - val_loss: 1.4826 - val_accuracy: 0.4893\nEpoch 28/50\n49/49 [==============================] - 4s 74ms/step - loss: 0.8888 - accuracy: 0.7063 - val_loss: 1.4748 - val_accuracy: 0.4996\nEpoch 29/50\n49/49 [==============================] - 3s 71ms/step - loss: 0.8677 - accuracy: 0.7150 - val_loss: 1.4853 - val_accuracy: 0.4968\nEpoch 30/50\n49/49 [==============================] - 3s 70ms/step - loss: 0.8485 - accuracy: 0.7203 - val_loss: 1.4931 - val_accuracy: 0.4922\nEpoch 31/50\n49/49 [==============================] - 4s 72ms/step - loss: 0.8278 - accuracy: 0.7284 - val_loss: 1.5006 - val_accuracy: 0.4981\nEpoch 32/50\n49/49 [==============================] - 3s 71ms/step - loss: 0.8096 - accuracy: 0.7385 - val_loss: 1.4978 - val_accuracy: 0.4983\nEpoch 33/50\n49/49 [==============================] - 4s 73ms/step - loss: 0.7892 - accuracy: 0.7448 - val_loss: 1.5063 - val_accuracy: 0.4903\nEpoch 34/50\n49/49 [==============================] - 4s 72ms/step - loss: 0.7716 - accuracy: 0.7522 - val_loss: 1.5353 - val_accuracy: 0.4887\nEpoch 35/50\n49/49 [==============================] - 4s 72ms/step - loss: 0.7519 - accuracy: 0.7581 - val_loss: 1.5157 - val_accuracy: 0.4970\nEpoch 00035: early stopping\nModel: \"model_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_2 (InputLayer)         [(None, 3072)]            0         \n_________________________________________________________________\nhidden_layer1 (Dense)        (None, 512)               1573376   \n_________________________________________________________________\nbatch_normalization_3 (Batch (None, 512)               2048      \n_________________________________________________________________\nhidden_layer2 (Dense)        (None, 256)               131328    \n_________________________________________________________________\nbatch_normalization_4 (Batch (None, 256)               1024      \n_________________________________________________________________\nhidden_layer3 (Dense)        (None, 128)               32896     \n_________________________________________________________________\nbatch_normalization_5 (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 1/50\n49/49 [==============================] - 4s 74ms/step - loss: 2.2105 - accuracy: 0.2723 - val_loss: 2.1327 - val_accuracy: 0.2009\nEpoch 2/50\n49/49 [==============================] - 4s 72ms/step - loss: 1.7436 - accuracy: 0.3897 - val_loss: 1.9828 - val_accuracy: 0.3093\nEpoch 3/50\n49/49 [==============================] - 4s 72ms/step - loss: 1.6239 - accuracy: 0.4297 - val_loss: 1.8499 - val_accuracy: 0.3599\nEpoch 4/50\n49/49 [==============================] - 4s 73ms/step - loss: 1.5549 - accuracy: 0.4561 - val_loss: 1.7348 - val_accuracy: 0.3904\nEpoch 5/50\n49/49 [==============================] - 4s 74ms/step - loss: 1.5019 - accuracy: 0.4740 - val_loss: 1.6745 - val_accuracy: 0.4068\nEpoch 6/50\n49/49 [==============================] - 4s 75ms/step - loss: 1.4582 - accuracy: 0.4910 - val_loss: 1.5982 - val_accuracy: 0.4372\nEpoch 7/50\n49/49 [==============================] - 4s 72ms/step - loss: 1.4159 - accuracy: 0.5057 - val_loss: 1.5626 - val_accuracy: 0.4510\nEpoch 8/50\n49/49 [==============================] - 4s 73ms/step - loss: 1.3815 - accuracy: 0.5177 - val_loss: 1.5527 - val_accuracy: 0.4590\nEpoch 9/50\n49/49 [==============================] - 4s 72ms/step - loss: 1.3507 - accuracy: 0.5286 - val_loss: 1.5137 - val_accuracy: 0.4660\nEpoch 10/50\n49/49 [==============================] - 3s 71ms/step - loss: 1.3190 - accuracy: 0.5400 - val_loss: 1.4956 - val_accuracy: 0.4693\nEpoch 11/50\n49/49 [==============================] - 4s 72ms/step - loss: 1.2910 - accuracy: 0.5511 - val_loss: 1.4954 - val_accuracy: 0.4722\nEpoch 12/50\n49/49 [==============================] - 4s 72ms/step - loss: 1.2644 - accuracy: 0.5596 - val_loss: 1.4796 - val_accuracy: 0.4755\nEpoch 13/50\n49/49 [==============================] - 3s 71ms/step - loss: 1.2387 - accuracy: 0.5714 - val_loss: 1.4911 - val_accuracy: 0.4721\nEpoch 14/50\n49/49 [==============================] - 4s 73ms/step - loss: 1.2131 - accuracy: 0.5801 - val_loss: 1.4696 - val_accuracy: 0.4775\nEpoch 15/50\n49/49 [==============================] - 4s 73ms/step - loss: 1.1890 - accuracy: 0.5898 - val_loss: 1.4678 - val_accuracy: 0.4822\nEpoch 16/50\n49/49 [==============================] - 4s 72ms/step - loss: 1.1648 - accuracy: 0.5995 - val_loss: 1.4685 - val_accuracy: 0.4796\nEpoch 17/50\n49/49 [==============================] - 4s 72ms/step - loss: 1.1408 - accuracy: 0.6077 - val_loss: 1.4780 - val_accuracy: 0.4796\nEpoch 18/50\n49/49 [==============================] - 4s 72ms/step - loss: 1.1171 - accuracy: 0.6174 - val_loss: 1.4536 - val_accuracy: 0.4896\nEpoch 19/50\n49/49 [==============================] - 4s 74ms/step - loss: 1.0968 - accuracy: 0.6237 - val_loss: 1.4580 - val_accuracy: 0.4875\nEpoch 20/50\n49/49 [==============================] - 4s 73ms/step - loss: 1.0750 - accuracy: 0.6330 - val_loss: 1.4616 - val_accuracy: 0.4853\nEpoch 21/50\n49/49 [==============================] - 4s 72ms/step - loss: 1.0529 - accuracy: 0.6411 - val_loss: 1.4711 - val_accuracy: 0.4843\nEpoch 22/50\n49/49 [==============================] - 4s 73ms/step - loss: 1.0322 - accuracy: 0.6477 - val_loss: 1.4824 - val_accuracy: 0.4869\nEpoch 23/50\n49/49 [==============================] - 4s 76ms/step - loss: 1.0108 - accuracy: 0.6572 - val_loss: 1.4535 - val_accuracy: 0.4881\nEpoch 24/50\n49/49 [==============================] - 4s 75ms/step - loss: 0.9902 - accuracy: 0.6657 - val_loss: 1.4680 - val_accuracy: 0.4817\nEpoch 25/50\n49/49 [==============================] - 4s 72ms/step - loss: 0.9711 - accuracy: 0.6718 - val_loss: 1.4643 - val_accuracy: 0.4859\nEpoch 26/50\n49/49 [==============================] - 4s 74ms/step - loss: 0.9487 - accuracy: 0.6823 - val_loss: 1.4667 - val_accuracy: 0.4901\nEpoch 27/50\n49/49 [==============================] - 4s 73ms/step - loss: 0.9280 - accuracy: 0.6910 - val_loss: 1.4543 - val_accuracy: 0.4917\nEpoch 28/50\n49/49 [==============================] - 4s 73ms/step - loss: 0.9079 - accuracy: 0.6976 - val_loss: 1.4775 - val_accuracy: 0.4900\nEpoch 29/50\n49/49 [==============================] - 4s 73ms/step - loss: 0.8891 - accuracy: 0.7065 - val_loss: 1.4734 - val_accuracy: 0.4887\nEpoch 30/50\n49/49 [==============================] - 4s 74ms/step - loss: 0.8712 - accuracy: 0.7119 - val_loss: 1.4934 - val_accuracy: 0.4956\nEpoch 31/50\n49/49 [==============================] - 4s 77ms/step - loss: 0.8519 - accuracy: 0.7196 - val_loss: 1.4979 - val_accuracy: 0.4880\nEpoch 32/50\n49/49 [==============================] - 4s 74ms/step - loss: 0.8316 - accuracy: 0.7275 - val_loss: 1.4894 - val_accuracy: 0.4896\nEpoch 33/50\n49/49 [==============================] - 4s 73ms/step - loss: 0.8108 - accuracy: 0.7360 - val_loss: 1.5087 - val_accuracy: 0.4886\nEpoch 34/50\n49/49 [==============================] - 4s 73ms/step - loss: 0.7937 - accuracy: 0.7428 - val_loss: 1.4998 - val_accuracy: 0.4937\nEpoch 35/50\n49/49 [==============================] - 4s 73ms/step - loss: 0.7743 - accuracy: 0.7512 - val_loss: 1.5193 - val_accuracy: 0.4896\nEpoch 36/50\n49/49 [==============================] - 4s 72ms/step - loss: 0.7523 - accuracy: 0.7599 - val_loss: 1.5035 - val_accuracy: 0.4918\nEpoch 37/50\n49/49 [==============================] - 4s 74ms/step - loss: 0.7350 - accuracy: 0.7663 - val_loss: 1.5177 - val_accuracy: 0.4964\nEpoch 38/50\n49/49 [==============================] - 4s 75ms/step - loss: 0.7176 - accuracy: 0.7726 - val_loss: 1.5266 - val_accuracy: 0.4951\nEpoch 39/50\n49/49 [==============================] - 4s 73ms/step - loss: 0.6991 - accuracy: 0.7801 - val_loss: 1.5330 - val_accuracy: 0.4911\nEpoch 40/50\n49/49 [==============================] - 4s 73ms/step - loss: 0.6804 - accuracy: 0.7872 - val_loss: 1.5455 - val_accuracy: 0.4881\nEpoch 41/50\n49/49 [==============================] - 4s 76ms/step - loss: 0.6628 - accuracy: 0.7943 - val_loss: 1.5719 - val_accuracy: 0.4860\nEpoch 42/50\n49/49 [==============================] - 4s 74ms/step - loss: 0.6478 - accuracy: 0.7999 - val_loss: 1.5778 - val_accuracy: 0.4906\nEpoch 43/50\n49/49 [==============================] - 4s 73ms/step - loss: 0.6282 - accuracy: 0.8080 - val_loss: 1.6042 - val_accuracy: 0.4876\nEpoch 44/50\n49/49 [==============================] - 4s 76ms/step - loss: 0.6110 - accuracy: 0.8157 - val_loss: 1.5775 - val_accuracy: 0.4932\nEpoch 45/50\n49/49 [==============================] - 4s 73ms/step - loss: 0.5956 - accuracy: 0.8202 - val_loss: 1.5806 - val_accuracy: 0.4906\nEpoch 46/50\n49/49 [==============================] - 4s 78ms/step - loss: 0.5760 - accuracy: 0.8293 - val_loss: 1.6225 - val_accuracy: 0.4890\nEpoch 47/50\n49/49 [==============================] - 4s 76ms/step - loss: 0.5603 - accuracy: 0.8352 - val_loss: 1.6209 - val_accuracy: 0.4899\nEpoch 48/50\n49/49 [==============================] - 3s 71ms/step - loss: 0.5432 - accuracy: 0.8429 - val_loss: 1.6400 - val_accuracy: 0.4902\nEpoch 49/50\n49/49 [==============================] - 3s 71ms/step - loss: 0.5251 - accuracy: 0.8502 - val_loss: 1.6427 - val_accuracy: 0.4911\nEpoch 50/50\n49/49 [==============================] - 3s 71ms/step - loss: 0.5095 - accuracy: 0.8553 - val_loss: 1.6659 - val_accuracy: 0.4896\n"
    }
   ],
   "source": [
    "# 載入 Callbacks\n",
    "\"\"\"Code Here\n",
    "設定 callbacks: Earlystop\n",
    "\"\"\"\n",
    "from keras.callbacks import EarlyStopping\n",
    "results = {}\n",
    "for i,p in enumerate(Patience):\n",
    "    earlystop = EarlyStopping(monitor=\"val_accuracy\", \n",
    "                            patience=p, \n",
    "                            verbose=1\n",
    "                            )\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:])\n",
    "    model.summary()\n",
    "    optimizer = keras.optimizers.SGD(lr=LEARNING_RATE, nesterov=True, momentum=MOMENTUM)\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",
    "            callbacks=[earlystop]\n",
    "            )\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-Patience-%s\" % str(p)\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": 8,
   "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.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",
   "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": "3.7.6-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}