Day_080_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 請結合前面的知識與程式碼，比較不同的 optimizer 與 learning rate 組合對訓練的結果與影響\n",
    "### 常見的 optimizer 包含\n",
    "* SGD\n",
    "* RMSprop\n",
    "* AdaGrad\n",
    "* Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import keras\n",
    "\n",
    "# Disable GPU\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = keras.datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_mlp(input_shape, output_units=10, num_neurons=[512, 256, 128]):\n",
    "    \"\"\"\n",
    "    Build your own model\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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 超參數設定\n",
    "\"\"\"\n",
    "Set your required experiment parameters\n",
    "\"\"\"\n",
    "LEARNING_RATE = [1e-2, 1e-3, 1e-4]\n",
    "EPOCHS = 50\n",
    "BATCH_SIZE = 256\n",
    "MOMENTUM = 0.95\n",
    "NESTEROV = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment with Optimizers = SGD\n",
      "learning_rate =  0.01\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.8215 - accuracy: 0.3461 - val_loss: 1.7328 - val_accuracy: 0.3782\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6029 - accuracy: 0.4322 - val_loss: 1.6801 - val_accuracy: 0.3999\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5127 - accuracy: 0.4635 - val_loss: 1.5497 - val_accuracy: 0.4414\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.4560 - accuracy: 0.4832 - val_loss: 1.5702 - val_accuracy: 0.4510\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.4042 - accuracy: 0.4985 - val_loss: 1.4205 - val_accuracy: 0.4934\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3730 - accuracy: 0.5118 - val_loss: 1.4258 - val_accuracy: 0.4918\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3306 - accuracy: 0.5276 - val_loss: 1.4424 - val_accuracy: 0.4910\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2960 - accuracy: 0.5405 - val_loss: 1.4024 - val_accuracy: 0.5045\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2659 - accuracy: 0.5491 - val_loss: 1.4672 - val_accuracy: 0.4768\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2343 - accuracy: 0.5617 - val_loss: 1.3866 - val_accuracy: 0.5154\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2069 - accuracy: 0.5710 - val_loss: 1.3292 - val_accuracy: 0.5318\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1752 - accuracy: 0.5825 - val_loss: 1.4485 - val_accuracy: 0.4891\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1475 - accuracy: 0.5919 - val_loss: 1.3351 - val_accuracy: 0.5343\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1188 - accuracy: 0.6011 - val_loss: 1.3526 - val_accuracy: 0.5251\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1043 - accuracy: 0.6092 - val_loss: 1.3812 - val_accuracy: 0.5163\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0762 - accuracy: 0.6176 - val_loss: 1.3365 - val_accuracy: 0.5304\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0473 - accuracy: 0.6285 - val_loss: 1.3987 - val_accuracy: 0.5198\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0209 - accuracy: 0.6352 - val_loss: 1.3457 - val_accuracy: 0.5281\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0005 - accuracy: 0.6420 - val_loss: 1.3981 - val_accuracy: 0.5265\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.9753 - accuracy: 0.6515 - val_loss: 1.3494 - val_accuracy: 0.5428\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.9498 - accuracy: 0.6617 - val_loss: 1.3833 - val_accuracy: 0.5342\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.9226 - accuracy: 0.6718 - val_loss: 1.3931 - val_accuracy: 0.5295\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.8993 - accuracy: 0.6778 - val_loss: 1.3885 - val_accuracy: 0.5342\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.8693 - accuracy: 0.6898 - val_loss: 1.4398 - val_accuracy: 0.5340\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.8487 - accuracy: 0.6969 - val_loss: 1.4162 - val_accuracy: 0.5322\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.8295 - accuracy: 0.7038 - val_loss: 1.3943 - val_accuracy: 0.5359\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.8062 - accuracy: 0.7089 - val_loss: 1.6166 - val_accuracy: 0.5167\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.7847 - accuracy: 0.7184 - val_loss: 1.5359 - val_accuracy: 0.5307\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.7544 - accuracy: 0.7299 - val_loss: 1.5718 - val_accuracy: 0.5225\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.7361 - accuracy: 0.7375 - val_loss: 1.5418 - val_accuracy: 0.5336\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.7187 - accuracy: 0.7422 - val_loss: 1.5431 - val_accuracy: 0.5338\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.6953 - accuracy: 0.7514 - val_loss: 1.6345 - val_accuracy: 0.5214\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.6687 - accuracy: 0.7598 - val_loss: 1.6122 - val_accuracy: 0.5313\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.6566 - accuracy: 0.7650 - val_loss: 1.7186 - val_accuracy: 0.5178\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.6367 - accuracy: 0.7719 - val_loss: 1.6818 - val_accuracy: 0.5275\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.6072 - accuracy: 0.7824 - val_loss: 1.6892 - val_accuracy: 0.5323\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.5930 - accuracy: 0.7869 - val_loss: 1.6761 - val_accuracy: 0.5312\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.5743 - accuracy: 0.7936 - val_loss: 1.7772 - val_accuracy: 0.5246\n",
      "Epoch 39/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.5516 - accuracy: 0.8011 - val_loss: 1.7799 - val_accuracy: 0.5313\n",
      "Epoch 40/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.5347 - accuracy: 0.8070 - val_loss: 1.7897 - val_accuracy: 0.5334\n",
      "Epoch 41/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.5120 - accuracy: 0.8179 - val_loss: 1.9981 - val_accuracy: 0.5231\n",
      "Epoch 42/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.4997 - accuracy: 0.8217 - val_loss: 1.9062 - val_accuracy: 0.5300\n",
      "Epoch 43/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.4839 - accuracy: 0.8264 - val_loss: 2.0203 - val_accuracy: 0.5297\n",
      "Epoch 44/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.4688 - accuracy: 0.8301 - val_loss: 2.0519 - val_accuracy: 0.5269\n",
      "Epoch 45/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.4506 - accuracy: 0.8367 - val_loss: 2.0997 - val_accuracy: 0.5214\n",
      "Epoch 46/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.4385 - accuracy: 0.8416 - val_loss: 2.0624 - val_accuracy: 0.5235\n",
      "Epoch 47/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.4161 - accuracy: 0.8523 - val_loss: 2.2483 - val_accuracy: 0.5113\n",
      "Epoch 48/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.4088 - accuracy: 0.8536 - val_loss: 2.1870 - val_accuracy: 0.5330\n",
      "Epoch 49/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.3903 - accuracy: 0.8601 - val_loss: 2.2782 - val_accuracy: 0.5163\n",
      "Epoch 50/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 10ms/step - loss: 0.3838 - accuracy: 0.8598 - val_loss: 2.3329 - val_accuracy: 0.5197\n",
      "Experiment with Optimizers = SGD\n",
      "learning_rate =  0.001\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.0393 - accuracy: 0.2700 - val_loss: 1.8669 - val_accuracy: 0.3476\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8037 - accuracy: 0.3677 - val_loss: 1.7503 - val_accuracy: 0.3863\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7237 - accuracy: 0.3950 - val_loss: 1.6899 - val_accuracy: 0.4033\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.6678 - accuracy: 0.4145 - val_loss: 1.6450 - val_accuracy: 0.4229\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6229 - accuracy: 0.4308 - val_loss: 1.6084 - val_accuracy: 0.4345\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5843 - accuracy: 0.4432 - val_loss: 1.5791 - val_accuracy: 0.4403\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5514 - accuracy: 0.4571 - val_loss: 1.5562 - val_accuracy: 0.4564\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5198 - accuracy: 0.4668 - val_loss: 1.5297 - val_accuracy: 0.4530\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.4939 - accuracy: 0.4761 - val_loss: 1.5004 - val_accuracy: 0.4700\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.4686 - accuracy: 0.4845 - val_loss: 1.4824 - val_accuracy: 0.4718\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.4425 - accuracy: 0.4955 - val_loss: 1.4770 - val_accuracy: 0.4766\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.4211 - accuracy: 0.5027 - val_loss: 1.4685 - val_accuracy: 0.4797\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3996 - accuracy: 0.5098 - val_loss: 1.4508 - val_accuracy: 0.4816\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3788 - accuracy: 0.5167 - val_loss: 1.4527 - val_accuracy: 0.4835\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3594 - accuracy: 0.5225 - val_loss: 1.4129 - val_accuracy: 0.4969\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3404 - accuracy: 0.5290 - val_loss: 1.4052 - val_accuracy: 0.5011\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3237 - accuracy: 0.5372 - val_loss: 1.4160 - val_accuracy: 0.4984\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3074 - accuracy: 0.5412 - val_loss: 1.4065 - val_accuracy: 0.5018\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2909 - accuracy: 0.5472 - val_loss: 1.3756 - val_accuracy: 0.5115\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2733 - accuracy: 0.5533 - val_loss: 1.3839 - val_accuracy: 0.5070\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2580 - accuracy: 0.5577 - val_loss: 1.3695 - val_accuracy: 0.5153\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2444 - accuracy: 0.5651 - val_loss: 1.3903 - val_accuracy: 0.5117\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2297 - accuracy: 0.5665 - val_loss: 1.3818 - val_accuracy: 0.5157\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2135 - accuracy: 0.5733 - val_loss: 1.3693 - val_accuracy: 0.5130\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2008 - accuracy: 0.5789 - val_loss: 1.3477 - val_accuracy: 0.5260\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1847 - accuracy: 0.5836 - val_loss: 1.3867 - val_accuracy: 0.5112\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1731 - accuracy: 0.5869 - val_loss: 1.3606 - val_accuracy: 0.5186\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1604 - accuracy: 0.5924 - val_loss: 1.3501 - val_accuracy: 0.5247\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1454 - accuracy: 0.5989 - val_loss: 1.3398 - val_accuracy: 0.5213\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1344 - accuracy: 0.6013 - val_loss: 1.3303 - val_accuracy: 0.5295\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1190 - accuracy: 0.6053 - val_loss: 1.3356 - val_accuracy: 0.5276\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1040 - accuracy: 0.6130 - val_loss: 1.3733 - val_accuracy: 0.5181\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0933 - accuracy: 0.6165 - val_loss: 1.3392 - val_accuracy: 0.5329\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0780 - accuracy: 0.6217 - val_loss: 1.3301 - val_accuracy: 0.5370\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0695 - accuracy: 0.6243 - val_loss: 1.3337 - val_accuracy: 0.5255\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0537 - accuracy: 0.6307 - val_loss: 1.3573 - val_accuracy: 0.5318\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0414 - accuracy: 0.6344 - val_loss: 1.3981 - val_accuracy: 0.5158\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0285 - accuracy: 0.6384 - val_loss: 1.3617 - val_accuracy: 0.5312\n",
      "Epoch 39/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0174 - accuracy: 0.6411 - val_loss: 1.3540 - val_accuracy: 0.5288\n",
      "Epoch 40/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0072 - accuracy: 0.6474 - val_loss: 1.3562 - val_accuracy: 0.5327\n",
      "Epoch 41/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.9934 - accuracy: 0.6513 - val_loss: 1.3492 - val_accuracy: 0.5319\n",
      "Epoch 42/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.9812 - accuracy: 0.6556 - val_loss: 1.3555 - val_accuracy: 0.5328\n",
      "Epoch 43/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.9689 - accuracy: 0.6590 - val_loss: 1.3819 - val_accuracy: 0.5247\n",
      "Epoch 44/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.9599 - accuracy: 0.6641 - val_loss: 1.4108 - val_accuracy: 0.5161\n",
      "Epoch 45/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.9459 - accuracy: 0.6689 - val_loss: 1.4216 - val_accuracy: 0.5144\n",
      "Epoch 46/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.9320 - accuracy: 0.6743 - val_loss: 1.3514 - val_accuracy: 0.5389\n",
      "Epoch 47/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.9187 - accuracy: 0.6765 - val_loss: 1.4132 - val_accuracy: 0.5248\n",
      "Epoch 48/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.9105 - accuracy: 0.6819 - val_loss: 1.4139 - val_accuracy: 0.5243\n",
      "Epoch 49/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8966 - accuracy: 0.6863 - val_loss: 1.3623 - val_accuracy: 0.5371\n",
      "Epoch 50/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.8841 - accuracy: 0.6924 - val_loss: 1.4083 - val_accuracy: 0.5238\n",
      "Experiment with Optimizers = SGD\n",
      "learning_rate =  0.0001\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.2506 - accuracy: 0.1638 - val_loss: 2.1745 - val_accuracy: 0.2056\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.1281 - accuracy: 0.2354 - val_loss: 2.0894 - val_accuracy: 0.2528\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.0557 - accuracy: 0.2735 - val_loss: 2.0285 - val_accuracy: 0.2858\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.0013 - accuracy: 0.3028 - val_loss: 1.9795 - val_accuracy: 0.3127\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9586 - accuracy: 0.3211 - val_loss: 1.9426 - val_accuracy: 0.3217\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9244 - accuracy: 0.3330 - val_loss: 1.9115 - val_accuracy: 0.3307\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8960 - accuracy: 0.3417 - val_loss: 1.8865 - val_accuracy: 0.3417\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8719 - accuracy: 0.3503 - val_loss: 1.8646 - val_accuracy: 0.3486\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8519 - accuracy: 0.3561 - val_loss: 1.8458 - val_accuracy: 0.3541\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8343 - accuracy: 0.3610 - val_loss: 1.8301 - val_accuracy: 0.3602\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8188 - accuracy: 0.3667 - val_loss: 1.8161 - val_accuracy: 0.3650\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8051 - accuracy: 0.3712 - val_loss: 1.8029 - val_accuracy: 0.3694\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7921 - accuracy: 0.3754 - val_loss: 1.7911 - val_accuracy: 0.3734\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.7803 - accuracy: 0.3806 - val_loss: 1.7790 - val_accuracy: 0.3764\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7697 - accuracy: 0.3831 - val_loss: 1.7699 - val_accuracy: 0.3806\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7596 - accuracy: 0.3852 - val_loss: 1.7597 - val_accuracy: 0.3801\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7498 - accuracy: 0.3892 - val_loss: 1.7501 - val_accuracy: 0.3873\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7406 - accuracy: 0.3928 - val_loss: 1.7409 - val_accuracy: 0.3918\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7316 - accuracy: 0.3961 - val_loss: 1.7329 - val_accuracy: 0.3925\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7233 - accuracy: 0.3993 - val_loss: 1.7245 - val_accuracy: 0.3931\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7154 - accuracy: 0.4014 - val_loss: 1.7164 - val_accuracy: 0.3988\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7073 - accuracy: 0.4034 - val_loss: 1.7102 - val_accuracy: 0.4009\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7002 - accuracy: 0.4049 - val_loss: 1.7021 - val_accuracy: 0.4041\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6925 - accuracy: 0.4083 - val_loss: 1.6961 - val_accuracy: 0.4055\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6858 - accuracy: 0.4115 - val_loss: 1.6893 - val_accuracy: 0.4079\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6789 - accuracy: 0.4135 - val_loss: 1.6818 - val_accuracy: 0.4101\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6719 - accuracy: 0.4146 - val_loss: 1.6766 - val_accuracy: 0.4120\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6654 - accuracy: 0.4171 - val_loss: 1.6699 - val_accuracy: 0.4167\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6591 - accuracy: 0.4201 - val_loss: 1.6670 - val_accuracy: 0.4145\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6531 - accuracy: 0.4219 - val_loss: 1.6582 - val_accuracy: 0.4193\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6468 - accuracy: 0.4231 - val_loss: 1.6527 - val_accuracy: 0.4158\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.6408 - accuracy: 0.4253 - val_loss: 1.6480 - val_accuracy: 0.4221\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6353 - accuracy: 0.4276 - val_loss: 1.6432 - val_accuracy: 0.4234\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6298 - accuracy: 0.4291 - val_loss: 1.6365 - val_accuracy: 0.4257\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6239 - accuracy: 0.4324 - val_loss: 1.6317 - val_accuracy: 0.4288\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6186 - accuracy: 0.4331 - val_loss: 1.6269 - val_accuracy: 0.4303\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6135 - accuracy: 0.4351 - val_loss: 1.6219 - val_accuracy: 0.4270\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6081 - accuracy: 0.4376 - val_loss: 1.6202 - val_accuracy: 0.4301\n",
      "Epoch 39/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6031 - accuracy: 0.4391 - val_loss: 1.6126 - val_accuracy: 0.4330\n",
      "Epoch 40/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5984 - accuracy: 0.4394 - val_loss: 1.6084 - val_accuracy: 0.4350\n",
      "Epoch 41/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5934 - accuracy: 0.4415 - val_loss: 1.6058 - val_accuracy: 0.4348\n",
      "Epoch 42/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5886 - accuracy: 0.4440 - val_loss: 1.6028 - val_accuracy: 0.4361\n",
      "Epoch 43/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5839 - accuracy: 0.4447 - val_loss: 1.5981 - val_accuracy: 0.4373\n",
      "Epoch 44/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5795 - accuracy: 0.4461 - val_loss: 1.5917 - val_accuracy: 0.4394\n",
      "Epoch 45/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5748 - accuracy: 0.4486 - val_loss: 1.5877 - val_accuracy: 0.4406\n",
      "Epoch 46/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5700 - accuracy: 0.4495 - val_loss: 1.5842 - val_accuracy: 0.4427\n",
      "Epoch 47/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5658 - accuracy: 0.4525 - val_loss: 1.5813 - val_accuracy: 0.4403\n",
      "Epoch 48/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5619 - accuracy: 0.4529 - val_loss: 1.5779 - val_accuracy: 0.4412\n",
      "Epoch 49/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5574 - accuracy: 0.4554 - val_loss: 1.5733 - val_accuracy: 0.4452\n",
      "Epoch 50/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5534 - accuracy: 0.4560 - val_loss: 1.5693 - val_accuracy: 0.4458\n",
      "Experiment with Optimizers = RMSprop\n",
      "learning_rate =  0.01\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 54.1004 - accuracy: 0.0989 - val_loss: 2.3158 - val_accuracy: 0.1000\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3104 - accuracy: 0.0997 - val_loss: 2.3092 - val_accuracy: 0.1000\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3108 - accuracy: 0.1009 - val_loss: 2.3114 - val_accuracy: 0.1000\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3103 - accuracy: 0.0987 - val_loss: 2.3073 - val_accuracy: 0.1000\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3105 - accuracy: 0.1003 - val_loss: 2.3101 - val_accuracy: 0.1000\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3105 - accuracy: 0.1000 - val_loss: 2.3216 - val_accuracy: 0.1000\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3097 - accuracy: 0.0994 - val_loss: 2.3093 - val_accuracy: 0.1000\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3088 - accuracy: 0.0980 - val_loss: 2.3121 - val_accuracy: 0.1000\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3114 - accuracy: 0.0987 - val_loss: 2.3116 - val_accuracy: 0.1000\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3108 - accuracy: 0.0995 - val_loss: 2.3089 - val_accuracy: 0.1000\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3092 - accuracy: 0.1009 - val_loss: 2.3077 - val_accuracy: 0.1000\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3090 - accuracy: 0.1006 - val_loss: 2.3135 - val_accuracy: 0.1000\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3104 - accuracy: 0.0976 - val_loss: 2.3145 - val_accuracy: 0.1000\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3106 - accuracy: 0.1000 - val_loss: 2.3146 - val_accuracy: 0.1000\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3103 - accuracy: 0.1009 - val_loss: 2.3086 - val_accuracy: 0.1000\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3083 - accuracy: 0.1006 - val_loss: 2.3070 - val_accuracy: 0.1000\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3100 - accuracy: 0.0982 - val_loss: 2.3080 - val_accuracy: 0.1000\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3092 - accuracy: 0.1010 - val_loss: 2.3069 - val_accuracy: 0.1000\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3103 - accuracy: 0.1001 - val_loss: 2.3078 - val_accuracy: 0.1000\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3097 - accuracy: 0.0986 - val_loss: 2.3108 - val_accuracy: 0.1000\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3094 - accuracy: 0.0983 - val_loss: 2.3057 - val_accuracy: 0.1000\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3098 - accuracy: 0.0993 - val_loss: 2.3144 - val_accuracy: 0.1000\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3102 - accuracy: 0.0981 - val_loss: 2.3079 - val_accuracy: 0.1000\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3084 - accuracy: 0.0988 - val_loss: 2.3060 - val_accuracy: 0.1000\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3084 - accuracy: 0.0987 - val_loss: 2.3112 - val_accuracy: 0.1000\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3103 - accuracy: 0.1017 - val_loss: 2.3075 - val_accuracy: 0.1000\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3107 - accuracy: 0.0997 - val_loss: 2.3078 - val_accuracy: 0.1000\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3091 - accuracy: 0.0975 - val_loss: 2.3135 - val_accuracy: 0.1000\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3115 - accuracy: 0.0991 - val_loss: 2.3090 - val_accuracy: 0.1000\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3104 - accuracy: 0.0994 - val_loss: 2.3038 - val_accuracy: 0.1000\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3104 - accuracy: 0.0975 - val_loss: 2.3087 - val_accuracy: 0.1000\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3108 - accuracy: 0.0973 - val_loss: 2.3098 - val_accuracy: 0.1000\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3093 - accuracy: 0.1005 - val_loss: 2.3075 - val_accuracy: 0.1000\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3096 - accuracy: 0.1015 - val_loss: 2.3142 - val_accuracy: 0.1000\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3108 - accuracy: 0.0998 - val_loss: 2.3103 - val_accuracy: 0.1000\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3098 - accuracy: 0.0992 - val_loss: 2.3104 - val_accuracy: 0.1000\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3101 - accuracy: 0.0991 - val_loss: 2.3096 - val_accuracy: 0.1000\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3109 - accuracy: 0.0986 - val_loss: 2.3062 - val_accuracy: 0.1000\n",
      "Epoch 39/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3110 - accuracy: 0.0992 - val_loss: 2.3076 - val_accuracy: 0.1000\n",
      "Epoch 40/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3109 - accuracy: 0.0993 - val_loss: 2.3099 - val_accuracy: 0.1000\n",
      "Epoch 41/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3089 - accuracy: 0.1021 - val_loss: 2.3092 - val_accuracy: 0.1000\n",
      "Epoch 42/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3100 - accuracy: 0.0993 - val_loss: 2.3061 - val_accuracy: 0.1000\n",
      "Epoch 43/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3089 - accuracy: 0.1000 - val_loss: 2.3076 - val_accuracy: 0.1000\n",
      "Epoch 44/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3104 - accuracy: 0.0994 - val_loss: 2.3151 - val_accuracy: 0.1000\n",
      "Epoch 45/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3109 - accuracy: 0.0986 - val_loss: 2.3086 - val_accuracy: 0.1000\n",
      "Epoch 46/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3110 - accuracy: 0.0988 - val_loss: 2.3062 - val_accuracy: 0.1000\n",
      "Epoch 47/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 12ms/step - loss: 2.3104 - accuracy: 0.0992 - val_loss: 2.3113 - val_accuracy: 0.1000\n",
      "Epoch 48/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3093 - accuracy: 0.0995 - val_loss: 2.3062 - val_accuracy: 0.1000\n",
      "Epoch 49/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3110 - accuracy: 0.0981 - val_loss: 2.3088 - val_accuracy: 0.1000\n",
      "Epoch 50/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.3105 - accuracy: 0.1005 - val_loss: 2.3194 - val_accuracy: 0.1000\n",
      "Experiment with Optimizers = RMSprop\n",
      "learning_rate =  0.001\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 4.5376 - accuracy: 0.1235 - val_loss: 2.0992 - val_accuracy: 0.1569\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1248 - accuracy: 0.1708 - val_loss: 2.1379 - val_accuracy: 0.1673\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1058 - accuracy: 0.1731 - val_loss: 2.1844 - val_accuracy: 0.1649\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1180 - accuracy: 0.1719 - val_loss: 2.0893 - val_accuracy: 0.1784\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.1012 - accuracy: 0.1738 - val_loss: 2.1499 - val_accuracy: 0.1556\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0943 - accuracy: 0.1755 - val_loss: 2.0821 - val_accuracy: 0.1878\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1074 - accuracy: 0.1757 - val_loss: 2.0953 - val_accuracy: 0.1731\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1037 - accuracy: 0.1746 - val_loss: 2.1306 - val_accuracy: 0.1612\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1391 - accuracy: 0.1676 - val_loss: 2.1487 - val_accuracy: 0.1674\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0919 - accuracy: 0.1817 - val_loss: 2.0653 - val_accuracy: 0.1701\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0896 - accuracy: 0.1812 - val_loss: 2.0675 - val_accuracy: 0.1843\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.0867 - accuracy: 0.1830 - val_loss: 2.0746 - val_accuracy: 0.1871\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0949 - accuracy: 0.1809 - val_loss: 2.1048 - val_accuracy: 0.1769\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1020 - accuracy: 0.1731 - val_loss: 2.1040 - val_accuracy: 0.1758\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1008 - accuracy: 0.1804 - val_loss: 2.0960 - val_accuracy: 0.1773\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0808 - accuracy: 0.1820 - val_loss: 2.0633 - val_accuracy: 0.1901\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0738 - accuracy: 0.1853 - val_loss: 2.0633 - val_accuracy: 0.1882\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0905 - accuracy: 0.1778 - val_loss: 2.0796 - val_accuracy: 0.1753\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0903 - accuracy: 0.1789 - val_loss: 2.1605 - val_accuracy: 0.1584\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1311 - accuracy: 0.1686 - val_loss: 2.0637 - val_accuracy: 0.1885\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0996 - accuracy: 0.1807 - val_loss: 2.0794 - val_accuracy: 0.1872\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.0842 - accuracy: 0.1857 - val_loss: 2.0745 - val_accuracy: 0.1767\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.0988 - accuracy: 0.1780 - val_loss: 2.1461 - val_accuracy: 0.1758\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0924 - accuracy: 0.1785 - val_loss: 2.0795 - val_accuracy: 0.1721\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0922 - accuracy: 0.1832 - val_loss: 2.0585 - val_accuracy: 0.1938\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0907 - accuracy: 0.1811 - val_loss: 2.0681 - val_accuracy: 0.1810\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0690 - accuracy: 0.1835 - val_loss: 2.0779 - val_accuracy: 0.1903\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0976 - accuracy: 0.1779 - val_loss: 2.0528 - val_accuracy: 0.1928\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0741 - accuracy: 0.1844 - val_loss: 2.0782 - val_accuracy: 0.1825\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0757 - accuracy: 0.1838 - val_loss: 2.1603 - val_accuracy: 0.1751\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.0970 - accuracy: 0.1799 - val_loss: 2.0930 - val_accuracy: 0.1798\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.0785 - accuracy: 0.1814 - val_loss: 2.0635 - val_accuracy: 0.1899\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.0703 - accuracy: 0.1901 - val_loss: 2.0792 - val_accuracy: 0.1908\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0922 - accuracy: 0.1839 - val_loss: 2.0644 - val_accuracy: 0.1878\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0724 - accuracy: 0.1861 - val_loss: 2.0509 - val_accuracy: 0.1892\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0841 - accuracy: 0.1859 - val_loss: 2.0641 - val_accuracy: 0.1861\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0751 - accuracy: 0.1840 - val_loss: 2.0714 - val_accuracy: 0.1902\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0786 - accuracy: 0.1834 - val_loss: 2.0913 - val_accuracy: 0.1899\n",
      "Epoch 39/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1151 - accuracy: 0.1750 - val_loss: 2.0871 - val_accuracy: 0.1857\n",
      "Epoch 40/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.1058 - accuracy: 0.1766 - val_loss: 2.1064 - val_accuracy: 0.1851\n",
      "Epoch 41/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.0814 - accuracy: 0.1832 - val_loss: 2.0796 - val_accuracy: 0.1760\n",
      "Epoch 42/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0881 - accuracy: 0.1826 - val_loss: 2.1350 - val_accuracy: 0.1862\n",
      "Epoch 43/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0996 - accuracy: 0.1788 - val_loss: 2.0788 - val_accuracy: 0.1923\n",
      "Epoch 44/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.0822 - accuracy: 0.1809 - val_loss: 2.0705 - val_accuracy: 0.1871\n",
      "Epoch 45/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.1007 - accuracy: 0.1801 - val_loss: 2.0724 - val_accuracy: 0.1756\n",
      "Epoch 46/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1239 - accuracy: 0.1704 - val_loss: 2.0828 - val_accuracy: 0.1740\n",
      "Epoch 47/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0861 - accuracy: 0.1746 - val_loss: 2.0775 - val_accuracy: 0.1804\n",
      "Epoch 48/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0965 - accuracy: 0.1762 - val_loss: 2.0656 - val_accuracy: 0.1897\n",
      "Epoch 49/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.1045 - accuracy: 0.1765 - val_loss: 2.1284 - val_accuracy: 0.1737\n",
      "Epoch 50/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 2.1283 - accuracy: 0.1689 - val_loss: 2.1230 - val_accuracy: 0.1761\n",
      "Experiment with Optimizers = RMSprop\n",
      "learning_rate =  0.0001\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.9274 - accuracy: 0.3013 - val_loss: 1.7910 - val_accuracy: 0.3463\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.7120 - accuracy: 0.3836 - val_loss: 1.6481 - val_accuracy: 0.4099\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.6077 - accuracy: 0.4280 - val_loss: 1.5878 - val_accuracy: 0.4257\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5419 - accuracy: 0.4513 - val_loss: 1.5598 - val_accuracy: 0.4470\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5050 - accuracy: 0.4624 - val_loss: 1.4958 - val_accuracy: 0.4675\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4681 - accuracy: 0.4735 - val_loss: 1.4806 - val_accuracy: 0.4696\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4323 - accuracy: 0.4884 - val_loss: 1.4947 - val_accuracy: 0.4624\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4056 - accuracy: 0.4989 - val_loss: 1.4837 - val_accuracy: 0.4768\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.3932 - accuracy: 0.5040 - val_loss: 1.4615 - val_accuracy: 0.4835\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.3441 - accuracy: 0.5213 - val_loss: 1.4064 - val_accuracy: 0.4999\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.3321 - accuracy: 0.5238 - val_loss: 1.4258 - val_accuracy: 0.4979\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.3109 - accuracy: 0.5289 - val_loss: 1.4111 - val_accuracy: 0.5012\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2795 - accuracy: 0.5428 - val_loss: 1.4086 - val_accuracy: 0.5034\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2627 - accuracy: 0.5493 - val_loss: 1.4223 - val_accuracy: 0.5027\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2384 - accuracy: 0.5564 - val_loss: 1.4253 - val_accuracy: 0.4999\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2362 - accuracy: 0.5590 - val_loss: 1.4309 - val_accuracy: 0.5003\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2044 - accuracy: 0.5694 - val_loss: 1.4402 - val_accuracy: 0.4968\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.1816 - accuracy: 0.5766 - val_loss: 1.4358 - val_accuracy: 0.5038\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.1711 - accuracy: 0.5791 - val_loss: 1.4047 - val_accuracy: 0.5058\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1386 - accuracy: 0.5915 - val_loss: 1.4500 - val_accuracy: 0.5043\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1362 - accuracy: 0.5931 - val_loss: 1.4457 - val_accuracy: 0.5050\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1084 - accuracy: 0.6027 - val_loss: 1.4229 - val_accuracy: 0.5209\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0978 - accuracy: 0.6046 - val_loss: 1.4469 - val_accuracy: 0.5155\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0818 - accuracy: 0.6116 - val_loss: 1.5479 - val_accuracy: 0.4958\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0731 - accuracy: 0.6159 - val_loss: 1.4822 - val_accuracy: 0.5151\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0553 - accuracy: 0.6207 - val_loss: 1.4928 - val_accuracy: 0.5013\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.0390 - accuracy: 0.6257 - val_loss: 1.5410 - val_accuracy: 0.5070\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.0323 - accuracy: 0.6304 - val_loss: 1.5215 - val_accuracy: 0.5139\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0120 - accuracy: 0.6369 - val_loss: 1.5234 - val_accuracy: 0.5106\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9941 - accuracy: 0.6436 - val_loss: 1.5807 - val_accuracy: 0.5075\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9648 - accuracy: 0.6544 - val_loss: 1.5467 - val_accuracy: 0.5093\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9769 - accuracy: 0.6514 - val_loss: 1.5794 - val_accuracy: 0.5092\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9371 - accuracy: 0.6630 - val_loss: 1.6549 - val_accuracy: 0.5060\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9416 - accuracy: 0.6626 - val_loss: 1.5687 - val_accuracy: 0.5071\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9258 - accuracy: 0.6650 - val_loss: 1.6366 - val_accuracy: 0.5114\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9208 - accuracy: 0.6692 - val_loss: 1.6963 - val_accuracy: 0.5071\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 0.9139 - accuracy: 0.6707 - val_loss: 1.6748 - val_accuracy: 0.4964\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 0.8981 - accuracy: 0.6780 - val_loss: 1.6776 - val_accuracy: 0.5093\n",
      "Epoch 39/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8958 - accuracy: 0.6782 - val_loss: 1.7179 - val_accuracy: 0.5034\n",
      "Epoch 40/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8800 - accuracy: 0.6825 - val_loss: 1.7326 - val_accuracy: 0.5028\n",
      "Epoch 41/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8724 - accuracy: 0.6864 - val_loss: 1.8532 - val_accuracy: 0.5041\n",
      "Epoch 42/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8643 - accuracy: 0.6915 - val_loss: 1.8561 - val_accuracy: 0.4903\n",
      "Epoch 43/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8391 - accuracy: 0.6987 - val_loss: 1.8498 - val_accuracy: 0.4999\n",
      "Epoch 44/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8534 - accuracy: 0.6929 - val_loss: 1.8095 - val_accuracy: 0.5074\n",
      "Epoch 45/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8487 - accuracy: 0.6959 - val_loss: 1.9326 - val_accuracy: 0.5015\n",
      "Epoch 46/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 0.8459 - accuracy: 0.6965 - val_loss: 1.8911 - val_accuracy: 0.5001\n",
      "Epoch 47/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8176 - accuracy: 0.7061 - val_loss: 1.9624 - val_accuracy: 0.4993\n",
      "Epoch 48/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8360 - accuracy: 0.6997 - val_loss: 1.9474 - val_accuracy: 0.4988\n",
      "Epoch 49/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.7980 - accuracy: 0.7135 - val_loss: 1.9874 - val_accuracy: 0.4916\n",
      "Epoch 50/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.7980 - accuracy: 0.7120 - val_loss: 1.9916 - val_accuracy: 0.4900\n",
      "Experiment with Optimizers = AdaGrad\n",
      "learning_rate =  0.01\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9976 - accuracy: 0.2812 - val_loss: 1.8880 - val_accuracy: 0.3171\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8218 - accuracy: 0.3515 - val_loss: 1.7800 - val_accuracy: 0.3706\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7420 - accuracy: 0.3840 - val_loss: 1.7091 - val_accuracy: 0.3908\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6860 - accuracy: 0.4028 - val_loss: 1.7723 - val_accuracy: 0.3716\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6365 - accuracy: 0.4215 - val_loss: 1.6332 - val_accuracy: 0.4183\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5997 - accuracy: 0.4329 - val_loss: 1.6100 - val_accuracy: 0.4198\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5757 - accuracy: 0.4412 - val_loss: 1.6042 - val_accuracy: 0.4178\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5466 - accuracy: 0.4538 - val_loss: 1.6587 - val_accuracy: 0.4070\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5247 - accuracy: 0.4593 - val_loss: 1.6317 - val_accuracy: 0.4071\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5009 - accuracy: 0.4715 - val_loss: 1.5563 - val_accuracy: 0.4562\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.4699 - accuracy: 0.4796 - val_loss: 1.4927 - val_accuracy: 0.4717\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.4596 - accuracy: 0.4843 - val_loss: 1.6404 - val_accuracy: 0.4195\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.4374 - accuracy: 0.4930 - val_loss: 1.4985 - val_accuracy: 0.4668\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.4207 - accuracy: 0.4999 - val_loss: 1.4673 - val_accuracy: 0.4773\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4041 - accuracy: 0.5027 - val_loss: 1.5793 - val_accuracy: 0.4368\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3856 - accuracy: 0.5112 - val_loss: 1.4482 - val_accuracy: 0.4883\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3718 - accuracy: 0.5168 - val_loss: 1.4663 - val_accuracy: 0.4853\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3538 - accuracy: 0.5231 - val_loss: 1.4637 - val_accuracy: 0.4861\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3486 - accuracy: 0.5255 - val_loss: 1.5571 - val_accuracy: 0.4543\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3308 - accuracy: 0.5314 - val_loss: 1.4122 - val_accuracy: 0.4993\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3221 - accuracy: 0.5360 - val_loss: 1.5360 - val_accuracy: 0.4598\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.3065 - accuracy: 0.5397 - val_loss: 1.4325 - val_accuracy: 0.4862\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2923 - accuracy: 0.5466 - val_loss: 1.5310 - val_accuracy: 0.4553\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2804 - accuracy: 0.5510 - val_loss: 1.4560 - val_accuracy: 0.4829\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2707 - accuracy: 0.5525 - val_loss: 1.4502 - val_accuracy: 0.4817\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2554 - accuracy: 0.5579 - val_loss: 1.3882 - val_accuracy: 0.5125\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2419 - accuracy: 0.5635 - val_loss: 1.4326 - val_accuracy: 0.4882\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2318 - accuracy: 0.5683 - val_loss: 1.4740 - val_accuracy: 0.4856\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2228 - accuracy: 0.5725 - val_loss: 1.4144 - val_accuracy: 0.4908\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2153 - accuracy: 0.5745 - val_loss: 1.3815 - val_accuracy: 0.5161\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.2044 - accuracy: 0.5795 - val_loss: 1.4796 - val_accuracy: 0.4634\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1911 - accuracy: 0.5822 - val_loss: 1.3647 - val_accuracy: 0.5122\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1806 - accuracy: 0.5867 - val_loss: 1.3601 - val_accuracy: 0.5240\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1713 - accuracy: 0.5910 - val_loss: 1.3660 - val_accuracy: 0.5166\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1612 - accuracy: 0.5933 - val_loss: 1.4309 - val_accuracy: 0.4919\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1484 - accuracy: 0.5955 - val_loss: 1.3466 - val_accuracy: 0.5245\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1413 - accuracy: 0.6015 - val_loss: 1.3627 - val_accuracy: 0.5178\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1299 - accuracy: 0.6059 - val_loss: 1.5383 - val_accuracy: 0.4644\n",
      "Epoch 39/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1265 - accuracy: 0.6067 - val_loss: 1.4118 - val_accuracy: 0.5029\n",
      "Epoch 40/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1135 - accuracy: 0.6106 - val_loss: 1.4978 - val_accuracy: 0.4713\n",
      "Epoch 41/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.1020 - accuracy: 0.6162 - val_loss: 1.4544 - val_accuracy: 0.4881\n",
      "Epoch 42/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0971 - accuracy: 0.6174 - val_loss: 1.3736 - val_accuracy: 0.5086\n",
      "Epoch 43/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0832 - accuracy: 0.6205 - val_loss: 1.4499 - val_accuracy: 0.5057\n",
      "Epoch 44/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0758 - accuracy: 0.6265 - val_loss: 1.3663 - val_accuracy: 0.5181\n",
      "Epoch 45/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0637 - accuracy: 0.6300 - val_loss: 1.3630 - val_accuracy: 0.5170\n",
      "Epoch 46/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0546 - accuracy: 0.6329 - val_loss: 1.3452 - val_accuracy: 0.5256\n",
      "Epoch 47/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0389 - accuracy: 0.6404 - val_loss: 1.4317 - val_accuracy: 0.5015\n",
      "Epoch 48/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0338 - accuracy: 0.6401 - val_loss: 1.3705 - val_accuracy: 0.5167\n",
      "Epoch 49/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0267 - accuracy: 0.6424 - val_loss: 1.6158 - val_accuracy: 0.4596\n",
      "Epoch 50/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.0216 - accuracy: 0.6451 - val_loss: 1.4541 - val_accuracy: 0.4941\n",
      "Experiment with Optimizers = AdaGrad\n",
      "learning_rate =  0.001\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1953 - accuracy: 0.2043 - val_loss: 2.1086 - val_accuracy: 0.2548\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.0563 - accuracy: 0.2693 - val_loss: 2.0114 - val_accuracy: 0.2885\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9761 - accuracy: 0.3061 - val_loss: 1.9497 - val_accuracy: 0.3167\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9231 - accuracy: 0.3270 - val_loss: 1.9029 - val_accuracy: 0.3397\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8849 - accuracy: 0.3409 - val_loss: 1.8723 - val_accuracy: 0.3509\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8559 - accuracy: 0.3515 - val_loss: 1.8460 - val_accuracy: 0.3536\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8323 - accuracy: 0.3599 - val_loss: 1.8313 - val_accuracy: 0.3632\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8127 - accuracy: 0.3678 - val_loss: 1.8097 - val_accuracy: 0.3688\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7945 - accuracy: 0.3739 - val_loss: 1.7907 - val_accuracy: 0.3758\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7787 - accuracy: 0.3803 - val_loss: 1.7801 - val_accuracy: 0.3728\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7648 - accuracy: 0.3848 - val_loss: 1.7657 - val_accuracy: 0.3775\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7520 - accuracy: 0.3895 - val_loss: 1.7553 - val_accuracy: 0.3856\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7400 - accuracy: 0.3947 - val_loss: 1.7374 - val_accuracy: 0.3926\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7284 - accuracy: 0.3964 - val_loss: 1.7393 - val_accuracy: 0.3909\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7180 - accuracy: 0.4020 - val_loss: 1.7246 - val_accuracy: 0.4011\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.7083 - accuracy: 0.4045 - val_loss: 1.7114 - val_accuracy: 0.4025\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6988 - accuracy: 0.4073 - val_loss: 1.7018 - val_accuracy: 0.4130\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6897 - accuracy: 0.4115 - val_loss: 1.6934 - val_accuracy: 0.4097\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6815 - accuracy: 0.4148 - val_loss: 1.6870 - val_accuracy: 0.4168\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6727 - accuracy: 0.4171 - val_loss: 1.6794 - val_accuracy: 0.4138\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.6648 - accuracy: 0.4201 - val_loss: 1.6686 - val_accuracy: 0.4196\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6575 - accuracy: 0.4226 - val_loss: 1.6641 - val_accuracy: 0.4224\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6506 - accuracy: 0.4267 - val_loss: 1.6580 - val_accuracy: 0.4204\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6433 - accuracy: 0.4283 - val_loss: 1.6480 - val_accuracy: 0.4297\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6364 - accuracy: 0.4311 - val_loss: 1.6433 - val_accuracy: 0.4271\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6301 - accuracy: 0.4332 - val_loss: 1.6409 - val_accuracy: 0.4288\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6241 - accuracy: 0.4364 - val_loss: 1.6359 - val_accuracy: 0.4304\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.6178 - accuracy: 0.4370 - val_loss: 1.6275 - val_accuracy: 0.4339\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6116 - accuracy: 0.4400 - val_loss: 1.6305 - val_accuracy: 0.4297\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.6058 - accuracy: 0.4414 - val_loss: 1.6361 - val_accuracy: 0.4196\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.6007 - accuracy: 0.4442 - val_loss: 1.6128 - val_accuracy: 0.4359\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5946 - accuracy: 0.4455 - val_loss: 1.6145 - val_accuracy: 0.4352\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5894 - accuracy: 0.4470 - val_loss: 1.6060 - val_accuracy: 0.4402\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5841 - accuracy: 0.4501 - val_loss: 1.5995 - val_accuracy: 0.4416\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5794 - accuracy: 0.4506 - val_loss: 1.5941 - val_accuracy: 0.4412\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5743 - accuracy: 0.4542 - val_loss: 1.5877 - val_accuracy: 0.4452\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5696 - accuracy: 0.4542 - val_loss: 1.5892 - val_accuracy: 0.4419\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5646 - accuracy: 0.4560 - val_loss: 1.5866 - val_accuracy: 0.4464\n",
      "Epoch 39/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5601 - accuracy: 0.4576 - val_loss: 1.5881 - val_accuracy: 0.4438\n",
      "Epoch 40/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5559 - accuracy: 0.4584 - val_loss: 1.5759 - val_accuracy: 0.4448\n",
      "Epoch 41/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5509 - accuracy: 0.4601 - val_loss: 1.5698 - val_accuracy: 0.4499\n",
      "Epoch 42/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5469 - accuracy: 0.4606 - val_loss: 1.5718 - val_accuracy: 0.4443\n",
      "Epoch 43/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5427 - accuracy: 0.4635 - val_loss: 1.5645 - val_accuracy: 0.4522\n",
      "Epoch 44/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5383 - accuracy: 0.4636 - val_loss: 1.5602 - val_accuracy: 0.4525\n",
      "Epoch 45/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5343 - accuracy: 0.4668 - val_loss: 1.5570 - val_accuracy: 0.4530\n",
      "Epoch 46/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5299 - accuracy: 0.4669 - val_loss: 1.5589 - val_accuracy: 0.4488\n",
      "Epoch 47/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5260 - accuracy: 0.4691 - val_loss: 1.5533 - val_accuracy: 0.4535\n",
      "Epoch 48/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.5218 - accuracy: 0.4693 - val_loss: 1.5487 - val_accuracy: 0.4556\n",
      "Epoch 49/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5186 - accuracy: 0.4711 - val_loss: 1.5427 - val_accuracy: 0.4551\n",
      "Epoch 50/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5141 - accuracy: 0.4724 - val_loss: 1.5427 - val_accuracy: 0.4551\n",
      "Experiment with Optimizers = AdaGrad\n",
      "learning_rate =  0.0001\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 3s 13ms/step - loss: 2.3178 - accuracy: 0.1293 - val_loss: 2.2855 - val_accuracy: 0.1479\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.2708 - accuracy: 0.1513 - val_loss: 2.2548 - val_accuracy: 0.1598\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.2424 - accuracy: 0.1758 - val_loss: 2.2288 - val_accuracy: 0.1919\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.2169 - accuracy: 0.2039 - val_loss: 2.2047 - val_accuracy: 0.2121\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1940 - accuracy: 0.2217 - val_loss: 2.1833 - val_accuracy: 0.2319\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1733 - accuracy: 0.2376 - val_loss: 2.1635 - val_accuracy: 0.2440\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1542 - accuracy: 0.2488 - val_loss: 2.1454 - val_accuracy: 0.2552\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.1366 - accuracy: 0.2588 - val_loss: 2.1285 - val_accuracy: 0.2652\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.1203 - accuracy: 0.2663 - val_loss: 2.1129 - val_accuracy: 0.2734\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.1050 - accuracy: 0.2724 - val_loss: 2.0979 - val_accuracy: 0.2782\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.0907 - accuracy: 0.2801 - val_loss: 2.0840 - val_accuracy: 0.2837\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.0772 - accuracy: 0.2852 - val_loss: 2.0708 - val_accuracy: 0.2874\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0644 - accuracy: 0.2891 - val_loss: 2.0585 - val_accuracy: 0.2912\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.0524 - accuracy: 0.2934 - val_loss: 2.0469 - val_accuracy: 0.2941\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 2.0410 - accuracy: 0.2972 - val_loss: 2.0357 - val_accuracy: 0.2971\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.0301 - accuracy: 0.3018 - val_loss: 2.0251 - val_accuracy: 0.3031\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.0197 - accuracy: 0.3044 - val_loss: 2.0148 - val_accuracy: 0.3066\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.0098 - accuracy: 0.3079 - val_loss: 2.0052 - val_accuracy: 0.3106\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 2.0004 - accuracy: 0.3097 - val_loss: 1.9961 - val_accuracy: 0.3132\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9916 - accuracy: 0.3119 - val_loss: 1.9879 - val_accuracy: 0.3138\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9835 - accuracy: 0.3144 - val_loss: 1.9798 - val_accuracy: 0.3173\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9757 - accuracy: 0.3168 - val_loss: 1.9723 - val_accuracy: 0.3184\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.9684 - accuracy: 0.3190 - val_loss: 1.9652 - val_accuracy: 0.3200\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.9616 - accuracy: 0.3213 - val_loss: 1.9586 - val_accuracy: 0.3226\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9550 - accuracy: 0.3228 - val_loss: 1.9522 - val_accuracy: 0.3244\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9488 - accuracy: 0.3254 - val_loss: 1.9462 - val_accuracy: 0.3254\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9429 - accuracy: 0.3273 - val_loss: 1.9406 - val_accuracy: 0.3278\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9373 - accuracy: 0.3288 - val_loss: 1.9350 - val_accuracy: 0.3290\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9319 - accuracy: 0.3303 - val_loss: 1.9297 - val_accuracy: 0.3310\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9267 - accuracy: 0.3326 - val_loss: 1.9246 - val_accuracy: 0.3307\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9217 - accuracy: 0.3338 - val_loss: 1.9197 - val_accuracy: 0.3330\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.9169 - accuracy: 0.3355 - val_loss: 1.9152 - val_accuracy: 0.3328\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9124 - accuracy: 0.3365 - val_loss: 1.9106 - val_accuracy: 0.3350\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9079 - accuracy: 0.3385 - val_loss: 1.9064 - val_accuracy: 0.3355\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.9036 - accuracy: 0.3388 - val_loss: 1.9022 - val_accuracy: 0.3376\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8995 - accuracy: 0.3397 - val_loss: 1.8983 - val_accuracy: 0.3388\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8956 - accuracy: 0.3418 - val_loss: 1.8946 - val_accuracy: 0.3400\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8918 - accuracy: 0.3425 - val_loss: 1.8910 - val_accuracy: 0.3402\n",
      "Epoch 39/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8882 - accuracy: 0.3446 - val_loss: 1.8873 - val_accuracy: 0.3416\n",
      "Epoch 40/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8848 - accuracy: 0.3446 - val_loss: 1.8840 - val_accuracy: 0.3419\n",
      "Epoch 41/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.8813 - accuracy: 0.3459 - val_loss: 1.8808 - val_accuracy: 0.3434\n",
      "Epoch 42/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8782 - accuracy: 0.3465 - val_loss: 1.8778 - val_accuracy: 0.3455\n",
      "Epoch 43/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8749 - accuracy: 0.3473 - val_loss: 1.8748 - val_accuracy: 0.3461\n",
      "Epoch 44/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8719 - accuracy: 0.3484 - val_loss: 1.8719 - val_accuracy: 0.3485\n",
      "Epoch 45/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8689 - accuracy: 0.3496 - val_loss: 1.8690 - val_accuracy: 0.3485\n",
      "Epoch 46/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8661 - accuracy: 0.3508 - val_loss: 1.8662 - val_accuracy: 0.3514\n",
      "Epoch 47/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8633 - accuracy: 0.3510 - val_loss: 1.8636 - val_accuracy: 0.3518\n",
      "Epoch 48/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8606 - accuracy: 0.3529 - val_loss: 1.8608 - val_accuracy: 0.3515\n",
      "Epoch 49/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8580 - accuracy: 0.3526 - val_loss: 1.8583 - val_accuracy: 0.3535\n",
      "Epoch 50/50\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 1.8554 - accuracy: 0.3540 - val_loss: 1.8558 - val_accuracy: 0.3521\n",
      "Experiment with Optimizers = Adam\n",
      "learning_rate =  0.01\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 3.1515 - accuracy: 0.2279 - val_loss: 1.9909 - val_accuracy: 0.2749\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.8221 - accuracy: 0.3367 - val_loss: 1.8612 - val_accuracy: 0.3208\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.7459 - accuracy: 0.3713 - val_loss: 1.6943 - val_accuracy: 0.3870\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.7044 - accuracy: 0.3884 - val_loss: 1.7295 - val_accuracy: 0.3742\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.6574 - accuracy: 0.4033 - val_loss: 1.7388 - val_accuracy: 0.3826\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.6389 - accuracy: 0.4087 - val_loss: 1.6299 - val_accuracy: 0.4160\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.6046 - accuracy: 0.4220 - val_loss: 1.6167 - val_accuracy: 0.4196\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5885 - accuracy: 0.4303 - val_loss: 1.5973 - val_accuracy: 0.4227\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.5765 - accuracy: 0.4327 - val_loss: 1.6165 - val_accuracy: 0.4221\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5781 - accuracy: 0.4322 - val_loss: 1.5604 - val_accuracy: 0.4411\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.5477 - accuracy: 0.4450 - val_loss: 1.5549 - val_accuracy: 0.4479\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.5282 - accuracy: 0.4498 - val_loss: 1.6399 - val_accuracy: 0.4114\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.5342 - accuracy: 0.4537 - val_loss: 1.5355 - val_accuracy: 0.4516\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5311 - accuracy: 0.4495 - val_loss: 1.5905 - val_accuracy: 0.4343\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5150 - accuracy: 0.4543 - val_loss: 1.5250 - val_accuracy: 0.4581\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5091 - accuracy: 0.4573 - val_loss: 1.5450 - val_accuracy: 0.4484\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5031 - accuracy: 0.4603 - val_loss: 1.5931 - val_accuracy: 0.4361\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.5133 - accuracy: 0.4564 - val_loss: 1.5427 - val_accuracy: 0.4518\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4951 - accuracy: 0.4617 - val_loss: 1.5451 - val_accuracy: 0.4507\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4874 - accuracy: 0.4671 - val_loss: 1.5495 - val_accuracy: 0.4497\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5020 - accuracy: 0.4601 - val_loss: 1.5449 - val_accuracy: 0.4481\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.4902 - accuracy: 0.4651 - val_loss: 1.5819 - val_accuracy: 0.4409\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4887 - accuracy: 0.4641 - val_loss: 1.5378 - val_accuracy: 0.4498\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4825 - accuracy: 0.4672 - val_loss: 1.5223 - val_accuracy: 0.4611\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4852 - accuracy: 0.4681 - val_loss: 1.5531 - val_accuracy: 0.4408\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4811 - accuracy: 0.4694 - val_loss: 1.6266 - val_accuracy: 0.4321\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4766 - accuracy: 0.4712 - val_loss: 1.5603 - val_accuracy: 0.4448\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.4751 - accuracy: 0.4707 - val_loss: 1.5751 - val_accuracy: 0.4505\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4762 - accuracy: 0.4698 - val_loss: 1.5606 - val_accuracy: 0.4438\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4610 - accuracy: 0.4738 - val_loss: 1.5322 - val_accuracy: 0.4516\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4569 - accuracy: 0.4779 - val_loss: 1.5839 - val_accuracy: 0.4369\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4604 - accuracy: 0.4789 - val_loss: 1.5513 - val_accuracy: 0.4441\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4794 - accuracy: 0.4670 - val_loss: 1.5971 - val_accuracy: 0.4341\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4590 - accuracy: 0.4771 - val_loss: 1.5821 - val_accuracy: 0.4370\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4627 - accuracy: 0.4771 - val_loss: 1.5538 - val_accuracy: 0.4556\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4597 - accuracy: 0.4780 - val_loss: 1.6611 - val_accuracy: 0.4165\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.4560 - accuracy: 0.4790 - val_loss: 1.4973 - val_accuracy: 0.4716\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4529 - accuracy: 0.4773 - val_loss: 1.5964 - val_accuracy: 0.4418\n",
      "Epoch 39/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4584 - accuracy: 0.4794 - val_loss: 1.5753 - val_accuracy: 0.4402\n",
      "Epoch 40/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4493 - accuracy: 0.4794 - val_loss: 1.5658 - val_accuracy: 0.4471\n",
      "Epoch 41/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4651 - accuracy: 0.4755 - val_loss: 1.5921 - val_accuracy: 0.4435\n",
      "Epoch 42/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4534 - accuracy: 0.4813 - val_loss: 1.6040 - val_accuracy: 0.4296\n",
      "Epoch 43/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4526 - accuracy: 0.4794 - val_loss: 1.6338 - val_accuracy: 0.4346\n",
      "Epoch 44/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4437 - accuracy: 0.4827 - val_loss: 1.6022 - val_accuracy: 0.4392\n",
      "Epoch 45/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4409 - accuracy: 0.4831 - val_loss: 1.5425 - val_accuracy: 0.4568\n",
      "Epoch 46/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.4418 - accuracy: 0.4840 - val_loss: 1.5497 - val_accuracy: 0.4552\n",
      "Epoch 47/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4436 - accuracy: 0.4840 - val_loss: 1.6086 - val_accuracy: 0.4374\n",
      "Epoch 48/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4365 - accuracy: 0.4896 - val_loss: 1.5193 - val_accuracy: 0.4604\n",
      "Epoch 49/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4386 - accuracy: 0.4873 - val_loss: 1.5449 - val_accuracy: 0.4552\n",
      "Epoch 50/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4390 - accuracy: 0.4894 - val_loss: 1.5085 - val_accuracy: 0.4692\n",
      "Experiment with Optimizers = Adam\n",
      "learning_rate =  0.001\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.9525 - accuracy: 0.3003 - val_loss: 1.8212 - val_accuracy: 0.3377\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.7201 - accuracy: 0.3844 - val_loss: 1.6802 - val_accuracy: 0.3907\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.6140 - accuracy: 0.4234 - val_loss: 1.5869 - val_accuracy: 0.4294\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5614 - accuracy: 0.4427 - val_loss: 1.5585 - val_accuracy: 0.4444\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5201 - accuracy: 0.4591 - val_loss: 1.5525 - val_accuracy: 0.4505\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4728 - accuracy: 0.4739 - val_loss: 1.4861 - val_accuracy: 0.4681\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4486 - accuracy: 0.4824 - val_loss: 1.4387 - val_accuracy: 0.4865\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4105 - accuracy: 0.4962 - val_loss: 1.4613 - val_accuracy: 0.4797\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.3761 - accuracy: 0.5093 - val_loss: 1.4136 - val_accuracy: 0.5002\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.3444 - accuracy: 0.5227 - val_loss: 1.4135 - val_accuracy: 0.5012\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.3206 - accuracy: 0.5306 - val_loss: 1.4164 - val_accuracy: 0.4986\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.3116 - accuracy: 0.5340 - val_loss: 1.4018 - val_accuracy: 0.5028\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2915 - accuracy: 0.5385 - val_loss: 1.4049 - val_accuracy: 0.5064\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.2638 - accuracy: 0.5495 - val_loss: 1.3867 - val_accuracy: 0.5127\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2424 - accuracy: 0.5570 - val_loss: 1.4178 - val_accuracy: 0.4970\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2253 - accuracy: 0.5639 - val_loss: 1.3922 - val_accuracy: 0.5087\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1967 - accuracy: 0.5724 - val_loss: 1.3883 - val_accuracy: 0.5190\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1830 - accuracy: 0.5791 - val_loss: 1.3765 - val_accuracy: 0.5187\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1539 - accuracy: 0.5883 - val_loss: 1.3715 - val_accuracy: 0.5219\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1324 - accuracy: 0.5971 - val_loss: 1.3543 - val_accuracy: 0.5304\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1131 - accuracy: 0.6040 - val_loss: 1.3796 - val_accuracy: 0.5176\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1096 - accuracy: 0.6043 - val_loss: 1.3606 - val_accuracy: 0.5281\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0764 - accuracy: 0.6168 - val_loss: 1.3898 - val_accuracy: 0.5189\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0553 - accuracy: 0.6237 - val_loss: 1.4022 - val_accuracy: 0.5218\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0383 - accuracy: 0.6308 - val_loss: 1.3858 - val_accuracy: 0.5277\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0210 - accuracy: 0.6336 - val_loss: 1.4256 - val_accuracy: 0.5174\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0080 - accuracy: 0.6393 - val_loss: 1.4242 - val_accuracy: 0.5184\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9899 - accuracy: 0.6476 - val_loss: 1.4152 - val_accuracy: 0.5263\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9676 - accuracy: 0.6526 - val_loss: 1.4204 - val_accuracy: 0.5251\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9501 - accuracy: 0.6609 - val_loss: 1.3889 - val_accuracy: 0.5377\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9303 - accuracy: 0.6689 - val_loss: 1.4647 - val_accuracy: 0.5197\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9223 - accuracy: 0.6683 - val_loss: 1.4539 - val_accuracy: 0.5291\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 0.8981 - accuracy: 0.6796 - val_loss: 1.4738 - val_accuracy: 0.5242\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8760 - accuracy: 0.6879 - val_loss: 1.4732 - val_accuracy: 0.5333\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8525 - accuracy: 0.6974 - val_loss: 1.5116 - val_accuracy: 0.5234\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8605 - accuracy: 0.6906 - val_loss: 1.4966 - val_accuracy: 0.5256\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8308 - accuracy: 0.7031 - val_loss: 1.5295 - val_accuracy: 0.5197\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8069 - accuracy: 0.7117 - val_loss: 1.5874 - val_accuracy: 0.5103\n",
      "Epoch 39/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8110 - accuracy: 0.7098 - val_loss: 1.5823 - val_accuracy: 0.5124\n",
      "Epoch 40/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 11ms/step - loss: 0.7829 - accuracy: 0.7188 - val_loss: 1.6132 - val_accuracy: 0.5191\n",
      "Epoch 41/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.7627 - accuracy: 0.7276 - val_loss: 1.5805 - val_accuracy: 0.5255\n",
      "Epoch 42/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.7556 - accuracy: 0.7299 - val_loss: 1.5789 - val_accuracy: 0.5234\n",
      "Epoch 43/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.7410 - accuracy: 0.7352 - val_loss: 1.6223 - val_accuracy: 0.5121\n",
      "Epoch 44/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.7269 - accuracy: 0.7415 - val_loss: 1.6587 - val_accuracy: 0.5125\n",
      "Epoch 45/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.7109 - accuracy: 0.7444 - val_loss: 1.6687 - val_accuracy: 0.5206\n",
      "Epoch 46/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.6940 - accuracy: 0.7535 - val_loss: 1.6736 - val_accuracy: 0.5134\n",
      "Epoch 47/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.6852 - accuracy: 0.7563 - val_loss: 1.7313 - val_accuracy: 0.5175\n",
      "Epoch 48/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.6705 - accuracy: 0.7607 - val_loss: 1.7624 - val_accuracy: 0.5119\n",
      "Epoch 49/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.6593 - accuracy: 0.7647 - val_loss: 1.7778 - val_accuracy: 0.5138\n",
      "Epoch 50/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.6572 - accuracy: 0.7651 - val_loss: 1.7611 - val_accuracy: 0.5212\n",
      "Experiment with Optimizers = Adam\n",
      "learning_rate =  0.0001\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 3072)]            0         \n",
      "_________________________________________________________________\n",
      "hidden_layer1 (Dense)        (None, 512)               1573376   \n",
      "_________________________________________________________________\n",
      "hidden_layer2 (Dense)        (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "hidden_layer3 (Dense)        (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,738,890\n",
      "Trainable params: 1,738,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.9204 - accuracy: 0.3124 - val_loss: 1.7880 - val_accuracy: 0.3670\n",
      "Epoch 2/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.7224 - accuracy: 0.3935 - val_loss: 1.6825 - val_accuracy: 0.4028\n",
      "Epoch 3/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.6361 - accuracy: 0.4225 - val_loss: 1.6090 - val_accuracy: 0.4326\n",
      "Epoch 4/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5815 - accuracy: 0.4425 - val_loss: 1.5782 - val_accuracy: 0.4425\n",
      "Epoch 5/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.5367 - accuracy: 0.4559 - val_loss: 1.5227 - val_accuracy: 0.4617\n",
      "Epoch 6/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4987 - accuracy: 0.4697 - val_loss: 1.5123 - val_accuracy: 0.4623\n",
      "Epoch 7/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4652 - accuracy: 0.4838 - val_loss: 1.4799 - val_accuracy: 0.4776\n",
      "Epoch 8/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4338 - accuracy: 0.4947 - val_loss: 1.4818 - val_accuracy: 0.4662\n",
      "Epoch 9/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.4137 - accuracy: 0.5035 - val_loss: 1.4543 - val_accuracy: 0.4877\n",
      "Epoch 10/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.3841 - accuracy: 0.5118 - val_loss: 1.4344 - val_accuracy: 0.4871\n",
      "Epoch 11/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.3660 - accuracy: 0.5205 - val_loss: 1.4099 - val_accuracy: 0.4966\n",
      "Epoch 12/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.3390 - accuracy: 0.5290 - val_loss: 1.4358 - val_accuracy: 0.4886\n",
      "Epoch 13/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.3176 - accuracy: 0.5363 - val_loss: 1.3893 - val_accuracy: 0.5027\n",
      "Epoch 14/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.3050 - accuracy: 0.5415 - val_loss: 1.3905 - val_accuracy: 0.5070\n",
      "Epoch 15/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2890 - accuracy: 0.5451 - val_loss: 1.3746 - val_accuracy: 0.5077\n",
      "Epoch 16/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2653 - accuracy: 0.5543 - val_loss: 1.3611 - val_accuracy: 0.5184\n",
      "Epoch 17/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2464 - accuracy: 0.5636 - val_loss: 1.3717 - val_accuracy: 0.5127\n",
      "Epoch 18/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2316 - accuracy: 0.5702 - val_loss: 1.3696 - val_accuracy: 0.5157\n",
      "Epoch 19/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.2117 - accuracy: 0.5746 - val_loss: 1.3465 - val_accuracy: 0.5213\n",
      "Epoch 20/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1976 - accuracy: 0.5805 - val_loss: 1.3868 - val_accuracy: 0.5089\n",
      "Epoch 21/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1863 - accuracy: 0.5834 - val_loss: 1.3484 - val_accuracy: 0.5283\n",
      "Epoch 22/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.1679 - accuracy: 0.5907 - val_loss: 1.3420 - val_accuracy: 0.5248\n",
      "Epoch 23/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.1544 - accuracy: 0.5951 - val_loss: 1.3672 - val_accuracy: 0.5159\n",
      "Epoch 24/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.1414 - accuracy: 0.6023 - val_loss: 1.3502 - val_accuracy: 0.5257\n",
      "Epoch 25/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.1265 - accuracy: 0.6075 - val_loss: 1.3309 - val_accuracy: 0.5334\n",
      "Epoch 26/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.1062 - accuracy: 0.6145 - val_loss: 1.3352 - val_accuracy: 0.5244\n",
      "Epoch 27/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.0973 - accuracy: 0.6168 - val_loss: 1.3360 - val_accuracy: 0.5328\n",
      "Epoch 28/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.0853 - accuracy: 0.6206 - val_loss: 1.3141 - val_accuracy: 0.5401\n",
      "Epoch 29/50\n",
      "196/196 [==============================] - 2s 13ms/step - loss: 1.0677 - accuracy: 0.6281 - val_loss: 1.3280 - val_accuracy: 0.5319\n",
      "Epoch 30/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.0603 - accuracy: 0.6294 - val_loss: 1.3205 - val_accuracy: 0.5349\n",
      "Epoch 31/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.0426 - accuracy: 0.6363 - val_loss: 1.3151 - val_accuracy: 0.5331\n",
      "Epoch 32/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 1.0270 - accuracy: 0.6443 - val_loss: 1.3183 - val_accuracy: 0.5353\n",
      "Epoch 33/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0180 - accuracy: 0.6466 - val_loss: 1.3171 - val_accuracy: 0.5375\n",
      "Epoch 34/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 1.0074 - accuracy: 0.6503 - val_loss: 1.3177 - val_accuracy: 0.5332\n",
      "Epoch 35/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9934 - accuracy: 0.6543 - val_loss: 1.3196 - val_accuracy: 0.5339\n",
      "Epoch 36/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9831 - accuracy: 0.6574 - val_loss: 1.3191 - val_accuracy: 0.5429\n",
      "Epoch 37/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9607 - accuracy: 0.6694 - val_loss: 1.3236 - val_accuracy: 0.5427\n",
      "Epoch 38/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9563 - accuracy: 0.6695 - val_loss: 1.3676 - val_accuracy: 0.5215\n",
      "Epoch 39/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 12ms/step - loss: 0.9335 - accuracy: 0.6794 - val_loss: 1.3318 - val_accuracy: 0.5385\n",
      "Epoch 40/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9307 - accuracy: 0.6788 - val_loss: 1.3773 - val_accuracy: 0.5225\n",
      "Epoch 41/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9214 - accuracy: 0.6810 - val_loss: 1.3280 - val_accuracy: 0.5424\n",
      "Epoch 42/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.9096 - accuracy: 0.6878 - val_loss: 1.3296 - val_accuracy: 0.5370\n",
      "Epoch 43/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8900 - accuracy: 0.6932 - val_loss: 1.3337 - val_accuracy: 0.5397\n",
      "Epoch 44/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8819 - accuracy: 0.6933 - val_loss: 1.3731 - val_accuracy: 0.5342\n",
      "Epoch 45/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8713 - accuracy: 0.7003 - val_loss: 1.3362 - val_accuracy: 0.5453\n",
      "Epoch 46/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8590 - accuracy: 0.7030 - val_loss: 1.3571 - val_accuracy: 0.5391\n",
      "Epoch 47/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8452 - accuracy: 0.7082 - val_loss: 1.3594 - val_accuracy: 0.5437\n",
      "Epoch 48/50\n",
      "196/196 [==============================] - 2s 12ms/step - loss: 0.8294 - accuracy: 0.7147 - val_loss: 1.3387 - val_accuracy: 0.5470\n",
      "Epoch 49/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8252 - accuracy: 0.7157 - val_loss: 1.3550 - val_accuracy: 0.5488\n",
      "Epoch 50/50\n",
      "196/196 [==============================] - 2s 11ms/step - loss: 0.8110 - accuracy: 0.7215 - val_loss: 1.3784 - val_accuracy: 0.5427\n"
     ]
    }
   ],
   "source": [
    "results = {}\n",
    "\"\"\"\n",
    "建立你的訓練與實驗迴圈並蒐集資料\n",
    "\"\"\"\n",
    "\n",
    "OPTIMIZERS = ['SGD', 'RMSprop', 'AdaGrad', 'Adam']\n",
    "\n",
    "\n",
    "for optimizers in OPTIMIZERS:\n",
    "    for lr in LEARNING_RATE:\n",
    "        sgd = keras.optimizers.SGD(learning_rate = lr, nesterov = NESTEROV, momentum = MOMENTUM)\n",
    "        rmsprop = keras.optimizers.RMSprop(learning_rate = lr, momentum = MOMENTUM)\n",
    "        adagrad = keras.optimizers.Adagrad(learning_rate = lr)\n",
    "        adam = keras.optimizers.Adam(learning_rate = lr)\n",
    "        OPT = {'SGD': sgd, 'RMSprop': rmsprop, 'AdaGrad': adagrad, 'Adam': adam}\n",
    "        \n",
    "        keras.backend.clear_session() # 把舊的 Graph 清掉\n",
    "        print(\"Experiment with Optimizers = %s\" % str(optimizers))\n",
    "        print(\"learning_rate = \", lr)\n",
    "        model = build_mlp(input_shape = x_train.shape[1:])\n",
    "        model.summary()\n",
    "        optimizer = OPT[optimizers]\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 = \"OPT-%s-%0.6f\" % (str(optimizers), lr)\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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\"\"\"\n",
    "將實驗結果繪出\n",
    "\"\"\"\n",
    "# color_bar = [\"r\", \"g\", \"b\", \"y\", \"m\", \"k\", \"r\", \"g\", \"b\", \"y\", \"m\", \"k\"]\n",
    "color_bar = ['#DDDDFF','#7D7DFF','#0000C6','#000079','#CEFFCE','#28FF28','#007500','#FFFF93', '#DDDDFF','#7D7DFF','#0000C6','#000079']\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "for i, cond in enumerate(results.keys()):\n",
    "    if i in [3,4,5,6,7,8,9,10,11]: continue\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",
    "    if i in [3,4,5,6,7,8,9,10,11]: continue\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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "for i, cond in enumerate(results.keys()):\n",
    "    if i in [0,1,2,6,7,8,9,10,11]: continue\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",
    "    if i in [0,1,2,6,7,8,9,10,11]: continue\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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "for i, cond in enumerate(results.keys()):\n",
    "    if i in [0,1,2,3,4,5,9,10,11]: continue\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",
    "    if i in [0,1,2,3,4,5,9,10,11]: continue\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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "for i, cond in enumerate(results.keys()):\n",
    "    if i in [0,1,2,3,4,5,6,7,8]: continue\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",
    "    if i in [0,1,2,3,4,5,6,7,8]: continue\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": "markdown",
   "metadata": {},
   "source": [
    "以此範例而言 SGD表現結果較佳，而在其他優化器下，可以看到一些因為學習速率太快造成精準度沒辦法達到比較高的現象出現(SGD則相反)"
   ]
  }
 ],
 "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",
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