3-CIFAR10-lrate/CIFAR10-lrate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning Rate Schedule & Adaptive Gradient Descent Algorithm\n",
    "\n",
    "* A CNN model is constructed to train on CIFAR-10\n",
    "\n",
    "* The following learning rate decay schedules are used : constant learning rate, time-based decay, step decay, exponential decay\n",
    "\n",
    "* The following adaptive graident descent algorithms are used : Adagrad, Adadelta, RMSProp, Adam\n",
    "    \n",
    "* Model performance of using different learning rate schedules or adaptive gradient descent algorithms are compared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.datasets import cifar10\n",
    "from keras.utils import np_utils\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.optimizers import SGD, Adagrad, Adadelta, RMSprop, Adam\n",
    "from keras.models import model_from_json\n",
    "from keras.callbacks import LearningRateScheduler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load CIFAR-10 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (10000, 32, 32, 3)\n",
      "10000 train samples\n",
      "2000 test samples\n"
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "num_classes = 2\n",
    "epochs = 100\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 32, 32   \n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()   \n",
    "\n",
    "# Only look at cats [=3] and dogs [=5]\n",
    "train_picks = np.ravel(np.logical_or(y_train==3,y_train==5))  \n",
    "test_picks = np.ravel(np.logical_or(y_test==3,y_test==5))     \n",
    "\n",
    "y_train = np.array(y_train[train_picks]==5,dtype=int)\n",
    "y_test = np.array(y_test[test_picks]==5,dtype=int)\n",
    "\n",
    "X_train = X_train[train_picks]\n",
    "X_test = X_test[test_picks]\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    X_train = X_train.reshape(X_train.shape[0], 3, img_rows, img_cols)\n",
    "    X_test = X_test.reshape(X_test.shape[0], 3, img_rows, img_cols)\n",
    "    input_shape = (3, img_rows, img_cols)\n",
    "else:\n",
    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)\n",
    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)\n",
    "    input_shape = (img_rows, img_cols, 3)\n",
    "\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "print('X_train shape:', X_train.shape)\n",
    "print(X_train.shape[0], 'train samples')\n",
    "print(X_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(np.ravel(y_train), num_classes)\n",
    "y_test = keras.utils.to_categorical(np.ravel(y_test), num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define function to construct CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def cnn_model() : \n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(4, kernel_size=(3, 3),activation='relu',input_shape=input_shape))\n",
    "    model.add(Conv2D(8, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(16, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(2, activation='softmax'))\n",
    "    # print(model.summary())\n",
    "    return(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define function to plot model accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_fig(i, history):\n",
    "    fig = plt.figure()\n",
    "    plt.plot(range(1,epochs+1),history.history['val_acc'],label='validation')\n",
    "    plt.plot(range(1,epochs+1),history.history['acc'],label='training')\n",
    "    plt.legend(loc=0)\n",
    "    plt.xlabel('epochs')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlim([1,epochs])\n",
    "#     plt.ylim([0,1])\n",
    "    plt.grid(True)\n",
    "    plt.title(\"Model Accuracy\")\n",
    "    plt.show()\n",
    "    fig.savefig('img/'+str(i)+'-accuracy.jpg')\n",
    "    plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constant learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      "6s - loss: 0.6928 - acc: 0.5151 - val_loss: 0.6873 - val_acc: 0.5415\n",
      "Epoch 2/100\n",
      "6s - loss: 0.6796 - acc: 0.5652 - val_loss: 0.7077 - val_acc: 0.5385\n",
      "Epoch 3/100\n",
      "6s - loss: 0.6666 - acc: 0.5918 - val_loss: 0.6614 - val_acc: 0.5910\n",
      "Epoch 4/100\n",
      "6s - loss: 0.6563 - acc: 0.6159 - val_loss: 0.6444 - val_acc: 0.6355\n",
      "Epoch 5/100\n",
      "6s - loss: 0.6442 - acc: 0.6309 - val_loss: 0.6213 - val_acc: 0.6605\n",
      "Epoch 6/100\n",
      "6s - loss: 0.6359 - acc: 0.6417 - val_loss: 0.6312 - val_acc: 0.6365\n",
      "Epoch 7/100\n",
      "6s - loss: 0.6311 - acc: 0.6462 - val_loss: 0.6132 - val_acc: 0.6595\n",
      "Epoch 8/100\n",
      "6s - loss: 0.6243 - acc: 0.6550 - val_loss: 0.6155 - val_acc: 0.6600\n",
      "Epoch 9/100\n",
      "6s - loss: 0.6152 - acc: 0.6577 - val_loss: 0.6032 - val_acc: 0.6635\n",
      "Epoch 10/100\n",
      "5s - loss: 0.6111 - acc: 0.6619 - val_loss: 0.6009 - val_acc: 0.6650\n",
      "Epoch 11/100\n",
      "6s - loss: 0.6069 - acc: 0.6644 - val_loss: 0.6116 - val_acc: 0.6565\n",
      "Epoch 12/100\n",
      "6s - loss: 0.6027 - acc: 0.6746 - val_loss: 0.6255 - val_acc: 0.6370\n",
      "Epoch 13/100\n",
      "5s - loss: 0.5984 - acc: 0.6776 - val_loss: 0.5834 - val_acc: 0.6810\n",
      "Epoch 14/100\n",
      "6s - loss: 0.5916 - acc: 0.6806 - val_loss: 0.5979 - val_acc: 0.6680\n",
      "Epoch 15/100\n",
      "5s - loss: 0.5888 - acc: 0.6820 - val_loss: 0.5804 - val_acc: 0.6825\n",
      "Epoch 16/100\n",
      "6s - loss: 0.5824 - acc: 0.6881 - val_loss: 0.5796 - val_acc: 0.6920\n",
      "Epoch 17/100\n",
      "5s - loss: 0.5767 - acc: 0.6879 - val_loss: 0.5754 - val_acc: 0.6960\n",
      "Epoch 18/100\n",
      "5s - loss: 0.5760 - acc: 0.6903 - val_loss: 0.5706 - val_acc: 0.6945\n",
      "Epoch 19/100\n",
      "6s - loss: 0.5658 - acc: 0.6959 - val_loss: 0.5806 - val_acc: 0.6885\n",
      "Epoch 20/100\n",
      "6s - loss: 0.5530 - acc: 0.7098 - val_loss: 0.5812 - val_acc: 0.6930\n",
      "Epoch 21/100\n",
      "5s - loss: 0.5616 - acc: 0.7044 - val_loss: 0.5639 - val_acc: 0.6955\n",
      "Epoch 22/100\n",
      "6s - loss: 0.5645 - acc: 0.7038 - val_loss: 0.5610 - val_acc: 0.7110\n",
      "Epoch 23/100\n",
      "5s - loss: 0.5496 - acc: 0.7131 - val_loss: 0.5645 - val_acc: 0.7025\n",
      "Epoch 24/100\n",
      "5s - loss: 0.5481 - acc: 0.7080 - val_loss: 0.5574 - val_acc: 0.7095\n",
      "Epoch 25/100\n",
      "5s - loss: 0.5392 - acc: 0.7198 - val_loss: 0.5687 - val_acc: 0.6935\n",
      "Epoch 26/100\n",
      "5s - loss: 0.5428 - acc: 0.7145 - val_loss: 0.5700 - val_acc: 0.7035\n",
      "Epoch 27/100\n",
      "5s - loss: 0.5366 - acc: 0.7139 - val_loss: 0.5733 - val_acc: 0.7070\n",
      "Epoch 28/100\n",
      "6s - loss: 0.5355 - acc: 0.7207 - val_loss: 0.5626 - val_acc: 0.7085\n",
      "Epoch 29/100\n",
      "6s - loss: 0.5245 - acc: 0.7264 - val_loss: 0.5710 - val_acc: 0.6920\n",
      "Epoch 30/100\n",
      "5s - loss: 0.5290 - acc: 0.7150 - val_loss: 0.5565 - val_acc: 0.7065\n",
      "Epoch 31/100\n",
      "6s - loss: 0.5279 - acc: 0.7226 - val_loss: 0.5543 - val_acc: 0.7020\n",
      "Epoch 32/100\n",
      "6s - loss: 0.5174 - acc: 0.7260 - val_loss: 0.5560 - val_acc: 0.7010\n",
      "Epoch 33/100\n",
      "5s - loss: 0.5172 - acc: 0.7263 - val_loss: 0.5980 - val_acc: 0.6760\n",
      "Epoch 34/100\n",
      "6s - loss: 0.5198 - acc: 0.7273 - val_loss: 0.5771 - val_acc: 0.6745\n",
      "Epoch 35/100\n",
      "5s - loss: 0.5075 - acc: 0.7280 - val_loss: 0.6885 - val_acc: 0.6210\n",
      "Epoch 36/100\n",
      "5s - loss: 0.5096 - acc: 0.7286 - val_loss: 0.5581 - val_acc: 0.7125\n",
      "Epoch 37/100\n",
      "6s - loss: 0.5077 - acc: 0.7268 - val_loss: 0.5544 - val_acc: 0.7090\n",
      "Epoch 38/100\n",
      "6s - loss: 0.5030 - acc: 0.7322 - val_loss: 0.5819 - val_acc: 0.6940\n",
      "Epoch 39/100\n",
      "5s - loss: 0.4961 - acc: 0.7421 - val_loss: 0.5565 - val_acc: 0.7125\n",
      "Epoch 40/100\n",
      "5s - loss: 0.4996 - acc: 0.7332 - val_loss: 0.5588 - val_acc: 0.7015\n",
      "Epoch 41/100\n",
      "5s - loss: 0.4975 - acc: 0.7326 - val_loss: 0.5533 - val_acc: 0.7195\n",
      "Epoch 42/100\n",
      "5s - loss: 0.4905 - acc: 0.7438 - val_loss: 0.5751 - val_acc: 0.6955\n",
      "Epoch 43/100\n",
      "5s - loss: 0.4919 - acc: 0.7393 - val_loss: 0.5781 - val_acc: 0.6925\n",
      "Epoch 44/100\n",
      "5s - loss: 0.4878 - acc: 0.7397 - val_loss: 0.5562 - val_acc: 0.7045\n",
      "Epoch 45/100\n",
      "6s - loss: 0.4821 - acc: 0.7436 - val_loss: 0.5528 - val_acc: 0.7155\n",
      "Epoch 46/100\n",
      "6s - loss: 0.4795 - acc: 0.7432 - val_loss: 0.5563 - val_acc: 0.7065\n",
      "Epoch 47/100\n",
      "6s - loss: 0.4754 - acc: 0.7500 - val_loss: 0.5741 - val_acc: 0.6915\n",
      "Epoch 48/100\n",
      "6s - loss: 0.4784 - acc: 0.7497 - val_loss: 0.5561 - val_acc: 0.7120\n",
      "Epoch 49/100\n",
      "6s - loss: 0.4788 - acc: 0.7398 - val_loss: 0.5543 - val_acc: 0.7200\n",
      "Epoch 50/100\n",
      "6s - loss: 0.4737 - acc: 0.7485 - val_loss: 0.6383 - val_acc: 0.6785\n",
      "Epoch 51/100\n",
      "6s - loss: 0.4732 - acc: 0.7472 - val_loss: 0.5690 - val_acc: 0.7025\n",
      "Epoch 52/100\n",
      "5s - loss: 0.4701 - acc: 0.7515 - val_loss: 0.5644 - val_acc: 0.7150\n",
      "Epoch 53/100\n",
      "5s - loss: 0.4647 - acc: 0.7575 - val_loss: 0.5582 - val_acc: 0.7175\n",
      "Epoch 54/100\n",
      "5s - loss: 0.4620 - acc: 0.7520 - val_loss: 0.5665 - val_acc: 0.7150\n",
      "Epoch 55/100\n",
      "5s - loss: 0.4637 - acc: 0.7501 - val_loss: 0.5662 - val_acc: 0.7025\n",
      "Epoch 56/100\n",
      "6s - loss: 0.4628 - acc: 0.7522 - val_loss: 0.5691 - val_acc: 0.6985\n",
      "Epoch 57/100\n",
      "5s - loss: 0.4703 - acc: 0.7511 - val_loss: 0.5628 - val_acc: 0.7075\n",
      "Epoch 58/100\n",
      "5s - loss: 0.4589 - acc: 0.7497 - val_loss: 0.5991 - val_acc: 0.7000\n",
      "Epoch 59/100\n",
      "5s - loss: 0.4590 - acc: 0.7545 - val_loss: 0.6092 - val_acc: 0.7015\n",
      "Epoch 60/100\n",
      "6s - loss: 0.4662 - acc: 0.7500 - val_loss: 0.5668 - val_acc: 0.7100\n",
      "Epoch 61/100\n",
      "6s - loss: 0.4578 - acc: 0.7481 - val_loss: 0.5608 - val_acc: 0.7105\n",
      "Epoch 62/100\n",
      "6s - loss: 0.4495 - acc: 0.7529 - val_loss: 0.5653 - val_acc: 0.7080\n",
      "Epoch 63/100\n",
      "5s - loss: 0.4516 - acc: 0.7539 - val_loss: 0.6061 - val_acc: 0.6995\n",
      "Epoch 64/100\n",
      "5s - loss: 0.4555 - acc: 0.7524 - val_loss: 0.5766 - val_acc: 0.6955\n",
      "Epoch 65/100\n",
      "6s - loss: 0.4502 - acc: 0.7579 - val_loss: 0.5883 - val_acc: 0.6845\n",
      "Epoch 66/100\n",
      "5s - loss: 0.4568 - acc: 0.7550 - val_loss: 0.5727 - val_acc: 0.7085\n",
      "Epoch 67/100\n",
      "5s - loss: 0.4567 - acc: 0.7524 - val_loss: 0.5593 - val_acc: 0.7085\n",
      "Epoch 68/100\n",
      "6s - loss: 0.4461 - acc: 0.7541 - val_loss: 0.5819 - val_acc: 0.7075\n",
      "Epoch 69/100\n",
      "6s - loss: 0.4473 - acc: 0.7555 - val_loss: 0.5800 - val_acc: 0.7040\n",
      "Epoch 70/100\n",
      "5s - loss: 0.4545 - acc: 0.7568 - val_loss: 0.5855 - val_acc: 0.6960\n",
      "Epoch 71/100\n",
      "6s - loss: 0.4412 - acc: 0.7613 - val_loss: 0.5889 - val_acc: 0.7020\n",
      "Epoch 72/100\n",
      "5s - loss: 0.4483 - acc: 0.7557 - val_loss: 0.5936 - val_acc: 0.7035\n",
      "Epoch 73/100\n",
      "5s - loss: 0.4408 - acc: 0.7585 - val_loss: 0.5836 - val_acc: 0.6870\n",
      "Epoch 74/100\n",
      "5s - loss: 0.4364 - acc: 0.7621 - val_loss: 0.5782 - val_acc: 0.7125\n",
      "Epoch 75/100\n",
      "5s - loss: 0.4362 - acc: 0.7688 - val_loss: 0.5898 - val_acc: 0.7010\n",
      "Epoch 76/100\n",
      "5s - loss: 0.4455 - acc: 0.7667 - val_loss: 0.6023 - val_acc: 0.7115\n",
      "Epoch 77/100\n",
      "5s - loss: 0.4364 - acc: 0.7654 - val_loss: 0.5857 - val_acc: 0.6985\n",
      "Epoch 78/100\n",
      "6s - loss: 0.4365 - acc: 0.7669 - val_loss: 0.5867 - val_acc: 0.7040\n",
      "Epoch 79/100\n",
      "5s - loss: 0.4412 - acc: 0.7595 - val_loss: 0.6257 - val_acc: 0.6905\n",
      "Epoch 80/100\n",
      "5s - loss: 0.4362 - acc: 0.7612 - val_loss: 0.6064 - val_acc: 0.6920\n",
      "Epoch 81/100\n",
      "5s - loss: 0.4361 - acc: 0.7644 - val_loss: 0.5894 - val_acc: 0.7065\n",
      "Epoch 82/100\n",
      "5s - loss: 0.4325 - acc: 0.7627 - val_loss: 0.6077 - val_acc: 0.6840\n",
      "Epoch 83/100\n",
      "6s - loss: 0.4214 - acc: 0.7727 - val_loss: 0.5924 - val_acc: 0.7035\n",
      "Epoch 84/100\n",
      "6s - loss: 0.4354 - acc: 0.7648 - val_loss: 0.5988 - val_acc: 0.6965\n",
      "Epoch 85/100\n",
      "5s - loss: 0.4335 - acc: 0.7629 - val_loss: 0.5740 - val_acc: 0.7130\n",
      "Epoch 86/100\n",
      "6s - loss: 0.4253 - acc: 0.7645 - val_loss: 0.5855 - val_acc: 0.6760\n",
      "Epoch 87/100\n",
      "5s - loss: 0.4329 - acc: 0.7668 - val_loss: 0.5782 - val_acc: 0.6790\n",
      "Epoch 88/100\n",
      "6s - loss: 0.4275 - acc: 0.7694 - val_loss: 0.5760 - val_acc: 0.7045\n",
      "Epoch 89/100\n",
      "6s - loss: 0.4293 - acc: 0.7603 - val_loss: 0.6023 - val_acc: 0.7030\n",
      "Epoch 90/100\n",
      "6s - loss: 0.4233 - acc: 0.7722 - val_loss: 0.6143 - val_acc: 0.7085\n",
      "Epoch 91/100\n",
      "6s - loss: 0.4226 - acc: 0.7700 - val_loss: 0.6113 - val_acc: 0.6945\n",
      "Epoch 92/100\n",
      "6s - loss: 0.4219 - acc: 0.7709 - val_loss: 0.5912 - val_acc: 0.7035\n",
      "Epoch 93/100\n",
      "5s - loss: 0.4252 - acc: 0.7637 - val_loss: 0.5928 - val_acc: 0.6945\n",
      "Epoch 94/100\n",
      "6s - loss: 0.4220 - acc: 0.7750 - val_loss: 0.5882 - val_acc: 0.6950\n",
      "Epoch 95/100\n",
      "6s - loss: 0.4225 - acc: 0.7701 - val_loss: 0.5935 - val_acc: 0.7030\n",
      "Epoch 96/100\n",
      "6s - loss: 0.4244 - acc: 0.7697 - val_loss: 0.6233 - val_acc: 0.6805\n",
      "Epoch 97/100\n",
      "6s - loss: 0.4211 - acc: 0.7683 - val_loss: 0.6320 - val_acc: 0.6935\n",
      "Epoch 98/100\n",
      "6s - loss: 0.4180 - acc: 0.7663 - val_loss: 0.6144 - val_acc: 0.7040\n",
      "Epoch 99/100\n",
      "6s - loss: 0.4297 - acc: 0.7666 - val_loss: 0.6075 - val_acc: 0.6830\n",
      "Epoch 100/100\n",
      "5s - loss: 0.4173 - acc: 0.7725 - val_loss: 0.5974 - val_acc: 0.7050\n"
     ]
    },
    {
     "data": {
      "image/png": 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mdfi5VFS6EqogUZEkLAKNNZTlwM+3Q1118zYp6+DwEhj3NPhHme4bMg+EBuK/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\n9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129445860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define CNN model\n",
    "model1 = cnn_model()\n",
    "\n",
    "# define SGD optimizer\n",
    "learning_rate = 0.1\n",
    "sgd = SGD(lr=learning_rate, momentum=0.0, decay=0.0, nesterov=False) # set to default except lr\n",
    "\n",
    "# compile the model\n",
    "model1.compile(loss=keras.losses.categorical_crossentropy, optimizer=sgd, metrics=['accuracy'])\n",
    "\n",
    "# fit the model\n",
    "history1 = model1.fit(X_train, y_train,\n",
    "                batch_size=batch_size,\n",
    "                epochs=epochs,\n",
    "                verbose=2,\n",
    "                validation_data=(X_test, y_test))\n",
    "\n",
    "# plot model accuracy\n",
    "plot_fig(1, history1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time-based decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      "6s - loss: 0.6921 - acc: 0.5194 - val_loss: 0.6911 - val_acc: 0.5075\n",
      "Epoch 2/100\n",
      "6s - loss: 0.6788 - acc: 0.5648 - val_loss: 0.6578 - val_acc: 0.6020\n",
      "Epoch 3/100\n",
      "6s - loss: 0.6700 - acc: 0.5883 - val_loss: 0.6746 - val_acc: 0.5685\n",
      "Epoch 4/100\n",
      "6s - loss: 0.6590 - acc: 0.6052 - val_loss: 0.6523 - val_acc: 0.6130\n",
      "Epoch 5/100\n",
      "6s - loss: 0.6526 - acc: 0.6094 - val_loss: 0.6362 - val_acc: 0.6335\n",
      "Epoch 6/100\n",
      "6s - loss: 0.6402 - acc: 0.6295 - val_loss: 0.6407 - val_acc: 0.6265\n",
      "Epoch 7/100\n",
      "6s - loss: 0.6336 - acc: 0.6412 - val_loss: 0.6278 - val_acc: 0.6430\n",
      "Epoch 8/100\n",
      "6s - loss: 0.6246 - acc: 0.6445 - val_loss: 0.6193 - val_acc: 0.6515\n",
      "Epoch 9/100\n",
      "6s - loss: 0.6198 - acc: 0.6518 - val_loss: 0.6175 - val_acc: 0.6460\n",
      "Epoch 10/100\n",
      "6s - loss: 0.6104 - acc: 0.6582 - val_loss: 0.6129 - val_acc: 0.6445\n",
      "Epoch 11/100\n",
      "6s - loss: 0.6065 - acc: 0.6637 - val_loss: 0.6052 - val_acc: 0.6580\n",
      "Epoch 12/100\n",
      "6s - loss: 0.5963 - acc: 0.6674 - val_loss: 0.6107 - val_acc: 0.6575\n",
      "Epoch 13/100\n",
      "6s - loss: 0.5932 - acc: 0.6744 - val_loss: 0.6003 - val_acc: 0.6635\n",
      "Epoch 14/100\n",
      "6s - loss: 0.5865 - acc: 0.6807 - val_loss: 0.6147 - val_acc: 0.6530\n",
      "Epoch 15/100\n",
      "6s - loss: 0.5868 - acc: 0.6808 - val_loss: 0.6015 - val_acc: 0.6605\n",
      "Epoch 16/100\n",
      "6s - loss: 0.5797 - acc: 0.6839 - val_loss: 0.5987 - val_acc: 0.6650\n",
      "Epoch 17/100\n",
      "6s - loss: 0.5764 - acc: 0.6894 - val_loss: 0.6055 - val_acc: 0.6490\n",
      "Epoch 18/100\n",
      "6s - loss: 0.5802 - acc: 0.6799 - val_loss: 0.5906 - val_acc: 0.6740\n",
      "Epoch 19/100\n",
      "6s - loss: 0.5745 - acc: 0.6946 - val_loss: 0.5959 - val_acc: 0.6715\n",
      "Epoch 20/100\n",
      "6s - loss: 0.5709 - acc: 0.6959 - val_loss: 0.5941 - val_acc: 0.6685\n",
      "Epoch 21/100\n",
      "6s - loss: 0.5680 - acc: 0.6953 - val_loss: 0.5901 - val_acc: 0.6750\n",
      "Epoch 22/100\n",
      "6s - loss: 0.5623 - acc: 0.6981 - val_loss: 0.5884 - val_acc: 0.6840\n",
      "Epoch 23/100\n",
      "6s - loss: 0.5577 - acc: 0.6993 - val_loss: 0.5849 - val_acc: 0.6805\n",
      "Epoch 24/100\n",
      "6s - loss: 0.5580 - acc: 0.7012 - val_loss: 0.5930 - val_acc: 0.6850\n",
      "Epoch 25/100\n",
      "6s - loss: 0.5547 - acc: 0.7016 - val_loss: 0.5874 - val_acc: 0.6800\n",
      "Epoch 26/100\n",
      "6s - loss: 0.5471 - acc: 0.7029 - val_loss: 0.5843 - val_acc: 0.6840\n",
      "Epoch 27/100\n",
      "6s - loss: 0.5455 - acc: 0.7112 - val_loss: 0.5914 - val_acc: 0.6815\n",
      "Epoch 28/100\n",
      "6s - loss: 0.5411 - acc: 0.7120 - val_loss: 0.5884 - val_acc: 0.6860\n",
      "Epoch 29/100\n",
      "6s - loss: 0.5422 - acc: 0.7118 - val_loss: 0.5828 - val_acc: 0.6860\n",
      "Epoch 30/100\n",
      "6s - loss: 0.5381 - acc: 0.7158 - val_loss: 0.5859 - val_acc: 0.6920\n",
      "Epoch 31/100\n",
      "6s - loss: 0.5366 - acc: 0.7139 - val_loss: 0.5856 - val_acc: 0.6875\n",
      "Epoch 32/100\n",
      "6s - loss: 0.5344 - acc: 0.7160 - val_loss: 0.5852 - val_acc: 0.6905\n",
      "Epoch 33/100\n",
      "6s - loss: 0.5337 - acc: 0.7156 - val_loss: 0.5848 - val_acc: 0.6865\n",
      "Epoch 34/100\n",
      "6s - loss: 0.5331 - acc: 0.7190 - val_loss: 0.5912 - val_acc: 0.6815\n",
      "Epoch 35/100\n",
      "6s - loss: 0.5285 - acc: 0.7215 - val_loss: 0.5884 - val_acc: 0.6810\n",
      "Epoch 36/100\n",
      "6s - loss: 0.5278 - acc: 0.7271 - val_loss: 0.5856 - val_acc: 0.6880\n",
      "Epoch 37/100\n",
      "6s - loss: 0.5255 - acc: 0.7223 - val_loss: 0.5812 - val_acc: 0.6975\n",
      "Epoch 38/100\n",
      "6s - loss: 0.5221 - acc: 0.7224 - val_loss: 0.5916 - val_acc: 0.6845\n",
      "Epoch 39/100\n",
      "6s - loss: 0.5213 - acc: 0.7214 - val_loss: 0.5803 - val_acc: 0.6900\n",
      "Epoch 40/100\n",
      "6s - loss: 0.5194 - acc: 0.7250 - val_loss: 0.5799 - val_acc: 0.6920\n",
      "Epoch 41/100\n",
      "6s - loss: 0.5209 - acc: 0.7250 - val_loss: 0.5863 - val_acc: 0.6840\n",
      "Epoch 42/100\n",
      "6s - loss: 0.5182 - acc: 0.7248 - val_loss: 0.5770 - val_acc: 0.6970\n",
      "Epoch 43/100\n",
      "6s - loss: 0.5124 - acc: 0.7275 - val_loss: 0.5783 - val_acc: 0.6980\n",
      "Epoch 44/100\n",
      "6s - loss: 0.5139 - acc: 0.7260 - val_loss: 0.5772 - val_acc: 0.6960\n",
      "Epoch 45/100\n",
      "6s - loss: 0.5064 - acc: 0.7358 - val_loss: 0.5783 - val_acc: 0.6980\n",
      "Epoch 46/100\n",
      "6s - loss: 0.5113 - acc: 0.7291 - val_loss: 0.5797 - val_acc: 0.7035\n",
      "Epoch 47/100\n",
      "6s - loss: 0.5071 - acc: 0.7317 - val_loss: 0.5773 - val_acc: 0.6970\n",
      "Epoch 48/100\n",
      "6s - loss: 0.5089 - acc: 0.7352 - val_loss: 0.5787 - val_acc: 0.6965\n",
      "Epoch 49/100\n",
      "6s - loss: 0.5077 - acc: 0.7344 - val_loss: 0.5800 - val_acc: 0.6980\n",
      "Epoch 50/100\n",
      "6s - loss: 0.5024 - acc: 0.7374 - val_loss: 0.5778 - val_acc: 0.6970\n",
      "Epoch 51/100\n",
      "6s - loss: 0.4985 - acc: 0.7334 - val_loss: 0.5792 - val_acc: 0.7040\n",
      "Epoch 52/100\n",
      "6s - loss: 0.4945 - acc: 0.7396 - val_loss: 0.5787 - val_acc: 0.7060\n",
      "Epoch 53/100\n",
      "6s - loss: 0.4961 - acc: 0.7324 - val_loss: 0.5814 - val_acc: 0.7005\n",
      "Epoch 54/100\n",
      "6s - loss: 0.4949 - acc: 0.7395 - val_loss: 0.5861 - val_acc: 0.7015\n",
      "Epoch 55/100\n",
      "6s - loss: 0.4929 - acc: 0.7415 - val_loss: 0.5805 - val_acc: 0.6990\n",
      "Epoch 56/100\n",
      "6s - loss: 0.4980 - acc: 0.7343 - val_loss: 0.5850 - val_acc: 0.7015\n",
      "Epoch 57/100\n",
      "6s - loss: 0.4923 - acc: 0.7424 - val_loss: 0.5785 - val_acc: 0.7005\n",
      "Epoch 58/100\n",
      "6s - loss: 0.4978 - acc: 0.7370 - val_loss: 0.5770 - val_acc: 0.7010\n",
      "Epoch 59/100\n",
      "6s - loss: 0.4895 - acc: 0.7389 - val_loss: 0.5779 - val_acc: 0.7045\n",
      "Epoch 60/100\n",
      "6s - loss: 0.4884 - acc: 0.7405 - val_loss: 0.5824 - val_acc: 0.6985\n",
      "Epoch 61/100\n",
      "6s - loss: 0.4840 - acc: 0.7399 - val_loss: 0.5772 - val_acc: 0.7075\n",
      "Epoch 62/100\n",
      "6s - loss: 0.4928 - acc: 0.7433 - val_loss: 0.5777 - val_acc: 0.7015\n",
      "Epoch 63/100\n",
      "6s - loss: 0.4852 - acc: 0.7441 - val_loss: 0.5812 - val_acc: 0.7055\n",
      "Epoch 64/100\n",
      "6s - loss: 0.4825 - acc: 0.7409 - val_loss: 0.5822 - val_acc: 0.7010\n",
      "Epoch 65/100\n",
      "6s - loss: 0.4836 - acc: 0.7407 - val_loss: 0.5814 - val_acc: 0.6980\n",
      "Epoch 66/100\n",
      "6s - loss: 0.4814 - acc: 0.7414 - val_loss: 0.5754 - val_acc: 0.7010\n",
      "Epoch 67/100\n",
      "6s - loss: 0.4824 - acc: 0.7447 - val_loss: 0.5801 - val_acc: 0.7090\n",
      "Epoch 68/100\n",
      "6s - loss: 0.4790 - acc: 0.7464 - val_loss: 0.5775 - val_acc: 0.7085\n",
      "Epoch 69/100\n",
      "6s - loss: 0.4776 - acc: 0.7522 - val_loss: 0.5796 - val_acc: 0.7075\n",
      "Epoch 70/100\n",
      "6s - loss: 0.4762 - acc: 0.7523 - val_loss: 0.5780 - val_acc: 0.7020\n",
      "Epoch 71/100\n",
      "6s - loss: 0.4760 - acc: 0.7472 - val_loss: 0.5812 - val_acc: 0.7045\n",
      "Epoch 72/100\n",
      "6s - loss: 0.4704 - acc: 0.7523 - val_loss: 0.5793 - val_acc: 0.7080\n",
      "Epoch 73/100\n",
      "6s - loss: 0.4766 - acc: 0.7517 - val_loss: 0.5793 - val_acc: 0.6980\n",
      "Epoch 74/100\n",
      "6s - loss: 0.4755 - acc: 0.7477 - val_loss: 0.5806 - val_acc: 0.7025\n",
      "Epoch 75/100\n",
      "6s - loss: 0.4761 - acc: 0.7439 - val_loss: 0.5752 - val_acc: 0.7025\n",
      "Epoch 76/100\n",
      "6s - loss: 0.4697 - acc: 0.7494 - val_loss: 0.5785 - val_acc: 0.7045\n",
      "Epoch 77/100\n",
      "6s - loss: 0.4720 - acc: 0.7507 - val_loss: 0.5835 - val_acc: 0.7045\n",
      "Epoch 78/100\n",
      "6s - loss: 0.4694 - acc: 0.7591 - val_loss: 0.5788 - val_acc: 0.7020\n",
      "Epoch 79/100\n",
      "6s - loss: 0.4755 - acc: 0.7485 - val_loss: 0.5792 - val_acc: 0.7035\n",
      "Epoch 80/100\n",
      "6s - loss: 0.4741 - acc: 0.7492 - val_loss: 0.5823 - val_acc: 0.7045\n",
      "Epoch 81/100\n",
      "6s - loss: 0.4680 - acc: 0.7503 - val_loss: 0.5822 - val_acc: 0.7025\n",
      "Epoch 82/100\n",
      "6s - loss: 0.4692 - acc: 0.7546 - val_loss: 0.5805 - val_acc: 0.7020\n",
      "Epoch 83/100\n",
      "6s - loss: 0.4630 - acc: 0.7536 - val_loss: 0.5823 - val_acc: 0.7000\n",
      "Epoch 84/100\n",
      "6s - loss: 0.4688 - acc: 0.7528 - val_loss: 0.5826 - val_acc: 0.7085\n",
      "Epoch 85/100\n",
      "6s - loss: 0.4616 - acc: 0.7528 - val_loss: 0.5817 - val_acc: 0.7080\n",
      "Epoch 86/100\n",
      "6s - loss: 0.4662 - acc: 0.7545 - val_loss: 0.5855 - val_acc: 0.7025\n",
      "Epoch 87/100\n",
      "6s - loss: 0.4659 - acc: 0.7532 - val_loss: 0.5817 - val_acc: 0.7020\n",
      "Epoch 88/100\n",
      "6s - loss: 0.4593 - acc: 0.7555 - val_loss: 0.5783 - val_acc: 0.7045\n",
      "Epoch 89/100\n",
      "6s - loss: 0.4662 - acc: 0.7534 - val_loss: 0.5782 - val_acc: 0.7080\n",
      "Epoch 90/100\n",
      "6s - loss: 0.4625 - acc: 0.7575 - val_loss: 0.5806 - val_acc: 0.7050\n",
      "Epoch 91/100\n",
      "6s - loss: 0.4576 - acc: 0.7592 - val_loss: 0.5818 - val_acc: 0.7035\n",
      "Epoch 92/100\n",
      "6s - loss: 0.4559 - acc: 0.7556 - val_loss: 0.5810 - val_acc: 0.7010\n",
      "Epoch 93/100\n",
      "6s - loss: 0.4619 - acc: 0.7535 - val_loss: 0.5832 - val_acc: 0.7065\n",
      "Epoch 94/100\n",
      "6s - loss: 0.4581 - acc: 0.7537 - val_loss: 0.5830 - val_acc: 0.7070\n",
      "Epoch 95/100\n",
      "6s - loss: 0.4581 - acc: 0.7557 - val_loss: 0.5837 - val_acc: 0.7045\n",
      "Epoch 96/100\n",
      "6s - loss: 0.4564 - acc: 0.7574 - val_loss: 0.5829 - val_acc: 0.7085\n",
      "Epoch 97/100\n",
      "6s - loss: 0.4606 - acc: 0.7538 - val_loss: 0.5811 - val_acc: 0.7055\n",
      "Epoch 98/100\n",
      "6s - loss: 0.4546 - acc: 0.7637 - val_loss: 0.5889 - val_acc: 0.7070\n",
      "Epoch 99/100\n",
      "6s - loss: 0.4582 - acc: 0.7525 - val_loss: 0.5882 - val_acc: 0.6995\n",
      "Epoch 100/100\n",
      "6s - loss: 0.4546 - acc: 0.7560 - val_loss: 0.5807 - val_acc: 0.7105\n"
     ]
    },
    {
     "data": {
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/mGNOZ/BLbAozh4QwPLxyUJJScvr8BV786QAv/XSA0ABXegR7Vdon+tR5Zny3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\nmVlbHCRmZtYWB4mZmbXFQWJmZm35f6La1/Kfg0HHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121d78f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define CNN model\n",
    "model2 = cnn_model()\n",
    "\n",
    "# define SGD optimizer\n",
    "learning_rate = 0.1\n",
    "decay_rate = learning_rate / epochs\n",
    "momentum = 0.5\n",
    "sgd = SGD(lr=learning_rate, momentum=momentum, decay=decay_rate, nesterov=False)\n",
    "\n",
    "# compile the model\n",
    "model2.compile(loss=keras.losses.categorical_crossentropy, optimizer=sgd, metrics=['accuracy'])\n",
    "\n",
    "# fit the model\n",
    "history2 = model2.fit(X_train, y_train, \n",
    "                     epochs=epochs, \n",
    "                     batch_size=batch_size,\n",
    "                     verbose=2, \n",
    "                     validation_data=(X_test, y_test))\n",
    "\n",
    "# plot model accuracy\n",
    "plot_fig(2, history2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      "lr: 0.1\n",
      "6s - loss: 0.6905 - acc: 0.5332 - val_loss: 0.6821 - val_acc: 0.5825\n",
      "Epoch 2/100\n",
      "lr: 0.1\n",
      "6s - loss: 0.6766 - acc: 0.5674 - val_loss: 0.6593 - val_acc: 0.6180\n",
      "Epoch 3/100\n",
      "lr: 0.1\n",
      "6s - loss: 0.6590 - acc: 0.6019 - val_loss: 0.6493 - val_acc: 0.6165\n",
      "Epoch 4/100\n",
      "lr: 0.1\n",
      "6s - loss: 0.6437 - acc: 0.6303 - val_loss: 0.6207 - val_acc: 0.6595\n",
      "Epoch 5/100\n",
      "lr: 0.1\n",
      "6s - loss: 0.6220 - acc: 0.6533 - val_loss: 0.6040 - val_acc: 0.6720\n",
      "Epoch 6/100\n",
      "lr: 0.1\n",
      "6s - loss: 0.6094 - acc: 0.6644 - val_loss: 0.5913 - val_acc: 0.6950\n",
      "Epoch 7/100\n",
      "lr: 0.1\n",
      "6s - loss: 0.5995 - acc: 0.6772 - val_loss: 0.5853 - val_acc: 0.6935\n",
      "Epoch 8/100\n",
      "lr: 0.1\n",
      "6s - loss: 0.5931 - acc: 0.6772 - val_loss: 0.5909 - val_acc: 0.6755\n",
      "Epoch 9/100\n",
      "lr: 0.1\n",
      "6s - loss: 0.5820 - acc: 0.6871 - val_loss: 0.5804 - val_acc: 0.6910\n",
      "Epoch 10/100\n",
      "lr: 0.05\n",
      "6s - loss: 0.5723 - acc: 0.6973 - val_loss: 0.5772 - val_acc: 0.6915\n",
      "Epoch 11/100\n",
      "lr: 0.05\n",
      "6s - loss: 0.5585 - acc: 0.7067 - val_loss: 0.5774 - val_acc: 0.6880\n",
      "Epoch 12/100\n",
      "lr: 0.05\n",
      "6s - loss: 0.5456 - acc: 0.7121 - val_loss: 0.5616 - val_acc: 0.7120\n",
      "Epoch 13/100\n",
      "lr: 0.05\n",
      "6s - loss: 0.5492 - acc: 0.7186 - val_loss: 0.5573 - val_acc: 0.7125\n",
      "Epoch 14/100\n",
      "lr: 0.05\n",
      "6s - loss: 0.5403 - acc: 0.7245 - val_loss: 0.5638 - val_acc: 0.7105\n",
      "Epoch 15/100\n",
      "lr: 0.05\n",
      "6s - loss: 0.5388 - acc: 0.7186 - val_loss: 0.5860 - val_acc: 0.6900\n",
      "Epoch 16/100\n",
      "lr: 0.05\n",
      "6s - loss: 0.5353 - acc: 0.7246 - val_loss: 0.5659 - val_acc: 0.7060\n",
      "Epoch 17/100\n",
      "lr: 0.05\n",
      "6s - loss: 0.5349 - acc: 0.7264 - val_loss: 0.5584 - val_acc: 0.7100\n",
      "Epoch 18/100\n",
      "lr: 0.05\n",
      "6s - loss: 0.5292 - acc: 0.7230 - val_loss: 0.5691 - val_acc: 0.7080\n",
      "Epoch 19/100\n",
      "lr: 0.05\n",
      "6s - loss: 0.5230 - acc: 0.7389 - val_loss: 0.5551 - val_acc: 0.7160\n",
      "Epoch 20/100\n",
      "lr: 0.025\n",
      "6s - loss: 0.5226 - acc: 0.7344 - val_loss: 0.5672 - val_acc: 0.7095\n",
      "Epoch 21/100\n",
      "lr: 0.025\n",
      "6s - loss: 0.5126 - acc: 0.7420 - val_loss: 0.5553 - val_acc: 0.7140\n",
      "Epoch 22/100\n",
      "lr: 0.025\n",
      "6s - loss: 0.5060 - acc: 0.7433 - val_loss: 0.5562 - val_acc: 0.7060\n",
      "Epoch 23/100\n",
      "lr: 0.025\n",
      "6s - loss: 0.5040 - acc: 0.7488 - val_loss: 0.5555 - val_acc: 0.7125\n",
      "Epoch 24/100\n",
      "lr: 0.025\n",
      "6s - loss: 0.4997 - acc: 0.7518 - val_loss: 0.5533 - val_acc: 0.7100\n",
      "Epoch 25/100\n",
      "lr: 0.025\n",
      "6s - loss: 0.5011 - acc: 0.7477 - val_loss: 0.5504 - val_acc: 0.7170\n",
      "Epoch 26/100\n",
      "lr: 0.025\n",
      "6s - loss: 0.4951 - acc: 0.7491 - val_loss: 0.5518 - val_acc: 0.7085\n",
      "Epoch 27/100\n",
      "lr: 0.025\n",
      "6s - loss: 0.4974 - acc: 0.7491 - val_loss: 0.5536 - val_acc: 0.7160\n",
      "Epoch 28/100\n",
      "lr: 0.025\n",
      "6s - loss: 0.4973 - acc: 0.7491 - val_loss: 0.5525 - val_acc: 0.7165\n",
      "Epoch 29/100\n",
      "lr: 0.025\n",
      "6s - loss: 0.4901 - acc: 0.7537 - val_loss: 0.5543 - val_acc: 0.7105\n",
      "Epoch 30/100\n",
      "lr: 0.0125\n",
      "6s - loss: 0.4894 - acc: 0.7521 - val_loss: 0.5559 - val_acc: 0.7065\n",
      "Epoch 31/100\n",
      "lr: 0.0125\n",
      "6s - loss: 0.4805 - acc: 0.7585 - val_loss: 0.5518 - val_acc: 0.7150\n",
      "Epoch 32/100\n",
      "lr: 0.0125\n",
      "6s - loss: 0.4797 - acc: 0.7563 - val_loss: 0.5546 - val_acc: 0.7180\n",
      "Epoch 33/100\n",
      "lr: 0.0125\n",
      "6s - loss: 0.4799 - acc: 0.7621 - val_loss: 0.5565 - val_acc: 0.7180\n",
      "Epoch 34/100\n",
      "lr: 0.0125\n",
      "6s - loss: 0.4736 - acc: 0.7690 - val_loss: 0.5516 - val_acc: 0.7120\n",
      "Epoch 35/100\n",
      "lr: 0.0125\n",
      "6s - loss: 0.4760 - acc: 0.7651 - val_loss: 0.5594 - val_acc: 0.7070\n",
      "Epoch 36/100\n",
      "lr: 0.0125\n",
      "6s - loss: 0.4775 - acc: 0.7647 - val_loss: 0.5565 - val_acc: 0.7185\n",
      "Epoch 37/100\n",
      "lr: 0.0125\n",
      "6s - loss: 0.4668 - acc: 0.7662 - val_loss: 0.5557 - val_acc: 0.7060\n",
      "Epoch 38/100\n",
      "lr: 0.0125\n",
      "6s - loss: 0.4737 - acc: 0.7635 - val_loss: 0.5537 - val_acc: 0.7140\n",
      "Epoch 39/100\n",
      "lr: 0.0125\n",
      "6s - loss: 0.4669 - acc: 0.7624 - val_loss: 0.5601 - val_acc: 0.7160\n",
      "Epoch 40/100\n",
      "lr: 0.00625\n",
      "6s - loss: 0.4715 - acc: 0.7600 - val_loss: 0.5515 - val_acc: 0.7140\n",
      "Epoch 41/100\n",
      "lr: 0.00625\n",
      "6s - loss: 0.4562 - acc: 0.7773 - val_loss: 0.5590 - val_acc: 0.7165\n",
      "Epoch 42/100\n",
      "lr: 0.00625\n",
      "6s - loss: 0.4662 - acc: 0.7701 - val_loss: 0.5565 - val_acc: 0.7170\n",
      "Epoch 43/100\n",
      "lr: 0.00625\n",
      "6s - loss: 0.4592 - acc: 0.7735 - val_loss: 0.5549 - val_acc: 0.7100\n",
      "Epoch 44/100\n",
      "lr: 0.00625\n",
      "6s - loss: 0.4632 - acc: 0.7687 - val_loss: 0.5589 - val_acc: 0.7100\n",
      "Epoch 45/100\n",
      "lr: 0.00625\n",
      "6s - loss: 0.4637 - acc: 0.7719 - val_loss: 0.5572 - val_acc: 0.7120\n",
      "Epoch 46/100\n",
      "lr: 0.00625\n",
      "6s - loss: 0.4626 - acc: 0.7739 - val_loss: 0.5547 - val_acc: 0.7155\n",
      "Epoch 47/100\n",
      "lr: 0.00625\n",
      "6s - loss: 0.4662 - acc: 0.7697 - val_loss: 0.5560 - val_acc: 0.7110\n",
      "Epoch 48/100\n",
      "lr: 0.00625\n",
      "6s - loss: 0.4587 - acc: 0.7663 - val_loss: 0.5586 - val_acc: 0.7135\n",
      "Epoch 49/100\n",
      "lr: 0.00625\n",
      "6s - loss: 0.4619 - acc: 0.7719 - val_loss: 0.5558 - val_acc: 0.7105\n",
      "Epoch 50/100\n",
      "lr: 0.003125\n",
      "6s - loss: 0.4584 - acc: 0.7714 - val_loss: 0.5558 - val_acc: 0.7135\n",
      "Epoch 51/100\n",
      "lr: 0.003125\n",
      "6s - loss: 0.4514 - acc: 0.7759 - val_loss: 0.5571 - val_acc: 0.7125\n",
      "Epoch 52/100\n",
      "lr: 0.003125\n",
      "6s - loss: 0.4603 - acc: 0.7685 - val_loss: 0.5558 - val_acc: 0.7130\n",
      "Epoch 53/100\n",
      "lr: 0.003125\n",
      "6s - loss: 0.4557 - acc: 0.7755 - val_loss: 0.5567 - val_acc: 0.7120\n",
      "Epoch 54/100\n",
      "lr: 0.003125\n",
      "6s - loss: 0.4558 - acc: 0.7730 - val_loss: 0.5581 - val_acc: 0.7130\n",
      "Epoch 55/100\n",
      "lr: 0.003125\n",
      "6s - loss: 0.4535 - acc: 0.7719 - val_loss: 0.5577 - val_acc: 0.7155\n",
      "Epoch 56/100\n",
      "lr: 0.003125\n",
      "6s - loss: 0.4551 - acc: 0.7764 - val_loss: 0.5581 - val_acc: 0.7170\n",
      "Epoch 57/100\n",
      "lr: 0.003125\n",
      "6s - loss: 0.4535 - acc: 0.7807 - val_loss: 0.5577 - val_acc: 0.7125\n",
      "Epoch 58/100\n",
      "lr: 0.003125\n",
      "6s - loss: 0.4527 - acc: 0.7732 - val_loss: 0.5575 - val_acc: 0.7130\n",
      "Epoch 59/100\n",
      "lr: 0.003125\n",
      "6s - loss: 0.4541 - acc: 0.7728 - val_loss: 0.5578 - val_acc: 0.7105\n",
      "Epoch 60/100\n",
      "lr: 0.0015625\n",
      "6s - loss: 0.4591 - acc: 0.7714 - val_loss: 0.5575 - val_acc: 0.7130\n",
      "Epoch 61/100\n",
      "lr: 0.0015625\n",
      "6s - loss: 0.4480 - acc: 0.7783 - val_loss: 0.5557 - val_acc: 0.7135\n",
      "Epoch 62/100\n",
      "lr: 0.0015625\n",
      "6s - loss: 0.4559 - acc: 0.7722 - val_loss: 0.5576 - val_acc: 0.7135\n",
      "Epoch 63/100\n",
      "lr: 0.0015625\n",
      "6s - loss: 0.4505 - acc: 0.7742 - val_loss: 0.5568 - val_acc: 0.7115\n",
      "Epoch 64/100\n",
      "lr: 0.0015625\n",
      "6s - loss: 0.4543 - acc: 0.7709 - val_loss: 0.5572 - val_acc: 0.7150\n",
      "Epoch 65/100\n",
      "lr: 0.0015625\n",
      "6s - loss: 0.4516 - acc: 0.7786 - val_loss: 0.5571 - val_acc: 0.7140\n",
      "Epoch 66/100\n",
      "lr: 0.0015625\n",
      "6s - loss: 0.4471 - acc: 0.7805 - val_loss: 0.5580 - val_acc: 0.7155\n",
      "Epoch 67/100\n",
      "lr: 0.0015625\n",
      "6s - loss: 0.4544 - acc: 0.7739 - val_loss: 0.5582 - val_acc: 0.7155\n",
      "Epoch 68/100\n",
      "lr: 0.0015625\n",
      "6s - loss: 0.4538 - acc: 0.7721 - val_loss: 0.5569 - val_acc: 0.7145\n",
      "Epoch 69/100\n",
      "lr: 0.0015625\n",
      "6s - loss: 0.4541 - acc: 0.7739 - val_loss: 0.5573 - val_acc: 0.7150\n",
      "Epoch 70/100\n",
      "lr: 0.00078125\n",
      "6s - loss: 0.4492 - acc: 0.7803 - val_loss: 0.5582 - val_acc: 0.7155\n",
      "Epoch 71/100\n",
      "lr: 0.00078125\n",
      "6s - loss: 0.4470 - acc: 0.7796 - val_loss: 0.5580 - val_acc: 0.7145\n",
      "Epoch 72/100\n",
      "lr: 0.00078125\n",
      "6s - loss: 0.4516 - acc: 0.7779 - val_loss: 0.5571 - val_acc: 0.7120\n",
      "Epoch 73/100\n",
      "lr: 0.00078125\n",
      "6s - loss: 0.4460 - acc: 0.7763 - val_loss: 0.5594 - val_acc: 0.7150\n",
      "Epoch 74/100\n",
      "lr: 0.00078125\n",
      "6s - loss: 0.4479 - acc: 0.7785 - val_loss: 0.5579 - val_acc: 0.7120\n",
      "Epoch 75/100\n",
      "lr: 0.00078125\n",
      "6s - loss: 0.4458 - acc: 0.7786 - val_loss: 0.5585 - val_acc: 0.7120\n",
      "Epoch 76/100\n",
      "lr: 0.00078125\n",
      "6s - loss: 0.4488 - acc: 0.7755 - val_loss: 0.5587 - val_acc: 0.7125\n",
      "Epoch 77/100\n",
      "lr: 0.00078125\n",
      "6s - loss: 0.4436 - acc: 0.7819 - val_loss: 0.5579 - val_acc: 0.7155\n",
      "Epoch 78/100\n",
      "lr: 0.00078125\n",
      "6s - loss: 0.4494 - acc: 0.7783 - val_loss: 0.5591 - val_acc: 0.7180\n",
      "Epoch 79/100\n",
      "lr: 0.00078125\n",
      "6s - loss: 0.4442 - acc: 0.7776 - val_loss: 0.5583 - val_acc: 0.7155\n",
      "Epoch 80/100\n",
      "lr: 0.000390625\n",
      "6s - loss: 0.4471 - acc: 0.7721 - val_loss: 0.5585 - val_acc: 0.7155\n",
      "Epoch 81/100\n",
      "lr: 0.000390625\n",
      "6s - loss: 0.4488 - acc: 0.7767 - val_loss: 0.5586 - val_acc: 0.7150\n",
      "Epoch 82/100\n",
      "lr: 0.000390625\n",
      "6s - loss: 0.4478 - acc: 0.7809 - val_loss: 0.5581 - val_acc: 0.7120\n",
      "Epoch 83/100\n",
      "lr: 0.000390625\n",
      "6s - loss: 0.4504 - acc: 0.7770 - val_loss: 0.5581 - val_acc: 0.7130\n",
      "Epoch 84/100\n",
      "lr: 0.000390625\n",
      "6s - loss: 0.4496 - acc: 0.7789 - val_loss: 0.5588 - val_acc: 0.7160\n",
      "Epoch 85/100\n",
      "lr: 0.000390625\n",
      "6s - loss: 0.4508 - acc: 0.7761 - val_loss: 0.5580 - val_acc: 0.7160\n",
      "Epoch 86/100\n",
      "lr: 0.000390625\n",
      "6s - loss: 0.4536 - acc: 0.7733 - val_loss: 0.5578 - val_acc: 0.7155\n",
      "Epoch 87/100\n",
      "lr: 0.000390625\n",
      "6s - loss: 0.4426 - acc: 0.7801 - val_loss: 0.5584 - val_acc: 0.7145\n",
      "Epoch 88/100\n",
      "lr: 0.000390625\n",
      "6s - loss: 0.4470 - acc: 0.7773 - val_loss: 0.5580 - val_acc: 0.7150\n",
      "Epoch 89/100\n",
      "lr: 0.000390625\n",
      "6s - loss: 0.4525 - acc: 0.7758 - val_loss: 0.5581 - val_acc: 0.7145\n",
      "Epoch 90/100\n",
      "lr: 0.0001953125\n",
      "6s - loss: 0.4469 - acc: 0.7779 - val_loss: 0.5579 - val_acc: 0.7135\n",
      "Epoch 91/100\n",
      "lr: 0.0001953125\n",
      "6s - loss: 0.4501 - acc: 0.7756 - val_loss: 0.5577 - val_acc: 0.7140\n",
      "Epoch 92/100\n",
      "lr: 0.0001953125\n",
      "6s - loss: 0.4481 - acc: 0.7755 - val_loss: 0.5578 - val_acc: 0.7130\n",
      "Epoch 93/100\n",
      "lr: 0.0001953125\n",
      "6s - loss: 0.4506 - acc: 0.7755 - val_loss: 0.5577 - val_acc: 0.7140\n",
      "Epoch 94/100\n",
      "lr: 0.0001953125\n",
      "6s - loss: 0.4537 - acc: 0.7692 - val_loss: 0.5582 - val_acc: 0.7165\n",
      "Epoch 95/100\n",
      "lr: 0.0001953125\n",
      "6s - loss: 0.4498 - acc: 0.7754 - val_loss: 0.5581 - val_acc: 0.7165\n",
      "Epoch 96/100\n",
      "lr: 0.0001953125\n",
      "6s - loss: 0.4477 - acc: 0.7774 - val_loss: 0.5580 - val_acc: 0.7135\n",
      "Epoch 97/100\n",
      "lr: 0.0001953125\n",
      "6s - loss: 0.4487 - acc: 0.7808 - val_loss: 0.5578 - val_acc: 0.7145\n",
      "Epoch 98/100\n",
      "lr: 0.0001953125\n",
      "6s - loss: 0.4458 - acc: 0.7827 - val_loss: 0.5579 - val_acc: 0.7145\n",
      "Epoch 99/100\n",
      "lr: 0.0001953125\n",
      "6s - loss: 0.4470 - acc: 0.7791 - val_loss: 0.5580 - val_acc: 0.7135\n",
      "Epoch 100/100\n",
      "lr: 9.765625e-05\n",
      "6s - loss: 0.4466 - acc: 0.7806 - val_loss: 0.5581 - val_acc: 0.7145\n"
     ]
    },
    {
     "data": {
      "image/png": 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MrngLBlwEX94NxyNN4/VFT5mGXoCsOPjwMijKgVu/hF51/u9997C5mYZdYRp2cxJMF9nr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\n0FovBhYDBIYO1X0DvYmImFq9weGnwHc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x121c37f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define CNN model\n",
    "model3 = cnn_model()\n",
    "\n",
    "# define SGD optimizer\n",
    "momentum = 0.5\n",
    "sgd = SGD(lr=0.0, momentum=momentum, decay=0.0, nesterov=False) \n",
    "\n",
    "# compile the model\n",
    "model3.compile(loss=keras.losses.categorical_crossentropy,optimizer=sgd, metrics=['accuracy'])\n",
    "\n",
    "# define step decay function\n",
    "class LossHistory(keras.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.losses = []\n",
    "        self.lr = []\n",
    "        \n",
    "    def on_epoch_end(self, batch, logs={}):\n",
    "        self.losses.append(logs.get('loss'))\n",
    "        self.lr.append(step_decay(len(self.losses)))\n",
    "        print('lr:', step_decay(len(self.losses)))\n",
    "\n",
    "def step_decay(epoch):\n",
    "    initial_lrate = 0.1\n",
    "    drop = 0.5\n",
    "    epochs_drop = 10.0\n",
    "    lrate = initial_lrate * math.pow(drop, math.floor((epoch)/epochs_drop))\n",
    "    return lrate\n",
    "\n",
    "# learning schedule callback\n",
    "loss_history = LossHistory()\n",
    "lrate = LearningRateScheduler(step_decay)\n",
    "callbacks_list = [loss_history, lrate]\n",
    "\n",
    "# fit the model\n",
    "history3 = model3.fit(X_train, y_train, \n",
    "                     validation_data=(X_test, y_test), \n",
    "                     epochs=epochs, \n",
    "                     batch_size=batch_size, \n",
    "                     callbacks=callbacks_list, \n",
    "                     verbose=2)\n",
    "\n",
    "# plot model accuracy\n",
    "plot_fig(3, history3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NO8fNLJPK1lQVER2SrgB+STIq6taIeErSpen+2cB84HRgGbAZ+FC67xFJdwKPAx3AE6TN\nUf1Fsnysaxxmlj1l7eOIiPkkySF/2+y87wO4fDfnXgtcW874eqMxV+sXAM0sk/zmeImSpirXOMws\ne5w4StSYq/V6HGaWSU4cJWrM1XgFQDPLJCeOEjXUucZhZtnkxFGixpz7OMwsm5w4StTgUVVmllFO\nHCVqqHMfh5llkxNHiZIXAF3jMLPsceIoUWNdreeqMrNMcuIoUUPOc1WZWTY5cZSosa6Wjh1BR6dr\nHWaWLU4cJWpMl491c5WZZY0TR4ka6rzuuJllkxNHiRpzybrjW13jMLOMceIoUVfi2OYah5lljBNH\nibqbqlzjMLNsceIoUXdTlWscZpYtThwlaugaVeUah5lljBNHiRrqXOMws2xy4ijRzvc43DluZhnj\nxFGinaOqPBzXzDLGiaNEfgHQzLKq4MQhqamcgQw0O0dVuXPczDJmr4lD0nGSFgFL0s9HSvp62SPr\n57qbqlzjMLNsKaTGcQPwLmANQET8HjixnEENBH4B0MyyqqCmqohY3mNT5n/NztXWUFsj93GYWebU\nFXDMcknHASEpB1wJLC5vWANDY12NR1WZWeYUUuO4FLgcGAusAI4CLitnUANFg9cdN7MMKqTGcUhE\n/H3+BknHAw+VJ6SBo7Guxn0cZpY5hdQ4vlrgtsxpzNV6VJWZZc5uaxySjgWOA/aTdFXern2A2kIu\nLulU4Mb0+Fsi4vM99ivdfzqwGbg4Ih5P940AbgHeAgTw4Yh4uMDnqoh61zjMLIP21FRVDwxLjxme\nt309cO7eLiypFrgZOAVoAx6TNC8iFuUddhowOf2aCXwj/ROShPKLiDhXUj3Q715AdI3DzLJot4kj\nIu4H7pf03Yh4voRrHwMsi4hnACTdAZwF5CeOs4DbIyKABZJGSDqApPZxInBxGks70F5CDGXVmKvx\ntOpmljmFdI5vlvQl4HCgsWtjRLxjL+eNBfLf/2ijuzaxp2PGAh3AauA7ko4EfgdcGRGbet5E0iXA\nJQBjxoyhtbW1gEfqG5vWb2Vje1T0nruzcePGfhFHf+Cy6Oay6Oay6NbbsigkcXwf+CFwBsnQ3A+S\n/FAvpzrgaOBjEfGIpBuBq4HP9jwwIuYAcwCmT58eLS0tZQ6t2w+WL+S5VzbT0lL9F+lbW1up5LP3\nZy6Lbi6Lbi6Lbr0ti0JGVY2KiG8D2yPi/oj4MLC32gYk73yMz/s8Lt1WyDFtQFtEPJJuv5MkkfQr\njblaL+RkZplTSOLYnv65UtK7JU0FRhZw3mPAZEmT0s7t84F5PY6ZB1ykxCxgXUSsjIiXSN5YPyQ9\n7iRe3zfSLzTU1fgFQDPLnEKaqv5dUjPwKZL3N/YBPrm3kyKiQ9IVwC9JhuPeGhFPSbo03T8bmE8y\nFHcZSYf4h/Iu8THg+2nSeabHvn4hGVXlznEzy5Y9Jo50SO3kiPgZsA54ezEXj4j5JMkhf9vsvO+D\nZDqTXZ37JDC9mPtVWqOnHDGzDNpjU1VEdALvr1AsA05D+gJgkv/MzLKhkKaqhyR9jWRk1c7hsF1v\neGdZ12JO7Z07aKgr6GV6M7MBr5DEcVT657/lbQsKG1k1qOUv5uTEYWZZsdfEERFF9WtkSUPX8rHb\nO2FIrsrRmJlVRkErANquNaY1Do+sMrMsceLoha4+Do+sMrMsceLohfw+DjOzrNhrH4ek9+5i8zrg\njxGxqu9DGji6ahyeWt3MsqSQUVX/ABwL/Cb93EIyW+0kSf8WEf9Zptj6ve6mKtc4zCw7CkkcdcBh\nEfEygKQxwO0kU6T/Fshs4uhuqnKNw8yyo5A+jvFdSSO1Kt32Kt0TIGZSd1OVaxxmlh2F1DhaJf0M\n+HH6+Zx021BgbdkiGwAac65xmFn2FJI4LidJFsenn28H7konKMz0y4Fdb4t7TQ4zy5JC3hwPkoWU\n7ix/OANLV43jJ0+8yJ9f2lDRe0vivOnjOPyNzRW9r5lZocNxvwC8AVD6FRGxT5lj6/eGN+Y4Ylwz\nf161gT+vqmziWLdlO1vaO/nCuUdU9L5mZoU0VX0ReE9ELC53MANNbY2Yd8UJVbn3O2+4n7Vb2qty\nbzPLtkJGVb3spNH/NA/JsW5Lpge1mVmVFFLjWCjph8B/Adu6NkbE3LJFZXvVPKSeFWu3VDsMM8ug\nQhLHPiTrgb8zb1sAThxV1Dwkx6IX11U7DDPLoEJGVX2oEoFYcdxUZWbVstvEIemfIuKLkr5KUsN4\nnYj4eFkjsz0a0ZRjU3sn2zt3kKv1JMdmVjl7qnF0dYgvrEQgVpzmdMXBdVu2M3pYQ5WjMbMs2W3i\niIifpn/eVrlwrFBOHGZWLYW8APhm4NPAxPzjI+Id5QvL9qa5qTtxmJlVUiGjqn4MzAZuATwpUz+x\ns8ax2YnDzCqrkMTRERHfKHskVpT8piozs0oqZDjOTyVdJukASSO7vsoeme3RCCcOM6uSQmocH0z/\n/J952wI4qO/DsULtkyaOtW6qMrMK22PikFQDXBgRD1UoHitQrraGofW1rnGYWcXtsakqInYAX6tQ\nLFakEU31ThxmVnGF9HHcJ+kcSSp7NFaUfYbkWOep1c2swgpJHB8lGZK7TdJ6SRskrS/k4pJOlfS0\npGWSrt7Ffkm6Kd3/B0lH99hfK+mJdM1z66F5SJ1rHGZWcXtNHBExPCJqIqI+IvZJP+919T9JtcDN\nwGnAFOD9kqb0OOw0YHL6dQnQc9jvlXRPfWI9jBjipiozq7yCZseTtK+kYySd2PVVwGnHAMsi4pmI\naAfuAM7qccxZwO2RWACMkHRAes9xwLtJXjy0XWgekvOoKjOruEKmHPkIyW/+44AngVnAw8DephwZ\nCyzP+9wGzCzgmLHASuA/gH8Chu8lvktIaiuMGTOG1tbWvYQ1eKx7pZ3XNm2ntbWVjRs3ZurZ98Rl\n0c1l0c1l0a23ZVHIexxXAjOABRHxdkmHAv+n5DsWQNIZwKqI+J2klj0dGxFzgDkA06dPj5aWPR4+\nqDwVy/jFc08z6/i3seChB8jSs+9Ja2uryyLlsujmsujW27IopKlqa0RsBZDUEBFLgEMKOG8FMD7v\n87h0WyHHHA+cKek5kiaud0j6XgH3zBRPO2Jm1VBI4miTNIJkzfFfSfoJ8HwB5z0GTJY0SVI9cD4w\nr8cx84CL0tFVs4B1EbEyIq6JiHERMTE979cRcWGhD5UVThxmVg2FLB37t+m310n6DdAM/KKA8zok\nXQH8EqgFbo2IpyRdmu6fDcwHTgeWkaxr7mVqizDCU6ubWRUU0seBpBOAyRHxHUn7kXRgP7u38yJi\nPklyyN82O+/7AC7fyzVagdZC4sya/KnVC/qLNDPrA3ttqpJ0LfAZ4Jp0Uw5wf0M/0JU41rrGYWYV\nVEgfx98CZwKbACLiRfYyRNYqY8SQesBNVWZWWYUkjva0SSkAJA0tb0hWqOGNdUhOHGZWWYUkjh9J\n+ibJW93/CNwLfKu8YVkhamrE8IY61m32RIdmVjmFjKq6XtIpwHqS9zf+JSJ+VfbIrCA7p1ZvrnYk\nZpYVBQ3GSROFk0U/1Dwk56YqM6uo3SYOSRtI+zV67iIZSbvXGXKt/JqH5DyqyswqareJIyI8cmoA\naG7K8eK6LdUOw8wypKBp1a3/ah6SY71rHGZWQU4cA1zXmhzJiGkzs/Jz4hjgRgzJ0bEj2NZZ7UjM\nLCucOAa4rmlHNm13jcPMKsOJY4Bz4jCzSnPiGOCam7oSR5UDMbPMcOIY4LpqHJs7XOMws8pw4hjg\nuhLHRjdVmVmFOHEMcDtrHG6qMrMKceIY4IY11FFbI3eOm1nFOHEMcJJoHpJz4jCzinHiGAScOMys\nkpw4BoHmITn3cZhZxThxDAKucZhZJRW0kJP1b81DcmzYHry0bmvF7z2ssY5hDf5nZJYl/h8/CIwe\n1sArW4JZ//e+it97WEMdj/zzSQx18jDLDP9vHwQubTmIWLuCNx9ySEXvu3jlem5/+HmefWUTbxnr\nRc/NssKJYxB4w/BG/mZ8jpZjDqzoff+0Yh23P/w8L7y62YnDLEPcOW4lmzCqCYAXXt1c5UjMrJKc\nOKxkwxtzjBxaz/NrnDjMssSJw3pl/MgmXnh1U7XDMLMKcuKwXpkwsslNVWYZU9bEIelUSU9LWibp\n6l3sl6Sb0v1/kHR0un28pN9IWiTpKUlXljNOK92EUU28uHYr2zt3VDsUM6uQsiUOSbXAzcBpwBTg\n/ZKm9DjsNGBy+nUJ8I10ewfwqYiYAswCLt/FudYPjB/ZROeOYMVrW6odiplVSDlrHMcAyyLimYho\nB+4AzupxzFnA7ZFYAIyQdEBErIyIxwEiYgOwGBhbxlitRBNGemSVWdaUM3GMBZbnfW7jr3/47/UY\nSROBqcAjfR6h9dqEUUMBeN6Jwywz+vULgJKGAXcBn4iI9bs55hKSZi7GjBlDa2tr5QLsRzZu3FiV\nZ98RQV0NPPTkEsZvfbbi99+VapVFf+Sy6Oay6Nbbsihn4lgBjM/7PC7dVtAxknIkSeP7ETF3dzeJ\niDnAHIDp06dHS0tLrwMfiFpbW6nWs0984n5i6FBaWqZX5f49VbMs+huXRTeXRbfelkU5m6oeAyZL\nmiSpHjgfmNfjmHnARenoqlnAuohYKUnAt4HFEfGVMsZofeDAkU1+CdAsQ8qWOCKiA7gC+CVJ5/aP\nIuIpSZdKujQ9bD7wDLAM+BZwWbr9eOADwDskPZl+nV6uWK13Dkzf5YjwmiBmWVDWPo6ImE+SHPK3\nzc77PoDLd3Heg4DKGZv1nQmjmtjc3smaTe2MHtZQ7XDMrMz85rj12oHpkFw3V5llgxOH9Vr3LLme\ns8osC5w4rNfG7ZsmjjV+e9wsC5w4rNcac7Xsv08jz7vGYZYJThzWJw4c1cQL7uMwywQnDusTB3p6\ndbPMcOKwPjFhZBOrNmxjS3tntUMxszJz4rA+caDXHzfLjH49yaENHF3vcvx44XIO2X94Re8tiZZD\n9vPLh2YV4sRhfeKg/YbRmKvhlgerM0Pu+TPG8/lzjqjKvc2yxonD+kTzkByPXHMy67dur/i9r5v3\nFA8sfYWIIJkf08zKyYnD+kxzU47mplzF7/v2Q9/AfUtW8cwrm3jTfsMqfn+zrHHnuA14J07eD4AH\nl75S5UjMssGJwwa8A0c1ceDIJh5w4jCrCCcOGxROmDyaBc+sYXvnjmqHYjboOXHYoPC2g0ezcVsH\nTy5fW+1QzAY9Jw4bFI5702hqhJurzCrAicMGheamHEeMG8EDS1dXOxSzQc+JwwaNt00eze+Xr2XT\ndq99blZOThw2aJxw8Gh2BCx51RMtmpWTXwC0QWPqgfsytL6WP77SyY4dla91SPjNdcsEJw4bNOrr\naph10CjuW7KKg/55fsXvf9Doocy97DhGNNVX/N5mleTEYYPKNacfyj4drzFx4qSK3re9s5Ovt/6F\nb7T+hWtOP6yi9zarNCcOG1QOfsNwzjq4npaWyRW/90vrtvGd/36Oi46byNgRQyp+f7NKcee4WR+5\n6p1vBuAr9/y5ypGYlZcTh1kfGTtiCBcfN5G5T7Sx5KX11Q7HrGycOMz60GUtb2J4Qx1f+PmSaodi\nVjbu4zDrQyOa6rns7Qfz+Z8v4ZSv3E9NhYfnSvDB4yby/mMOrOh9LVucOMz62MXHTWTV+m28uHZL\nxe/94rotXDP3j6zesI2PvePgit/fssGJw6yPNeZq+Zf3TKnKvbd37uAzd/6Br/zqz7y2uZ23DfP0\nK9b3nDjMBpFcbQ3Xn3ckI5rqufWhZ/n1PjXc+pdHKh7HyKH1XHTsBKZPHFnxe1v5lTVxSDoVuBGo\nBW6JiM/32K90/+nAZuDiiHi8kHPNbNdqasRnzziMsfsO4QcPLmHL9srP3fXbpauZ9/sXmT5hXz7y\ntkmMH9lU8RiEmDCqiaEN/v24r5WtRCXVAjcDpwBtwGOS5kXEorzDTgMmp18zgW8AMws818x2QxL/\ncMIk3tTxPC0tx1X8/pvbO/jRY8v51gPPcun3Hq/4/bvU1ojDDhjO9Akj6XhtO8sffq7iMTTU1bJ/\ncyNvHDGE/ZsbydVWZz6z+tqaPptLrZyp+BhgWUQ8AyDpDuAsIP+H/1nA7RERwAJJIyQdAEws4Fwz\n66ea6uvoHzKBAAAGaElEQVS4+PhJXDhrAg8/s4bN7ZWv9XTuCBavXM/C517jh48tT2pei5+qeBz9\nRW2NGN5Yx/DGOt5Y305LS+nXKmfiGAssz/vcRlKr2NsxYws8FwBJlwCXAIwZM4bW1tZeBT1Qbdy4\nMbPP3pPLolt/KYuGKt13Wj1MezN0HNzA6rWbGDp0aMVj2NYZvLo1WLM1WLt1B1WYuJkAtnXClo5g\nc0c7w2s6evXvYsA3/kXEHGAOwPTp06OlN2l0AGttbSWrz96Ty6Kby6Kby6Jbb8uinIljBTA+7/O4\ndFshx+QKONfMzKqgnFOOPAZMljRJUj1wPjCvxzHzgIuUmAWsi4iVBZ5rZmZVULYaR0R0SLoC+CXJ\nkNpbI+IpSZem+2cD80mG4i4jGY77oT2dW65YzcyscGXt44iI+STJIX/b7LzvA7i80HPNzKz6PDuu\nmZkVxYnDzMyK4sRhZmZFceIwM7OiKOmfHhwkrQaer3YcVTIaeKXaQfQTLotuLotuLotu+WUxISL2\nK+bkQZU4skzSwoiYXu04+gOXRTeXRTeXRbfeloWbqszMrChOHGZmVhQnjsFjTrUD6EdcFt1cFt1c\nFt16VRbu4zAzs6K4xmFmZkVx4jAzs6I4cQxAksZL+o2kRZKeknRlun2kpF9JWpr+uW+1Y60ESbWS\nnpD0s/RzVsthhKQ7JS2RtFjSsRkui0+m/zf+JOkHkhqzVBaSbpW0StKf8rbt9vklXSNpmaSnJb1r\nb9d34hiYOoBPRcQUYBZwuaQpwNXAfRExGbgv/ZwFVwKL8z5ntRxuBH4REYcCR5KUSebKQtJY4OPA\n9Ih4C8nSDOeTrbL4LnBqj227fP70Z8f5wOHpOV+XVLuniztxDEARsTIiHk+/30DyA2IscBZwW3rY\nbcDZ1YmwciSNA94N3JK3OYvl0AycCHwbICLaI2ItGSyLVB0wRFId0AS8SIbKIiJ+C7zaY/Punv8s\n4I6I2BYRz5Ksj3TMnq7vxDHASZoITAUeAcakKygCvASMqVJYlfQfwD8BO/K2ZbEcJgGrge+kzXa3\nSBpKBssiIlYA1wMvACtJVha9hwyWRQ+7e/6xwPK849rSbbvlxDGASRoG3AV8IiLW5+9LF8ka1GOt\nJZ0BrIqI3+3umCyUQ6oOOBr4RkRMBTbRoykmK2WRtt2fRZJM3wgMlXRh/jFZKYvd6e3zO3EMUJJy\nJEnj+xExN938sqQD0v0HAKuqFV+FHA+cKek54A7gHZK+R/bKAZLfEtsi4pH0850kiSSLZXEy8GxE\nrI6I7cBc4DiyWRb5dvf8K4DxeceNS7ftlhPHACRJJG3ZiyPiK3m75gEfTL//IPCTSsdWSRFxTUSM\ni4iJJJ17v46IC8lYOQBExEvAckmHpJtOAhaRwbIgaaKaJakp/b9yEkk/YBbLIt/unn8ecL6kBkmT\ngMnAo3u6kN8cH4AknQA8APyR7rb9fybp5/gRcCDJ9PJ/FxE9O8gGJUktwKcj4gxJo8hgOUg6imSQ\nQD3wDPAhkl8Os1gW/wq8j2QE4hPAR4BhZKQsJP0AaCGZPv1l4Frgv9jN80v6X8CHScrrExHx8z1e\n34nDzMyK4aYqMzMrihOHmZkVxYnDzMyK4sRhZmZFceIwM7OiOHGY9QOSWrpm9zXr75w4zMysKE4c\nZkWQdKGkRyU9Kemb6VogGyXdkK7/cJ+k/dJjj5K0QNIfJN3dtf6BpIMl3Svp95Iel/Sm9PLD8tbT\n+H761rNZv+PEYVYgSYeRvI18fEQcBXQCfw8MBRZGxOHA/SRv6QLcDnwmIo4gecu/a/v3gZsj4kiS\nOZS6ZiydCnwCmAIcRDIXl1m/U1ftAMwGkJOAacBjaWVgCMlEcTuAH6bHfA+Ym66PMSIi7k+33wb8\nWNJwYGxE3A0QEVsB0us9GhFt6ecngYnAg+V/LLPiOHGYFU7AbRFxzes2Sp/tcVyp8/hsy/u+E///\ntH7KTVVmhbsPOFfSG2DnGs4TSP4fnZsecwHwYESsA16T9LZ0+weA+9MVG9sknZ1eo0FSU0WfwqyX\n/BuNWYEiYpGk/w3cI6kG2A5cTrJo0jHpvlUk/SCQTF09O00MXbPVQpJEvinp39JrnFfBxzDrNc+O\na9ZLkjZGxLBqx2FWKW6qMjOzorjGYWZmRXGNw8zMiuLEYWZmRXHiMDOzojhxmJlZUZw4zMysKP8f\n45QdW1MAlg0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a6adda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot learning rate\n",
    "fig = plt.figure()\n",
    "plt.plot(range(1,epochs+1),loss_history.lr,label='learning rate')\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.xlim([1,epochs+1])\n",
    "plt.ylabel(\"learning rate\")\n",
    "plt.legend(loc=0)\n",
    "plt.grid(True)\n",
    "plt.title(\"Learning rate\")\n",
    "plt.show()\n",
    "fig.savefig('img/3-learning-rate.jpg')\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exponential decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      "lr: 0.0904837418036\n",
      "6s - loss: 0.6937 - acc: 0.5119 - val_loss: 0.6905 - val_acc: 0.5025\n",
      "Epoch 2/100\n",
      "lr: 0.0818730753078\n",
      "6s - loss: 0.6866 - acc: 0.5392 - val_loss: 0.6788 - val_acc: 0.5810\n",
      "Epoch 3/100\n",
      "lr: 0.0740818220682\n",
      "6s - loss: 0.6774 - acc: 0.5746 - val_loss: 0.6667 - val_acc: 0.5985\n",
      "Epoch 4/100\n",
      "lr: 0.0670320046036\n",
      "6s - loss: 0.6607 - acc: 0.6030 - val_loss: 0.6567 - val_acc: 0.6220\n",
      "Epoch 5/100\n",
      "lr: 0.0606530659713\n",
      "6s - loss: 0.6525 - acc: 0.6197 - val_loss: 0.6471 - val_acc: 0.6290\n",
      "Epoch 6/100\n",
      "lr: 0.0548811636094\n",
      "6s - loss: 0.6457 - acc: 0.6226 - val_loss: 0.6467 - val_acc: 0.6225\n",
      "Epoch 7/100\n",
      "lr: 0.0496585303791\n",
      "6s - loss: 0.6428 - acc: 0.6335 - val_loss: 0.6398 - val_acc: 0.6455\n",
      "Epoch 8/100\n",
      "lr: 0.0449328964117\n",
      "6s - loss: 0.6345 - acc: 0.6341 - val_loss: 0.6410 - val_acc: 0.6410\n",
      "Epoch 9/100\n",
      "lr: 0.0406569659741\n",
      "6s - loss: 0.6276 - acc: 0.6351 - val_loss: 0.6325 - val_acc: 0.6505\n",
      "Epoch 10/100\n",
      "lr: 0.0367879441171\n",
      "6s - loss: 0.6257 - acc: 0.6461 - val_loss: 0.6337 - val_acc: 0.6445\n",
      "Epoch 11/100\n",
      "lr: 0.0332871083698\n",
      "6s - loss: 0.6212 - acc: 0.6470 - val_loss: 0.6309 - val_acc: 0.6515\n",
      "Epoch 12/100\n",
      "lr: 0.0301194211912\n",
      "6s - loss: 0.6195 - acc: 0.6545 - val_loss: 0.6397 - val_acc: 0.6420\n",
      "Epoch 13/100\n",
      "lr: 0.0272531793034\n",
      "6s - loss: 0.6189 - acc: 0.6511 - val_loss: 0.6280 - val_acc: 0.6470\n",
      "Epoch 14/100\n",
      "lr: 0.0246596963942\n",
      "6s - loss: 0.6130 - acc: 0.6650 - val_loss: 0.6259 - val_acc: 0.6500\n",
      "Epoch 15/100\n",
      "lr: 0.0223130160148\n",
      "6s - loss: 0.6087 - acc: 0.6603 - val_loss: 0.6221 - val_acc: 0.6600\n",
      "Epoch 16/100\n",
      "lr: 0.0201896517995\n",
      "6s - loss: 0.6107 - acc: 0.6570 - val_loss: 0.6192 - val_acc: 0.6585\n",
      "Epoch 17/100\n",
      "lr: 0.0182683524053\n",
      "6s - loss: 0.6063 - acc: 0.6639 - val_loss: 0.6177 - val_acc: 0.6630\n",
      "Epoch 18/100\n",
      "lr: 0.0165298888222\n",
      "6s - loss: 0.6071 - acc: 0.6662 - val_loss: 0.6165 - val_acc: 0.6685\n",
      "Epoch 19/100\n",
      "lr: 0.0149568619223\n",
      "6s - loss: 0.6099 - acc: 0.6550 - val_loss: 0.6165 - val_acc: 0.6665\n",
      "Epoch 20/100\n",
      "lr: 0.0135335283237\n",
      "6s - loss: 0.6031 - acc: 0.6624 - val_loss: 0.6180 - val_acc: 0.6565\n",
      "Epoch 21/100\n",
      "lr: 0.0122456428253\n",
      "6s - loss: 0.6038 - acc: 0.6673 - val_loss: 0.6158 - val_acc: 0.6660\n",
      "Epoch 22/100\n",
      "lr: 0.0110803158362\n",
      "6s - loss: 0.6002 - acc: 0.6727 - val_loss: 0.6171 - val_acc: 0.6630\n",
      "Epoch 23/100\n",
      "lr: 0.0100258843723\n",
      "6s - loss: 0.5984 - acc: 0.6736 - val_loss: 0.6164 - val_acc: 0.6610\n",
      "Epoch 24/100\n",
      "lr: 0.00907179532894\n",
      "6s - loss: 0.6023 - acc: 0.6645 - val_loss: 0.6182 - val_acc: 0.6590\n",
      "Epoch 25/100\n",
      "lr: 0.00820849986239\n",
      "6s - loss: 0.5978 - acc: 0.6695 - val_loss: 0.6141 - val_acc: 0.6675\n",
      "Epoch 26/100\n",
      "lr: 0.00742735782143\n",
      "6s - loss: 0.5955 - acc: 0.6755 - val_loss: 0.6163 - val_acc: 0.6650\n",
      "Epoch 27/100\n",
      "lr: 0.00672055127397\n",
      "6s - loss: 0.5939 - acc: 0.6711 - val_loss: 0.6130 - val_acc: 0.6750\n",
      "Epoch 28/100\n",
      "lr: 0.00608100626252\n",
      "6s - loss: 0.5947 - acc: 0.6736 - val_loss: 0.6141 - val_acc: 0.6720\n",
      "Epoch 29/100\n",
      "lr: 0.00550232200564\n",
      "6s - loss: 0.5991 - acc: 0.6697 - val_loss: 0.6150 - val_acc: 0.6710\n",
      "Epoch 30/100\n",
      "lr: 0.00497870683679\n",
      "6s - loss: 0.5985 - acc: 0.6729 - val_loss: 0.6131 - val_acc: 0.6730\n",
      "Epoch 31/100\n",
      "lr: 0.00450492023936\n",
      "6s - loss: 0.5845 - acc: 0.6848 - val_loss: 0.6118 - val_acc: 0.6680\n",
      "Epoch 32/100\n",
      "lr: 0.00407622039784\n",
      "6s - loss: 0.5933 - acc: 0.6739 - val_loss: 0.6118 - val_acc: 0.6705\n",
      "Epoch 33/100\n",
      "lr: 0.00368831674012\n",
      "6s - loss: 0.5950 - acc: 0.6782 - val_loss: 0.6135 - val_acc: 0.6665\n",
      "Epoch 34/100\n",
      "lr: 0.00333732699603\n",
      "6s - loss: 0.5906 - acc: 0.6763 - val_loss: 0.6119 - val_acc: 0.6745\n",
      "Epoch 35/100\n",
      "lr: 0.00301973834223\n",
      "6s - loss: 0.5896 - acc: 0.6802 - val_loss: 0.6115 - val_acc: 0.6680\n",
      "Epoch 36/100\n",
      "lr: 0.00273237224473\n",
      "6s - loss: 0.5909 - acc: 0.6796 - val_loss: 0.6116 - val_acc: 0.6720\n",
      "Epoch 37/100\n",
      "lr: 0.00247235264703\n",
      "6s - loss: 0.5921 - acc: 0.6759 - val_loss: 0.6114 - val_acc: 0.6700\n",
      "Epoch 38/100\n",
      "lr: 0.00223707718562\n",
      "6s - loss: 0.5896 - acc: 0.6769 - val_loss: 0.6117 - val_acc: 0.6735\n",
      "Epoch 39/100\n",
      "lr: 0.00202419114458\n",
      "6s - loss: 0.5945 - acc: 0.6737 - val_loss: 0.6115 - val_acc: 0.6725\n",
      "Epoch 40/100\n",
      "lr: 0.00183156388887\n",
      "6s - loss: 0.5944 - acc: 0.6721 - val_loss: 0.6112 - val_acc: 0.6725\n",
      "Epoch 41/100\n",
      "lr: 0.00165726754018\n",
      "6s - loss: 0.5899 - acc: 0.6783 - val_loss: 0.6118 - val_acc: 0.6695\n",
      "Epoch 42/100\n",
      "lr: 0.00149955768205\n",
      "6s - loss: 0.5847 - acc: 0.6764 - val_loss: 0.6116 - val_acc: 0.6640\n",
      "Epoch 43/100\n",
      "lr: 0.00135685590122\n",
      "6s - loss: 0.5867 - acc: 0.6824 - val_loss: 0.6113 - val_acc: 0.6660\n",
      "Epoch 44/100\n",
      "lr: 0.00122773399031\n",
      "6s - loss: 0.5901 - acc: 0.6806 - val_loss: 0.6116 - val_acc: 0.6655\n",
      "Epoch 45/100\n",
      "lr: 0.00111089965382\n",
      "6s - loss: 0.5902 - acc: 0.6796 - val_loss: 0.6111 - val_acc: 0.6670\n",
      "Epoch 46/100\n",
      "lr: 0.00100518357446\n",
      "6s - loss: 0.5887 - acc: 0.6730 - val_loss: 0.6116 - val_acc: 0.6650\n",
      "Epoch 47/100\n",
      "lr: 0.00090952771017\n",
      "6s - loss: 0.5875 - acc: 0.6805 - val_loss: 0.6111 - val_acc: 0.6670\n",
      "Epoch 48/100\n",
      "lr: 0.000822974704902\n",
      "6s - loss: 0.5931 - acc: 0.6667 - val_loss: 0.6112 - val_acc: 0.6685\n",
      "Epoch 49/100\n",
      "lr: 0.000744658307092\n",
      "6s - loss: 0.5837 - acc: 0.6797 - val_loss: 0.6109 - val_acc: 0.6655\n",
      "Epoch 50/100\n",
      "lr: 0.000673794699909\n",
      "6s - loss: 0.5917 - acc: 0.6784 - val_loss: 0.6110 - val_acc: 0.6665\n",
      "Epoch 51/100\n",
      "lr: 0.000609674656552\n",
      "6s - loss: 0.5829 - acc: 0.6803 - val_loss: 0.6108 - val_acc: 0.6650\n",
      "Epoch 52/100\n",
      "lr: 0.000551656442076\n",
      "6s - loss: 0.5891 - acc: 0.6800 - val_loss: 0.6108 - val_acc: 0.6685\n",
      "Epoch 53/100\n",
      "lr: 0.000499159390691\n",
      "6s - loss: 0.5926 - acc: 0.6736 - val_loss: 0.6108 - val_acc: 0.6680\n",
      "Epoch 54/100\n",
      "lr: 0.000451658094261\n",
      "6s - loss: 0.5888 - acc: 0.6792 - val_loss: 0.6109 - val_acc: 0.6650\n",
      "Epoch 55/100\n",
      "lr: 0.000408677143846\n",
      "6s - loss: 0.5877 - acc: 0.6792 - val_loss: 0.6108 - val_acc: 0.6660\n",
      "Epoch 56/100\n",
      "lr: 0.000369786371648\n",
      "6s - loss: 0.5898 - acc: 0.6760 - val_loss: 0.6107 - val_acc: 0.6665\n",
      "Epoch 57/100\n",
      "lr: 0.000334596545747\n",
      "6s - loss: 0.5840 - acc: 0.6831 - val_loss: 0.6105 - val_acc: 0.6645\n",
      "Epoch 58/100\n",
      "lr: 0.000302755474538\n",
      "6s - loss: 0.5893 - acc: 0.6747 - val_loss: 0.6107 - val_acc: 0.6685\n",
      "Epoch 59/100\n",
      "lr: 0.000273944481877\n",
      "6s - loss: 0.5841 - acc: 0.6855 - val_loss: 0.6109 - val_acc: 0.6675\n",
      "Epoch 60/100\n",
      "lr: 0.000247875217667\n",
      "6s - loss: 0.5822 - acc: 0.6812 - val_loss: 0.6107 - val_acc: 0.6685\n",
      "Epoch 61/100\n",
      "lr: 0.000224286771949\n",
      "6s - loss: 0.5906 - acc: 0.6774 - val_loss: 0.6106 - val_acc: 0.6660\n",
      "Epoch 62/100\n",
      "lr: 0.00020294306363\n",
      "6s - loss: 0.5840 - acc: 0.6840 - val_loss: 0.6105 - val_acc: 0.6645\n",
      "Epoch 63/100\n",
      "lr: 0.000183630477703\n",
      "6s - loss: 0.5846 - acc: 0.6846 - val_loss: 0.6105 - val_acc: 0.6645\n",
      "Epoch 64/100\n",
      "lr: 0.000166155727317\n",
      "6s - loss: 0.5868 - acc: 0.6757 - val_loss: 0.6106 - val_acc: 0.6650\n",
      "Epoch 65/100\n",
      "lr: 0.000150343919298\n",
      "6s - loss: 0.5906 - acc: 0.6744 - val_loss: 0.6106 - val_acc: 0.6640\n",
      "Epoch 66/100\n",
      "lr: 0.000136036803755\n",
      "6s - loss: 0.5856 - acc: 0.6731 - val_loss: 0.6106 - val_acc: 0.6640\n",
      "Epoch 67/100\n",
      "lr: 0.000123091190267\n",
      "6s - loss: 0.5880 - acc: 0.6758 - val_loss: 0.6105 - val_acc: 0.6630\n",
      "Epoch 68/100\n",
      "lr: 0.000111377514784\n",
      "6s - loss: 0.5887 - acc: 0.6784 - val_loss: 0.6105 - val_acc: 0.6645\n",
      "Epoch 69/100\n",
      "lr: 0.000100778542905\n",
      "6s - loss: 0.5855 - acc: 0.6821 - val_loss: 0.6105 - val_acc: 0.6645\n",
      "Epoch 70/100\n",
      "lr: 9.11881965555e-05\n",
      "6s - loss: 0.5891 - acc: 0.6813 - val_loss: 0.6105 - val_acc: 0.6640\n",
      "Epoch 71/100\n",
      "lr: 8.25104923266e-05\n",
      "6s - loss: 0.5867 - acc: 0.6810 - val_loss: 0.6105 - val_acc: 0.6645\n",
      "Epoch 72/100\n",
      "lr: 7.46585808377e-05\n",
      "6s - loss: 0.5863 - acc: 0.6843 - val_loss: 0.6105 - val_acc: 0.6640\n",
      "Epoch 73/100\n",
      "lr: 6.75538775194e-05\n",
      "6s - loss: 0.5861 - acc: 0.6770 - val_loss: 0.6105 - val_acc: 0.6640\n",
      "Epoch 74/100\n",
      "lr: 6.1125276113e-05\n",
      "6s - loss: 0.5924 - acc: 0.6828 - val_loss: 0.6105 - val_acc: 0.6640\n",
      "Epoch 75/100\n",
      "lr: 5.53084370148e-05\n",
      "6s - loss: 0.5841 - acc: 0.6783 - val_loss: 0.6105 - val_acc: 0.6640\n",
      "Epoch 76/100\n",
      "lr: 5.00451433441e-05\n",
      "6s - loss: 0.5888 - acc: 0.6766 - val_loss: 0.6104 - val_acc: 0.6635\n",
      "Epoch 77/100\n",
      "lr: 4.52827182887e-05\n",
      "6s - loss: 0.5826 - acc: 0.6800 - val_loss: 0.6104 - val_acc: 0.6635\n",
      "Epoch 78/100\n",
      "lr: 4.0973497898e-05\n",
      "6s - loss: 0.5822 - acc: 0.6832 - val_loss: 0.6104 - val_acc: 0.6635\n",
      "Epoch 79/100\n",
      "lr: 3.70743540459e-05\n",
      "6s - loss: 0.5882 - acc: 0.6796 - val_loss: 0.6104 - val_acc: 0.6640\n",
      "Epoch 80/100\n",
      "lr: 3.35462627903e-05\n",
      "6s - loss: 0.5861 - acc: 0.6757 - val_loss: 0.6104 - val_acc: 0.6645\n",
      "Epoch 81/100\n",
      "lr: 3.03539138079e-05\n",
      "6s - loss: 0.5903 - acc: 0.6714 - val_loss: 0.6104 - val_acc: 0.6650\n",
      "Epoch 82/100\n",
      "lr: 2.74653569972e-05\n",
      "6s - loss: 0.5842 - acc: 0.6776 - val_loss: 0.6104 - val_acc: 0.6650\n",
      "Epoch 83/100\n",
      "lr: 2.48516827108e-05\n",
      "6s - loss: 0.5878 - acc: 0.6789 - val_loss: 0.6104 - val_acc: 0.6650\n",
      "Epoch 84/100\n",
      "lr: 2.24867324179e-05\n",
      "6s - loss: 0.5854 - acc: 0.6824 - val_loss: 0.6103 - val_acc: 0.6650\n",
      "Epoch 85/100\n",
      "lr: 2.03468369011e-05\n",
      "6s - loss: 0.5878 - acc: 0.6819 - val_loss: 0.6104 - val_acc: 0.6650\n",
      "Epoch 86/100\n",
      "lr: 1.84105793668e-05\n",
      "6s - loss: 0.5853 - acc: 0.6780 - val_loss: 0.6104 - val_acc: 0.6650\n",
      "Epoch 87/100\n",
      "lr: 1.66585810988e-05\n",
      "6s - loss: 0.5869 - acc: 0.6772 - val_loss: 0.6104 - val_acc: 0.6650\n",
      "Epoch 88/100\n",
      "lr: 1.50733075095e-05\n",
      "6s - loss: 0.5870 - acc: 0.6756 - val_loss: 0.6104 - val_acc: 0.6645\n",
      "Epoch 89/100\n",
      "lr: 1.36388926482e-05\n",
      "6s - loss: 0.5861 - acc: 0.6805 - val_loss: 0.6104 - val_acc: 0.6645\n",
      "Epoch 90/100\n",
      "lr: 1.23409804087e-05\n",
      "6s - loss: 0.5839 - acc: 0.6811 - val_loss: 0.6104 - val_acc: 0.6645\n",
      "Epoch 91/100\n",
      "lr: 1.1166580849e-05\n",
      "6s - loss: 0.5871 - acc: 0.6781 - val_loss: 0.6104 - val_acc: 0.6645\n",
      "Epoch 92/100\n",
      "lr: 1.01039401837e-05\n",
      "6s - loss: 0.5834 - acc: 0.6846 - val_loss: 0.6104 - val_acc: 0.6645\n",
      "Epoch 93/100\n",
      "lr: 9.14242314782e-06\n",
      "6s - loss: 0.5900 - acc: 0.6767 - val_loss: 0.6104 - val_acc: 0.6645\n",
      "Epoch 94/100\n",
      "lr: 8.27240655566e-06\n",
      "6s - loss: 0.5860 - acc: 0.6764 - val_loss: 0.6104 - val_acc: 0.6645\n",
      "Epoch 95/100\n",
      "lr: 7.48518298877e-06\n",
      "6s - loss: 0.5861 - acc: 0.6752 - val_loss: 0.6104 - val_acc: 0.6640\n",
      "Epoch 96/100\n",
      "lr: 6.77287364909e-06\n",
      "6s - loss: 0.5838 - acc: 0.6836 - val_loss: 0.6104 - val_acc: 0.6640\n",
      "Epoch 97/100\n",
      "lr: 6.12834950532e-06\n",
      "6s - loss: 0.5892 - acc: 0.6744 - val_loss: 0.6104 - val_acc: 0.6640\n",
      "Epoch 98/100\n",
      "lr: 5.54515994322e-06\n",
      "6s - loss: 0.5862 - acc: 0.6796 - val_loss: 0.6104 - val_acc: 0.6640\n",
      "Epoch 99/100\n",
      "lr: 5.01746820562e-06\n",
      "6s - loss: 0.5900 - acc: 0.6775 - val_loss: 0.6104 - val_acc: 0.6640\n",
      "Epoch 100/100\n",
      "lr: 4.53999297625e-06\n",
      "6s - loss: 0.5891 - acc: 0.6799 - val_loss: 0.6104 - val_acc: 0.6640\n"
     ]
    },
    {
     "data": {
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s9a4r1dG8Yj79ZS8bUisO856RXcij09dzwfM/8+pWGxAOmQb85qtMMrKrH8Z/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\nT1iCMcYY4wlLMMYYYzzx/8ggAxdmuK+7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b11a7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define CNN model\n",
    "model4 = cnn_model()\n",
    "\n",
    "# define SGD optimizer\n",
    "momentum = 0.8\n",
    "sgd = SGD(lr=0.0, momentum=momentum, decay=0.0, nesterov=False)\n",
    "\n",
    "# compile the model\n",
    "model4.compile(loss=keras.losses.categorical_crossentropy, optimizer=sgd, metrics=['accuracy'])\n",
    "\n",
    "# define step decay function\n",
    "class LossHistory_(keras.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.losses = []\n",
    "        self.lr = []\n",
    "        \n",
    "    def on_epoch_end(self, batch, logs={}):\n",
    "        self.losses.append(logs.get('loss'))\n",
    "        self.lr.append(exp_decay(len(self.losses)))\n",
    "        print('lr:', exp_decay(len(self.losses)))\n",
    "\n",
    "def exp_decay(epoch):\n",
    "    initial_lrate = 0.1\n",
    "    k = 0.1\n",
    "    lrate = initial_lrate * np.exp(-k*epoch)\n",
    "    return lrate\n",
    "\n",
    "# learning schedule callback\n",
    "loss_history_ = LossHistory_()\n",
    "lrate_ = LearningRateScheduler(exp_decay)\n",
    "callbacks_list_ = [loss_history_, lrate_]\n",
    "\n",
    "# fit the model\n",
    "history4 = model4.fit(X_train, y_train, \n",
    "     validation_data=(X_test, y_test), \n",
    "     epochs=epochs, \n",
    "     batch_size=batch_size, \n",
    "     callbacks=callbacks_list_, \n",
    "     verbose=2)\n",
    "\n",
    "# plot model accuracy\n",
    "plot_fig(4, history4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BzAHGARea2bgW1eYAoyPT5cCvI+V1wLfdfRwwE7iqlXWTRkYojdsumESDwzUP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\n3JMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121b58358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot learning rate\n",
    "fig = plt.figure()\n",
    "plt.plot(range(1,epochs+1),loss_history_.lr,label='learning rate')\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.xlim([1,epochs+1])\n",
    "plt.ylabel(\"learning rate\")\n",
    "plt.legend(loc=0)\n",
    "plt.grid(True)\n",
    "plt.title(\"Learning rate\")\n",
    "plt.show()\n",
    "fig.savefig('img/4-learning-rate.jpg')\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaptive gradient descent algorithms : Adagrad, Adadelta, RMSprop, Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      "6s - loss: 0.6982 - acc: 0.5112 - val_loss: 0.6902 - val_acc: 0.5740\n",
      "Epoch 2/100\n",
      "6s - loss: 0.6899 - acc: 0.5308 - val_loss: 0.6891 - val_acc: 0.5590\n",
      "Epoch 3/100\n",
      "6s - loss: 0.6887 - acc: 0.5394 - val_loss: 0.6859 - val_acc: 0.5870\n",
      "Epoch 4/100\n",
      "6s - loss: 0.6879 - acc: 0.5417 - val_loss: 0.6879 - val_acc: 0.5610\n",
      "Epoch 5/100\n",
      "6s - loss: 0.6870 - acc: 0.5476 - val_loss: 0.6843 - val_acc: 0.6030\n",
      "Epoch 6/100\n",
      "6s - loss: 0.6851 - acc: 0.5562 - val_loss: 0.6827 - val_acc: 0.5945\n",
      "Epoch 7/100\n",
      "6s - loss: 0.6833 - acc: 0.5621 - val_loss: 0.6792 - val_acc: 0.6185\n",
      "Epoch 8/100\n",
      "6s - loss: 0.6814 - acc: 0.5761 - val_loss: 0.6768 - val_acc: 0.6180\n",
      "Epoch 9/100\n",
      "6s - loss: 0.6797 - acc: 0.5793 - val_loss: 0.6748 - val_acc: 0.6235\n",
      "Epoch 10/100\n",
      "6s - loss: 0.6752 - acc: 0.5867 - val_loss: 0.6708 - val_acc: 0.6370\n",
      "Epoch 11/100\n",
      "6s - loss: 0.6742 - acc: 0.5936 - val_loss: 0.6666 - val_acc: 0.6435\n",
      "Epoch 12/100\n",
      "6s - loss: 0.6721 - acc: 0.6003 - val_loss: 0.6629 - val_acc: 0.6435\n",
      "Epoch 13/100\n",
      "6s - loss: 0.6680 - acc: 0.6129 - val_loss: 0.6613 - val_acc: 0.6535\n",
      "Epoch 14/100\n",
      "6s - loss: 0.6612 - acc: 0.6102 - val_loss: 0.6526 - val_acc: 0.6515\n",
      "Epoch 15/100\n",
      "6s - loss: 0.6556 - acc: 0.6161 - val_loss: 0.6380 - val_acc: 0.6595\n",
      "Epoch 16/100\n",
      "6s - loss: 0.6486 - acc: 0.6152 - val_loss: 0.6362 - val_acc: 0.6530\n",
      "Epoch 17/100\n",
      "6s - loss: 0.6381 - acc: 0.6251 - val_loss: 0.6270 - val_acc: 0.6615\n",
      "Epoch 18/100\n",
      "6s - loss: 0.6409 - acc: 0.6230 - val_loss: 0.6233 - val_acc: 0.6590\n",
      "Epoch 19/100\n",
      "6s - loss: 0.6327 - acc: 0.6264 - val_loss: 0.6181 - val_acc: 0.6735\n",
      "Epoch 20/100\n",
      "6s - loss: 0.6276 - acc: 0.6364 - val_loss: 0.6124 - val_acc: 0.6735\n",
      "Epoch 21/100\n",
      "6s - loss: 0.6238 - acc: 0.6434 - val_loss: 0.6082 - val_acc: 0.6670\n",
      "Epoch 22/100\n",
      "6s - loss: 0.6223 - acc: 0.6389 - val_loss: 0.6052 - val_acc: 0.6790\n",
      "Epoch 23/100\n",
      "6s - loss: 0.6151 - acc: 0.6492 - val_loss: 0.6006 - val_acc: 0.6850\n",
      "Epoch 24/100\n",
      "6s - loss: 0.6115 - acc: 0.6574 - val_loss: 0.6039 - val_acc: 0.6755\n",
      "Epoch 25/100\n",
      "6s - loss: 0.6113 - acc: 0.6573 - val_loss: 0.5987 - val_acc: 0.6805\n",
      "Epoch 26/100\n",
      "6s - loss: 0.6059 - acc: 0.6618 - val_loss: 0.5948 - val_acc: 0.6750\n",
      "Epoch 27/100\n",
      "6s - loss: 0.6071 - acc: 0.6614 - val_loss: 0.5934 - val_acc: 0.6815\n",
      "Epoch 28/100\n",
      "6s - loss: 0.6016 - acc: 0.6650 - val_loss: 0.5921 - val_acc: 0.6820\n",
      "Epoch 29/100\n",
      "6s - loss: 0.5995 - acc: 0.6729 - val_loss: 0.5903 - val_acc: 0.6870\n",
      "Epoch 30/100\n",
      "6s - loss: 0.5919 - acc: 0.6712 - val_loss: 0.5884 - val_acc: 0.6850\n",
      "Epoch 31/100\n",
      "6s - loss: 0.5939 - acc: 0.6732 - val_loss: 0.5860 - val_acc: 0.6835\n",
      "Epoch 32/100\n",
      "6s - loss: 0.5949 - acc: 0.6681 - val_loss: 0.5896 - val_acc: 0.6825\n",
      "Epoch 33/100\n",
      "6s - loss: 0.5912 - acc: 0.6823 - val_loss: 0.5838 - val_acc: 0.6960\n",
      "Epoch 34/100\n",
      "6s - loss: 0.5905 - acc: 0.6741 - val_loss: 0.5933 - val_acc: 0.6715\n",
      "Epoch 35/100\n",
      "6s - loss: 0.5844 - acc: 0.6812 - val_loss: 0.5786 - val_acc: 0.6910\n",
      "Epoch 36/100\n",
      "6s - loss: 0.5830 - acc: 0.6853 - val_loss: 0.5880 - val_acc: 0.6830\n",
      "Epoch 37/100\n",
      "6s - loss: 0.5838 - acc: 0.6799 - val_loss: 0.5795 - val_acc: 0.6835\n",
      "Epoch 38/100\n",
      "6s - loss: 0.5799 - acc: 0.6825 - val_loss: 0.5778 - val_acc: 0.6885\n",
      "Epoch 39/100\n",
      "6s - loss: 0.5809 - acc: 0.6839 - val_loss: 0.5830 - val_acc: 0.6850\n",
      "Epoch 40/100\n",
      "6s - loss: 0.5766 - acc: 0.6862 - val_loss: 0.5746 - val_acc: 0.6960\n",
      "Epoch 41/100\n",
      "6s - loss: 0.5772 - acc: 0.6874 - val_loss: 0.5809 - val_acc: 0.6900\n",
      "Epoch 42/100\n",
      "6s - loss: 0.5773 - acc: 0.6872 - val_loss: 0.5743 - val_acc: 0.6895\n",
      "Epoch 43/100\n",
      "6s - loss: 0.5753 - acc: 0.6912 - val_loss: 0.5721 - val_acc: 0.6965\n",
      "Epoch 44/100\n",
      "6s - loss: 0.5758 - acc: 0.6876 - val_loss: 0.5742 - val_acc: 0.6915\n",
      "Epoch 45/100\n",
      "6s - loss: 0.5725 - acc: 0.6941 - val_loss: 0.5766 - val_acc: 0.6905\n",
      "Epoch 46/100\n",
      "6s - loss: 0.5738 - acc: 0.6885 - val_loss: 0.5706 - val_acc: 0.6940\n",
      "Epoch 47/100\n",
      "6s - loss: 0.5704 - acc: 0.6870 - val_loss: 0.5707 - val_acc: 0.7005\n",
      "Epoch 48/100\n",
      "6s - loss: 0.5680 - acc: 0.7001 - val_loss: 0.5692 - val_acc: 0.6965\n",
      "Epoch 49/100\n",
      "6s - loss: 0.5711 - acc: 0.6888 - val_loss: 0.5719 - val_acc: 0.6900\n",
      "Epoch 50/100\n",
      "6s - loss: 0.5670 - acc: 0.6961 - val_loss: 0.5695 - val_acc: 0.6905\n",
      "Epoch 51/100\n",
      "6s - loss: 0.5655 - acc: 0.6971 - val_loss: 0.5673 - val_acc: 0.6935\n",
      "Epoch 52/100\n",
      "6s - loss: 0.5632 - acc: 0.6950 - val_loss: 0.5678 - val_acc: 0.6950\n",
      "Epoch 53/100\n",
      "6s - loss: 0.5625 - acc: 0.6984 - val_loss: 0.5684 - val_acc: 0.6975\n",
      "Epoch 54/100\n",
      "6s - loss: 0.5590 - acc: 0.7024 - val_loss: 0.5664 - val_acc: 0.6990\n",
      "Epoch 55/100\n",
      "6s - loss: 0.5605 - acc: 0.6951 - val_loss: 0.5664 - val_acc: 0.6950\n",
      "Epoch 56/100\n",
      "6s - loss: 0.5626 - acc: 0.7020 - val_loss: 0.5648 - val_acc: 0.6985\n",
      "Epoch 57/100\n",
      "6s - loss: 0.5547 - acc: 0.7045 - val_loss: 0.5652 - val_acc: 0.6915\n",
      "Epoch 58/100\n",
      "6s - loss: 0.5563 - acc: 0.7066 - val_loss: 0.5689 - val_acc: 0.6925\n",
      "Epoch 59/100\n",
      "6s - loss: 0.5616 - acc: 0.7104 - val_loss: 0.5649 - val_acc: 0.7010\n",
      "Epoch 60/100\n",
      "6s - loss: 0.5511 - acc: 0.7086 - val_loss: 0.5612 - val_acc: 0.7075\n",
      "Epoch 61/100\n",
      "6s - loss: 0.5546 - acc: 0.7158 - val_loss: 0.5663 - val_acc: 0.6985\n",
      "Epoch 62/100\n",
      "6s - loss: 0.5560 - acc: 0.7077 - val_loss: 0.5619 - val_acc: 0.7045\n",
      "Epoch 63/100\n",
      "6s - loss: 0.5554 - acc: 0.7087 - val_loss: 0.5617 - val_acc: 0.7025\n",
      "Epoch 64/100\n",
      "6s - loss: 0.5513 - acc: 0.7081 - val_loss: 0.5622 - val_acc: 0.7080\n",
      "Epoch 65/100\n",
      "6s - loss: 0.5501 - acc: 0.7091 - val_loss: 0.5664 - val_acc: 0.7005\n",
      "Epoch 66/100\n",
      "6s - loss: 0.5530 - acc: 0.7127 - val_loss: 0.5602 - val_acc: 0.7065\n",
      "Epoch 67/100\n",
      "6s - loss: 0.5481 - acc: 0.7103 - val_loss: 0.5616 - val_acc: 0.7020\n",
      "Epoch 68/100\n",
      "6s - loss: 0.5490 - acc: 0.7123 - val_loss: 0.5601 - val_acc: 0.7015\n",
      "Epoch 69/100\n",
      "6s - loss: 0.5546 - acc: 0.7096 - val_loss: 0.5618 - val_acc: 0.7010\n",
      "Epoch 70/100\n",
      "6s - loss: 0.5472 - acc: 0.7118 - val_loss: 0.5622 - val_acc: 0.7045\n",
      "Epoch 71/100\n",
      "6s - loss: 0.5439 - acc: 0.7139 - val_loss: 0.5613 - val_acc: 0.7030\n",
      "Epoch 72/100\n",
      "6s - loss: 0.5498 - acc: 0.7113 - val_loss: 0.5604 - val_acc: 0.7020\n",
      "Epoch 73/100\n",
      "6s - loss: 0.5445 - acc: 0.7139 - val_loss: 0.5600 - val_acc: 0.7045\n",
      "Epoch 74/100\n",
      "6s - loss: 0.5406 - acc: 0.7175 - val_loss: 0.5583 - val_acc: 0.7075\n",
      "Epoch 75/100\n",
      "6s - loss: 0.5467 - acc: 0.7151 - val_loss: 0.5601 - val_acc: 0.7045\n",
      "Epoch 76/100\n",
      "6s - loss: 0.5428 - acc: 0.7176 - val_loss: 0.5582 - val_acc: 0.7045\n",
      "Epoch 77/100\n",
      "6s - loss: 0.5487 - acc: 0.7111 - val_loss: 0.5586 - val_acc: 0.6970\n",
      "Epoch 78/100\n",
      "6s - loss: 0.5429 - acc: 0.7124 - val_loss: 0.5641 - val_acc: 0.6970\n",
      "Epoch 79/100\n",
      "6s - loss: 0.5384 - acc: 0.7243 - val_loss: 0.5600 - val_acc: 0.6990\n",
      "Epoch 80/100\n",
      "6s - loss: 0.5378 - acc: 0.7238 - val_loss: 0.5596 - val_acc: 0.7010\n",
      "Epoch 81/100\n",
      "6s - loss: 0.5349 - acc: 0.7273 - val_loss: 0.5580 - val_acc: 0.7015\n",
      "Epoch 82/100\n",
      "6s - loss: 0.5346 - acc: 0.7267 - val_loss: 0.5588 - val_acc: 0.6985\n",
      "Epoch 83/100\n",
      "6s - loss: 0.5314 - acc: 0.7267 - val_loss: 0.5581 - val_acc: 0.7005\n",
      "Epoch 84/100\n",
      "6s - loss: 0.5318 - acc: 0.7312 - val_loss: 0.5576 - val_acc: 0.7050\n",
      "Epoch 85/100\n",
      "6s - loss: 0.5332 - acc: 0.7257 - val_loss: 0.5588 - val_acc: 0.7015\n",
      "Epoch 86/100\n",
      "6s - loss: 0.5297 - acc: 0.7263 - val_loss: 0.5564 - val_acc: 0.6995\n",
      "Epoch 87/100\n",
      "6s - loss: 0.5327 - acc: 0.7265 - val_loss: 0.5567 - val_acc: 0.6980\n",
      "Epoch 88/100\n",
      "6s - loss: 0.5272 - acc: 0.7325 - val_loss: 0.5595 - val_acc: 0.6995\n",
      "Epoch 89/100\n",
      "6s - loss: 0.5282 - acc: 0.7274 - val_loss: 0.5569 - val_acc: 0.7000\n",
      "Epoch 90/100\n",
      "6s - loss: 0.5244 - acc: 0.7327 - val_loss: 0.5572 - val_acc: 0.7005\n",
      "Epoch 91/100\n",
      "6s - loss: 0.5264 - acc: 0.7275 - val_loss: 0.5557 - val_acc: 0.7090\n",
      "Epoch 92/100\n",
      "6s - loss: 0.5242 - acc: 0.7341 - val_loss: 0.5560 - val_acc: 0.7000\n",
      "Epoch 93/100\n",
      "6s - loss: 0.5252 - acc: 0.7341 - val_loss: 0.5555 - val_acc: 0.7055\n",
      "Epoch 94/100\n",
      "6s - loss: 0.5230 - acc: 0.7325 - val_loss: 0.5589 - val_acc: 0.6950\n",
      "Epoch 95/100\n",
      "6s - loss: 0.5245 - acc: 0.7323 - val_loss: 0.5566 - val_acc: 0.6975\n",
      "Epoch 96/100\n",
      "6s - loss: 0.5254 - acc: 0.7302 - val_loss: 0.5570 - val_acc: 0.6990\n",
      "Epoch 97/100\n",
      "6s - loss: 0.5269 - acc: 0.7303 - val_loss: 0.5553 - val_acc: 0.6965\n",
      "Epoch 98/100\n",
      "6s - loss: 0.5159 - acc: 0.7416 - val_loss: 0.5551 - val_acc: 0.7055\n",
      "Epoch 99/100\n",
      "6s - loss: 0.5261 - acc: 0.7322 - val_loss: 0.5569 - val_acc: 0.6975\n",
      "Epoch 100/100\n",
      "6s - loss: 0.5126 - acc: 0.7368 - val_loss: 0.5550 - val_acc: 0.7040\n",
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      "7s - loss: 0.6981 - acc: 0.5202 - val_loss: 0.6887 - val_acc: 0.5255\n",
      "Epoch 2/100\n",
      "6s - loss: 0.6882 - acc: 0.5367 - val_loss: 0.6826 - val_acc: 0.5940\n",
      "Epoch 3/100\n",
      "6s - loss: 0.6836 - acc: 0.5517 - val_loss: 0.6752 - val_acc: 0.5585\n",
      "Epoch 4/100\n",
      "6s - loss: 0.6771 - acc: 0.5680 - val_loss: 0.6653 - val_acc: 0.6115\n",
      "Epoch 5/100\n",
      "6s - loss: 0.6679 - acc: 0.5923 - val_loss: 0.6535 - val_acc: 0.6250\n",
      "Epoch 6/100\n",
      "6s - loss: 0.6552 - acc: 0.6101 - val_loss: 0.6434 - val_acc: 0.6340\n",
      "Epoch 7/100\n",
      "6s - loss: 0.6496 - acc: 0.6218 - val_loss: 0.6328 - val_acc: 0.6455\n",
      "Epoch 8/100\n",
      "6s - loss: 0.6429 - acc: 0.6329 - val_loss: 0.6268 - val_acc: 0.6525\n",
      "Epoch 9/100\n",
      "6s - loss: 0.6373 - acc: 0.6395 - val_loss: 0.6263 - val_acc: 0.6465\n",
      "Epoch 10/100\n",
      "6s - loss: 0.6257 - acc: 0.6484 - val_loss: 0.6171 - val_acc: 0.6570\n",
      "Epoch 11/100\n",
      "6s - loss: 0.6207 - acc: 0.6582 - val_loss: 0.6184 - val_acc: 0.6515\n",
      "Epoch 12/100\n",
      "6s - loss: 0.6152 - acc: 0.6631 - val_loss: 0.6040 - val_acc: 0.6680\n",
      "Epoch 13/100\n",
      "6s - loss: 0.6095 - acc: 0.6646 - val_loss: 0.5998 - val_acc: 0.6715\n",
      "Epoch 14/100\n",
      "6s - loss: 0.6088 - acc: 0.6689 - val_loss: 0.5968 - val_acc: 0.6755\n",
      "Epoch 15/100\n",
      "6s - loss: 0.6023 - acc: 0.6709 - val_loss: 0.5967 - val_acc: 0.6725\n",
      "Epoch 16/100\n",
      "6s - loss: 0.5976 - acc: 0.6814 - val_loss: 0.5881 - val_acc: 0.6690\n",
      "Epoch 17/100\n",
      "6s - loss: 0.5910 - acc: 0.6814 - val_loss: 0.5815 - val_acc: 0.6870\n",
      "Epoch 18/100\n",
      "6s - loss: 0.5859 - acc: 0.6919 - val_loss: 0.5785 - val_acc: 0.6790\n",
      "Epoch 19/100\n",
      "6s - loss: 0.5806 - acc: 0.6937 - val_loss: 0.5746 - val_acc: 0.6900\n",
      "Epoch 20/100\n",
      "6s - loss: 0.5756 - acc: 0.6977 - val_loss: 0.5741 - val_acc: 0.6865\n",
      "Epoch 21/100\n",
      "6s - loss: 0.5739 - acc: 0.6972 - val_loss: 0.5708 - val_acc: 0.6880\n",
      "Epoch 22/100\n",
      "6s - loss: 0.5688 - acc: 0.7021 - val_loss: 0.5665 - val_acc: 0.6935\n",
      "Epoch 23/100\n",
      "6s - loss: 0.5651 - acc: 0.7078 - val_loss: 0.5618 - val_acc: 0.7010\n",
      "Epoch 24/100\n",
      "6s - loss: 0.5601 - acc: 0.7092 - val_loss: 0.5657 - val_acc: 0.6935\n",
      "Epoch 25/100\n",
      "6s - loss: 0.5597 - acc: 0.7075 - val_loss: 0.5588 - val_acc: 0.7005\n",
      "Epoch 26/100\n",
      "6s - loss: 0.5589 - acc: 0.7123 - val_loss: 0.5602 - val_acc: 0.6965\n",
      "Epoch 27/100\n",
      "6s - loss: 0.5508 - acc: 0.7210 - val_loss: 0.5572 - val_acc: 0.7035\n",
      "Epoch 28/100\n",
      "6s - loss: 0.5531 - acc: 0.7137 - val_loss: 0.5664 - val_acc: 0.6930\n",
      "Epoch 29/100\n",
      "6s - loss: 0.5488 - acc: 0.7164 - val_loss: 0.5676 - val_acc: 0.6840\n",
      "Epoch 30/100\n",
      "6s - loss: 0.5488 - acc: 0.7228 - val_loss: 0.5530 - val_acc: 0.7020\n",
      "Epoch 31/100\n",
      "6s - loss: 0.5447 - acc: 0.7236 - val_loss: 0.5514 - val_acc: 0.7020\n",
      "Epoch 32/100\n",
      "6s - loss: 0.5377 - acc: 0.7286 - val_loss: 0.5691 - val_acc: 0.6925\n",
      "Epoch 33/100\n",
      "6s - loss: 0.5365 - acc: 0.7249 - val_loss: 0.5506 - val_acc: 0.7040\n",
      "Epoch 34/100\n",
      "6s - loss: 0.5379 - acc: 0.7222 - val_loss: 0.5489 - val_acc: 0.7120\n",
      "Epoch 35/100\n",
      "6s - loss: 0.5308 - acc: 0.7295 - val_loss: 0.5500 - val_acc: 0.7045\n",
      "Epoch 36/100\n",
      "6s - loss: 0.5336 - acc: 0.7275 - val_loss: 0.5588 - val_acc: 0.6965\n",
      "Epoch 37/100\n",
      "6s - loss: 0.5261 - acc: 0.7310 - val_loss: 0.5546 - val_acc: 0.7025\n",
      "Epoch 38/100\n",
      "6s - loss: 0.5255 - acc: 0.7294 - val_loss: 0.5579 - val_acc: 0.6995\n",
      "Epoch 39/100\n",
      "6s - loss: 0.5228 - acc: 0.7357 - val_loss: 0.5452 - val_acc: 0.7080\n",
      "Epoch 40/100\n",
      "6s - loss: 0.5204 - acc: 0.7389 - val_loss: 0.5462 - val_acc: 0.7105\n",
      "Epoch 41/100\n",
      "6s - loss: 0.5144 - acc: 0.7388 - val_loss: 0.5450 - val_acc: 0.7060\n",
      "Epoch 42/100\n",
      "6s - loss: 0.5217 - acc: 0.7421 - val_loss: 0.5444 - val_acc: 0.7105\n",
      "Epoch 43/100\n",
      "6s - loss: 0.5240 - acc: 0.7363 - val_loss: 0.5445 - val_acc: 0.7130\n",
      "Epoch 44/100\n",
      "6s - loss: 0.5149 - acc: 0.7413 - val_loss: 0.5661 - val_acc: 0.6905\n",
      "Epoch 45/100\n",
      "6s - loss: 0.5171 - acc: 0.7433 - val_loss: 0.5439 - val_acc: 0.7180\n",
      "Epoch 46/100\n",
      "6s - loss: 0.5131 - acc: 0.7395 - val_loss: 0.5430 - val_acc: 0.7115\n",
      "Epoch 47/100\n",
      "6s - loss: 0.5080 - acc: 0.7427 - val_loss: 0.5438 - val_acc: 0.7095\n",
      "Epoch 48/100\n",
      "6s - loss: 0.5106 - acc: 0.7437 - val_loss: 0.5461 - val_acc: 0.7140\n",
      "Epoch 49/100\n",
      "6s - loss: 0.5068 - acc: 0.7509 - val_loss: 0.5435 - val_acc: 0.7115\n",
      "Epoch 50/100\n",
      "6s - loss: 0.5061 - acc: 0.7486 - val_loss: 0.5431 - val_acc: 0.7120\n",
      "Epoch 51/100\n",
      "6s - loss: 0.5050 - acc: 0.7453 - val_loss: 0.5415 - val_acc: 0.7075\n",
      "Epoch 52/100\n",
      "6s - loss: 0.5051 - acc: 0.7500 - val_loss: 0.5402 - val_acc: 0.7105\n",
      "Epoch 53/100\n",
      "6s - loss: 0.5020 - acc: 0.7528 - val_loss: 0.5456 - val_acc: 0.7185\n",
      "Epoch 54/100\n",
      "6s - loss: 0.4995 - acc: 0.7535 - val_loss: 0.5405 - val_acc: 0.7120\n",
      "Epoch 55/100\n",
      "6s - loss: 0.5007 - acc: 0.7495 - val_loss: 0.5406 - val_acc: 0.7115\n",
      "Epoch 56/100\n",
      "6s - loss: 0.4999 - acc: 0.7516 - val_loss: 0.5437 - val_acc: 0.7120\n",
      "Epoch 57/100\n",
      "6s - loss: 0.4961 - acc: 0.7534 - val_loss: 0.5486 - val_acc: 0.7090\n",
      "Epoch 58/100\n",
      "6s - loss: 0.4932 - acc: 0.7561 - val_loss: 0.5457 - val_acc: 0.7165\n",
      "Epoch 59/100\n",
      "6s - loss: 0.4952 - acc: 0.7551 - val_loss: 0.5427 - val_acc: 0.7175\n",
      "Epoch 60/100\n",
      "6s - loss: 0.4952 - acc: 0.7541 - val_loss: 0.5451 - val_acc: 0.7080\n",
      "Epoch 61/100\n",
      "6s - loss: 0.4886 - acc: 0.7577 - val_loss: 0.5506 - val_acc: 0.7115\n",
      "Epoch 62/100\n",
      "6s - loss: 0.4943 - acc: 0.7565 - val_loss: 0.5403 - val_acc: 0.7110\n",
      "Epoch 63/100\n",
      "6s - loss: 0.4930 - acc: 0.7491 - val_loss: 0.5404 - val_acc: 0.7075\n",
      "Epoch 64/100\n",
      "6s - loss: 0.4878 - acc: 0.7560 - val_loss: 0.5422 - val_acc: 0.7130\n",
      "Epoch 65/100\n",
      "6s - loss: 0.4884 - acc: 0.7574 - val_loss: 0.5392 - val_acc: 0.7090\n",
      "Epoch 66/100\n",
      "6s - loss: 0.4873 - acc: 0.7593 - val_loss: 0.5399 - val_acc: 0.7165\n",
      "Epoch 67/100\n",
      "6s - loss: 0.4881 - acc: 0.7588 - val_loss: 0.5408 - val_acc: 0.7145\n",
      "Epoch 68/100\n",
      "6s - loss: 0.4867 - acc: 0.7555 - val_loss: 0.5400 - val_acc: 0.7115\n",
      "Epoch 69/100\n",
      "6s - loss: 0.4770 - acc: 0.7650 - val_loss: 0.5458 - val_acc: 0.7130\n",
      "Epoch 70/100\n",
      "6s - loss: 0.4767 - acc: 0.7636 - val_loss: 0.5451 - val_acc: 0.7215\n",
      "Epoch 71/100\n",
      "6s - loss: 0.4758 - acc: 0.7649 - val_loss: 0.5409 - val_acc: 0.7155\n",
      "Epoch 72/100\n",
      "6s - loss: 0.4773 - acc: 0.7662 - val_loss: 0.5380 - val_acc: 0.7105\n",
      "Epoch 73/100\n",
      "6s - loss: 0.4745 - acc: 0.7622 - val_loss: 0.5466 - val_acc: 0.7170\n",
      "Epoch 74/100\n",
      "6s - loss: 0.4766 - acc: 0.7644 - val_loss: 0.5425 - val_acc: 0.7130\n",
      "Epoch 75/100\n",
      "6s - loss: 0.4688 - acc: 0.7670 - val_loss: 0.5487 - val_acc: 0.7160\n",
      "Epoch 76/100\n",
      "6s - loss: 0.4709 - acc: 0.7652 - val_loss: 0.5450 - val_acc: 0.7190\n",
      "Epoch 77/100\n",
      "6s - loss: 0.4669 - acc: 0.7660 - val_loss: 0.5447 - val_acc: 0.7195\n",
      "Epoch 78/100\n",
      "6s - loss: 0.4699 - acc: 0.7643 - val_loss: 0.5453 - val_acc: 0.7165\n",
      "Epoch 79/100\n",
      "6s - loss: 0.4641 - acc: 0.7756 - val_loss: 0.5490 - val_acc: 0.7125\n",
      "Epoch 80/100\n",
      "6s - loss: 0.4705 - acc: 0.7687 - val_loss: 0.5436 - val_acc: 0.7225\n",
      "Epoch 81/100\n",
      "6s - loss: 0.4675 - acc: 0.7668 - val_loss: 0.5392 - val_acc: 0.7195\n",
      "Epoch 82/100\n",
      "6s - loss: 0.4638 - acc: 0.7687 - val_loss: 0.5474 - val_acc: 0.7215\n",
      "Epoch 83/100\n",
      "6s - loss: 0.4657 - acc: 0.7725 - val_loss: 0.5404 - val_acc: 0.7205\n",
      "Epoch 84/100\n",
      "6s - loss: 0.4597 - acc: 0.7727 - val_loss: 0.5394 - val_acc: 0.7180\n",
      "Epoch 85/100\n",
      "6s - loss: 0.4649 - acc: 0.7672 - val_loss: 0.5393 - val_acc: 0.7160\n",
      "Epoch 86/100\n",
      "6s - loss: 0.4684 - acc: 0.7673 - val_loss: 0.5426 - val_acc: 0.7220\n",
      "Epoch 87/100\n",
      "6s - loss: 0.4648 - acc: 0.7699 - val_loss: 0.5527 - val_acc: 0.7070\n",
      "Epoch 88/100\n",
      "6s - loss: 0.4615 - acc: 0.7732 - val_loss: 0.5399 - val_acc: 0.7200\n",
      "Epoch 89/100\n",
      "6s - loss: 0.4549 - acc: 0.7752 - val_loss: 0.5425 - val_acc: 0.7185\n",
      "Epoch 90/100\n",
      "6s - loss: 0.4614 - acc: 0.7720 - val_loss: 0.5464 - val_acc: 0.7035\n",
      "Epoch 91/100\n",
      "6s - loss: 0.4575 - acc: 0.7751 - val_loss: 0.5411 - val_acc: 0.7130\n",
      "Epoch 92/100\n",
      "6s - loss: 0.4584 - acc: 0.7756 - val_loss: 0.5377 - val_acc: 0.7195\n",
      "Epoch 93/100\n",
      "6s - loss: 0.4564 - acc: 0.7772 - val_loss: 0.5465 - val_acc: 0.7180\n",
      "Epoch 94/100\n",
      "6s - loss: 0.4550 - acc: 0.7768 - val_loss: 0.5429 - val_acc: 0.7115\n",
      "Epoch 95/100\n",
      "6s - loss: 0.4469 - acc: 0.7832 - val_loss: 0.5532 - val_acc: 0.7125\n",
      "Epoch 96/100\n",
      "6s - loss: 0.4519 - acc: 0.7773 - val_loss: 0.5517 - val_acc: 0.7205\n",
      "Epoch 97/100\n",
      "6s - loss: 0.4530 - acc: 0.7755 - val_loss: 0.5398 - val_acc: 0.7145\n",
      "Epoch 98/100\n",
      "6s - loss: 0.4506 - acc: 0.7724 - val_loss: 0.5534 - val_acc: 0.7155\n",
      "Epoch 99/100\n",
      "6s - loss: 0.4493 - acc: 0.7801 - val_loss: 0.5452 - val_acc: 0.7170\n",
      "Epoch 100/100\n",
      "6s - loss: 0.4514 - acc: 0.7741 - val_loss: 0.5521 - val_acc: 0.7210\n",
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      "6s - loss: 0.6869 - acc: 0.5469 - val_loss: 0.6637 - val_acc: 0.6165\n",
      "Epoch 2/100\n",
      "6s - loss: 0.6584 - acc: 0.6155 - val_loss: 0.6352 - val_acc: 0.6490\n",
      "Epoch 3/100\n",
      "6s - loss: 0.6365 - acc: 0.6459 - val_loss: 0.6675 - val_acc: 0.6145\n",
      "Epoch 4/100\n",
      "6s - loss: 0.6240 - acc: 0.6519 - val_loss: 0.6124 - val_acc: 0.6610\n",
      "Epoch 5/100\n",
      "6s - loss: 0.6117 - acc: 0.6688 - val_loss: 0.6064 - val_acc: 0.6690\n",
      "Epoch 6/100\n",
      "6s - loss: 0.6007 - acc: 0.6803 - val_loss: 0.5915 - val_acc: 0.6775\n",
      "Epoch 7/100\n",
      "6s - loss: 0.5886 - acc: 0.6867 - val_loss: 0.5919 - val_acc: 0.6685\n",
      "Epoch 8/100\n",
      "6s - loss: 0.5846 - acc: 0.6917 - val_loss: 0.5779 - val_acc: 0.6840\n",
      "Epoch 9/100\n",
      "6s - loss: 0.5732 - acc: 0.6953 - val_loss: 0.5723 - val_acc: 0.6890\n",
      "Epoch 10/100\n",
      "6s - loss: 0.5708 - acc: 0.6975 - val_loss: 0.5754 - val_acc: 0.6825\n",
      "Epoch 11/100\n",
      "6s - loss: 0.5625 - acc: 0.7121 - val_loss: 0.5683 - val_acc: 0.6920\n",
      "Epoch 12/100\n",
      "6s - loss: 0.5554 - acc: 0.7109 - val_loss: 0.5648 - val_acc: 0.7000\n",
      "Epoch 13/100\n",
      "6s - loss: 0.5485 - acc: 0.7145 - val_loss: 0.5592 - val_acc: 0.6895\n",
      "Epoch 14/100\n",
      "6s - loss: 0.5419 - acc: 0.7243 - val_loss: 0.5590 - val_acc: 0.6950\n",
      "Epoch 15/100\n",
      "6s - loss: 0.5389 - acc: 0.7229 - val_loss: 0.5628 - val_acc: 0.6965\n",
      "Epoch 16/100\n",
      "6s - loss: 0.5338 - acc: 0.7322 - val_loss: 0.5516 - val_acc: 0.6995\n",
      "Epoch 17/100\n",
      "6s - loss: 0.5340 - acc: 0.7304 - val_loss: 0.5563 - val_acc: 0.7030\n",
      "Epoch 18/100\n",
      "6s - loss: 0.5244 - acc: 0.7335 - val_loss: 0.5611 - val_acc: 0.6955\n",
      "Epoch 19/100\n",
      "6s - loss: 0.5263 - acc: 0.7332 - val_loss: 0.5619 - val_acc: 0.7040\n",
      "Epoch 20/100\n",
      "6s - loss: 0.5200 - acc: 0.7363 - val_loss: 0.5697 - val_acc: 0.7065\n",
      "Epoch 21/100\n",
      "6s - loss: 0.5153 - acc: 0.7422 - val_loss: 0.5586 - val_acc: 0.7130\n",
      "Epoch 22/100\n",
      "6s - loss: 0.5150 - acc: 0.7443 - val_loss: 0.5574 - val_acc: 0.7165\n",
      "Epoch 23/100\n",
      "6s - loss: 0.5068 - acc: 0.7469 - val_loss: 0.5515 - val_acc: 0.7030\n",
      "Epoch 24/100\n",
      "6s - loss: 0.5092 - acc: 0.7448 - val_loss: 0.5555 - val_acc: 0.7005\n",
      "Epoch 25/100\n",
      "6s - loss: 0.4969 - acc: 0.7516 - val_loss: 0.5514 - val_acc: 0.7195\n",
      "Epoch 26/100\n",
      "6s - loss: 0.5032 - acc: 0.7527 - val_loss: 0.5692 - val_acc: 0.7145\n",
      "Epoch 27/100\n",
      "6s - loss: 0.4914 - acc: 0.7558 - val_loss: 0.5633 - val_acc: 0.7080\n",
      "Epoch 28/100\n",
      "6s - loss: 0.4920 - acc: 0.7608 - val_loss: 0.5520 - val_acc: 0.7075\n",
      "Epoch 29/100\n",
      "6s - loss: 0.4931 - acc: 0.7514 - val_loss: 0.5578 - val_acc: 0.6995\n",
      "Epoch 30/100\n",
      "6s - loss: 0.4905 - acc: 0.7675 - val_loss: 0.5529 - val_acc: 0.7140\n",
      "Epoch 31/100\n",
      "6s - loss: 0.4872 - acc: 0.7582 - val_loss: 0.5587 - val_acc: 0.7160\n",
      "Epoch 32/100\n",
      "6s - loss: 0.4870 - acc: 0.7597 - val_loss: 0.5502 - val_acc: 0.7195\n",
      "Epoch 33/100\n",
      "6s - loss: 0.4820 - acc: 0.7577 - val_loss: 0.5546 - val_acc: 0.7250\n",
      "Epoch 34/100\n",
      "6s - loss: 0.4861 - acc: 0.7652 - val_loss: 0.5659 - val_acc: 0.7190\n",
      "Epoch 35/100\n",
      "6s - loss: 0.4792 - acc: 0.7652 - val_loss: 0.5564 - val_acc: 0.7160\n",
      "Epoch 36/100\n",
      "6s - loss: 0.4746 - acc: 0.7669 - val_loss: 0.5673 - val_acc: 0.7155\n",
      "Epoch 37/100\n",
      "6s - loss: 0.4731 - acc: 0.7700 - val_loss: 0.5597 - val_acc: 0.7285\n",
      "Epoch 38/100\n",
      "6s - loss: 0.4734 - acc: 0.7693 - val_loss: 0.5480 - val_acc: 0.7155\n",
      "Epoch 39/100\n",
      "6s - loss: 0.4753 - acc: 0.7668 - val_loss: 0.5599 - val_acc: 0.7290\n",
      "Epoch 40/100\n",
      "6s - loss: 0.4669 - acc: 0.7736 - val_loss: 0.6094 - val_acc: 0.7275\n",
      "Epoch 41/100\n",
      "6s - loss: 0.4730 - acc: 0.7684 - val_loss: 0.5690 - val_acc: 0.7250\n",
      "Epoch 42/100\n",
      "6s - loss: 0.4700 - acc: 0.7730 - val_loss: 0.5682 - val_acc: 0.7230\n",
      "Epoch 43/100\n",
      "6s - loss: 0.4679 - acc: 0.7705 - val_loss: 0.5608 - val_acc: 0.7270\n",
      "Epoch 44/100\n",
      "6s - loss: 0.4606 - acc: 0.7769 - val_loss: 0.5670 - val_acc: 0.7250\n",
      "Epoch 45/100\n",
      "6s - loss: 0.4662 - acc: 0.7752 - val_loss: 0.5701 - val_acc: 0.7190\n",
      "Epoch 46/100\n",
      "6s - loss: 0.4579 - acc: 0.7751 - val_loss: 0.5686 - val_acc: 0.7195\n",
      "Epoch 47/100\n",
      "6s - loss: 0.4586 - acc: 0.7745 - val_loss: 0.5545 - val_acc: 0.7210\n",
      "Epoch 48/100\n",
      "6s - loss: 0.4631 - acc: 0.7755 - val_loss: 0.5961 - val_acc: 0.7235\n",
      "Epoch 49/100\n",
      "6s - loss: 0.4601 - acc: 0.7736 - val_loss: 0.5489 - val_acc: 0.7170\n",
      "Epoch 50/100\n",
      "6s - loss: 0.4562 - acc: 0.7776 - val_loss: 0.5604 - val_acc: 0.7290\n",
      "Epoch 51/100\n",
      "6s - loss: 0.4497 - acc: 0.7834 - val_loss: 0.5621 - val_acc: 0.7300\n",
      "Epoch 52/100\n",
      "6s - loss: 0.4562 - acc: 0.7844 - val_loss: 0.5550 - val_acc: 0.7195\n",
      "Epoch 53/100\n",
      "6s - loss: 0.4587 - acc: 0.7744 - val_loss: 0.5612 - val_acc: 0.7235\n",
      "Epoch 54/100\n",
      "6s - loss: 0.4533 - acc: 0.7810 - val_loss: 0.5464 - val_acc: 0.7240\n",
      "Epoch 55/100\n",
      "6s - loss: 0.4507 - acc: 0.7814 - val_loss: 0.5500 - val_acc: 0.7190\n",
      "Epoch 56/100\n",
      "6s - loss: 0.4517 - acc: 0.7818 - val_loss: 0.5507 - val_acc: 0.7165\n",
      "Epoch 57/100\n",
      "6s - loss: 0.4528 - acc: 0.7840 - val_loss: 0.5537 - val_acc: 0.7205\n",
      "Epoch 58/100\n",
      "6s - loss: 0.4524 - acc: 0.7810 - val_loss: 0.5588 - val_acc: 0.7130\n",
      "Epoch 59/100\n",
      "6s - loss: 0.4427 - acc: 0.7877 - val_loss: 0.5662 - val_acc: 0.7250\n",
      "Epoch 60/100\n",
      "6s - loss: 0.4557 - acc: 0.7776 - val_loss: 0.5960 - val_acc: 0.7255\n",
      "Epoch 61/100\n",
      "6s - loss: 0.4439 - acc: 0.7861 - val_loss: 0.5654 - val_acc: 0.7150\n",
      "Epoch 62/100\n",
      "6s - loss: 0.4518 - acc: 0.7818 - val_loss: 0.5508 - val_acc: 0.7165\n",
      "Epoch 63/100\n",
      "6s - loss: 0.4470 - acc: 0.7861 - val_loss: 0.5663 - val_acc: 0.7295\n",
      "Epoch 64/100\n",
      "6s - loss: 0.4457 - acc: 0.7885 - val_loss: 0.5481 - val_acc: 0.7140\n",
      "Epoch 65/100\n",
      "6s - loss: 0.4509 - acc: 0.7804 - val_loss: 0.5587 - val_acc: 0.7200\n",
      "Epoch 66/100\n",
      "6s - loss: 0.4513 - acc: 0.7828 - val_loss: 0.5528 - val_acc: 0.7040\n",
      "Epoch 67/100\n",
      "6s - loss: 0.4551 - acc: 0.7743 - val_loss: 0.5696 - val_acc: 0.7300\n",
      "Epoch 68/100\n",
      "6s - loss: 0.4489 - acc: 0.7891 - val_loss: 0.5451 - val_acc: 0.7200\n",
      "Epoch 69/100\n",
      "6s - loss: 0.4512 - acc: 0.7836 - val_loss: 0.5502 - val_acc: 0.7090\n",
      "Epoch 70/100\n",
      "6s - loss: 0.4434 - acc: 0.7873 - val_loss: 0.5746 - val_acc: 0.7295\n",
      "Epoch 71/100\n",
      "6s - loss: 0.4440 - acc: 0.7837 - val_loss: 0.5632 - val_acc: 0.7335\n",
      "Epoch 72/100\n",
      "6s - loss: 0.4457 - acc: 0.7839 - val_loss: 0.5788 - val_acc: 0.7280\n",
      "Epoch 73/100\n",
      "6s - loss: 0.4463 - acc: 0.7850 - val_loss: 0.5696 - val_acc: 0.7305\n",
      "Epoch 74/100\n",
      "6s - loss: 0.4459 - acc: 0.7865 - val_loss: 0.5741 - val_acc: 0.7320\n",
      "Epoch 75/100\n",
      "6s - loss: 0.4393 - acc: 0.7881 - val_loss: 0.5735 - val_acc: 0.7295\n",
      "Epoch 76/100\n",
      "6s - loss: 0.4416 - acc: 0.7843 - val_loss: 0.5616 - val_acc: 0.7320\n",
      "Epoch 77/100\n",
      "6s - loss: 0.4343 - acc: 0.7906 - val_loss: 0.5771 - val_acc: 0.7240\n",
      "Epoch 78/100\n",
      "6s - loss: 0.4353 - acc: 0.7903 - val_loss: 0.5491 - val_acc: 0.7285\n",
      "Epoch 79/100\n",
      "6s - loss: 0.4396 - acc: 0.7894 - val_loss: 0.5538 - val_acc: 0.7120\n",
      "Epoch 80/100\n",
      "6s - loss: 0.4432 - acc: 0.7833 - val_loss: 0.5631 - val_acc: 0.7280\n",
      "Epoch 81/100\n",
      "6s - loss: 0.4405 - acc: 0.7902 - val_loss: 0.6005 - val_acc: 0.7330\n",
      "Epoch 82/100\n",
      "6s - loss: 0.4451 - acc: 0.7824 - val_loss: 0.5975 - val_acc: 0.7380\n",
      "Epoch 83/100\n",
      "6s - loss: 0.4403 - acc: 0.7897 - val_loss: 0.5559 - val_acc: 0.7090\n",
      "Epoch 84/100\n",
      "6s - loss: 0.4415 - acc: 0.7853 - val_loss: 0.5563 - val_acc: 0.7245\n",
      "Epoch 85/100\n",
      "6s - loss: 0.4382 - acc: 0.7871 - val_loss: 0.5580 - val_acc: 0.7250\n",
      "Epoch 86/100\n",
      "6s - loss: 0.4435 - acc: 0.7825 - val_loss: 0.5689 - val_acc: 0.7280\n",
      "Epoch 87/100\n",
      "6s - loss: 0.4399 - acc: 0.7889 - val_loss: 0.5792 - val_acc: 0.7315\n",
      "Epoch 88/100\n",
      "6s - loss: 0.4421 - acc: 0.7894 - val_loss: 0.5601 - val_acc: 0.7125\n",
      "Epoch 89/100\n",
      "6s - loss: 0.4410 - acc: 0.7852 - val_loss: 0.5515 - val_acc: 0.7110\n",
      "Epoch 90/100\n",
      "6s - loss: 0.4396 - acc: 0.7847 - val_loss: 0.5748 - val_acc: 0.7125\n",
      "Epoch 91/100\n",
      "6s - loss: 0.4440 - acc: 0.7843 - val_loss: 0.5618 - val_acc: 0.7260\n",
      "Epoch 92/100\n",
      "6s - loss: 0.4445 - acc: 0.7848 - val_loss: 0.5662 - val_acc: 0.7220\n",
      "Epoch 93/100\n",
      "6s - loss: 0.4356 - acc: 0.7919 - val_loss: 0.5556 - val_acc: 0.7315\n",
      "Epoch 94/100\n",
      "6s - loss: 0.4362 - acc: 0.7926 - val_loss: 0.6259 - val_acc: 0.7255\n",
      "Epoch 95/100\n",
      "6s - loss: 0.4376 - acc: 0.7908 - val_loss: 0.5707 - val_acc: 0.7235\n",
      "Epoch 96/100\n",
      "6s - loss: 0.4399 - acc: 0.7862 - val_loss: 0.6022 - val_acc: 0.7300\n",
      "Epoch 97/100\n",
      "6s - loss: 0.4296 - acc: 0.7929 - val_loss: 0.5546 - val_acc: 0.7205\n",
      "Epoch 98/100\n",
      "6s - loss: 0.4310 - acc: 0.7937 - val_loss: 0.5746 - val_acc: 0.7270\n",
      "Epoch 99/100\n",
      "6s - loss: 0.4443 - acc: 0.7841 - val_loss: 0.5551 - val_acc: 0.7020\n",
      "Epoch 100/100\n",
      "6s - loss: 0.4393 - acc: 0.7848 - val_loss: 0.5979 - val_acc: 0.7345\n",
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      "6s - loss: 0.7004 - acc: 0.5061 - val_loss: 0.6876 - val_acc: 0.5705\n",
      "Epoch 2/100\n",
      "6s - loss: 0.6875 - acc: 0.5261 - val_loss: 0.6787 - val_acc: 0.6005\n",
      "Epoch 3/100\n",
      "6s - loss: 0.6798 - acc: 0.5497 - val_loss: 0.6682 - val_acc: 0.6220\n",
      "Epoch 4/100\n",
      "6s - loss: 0.6683 - acc: 0.5757 - val_loss: 0.6421 - val_acc: 0.6295\n",
      "Epoch 5/100\n",
      "6s - loss: 0.6584 - acc: 0.5900 - val_loss: 0.6448 - val_acc: 0.6400\n",
      "Epoch 6/100\n",
      "6s - loss: 0.6489 - acc: 0.6075 - val_loss: 0.6350 - val_acc: 0.6520\n",
      "Epoch 7/100\n",
      "6s - loss: 0.6385 - acc: 0.6305 - val_loss: 0.6288 - val_acc: 0.6395\n",
      "Epoch 8/100\n",
      "6s - loss: 0.6253 - acc: 0.6437 - val_loss: 0.6200 - val_acc: 0.6610\n",
      "Epoch 9/100\n",
      "6s - loss: 0.6168 - acc: 0.6539 - val_loss: 0.6164 - val_acc: 0.6560\n",
      "Epoch 10/100\n",
      "6s - loss: 0.6092 - acc: 0.6618 - val_loss: 0.6068 - val_acc: 0.6680\n",
      "Epoch 11/100\n",
      "6s - loss: 0.5999 - acc: 0.6660 - val_loss: 0.5978 - val_acc: 0.6775\n",
      "Epoch 12/100\n",
      "6s - loss: 0.5908 - acc: 0.6765 - val_loss: 0.5939 - val_acc: 0.6710\n",
      "Epoch 13/100\n",
      "6s - loss: 0.5830 - acc: 0.6817 - val_loss: 0.5886 - val_acc: 0.6725\n",
      "Epoch 14/100\n",
      "6s - loss: 0.5817 - acc: 0.6852 - val_loss: 0.5824 - val_acc: 0.6855\n",
      "Epoch 15/100\n",
      "6s - loss: 0.5739 - acc: 0.6934 - val_loss: 0.5837 - val_acc: 0.6880\n",
      "Epoch 16/100\n",
      "6s - loss: 0.5626 - acc: 0.7052 - val_loss: 0.5749 - val_acc: 0.6985\n",
      "Epoch 17/100\n",
      "6s - loss: 0.5600 - acc: 0.7061 - val_loss: 0.5731 - val_acc: 0.6960\n",
      "Epoch 18/100\n",
      "6s - loss: 0.5434 - acc: 0.7157 - val_loss: 0.5746 - val_acc: 0.6935\n",
      "Epoch 19/100\n",
      "6s - loss: 0.5427 - acc: 0.7191 - val_loss: 0.5755 - val_acc: 0.6920\n",
      "Epoch 20/100\n",
      "6s - loss: 0.5359 - acc: 0.7232 - val_loss: 0.5702 - val_acc: 0.6915\n",
      "Epoch 21/100\n",
      "6s - loss: 0.5336 - acc: 0.7198 - val_loss: 0.5741 - val_acc: 0.7040\n",
      "Epoch 22/100\n",
      "6s - loss: 0.5250 - acc: 0.7307 - val_loss: 0.5618 - val_acc: 0.7060\n",
      "Epoch 23/100\n",
      "6s - loss: 0.5246 - acc: 0.7300 - val_loss: 0.5611 - val_acc: 0.7105\n",
      "Epoch 24/100\n",
      "6s - loss: 0.5207 - acc: 0.7334 - val_loss: 0.5607 - val_acc: 0.7070\n",
      "Epoch 25/100\n",
      "6s - loss: 0.5199 - acc: 0.7378 - val_loss: 0.5644 - val_acc: 0.7025\n",
      "Epoch 26/100\n",
      "6s - loss: 0.5016 - acc: 0.7432 - val_loss: 0.5575 - val_acc: 0.7180\n",
      "Epoch 27/100\n",
      "6s - loss: 0.5041 - acc: 0.7421 - val_loss: 0.5608 - val_acc: 0.7095\n",
      "Epoch 28/100\n",
      "6s - loss: 0.5048 - acc: 0.7434 - val_loss: 0.5605 - val_acc: 0.7020\n",
      "Epoch 29/100\n",
      "6s - loss: 0.4960 - acc: 0.7504 - val_loss: 0.5611 - val_acc: 0.6990\n",
      "Epoch 30/100\n",
      "6s - loss: 0.4932 - acc: 0.7521 - val_loss: 0.5631 - val_acc: 0.7050\n",
      "Epoch 31/100\n",
      "6s - loss: 0.4898 - acc: 0.7504 - val_loss: 0.5593 - val_acc: 0.6985\n",
      "Epoch 32/100\n",
      "6s - loss: 0.4831 - acc: 0.7612 - val_loss: 0.5579 - val_acc: 0.7025\n",
      "Epoch 33/100\n",
      "6s - loss: 0.4822 - acc: 0.7581 - val_loss: 0.5586 - val_acc: 0.7020\n",
      "Epoch 34/100\n",
      "6s - loss: 0.4792 - acc: 0.7621 - val_loss: 0.5618 - val_acc: 0.7010\n",
      "Epoch 35/100\n",
      "6s - loss: 0.4804 - acc: 0.7616 - val_loss: 0.5661 - val_acc: 0.7025\n",
      "Epoch 36/100\n",
      "6s - loss: 0.4784 - acc: 0.7607 - val_loss: 0.5564 - val_acc: 0.7060\n",
      "Epoch 37/100\n",
      "6s - loss: 0.4740 - acc: 0.7672 - val_loss: 0.5599 - val_acc: 0.7105\n",
      "Epoch 38/100\n",
      "6s - loss: 0.4737 - acc: 0.7624 - val_loss: 0.5623 - val_acc: 0.7050\n",
      "Epoch 39/100\n",
      "6s - loss: 0.4684 - acc: 0.7702 - val_loss: 0.5645 - val_acc: 0.7070\n",
      "Epoch 40/100\n",
      "6s - loss: 0.4671 - acc: 0.7667 - val_loss: 0.5648 - val_acc: 0.7065\n",
      "Epoch 41/100\n",
      "6s - loss: 0.4621 - acc: 0.7730 - val_loss: 0.5602 - val_acc: 0.7070\n",
      "Epoch 42/100\n",
      "6s - loss: 0.4630 - acc: 0.7731 - val_loss: 0.5624 - val_acc: 0.7070\n",
      "Epoch 43/100\n",
      "6s - loss: 0.4536 - acc: 0.7740 - val_loss: 0.5664 - val_acc: 0.7095\n",
      "Epoch 44/100\n",
      "6s - loss: 0.4473 - acc: 0.7795 - val_loss: 0.5676 - val_acc: 0.7025\n",
      "Epoch 45/100\n",
      "6s - loss: 0.4436 - acc: 0.7807 - val_loss: 0.5642 - val_acc: 0.7035\n",
      "Epoch 46/100\n",
      "6s - loss: 0.4543 - acc: 0.7746 - val_loss: 0.5679 - val_acc: 0.7165\n",
      "Epoch 47/100\n",
      "6s - loss: 0.4403 - acc: 0.7817 - val_loss: 0.5753 - val_acc: 0.7165\n",
      "Epoch 48/100\n",
      "6s - loss: 0.4530 - acc: 0.7755 - val_loss: 0.5687 - val_acc: 0.6965\n",
      "Epoch 49/100\n",
      "6s - loss: 0.4416 - acc: 0.7824 - val_loss: 0.5680 - val_acc: 0.7065\n",
      "Epoch 50/100\n",
      "6s - loss: 0.4327 - acc: 0.7899 - val_loss: 0.5707 - val_acc: 0.6975\n",
      "Epoch 51/100\n",
      "6s - loss: 0.4439 - acc: 0.7834 - val_loss: 0.5670 - val_acc: 0.7090\n",
      "Epoch 52/100\n",
      "6s - loss: 0.4405 - acc: 0.7831 - val_loss: 0.5658 - val_acc: 0.7095\n",
      "Epoch 53/100\n",
      "6s - loss: 0.4368 - acc: 0.7866 - val_loss: 0.5677 - val_acc: 0.7170\n",
      "Epoch 54/100\n",
      "6s - loss: 0.4392 - acc: 0.7817 - val_loss: 0.5626 - val_acc: 0.7085\n",
      "Epoch 55/100\n",
      "6s - loss: 0.4360 - acc: 0.7819 - val_loss: 0.5829 - val_acc: 0.7090\n",
      "Epoch 56/100\n",
      "6s - loss: 0.4306 - acc: 0.7909 - val_loss: 0.5769 - val_acc: 0.7100\n",
      "Epoch 57/100\n",
      "6s - loss: 0.4353 - acc: 0.7868 - val_loss: 0.5768 - val_acc: 0.7115\n",
      "Epoch 58/100\n",
      "6s - loss: 0.4262 - acc: 0.7918 - val_loss: 0.5732 - val_acc: 0.7090\n",
      "Epoch 59/100\n",
      "6s - loss: 0.4245 - acc: 0.7976 - val_loss: 0.5663 - val_acc: 0.7075\n",
      "Epoch 60/100\n",
      "6s - loss: 0.4198 - acc: 0.7926 - val_loss: 0.5698 - val_acc: 0.7165\n",
      "Epoch 61/100\n",
      "6s - loss: 0.4235 - acc: 0.7922 - val_loss: 0.5770 - val_acc: 0.7105\n",
      "Epoch 62/100\n",
      "6s - loss: 0.4221 - acc: 0.7957 - val_loss: 0.5730 - val_acc: 0.7240\n",
      "Epoch 63/100\n",
      "6s - loss: 0.4274 - acc: 0.7909 - val_loss: 0.5884 - val_acc: 0.7180\n",
      "Epoch 64/100\n",
      "6s - loss: 0.4249 - acc: 0.7926 - val_loss: 0.5769 - val_acc: 0.7140\n",
      "Epoch 65/100\n",
      "6s - loss: 0.4193 - acc: 0.7954 - val_loss: 0.5816 - val_acc: 0.7120\n",
      "Epoch 66/100\n",
      "6s - loss: 0.4197 - acc: 0.7953 - val_loss: 0.5777 - val_acc: 0.7045\n",
      "Epoch 67/100\n",
      "6s - loss: 0.4161 - acc: 0.7969 - val_loss: 0.5959 - val_acc: 0.7035\n",
      "Epoch 68/100\n",
      "6s - loss: 0.4107 - acc: 0.8025 - val_loss: 0.5908 - val_acc: 0.7040\n",
      "Epoch 69/100\n",
      "6s - loss: 0.4186 - acc: 0.7962 - val_loss: 0.5811 - val_acc: 0.7085\n",
      "Epoch 70/100\n",
      "6s - loss: 0.4156 - acc: 0.7987 - val_loss: 0.5829 - val_acc: 0.7165\n",
      "Epoch 71/100\n",
      "6s - loss: 0.4140 - acc: 0.7984 - val_loss: 0.5819 - val_acc: 0.7055\n",
      "Epoch 72/100\n",
      "6s - loss: 0.4122 - acc: 0.7974 - val_loss: 0.5937 - val_acc: 0.7090\n",
      "Epoch 73/100\n",
      "6s - loss: 0.4130 - acc: 0.8007 - val_loss: 0.5854 - val_acc: 0.7120\n",
      "Epoch 74/100\n",
      "6s - loss: 0.4132 - acc: 0.8000 - val_loss: 0.5911 - val_acc: 0.7185\n",
      "Epoch 75/100\n",
      "6s - loss: 0.4103 - acc: 0.7978 - val_loss: 0.6053 - val_acc: 0.7100\n",
      "Epoch 76/100\n",
      "6s - loss: 0.4109 - acc: 0.8014 - val_loss: 0.5952 - val_acc: 0.7090\n",
      "Epoch 77/100\n",
      "6s - loss: 0.4084 - acc: 0.8083 - val_loss: 0.6160 - val_acc: 0.7140\n",
      "Epoch 78/100\n",
      "6s - loss: 0.4107 - acc: 0.8004 - val_loss: 0.5889 - val_acc: 0.6975\n",
      "Epoch 79/100\n",
      "6s - loss: 0.4084 - acc: 0.8024 - val_loss: 0.5819 - val_acc: 0.7110\n",
      "Epoch 80/100\n",
      "6s - loss: 0.3940 - acc: 0.8124 - val_loss: 0.5895 - val_acc: 0.7150\n",
      "Epoch 81/100\n",
      "6s - loss: 0.4027 - acc: 0.8072 - val_loss: 0.5954 - val_acc: 0.7035\n",
      "Epoch 82/100\n",
      "6s - loss: 0.4073 - acc: 0.8043 - val_loss: 0.5873 - val_acc: 0.7065\n",
      "Epoch 83/100\n",
      "6s - loss: 0.4074 - acc: 0.8033 - val_loss: 0.5897 - val_acc: 0.7120\n",
      "Epoch 84/100\n",
      "6s - loss: 0.4118 - acc: 0.8008 - val_loss: 0.5820 - val_acc: 0.7230\n",
      "Epoch 85/100\n",
      "6s - loss: 0.4050 - acc: 0.8065 - val_loss: 0.5953 - val_acc: 0.7155\n",
      "Epoch 86/100\n",
      "6s - loss: 0.4011 - acc: 0.8052 - val_loss: 0.6047 - val_acc: 0.7145\n",
      "Epoch 87/100\n",
      "6s - loss: 0.3974 - acc: 0.8042 - val_loss: 0.5988 - val_acc: 0.7165\n",
      "Epoch 88/100\n",
      "6s - loss: 0.4039 - acc: 0.8060 - val_loss: 0.5999 - val_acc: 0.7205\n",
      "Epoch 89/100\n",
      "6s - loss: 0.4022 - acc: 0.8040 - val_loss: 0.5859 - val_acc: 0.7190\n",
      "Epoch 90/100\n",
      "6s - loss: 0.3984 - acc: 0.8047 - val_loss: 0.5867 - val_acc: 0.7155\n",
      "Epoch 91/100\n",
      "6s - loss: 0.3962 - acc: 0.8058 - val_loss: 0.5973 - val_acc: 0.7120\n",
      "Epoch 92/100\n",
      "6s - loss: 0.3938 - acc: 0.8127 - val_loss: 0.5809 - val_acc: 0.7200\n",
      "Epoch 93/100\n",
      "6s - loss: 0.4027 - acc: 0.8071 - val_loss: 0.5868 - val_acc: 0.7205\n",
      "Epoch 94/100\n",
      "6s - loss: 0.4044 - acc: 0.8016 - val_loss: 0.5927 - val_acc: 0.7185\n",
      "Epoch 95/100\n",
      "6s - loss: 0.3980 - acc: 0.8068 - val_loss: 0.5937 - val_acc: 0.7195\n",
      "Epoch 96/100\n",
      "6s - loss: 0.3998 - acc: 0.8103 - val_loss: 0.5874 - val_acc: 0.7180\n",
      "Epoch 97/100\n",
      "6s - loss: 0.3926 - acc: 0.8115 - val_loss: 0.5942 - val_acc: 0.7150\n",
      "Epoch 98/100\n",
      "6s - loss: 0.3976 - acc: 0.8074 - val_loss: 0.6007 - val_acc: 0.7140\n",
      "Epoch 99/100\n",
      "6s - loss: 0.4022 - acc: 0.8053 - val_loss: 0.5912 - val_acc: 0.7205\n",
      "Epoch 100/100\n",
      "6s - loss: 0.3966 - acc: 0.8056 - val_loss: 0.5911 - val_acc: 0.7130\n"
     ]
    }
   ],
   "source": [
    "# fit CNN model using Adagrad optimizer\n",
    "model5 = cnn_model()\n",
    "model5.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=Adagrad(lr=0.01, epsilon=1e-08, decay=0.0),\n",
    "              metrics=['accuracy'])\n",
    "history5 = model5.fit(X_train, y_train, \n",
    "                     validation_data=(X_test, y_test), \n",
    "                     epochs=epochs, \n",
    "                     batch_size=batch_size,\n",
    "                     verbose=2)\n",
    "\n",
    "# fit CNN model using Adadelta optimizer\n",
    "model6 = cnn_model()\n",
    "model6.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=Adadelta(lr=1.0, rho=0.95, epsilon=1e-08, decay=0.0),\n",
    "              metrics=['accuracy'])\n",
    "history6 = model6.fit(X_train, y_train, \n",
    "                     validation_data=(X_test, y_test), \n",
    "                     epochs=epochs, \n",
    "                     batch_size=batch_size,\n",
    "                     verbose=2)\n",
    "\n",
    "# fit CNN model using RMSprop optimizer\n",
    "model7 = cnn_model()\n",
    "model7.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0),\n",
    "              metrics=['accuracy'])\n",
    "history7 = model7.fit(X_train, y_train, \n",
    "                     validation_data=(X_test, y_test), \n",
    "                     epochs=epochs, \n",
    "                     batch_size=batch_size,\n",
    "                     verbose=2)\n",
    "\n",
    "# fit CNN model using Adam optimizer\n",
    "model8 = cnn_model()\n",
    "model8.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0),\n",
    "              metrics=['accuracy'])\n",
    "history8 = model8.fit(X_train, y_train, \n",
    "                     validation_data=(X_test, y_test), \n",
    "                     epochs=epochs, \n",
    "                     batch_size=batch_size,\n",
    "                     verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare model accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BKIg2Dn2R1D0JzyjngKsAXFGUPtrXVRKt6iDt5hl0bqs/HFyOcI9AqLAVTAas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izTpXloOw34TpgjNvlS2MLKL8Vp9R8fviGpc3Nzw9/fn7Nnz1JXW8Nq1Skmm8+LRCdutTgIGL8e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aVk2BV7T4oyK6SEvBFIpMWFOwsHFfagFDGaNbEIogf0oKBB1CjIQYGRRCGYEhWR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\nkiSpRwZwSZIkqUcGcEmSJKlHfwILQ/3VFuZBsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12dd01518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "plt.plot(range(epochs),history1.history['val_acc'],label='Constant lr')\n",
    "plt.plot(range(epochs),history2.history['val_acc'],label='Time-based')\n",
    "plt.plot(range(epochs),history3.history['val_acc'],label='Step decay')\n",
    "plt.plot(range(epochs),history4.history['val_acc'],label='Exponential decay')\n",
    "plt.plot(range(epochs),history5.history['val_acc'],label='Adagrad')\n",
    "plt.plot(range(epochs),history6.history['val_acc'],label='Adadelta')\n",
    "plt.plot(range(epochs),history7.history['val_acc'],label='RMSprop')\n",
    "plt.plot(range(epochs),history8.history['val_acc'],label='Adam')\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel('epochs')\n",
    "plt.xlim([0,epochs])\n",
    "plt.ylabel('accuracy om validation set')\n",
    "plt.grid(True)\n",
    "plt.title(\"Comparing Model Accuracy\")\n",
    "plt.show()\n",
    "fig.savefig('img/compare-accuracy.jpg')\n",
    "plt.close(fig)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}