Miscellaneous/learning_rate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Learning Rate Schedules and Adaptive Learning Rate Methods for Deep Learning](https://towardsdatascience.com/learning-rate-schedules-and-adaptive-learning-rate-methods-for-deep-learning-2c8f433990d1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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",
    "\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.utils import np_utils\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.models import model_from_json\n",
    "\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "\n",
    "from keras.optimizers import SGD, Adagrad, Adadelta, RMSprop, Adam\n",
    "from keras.callbacks import LearningRateScheduler\n",
    "\n",
    "import tensorflow as tf\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load and prepare CIFAR-10 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "NUM_CLASSES = 2\n",
    "EPOCHS = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Input image dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rows, img_cols = 32, 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For now, we will only consider images of **cats** and **dogs**!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.array(y_train[train_picks]==5, dtype=int)\n",
    "y_test = np.array(y_test[test_picks]==5, dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train[train_picks]\n",
    "X_test = X_test[test_picks]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reshape the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train /= 255\n",
    "X_test /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (10000, 32, 32, 3)\n",
      "Train samples: 10000\n",
      "Test samples: 2000\n"
     ]
    }
   ],
   "source": [
    "print('X_train shape: {}\\nTrain samples: {}\\nTest samples: {}'.format(X_train.shape, X_train.shape[0], X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert class vectors to binary class matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [
    "#### Method to build CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_cnn(optimizer):\n",
    "    model = Sequential([\n",
    "        Conv2D(4, kernel_size=(3, 3), activation='relu', input_shape=input_shape),\n",
    "        Conv2D(8, (3, 3), activation='relu'),\n",
    "        MaxPooling2D(pool_size=(2, 2)),\n",
    "        Dropout(0.25),\n",
    "        Flatten(),\n",
    "        Dense(16, activation='relu'),\n",
    "        Dropout(0.5),\n",
    "        Dense(2, activation='softmax'),\n",
    "    ])\n",
    "    \n",
    "    model.compile(optimizer=optimizer,\n",
    "                 loss=keras.losses.categorical_crossentropy,\n",
    "                 metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method to plot histories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(method, history):\n",
    "    fig = plt.figure()\n",
    "    \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()\n",
    "    \n",
    "    plt.xlim([1, EPOCHS])\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    \n",
    "    plt.title(method)\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method to plot learning rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_lr(method, loss_history):\n",
    "    fig = plt.figure()\n",
    "\n",
    "    plt.plot(range(1, EPOCHS+1), loss_history.lr, label='learning rate')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.xlim([1, EPOCHS+1])\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Learning rate')\n",
    "\n",
    "    plt.title('Learning Rate (' + method + ')')\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Constant learning rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define SGD optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.1\n",
    "sgd = SGD(lr=learning_rate, momentum=0.0, decay=0.0, nesterov=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "clr_model = build_cnn(sgd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      " - 2s - loss: 0.6880 - acc: 0.5357 - val_loss: 0.6733 - val_acc: 0.5975\n",
      "Epoch 2/100\n",
      " - 2s - loss: 0.6778 - acc: 0.5809 - val_loss: 0.6705 - val_acc: 0.5885\n",
      "Epoch 3/100\n",
      " - 2s - loss: 0.6708 - acc: 0.5914 - val_loss: 0.6536 - val_acc: 0.6335\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.6622 - acc: 0.6015 - val_loss: 0.6581 - val_acc: 0.6245\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.6568 - acc: 0.6162 - val_loss: 0.6476 - val_acc: 0.6235\n",
      "Epoch 6/100\n",
      " - 2s - loss: 0.6470 - acc: 0.6256 - val_loss: 0.6459 - val_acc: 0.6235\n",
      "Epoch 7/100\n",
      " - 2s - loss: 0.6390 - acc: 0.6384 - val_loss: 0.6285 - val_acc: 0.6435\n",
      "Epoch 8/100\n",
      " - 2s - loss: 0.6304 - acc: 0.6443 - val_loss: 0.6453 - val_acc: 0.6325\n",
      "Epoch 9/100\n",
      " - 2s - loss: 0.6248 - acc: 0.6507 - val_loss: 0.6105 - val_acc: 0.6630\n",
      "Epoch 10/100\n",
      " - 2s - loss: 0.6179 - acc: 0.6533 - val_loss: 0.6272 - val_acc: 0.6390\n",
      "Epoch 11/100\n",
      " - 2s - loss: 0.6081 - acc: 0.6619 - val_loss: 0.6803 - val_acc: 0.5715\n",
      "Epoch 12/100\n",
      " - 2s - loss: 0.6048 - acc: 0.6688 - val_loss: 0.5911 - val_acc: 0.6745\n",
      "Epoch 13/100\n",
      " - 2s - loss: 0.5991 - acc: 0.6758 - val_loss: 0.6057 - val_acc: 0.6575\n",
      "Epoch 14/100\n",
      " - 2s - loss: 0.5887 - acc: 0.6807 - val_loss: 0.6214 - val_acc: 0.6455\n",
      "Epoch 15/100\n",
      " - 2s - loss: 0.5910 - acc: 0.6785 - val_loss: 0.5855 - val_acc: 0.6905\n",
      "Epoch 16/100\n",
      " - 2s - loss: 0.5802 - acc: 0.6838 - val_loss: 0.5876 - val_acc: 0.6775\n",
      "Epoch 17/100\n",
      " - 2s - loss: 0.5801 - acc: 0.6837 - val_loss: 0.5737 - val_acc: 0.6845\n",
      "Epoch 18/100\n",
      " - 2s - loss: 0.5730 - acc: 0.6904 - val_loss: 0.5942 - val_acc: 0.6675\n",
      "Epoch 19/100\n",
      " - 2s - loss: 0.5692 - acc: 0.6927 - val_loss: 0.5796 - val_acc: 0.6785\n",
      "Epoch 20/100\n",
      " - 2s - loss: 0.5595 - acc: 0.6983 - val_loss: 0.5904 - val_acc: 0.6820\n",
      "Epoch 21/100\n",
      " - 2s - loss: 0.5650 - acc: 0.6983 - val_loss: 0.5900 - val_acc: 0.6845\n",
      "Epoch 22/100\n",
      " - 2s - loss: 0.5577 - acc: 0.6972 - val_loss: 0.5841 - val_acc: 0.6855\n",
      "Epoch 23/100\n",
      " - 2s - loss: 0.5451 - acc: 0.7120 - val_loss: 0.5774 - val_acc: 0.6845\n",
      "Epoch 24/100\n",
      " - 2s - loss: 0.5450 - acc: 0.7101 - val_loss: 0.5649 - val_acc: 0.6920\n",
      "Epoch 25/100\n",
      " - 2s - loss: 0.5436 - acc: 0.7049 - val_loss: 0.5649 - val_acc: 0.7025\n",
      "Epoch 26/100\n",
      " - 2s - loss: 0.5441 - acc: 0.7099 - val_loss: 0.5732 - val_acc: 0.6970\n",
      "Epoch 27/100\n",
      " - 2s - loss: 0.5377 - acc: 0.7151 - val_loss: 0.5822 - val_acc: 0.6890\n",
      "Epoch 28/100\n",
      " - 2s - loss: 0.5292 - acc: 0.7137 - val_loss: 0.5707 - val_acc: 0.6845\n",
      "Epoch 29/100\n",
      " - 2s - loss: 0.5364 - acc: 0.7151 - val_loss: 0.5876 - val_acc: 0.6775\n",
      "Epoch 30/100\n",
      " - 2s - loss: 0.5290 - acc: 0.7183 - val_loss: 0.5950 - val_acc: 0.6635\n",
      "Epoch 31/100\n",
      " - 2s - loss: 0.5277 - acc: 0.7174 - val_loss: 0.5810 - val_acc: 0.6765\n",
      "Epoch 32/100\n",
      " - 2s - loss: 0.5208 - acc: 0.7267 - val_loss: 0.6026 - val_acc: 0.6740\n",
      "Epoch 33/100\n",
      " - 2s - loss: 0.5148 - acc: 0.7295 - val_loss: 0.5968 - val_acc: 0.6590\n",
      "Epoch 34/100\n",
      " - 2s - loss: 0.5126 - acc: 0.7311 - val_loss: 0.5596 - val_acc: 0.7065\n",
      "Epoch 35/100\n",
      " - 2s - loss: 0.5145 - acc: 0.7334 - val_loss: 0.5638 - val_acc: 0.7045\n",
      "Epoch 36/100\n",
      " - 2s - loss: 0.5113 - acc: 0.7309 - val_loss: 0.5749 - val_acc: 0.6935\n",
      "Epoch 37/100\n",
      " - 2s - loss: 0.5084 - acc: 0.7297 - val_loss: 0.6042 - val_acc: 0.6745\n",
      "Epoch 38/100\n",
      " - 2s - loss: 0.5057 - acc: 0.7305 - val_loss: 0.5690 - val_acc: 0.6965\n",
      "Epoch 39/100\n",
      " - 2s - loss: 0.5087 - acc: 0.7298 - val_loss: 0.5728 - val_acc: 0.6940\n",
      "Epoch 40/100\n",
      " - 2s - loss: 0.4960 - acc: 0.7366 - val_loss: 0.5629 - val_acc: 0.6950\n",
      "Epoch 41/100\n",
      " - 2s - loss: 0.4950 - acc: 0.7379 - val_loss: 0.5607 - val_acc: 0.7040\n",
      "Epoch 42/100\n",
      " - 2s - loss: 0.4914 - acc: 0.7438 - val_loss: 0.5674 - val_acc: 0.6970\n",
      "Epoch 43/100\n",
      " - 2s - loss: 0.4961 - acc: 0.7358 - val_loss: 0.5695 - val_acc: 0.7025\n",
      "Epoch 44/100\n",
      " - 2s - loss: 0.4855 - acc: 0.7468 - val_loss: 0.5723 - val_acc: 0.7020\n",
      "Epoch 45/100\n",
      " - 2s - loss: 0.4877 - acc: 0.7435 - val_loss: 0.5830 - val_acc: 0.6940\n",
      "Epoch 46/100\n",
      " - 2s - loss: 0.4810 - acc: 0.7482 - val_loss: 0.5711 - val_acc: 0.7050\n",
      "Epoch 47/100\n",
      " - 2s - loss: 0.4839 - acc: 0.7414 - val_loss: 0.5702 - val_acc: 0.6960\n",
      "Epoch 48/100\n",
      " - 2s - loss: 0.4795 - acc: 0.7485 - val_loss: 0.5973 - val_acc: 0.6655\n",
      "Epoch 49/100\n",
      " - 2s - loss: 0.4719 - acc: 0.7465 - val_loss: 0.5737 - val_acc: 0.7025\n",
      "Epoch 50/100\n",
      " - 2s - loss: 0.4730 - acc: 0.7479 - val_loss: 0.5638 - val_acc: 0.7110\n",
      "Epoch 51/100\n",
      " - 2s - loss: 0.4660 - acc: 0.7524 - val_loss: 0.5613 - val_acc: 0.7130\n",
      "Epoch 52/100\n",
      " - 2s - loss: 0.4635 - acc: 0.7527 - val_loss: 0.5673 - val_acc: 0.7080\n",
      "Epoch 53/100\n",
      " - 2s - loss: 0.4712 - acc: 0.7503 - val_loss: 0.5936 - val_acc: 0.7000\n",
      "Epoch 54/100\n",
      " - 2s - loss: 0.4629 - acc: 0.7590 - val_loss: 0.5676 - val_acc: 0.6995\n",
      "Epoch 55/100\n",
      " - 2s - loss: 0.4583 - acc: 0.7528 - val_loss: 0.5699 - val_acc: 0.7000\n",
      "Epoch 56/100\n",
      " - 2s - loss: 0.4609 - acc: 0.7536 - val_loss: 0.5575 - val_acc: 0.7125\n",
      "Epoch 57/100\n",
      " - 2s - loss: 0.4615 - acc: 0.7512 - val_loss: 0.5638 - val_acc: 0.7095\n",
      "Epoch 58/100\n",
      " - 2s - loss: 0.4571 - acc: 0.7539 - val_loss: 0.6004 - val_acc: 0.6990\n",
      "Epoch 59/100\n",
      " - 2s - loss: 0.4561 - acc: 0.7580 - val_loss: 0.5667 - val_acc: 0.7090\n",
      "Epoch 60/100\n",
      " - 2s - loss: 0.4583 - acc: 0.7526 - val_loss: 0.5909 - val_acc: 0.6910\n",
      "Epoch 61/100\n",
      " - 2s - loss: 0.4489 - acc: 0.7583 - val_loss: 0.5553 - val_acc: 0.7085\n",
      "Epoch 62/100\n",
      " - 2s - loss: 0.4537 - acc: 0.7614 - val_loss: 0.6185 - val_acc: 0.6925\n",
      "Epoch 63/100\n",
      " - 2s - loss: 0.4501 - acc: 0.7616 - val_loss: 0.5849 - val_acc: 0.6970\n",
      "Epoch 64/100\n",
      " - 2s - loss: 0.4552 - acc: 0.7561 - val_loss: 0.5725 - val_acc: 0.7115\n",
      "Epoch 65/100\n",
      " - 2s - loss: 0.4384 - acc: 0.7657 - val_loss: 0.5675 - val_acc: 0.7005\n",
      "Epoch 66/100\n",
      " - 2s - loss: 0.4382 - acc: 0.7700 - val_loss: 0.5980 - val_acc: 0.6985\n",
      "Epoch 67/100\n",
      " - 2s - loss: 0.4450 - acc: 0.7625 - val_loss: 0.5821 - val_acc: 0.7105\n",
      "Epoch 68/100\n",
      " - 2s - loss: 0.4470 - acc: 0.7605 - val_loss: 0.5616 - val_acc: 0.7060\n",
      "Epoch 69/100\n",
      " - 2s - loss: 0.4375 - acc: 0.7680 - val_loss: 0.5856 - val_acc: 0.6910\n",
      "Epoch 70/100\n",
      " - 2s - loss: 0.4430 - acc: 0.7656 - val_loss: 0.5704 - val_acc: 0.7095\n",
      "Epoch 71/100\n",
      " - 2s - loss: 0.4363 - acc: 0.7684 - val_loss: 0.5785 - val_acc: 0.7075\n",
      "Epoch 72/100\n",
      " - 2s - loss: 0.4361 - acc: 0.7677 - val_loss: 0.5626 - val_acc: 0.7165\n",
      "Epoch 73/100\n",
      " - 2s - loss: 0.4404 - acc: 0.7669 - val_loss: 0.5589 - val_acc: 0.7050\n",
      "Epoch 74/100\n",
      " - 2s - loss: 0.4248 - acc: 0.7775 - val_loss: 0.8021 - val_acc: 0.6205\n",
      "Epoch 75/100\n",
      " - 2s - loss: 0.4421 - acc: 0.7654 - val_loss: 0.5639 - val_acc: 0.7090\n",
      "Epoch 76/100\n",
      " - 2s - loss: 0.4283 - acc: 0.7704 - val_loss: 0.5992 - val_acc: 0.6840\n",
      "Epoch 77/100\n",
      " - 2s - loss: 0.4333 - acc: 0.7727 - val_loss: 0.5980 - val_acc: 0.7000\n",
      "Epoch 78/100\n",
      " - 2s - loss: 0.4298 - acc: 0.7775 - val_loss: 0.5600 - val_acc: 0.7200\n",
      "Epoch 79/100\n",
      " - 2s - loss: 0.4257 - acc: 0.7749 - val_loss: 0.6022 - val_acc: 0.6900\n",
      "Epoch 80/100\n",
      " - 2s - loss: 0.4227 - acc: 0.7717 - val_loss: 0.5581 - val_acc: 0.7080\n",
      "Epoch 81/100\n",
      " - 2s - loss: 0.4247 - acc: 0.7779 - val_loss: 0.5741 - val_acc: 0.7105\n",
      "Epoch 82/100\n",
      " - 2s - loss: 0.4253 - acc: 0.7710 - val_loss: 0.6141 - val_acc: 0.6980\n",
      "Epoch 83/100\n",
      " - 2s - loss: 0.4195 - acc: 0.7789 - val_loss: 0.5722 - val_acc: 0.7030\n",
      "Epoch 84/100\n",
      " - 2s - loss: 0.4146 - acc: 0.7847 - val_loss: 0.5724 - val_acc: 0.7105\n",
      "Epoch 85/100\n",
      " - 2s - loss: 0.4113 - acc: 0.7815 - val_loss: 0.5690 - val_acc: 0.7145\n",
      "Epoch 86/100\n",
      " - 2s - loss: 0.4091 - acc: 0.7791 - val_loss: 0.5785 - val_acc: 0.6980\n",
      "Epoch 87/100\n",
      " - 2s - loss: 0.4233 - acc: 0.7710 - val_loss: 0.6209 - val_acc: 0.6710\n",
      "Epoch 88/100\n",
      " - 2s - loss: 0.4198 - acc: 0.7721 - val_loss: 0.5676 - val_acc: 0.7110\n",
      "Epoch 89/100\n",
      " - 2s - loss: 0.4219 - acc: 0.7766 - val_loss: 0.5591 - val_acc: 0.7170\n",
      "Epoch 90/100\n",
      " - 2s - loss: 0.4180 - acc: 0.7769 - val_loss: 0.5815 - val_acc: 0.7175\n",
      "Epoch 91/100\n",
      " - 2s - loss: 0.4121 - acc: 0.7818 - val_loss: 0.5908 - val_acc: 0.6915\n",
      "Epoch 92/100\n",
      " - 2s - loss: 0.4194 - acc: 0.7749 - val_loss: 0.5833 - val_acc: 0.6905\n",
      "Epoch 93/100\n",
      " - 2s - loss: 0.4192 - acc: 0.7721 - val_loss: 0.6789 - val_acc: 0.6320\n",
      "Epoch 94/100\n",
      " - 2s - loss: 0.4129 - acc: 0.7806 - val_loss: 0.5665 - val_acc: 0.7125\n",
      "Epoch 95/100\n",
      " - 2s - loss: 0.4173 - acc: 0.7767 - val_loss: 0.5665 - val_acc: 0.7115\n",
      "Epoch 96/100\n",
      " - 2s - loss: 0.4047 - acc: 0.7871 - val_loss: 0.5711 - val_acc: 0.7145\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/100\n",
      " - 2s - loss: 0.4085 - acc: 0.7789 - val_loss: 0.5859 - val_acc: 0.7075\n",
      "Epoch 98/100\n",
      " - 2s - loss: 0.4080 - acc: 0.7802 - val_loss: 0.5797 - val_acc: 0.7170\n",
      "Epoch 99/100\n",
      " - 2s - loss: 0.4175 - acc: 0.7759 - val_loss: 0.5835 - val_acc: 0.7180\n",
      "Epoch 100/100\n",
      " - 2s - loss: 0.4069 - acc: 0.7846 - val_loss: 0.5697 - val_acc: 0.7095\n"
     ]
    }
   ],
   "source": [
    "clr_history = clr_model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=2, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot('Constant Learning Rate', clr_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time-based decay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define SGD optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.1\n",
    "decay_rate = learning_rate / EPOCHS\n",
    "momentum = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = SGD(lr=learning_rate, momentum=momentum, decay=decay_rate, nesterov=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "tbd_model = build_cnn(sgd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      " - 2s - loss: 0.6928 - acc: 0.5093 - val_loss: 0.6839 - val_acc: 0.5610\n",
      "Epoch 2/100\n",
      " - 2s - loss: 0.6825 - acc: 0.5557 - val_loss: 0.6639 - val_acc: 0.5975\n",
      "Epoch 3/100\n",
      " - 2s - loss: 0.6705 - acc: 0.5860 - val_loss: 0.6587 - val_acc: 0.6280\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.6648 - acc: 0.5956 - val_loss: 0.6498 - val_acc: 0.6265\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.6514 - acc: 0.6118 - val_loss: 0.6297 - val_acc: 0.6510\n",
      "Epoch 6/100\n",
      " - 2s - loss: 0.6436 - acc: 0.6307 - val_loss: 0.6232 - val_acc: 0.6655\n",
      "Epoch 7/100\n",
      " - 2s - loss: 0.6367 - acc: 0.6367 - val_loss: 0.6238 - val_acc: 0.6330\n",
      "Epoch 8/100\n",
      " - 2s - loss: 0.6240 - acc: 0.6505 - val_loss: 0.6239 - val_acc: 0.6350\n",
      "Epoch 9/100\n",
      " - 2s - loss: 0.6189 - acc: 0.6525 - val_loss: 0.6284 - val_acc: 0.6425\n",
      "Epoch 10/100\n",
      " - 2s - loss: 0.6138 - acc: 0.6621 - val_loss: 0.6009 - val_acc: 0.6740\n",
      "Epoch 11/100\n",
      " - 2s - loss: 0.6031 - acc: 0.6641 - val_loss: 0.5934 - val_acc: 0.6670\n",
      "Epoch 12/100\n",
      " - 2s - loss: 0.5974 - acc: 0.6737 - val_loss: 0.5928 - val_acc: 0.6690\n",
      "Epoch 13/100\n",
      " - 2s - loss: 0.5936 - acc: 0.6748 - val_loss: 0.5926 - val_acc: 0.6735\n",
      "Epoch 14/100\n",
      " - 2s - loss: 0.5913 - acc: 0.6789 - val_loss: 0.5856 - val_acc: 0.6755\n",
      "Epoch 15/100\n",
      " - 2s - loss: 0.5854 - acc: 0.6747 - val_loss: 0.5864 - val_acc: 0.6830\n",
      "Epoch 16/100\n",
      " - 2s - loss: 0.5839 - acc: 0.6851 - val_loss: 0.5813 - val_acc: 0.6840\n",
      "Epoch 17/100\n",
      " - 2s - loss: 0.5781 - acc: 0.6888 - val_loss: 0.5805 - val_acc: 0.6835\n",
      "Epoch 18/100\n",
      " - 2s - loss: 0.5752 - acc: 0.6899 - val_loss: 0.5899 - val_acc: 0.6755\n",
      "Epoch 19/100\n",
      " - 2s - loss: 0.5716 - acc: 0.6885 - val_loss: 0.5847 - val_acc: 0.6705\n",
      "Epoch 20/100\n",
      " - 2s - loss: 0.5681 - acc: 0.6961 - val_loss: 0.5773 - val_acc: 0.6845\n",
      "Epoch 21/100\n",
      " - 2s - loss: 0.5651 - acc: 0.6979 - val_loss: 0.5737 - val_acc: 0.6835\n",
      "Epoch 22/100\n",
      " - 2s - loss: 0.5602 - acc: 0.7015 - val_loss: 0.5741 - val_acc: 0.6940\n",
      "Epoch 23/100\n",
      " - 2s - loss: 0.5602 - acc: 0.7007 - val_loss: 0.5685 - val_acc: 0.6930\n",
      "Epoch 24/100\n",
      " - 2s - loss: 0.5587 - acc: 0.7016 - val_loss: 0.5715 - val_acc: 0.6945\n",
      "Epoch 25/100\n",
      " - 2s - loss: 0.5571 - acc: 0.7069 - val_loss: 0.5672 - val_acc: 0.6940\n",
      "Epoch 26/100\n",
      " - 2s - loss: 0.5562 - acc: 0.7012 - val_loss: 0.5807 - val_acc: 0.6860\n",
      "Epoch 27/100\n",
      " - 2s - loss: 0.5510 - acc: 0.7108 - val_loss: 0.5685 - val_acc: 0.6945\n",
      "Epoch 28/100\n",
      " - 2s - loss: 0.5496 - acc: 0.7030 - val_loss: 0.5704 - val_acc: 0.6860\n",
      "Epoch 29/100\n",
      " - 2s - loss: 0.5486 - acc: 0.7092 - val_loss: 0.5693 - val_acc: 0.6955\n",
      "Epoch 30/100\n",
      " - 2s - loss: 0.5507 - acc: 0.7111 - val_loss: 0.5685 - val_acc: 0.6900\n",
      "Epoch 31/100\n",
      " - 2s - loss: 0.5476 - acc: 0.7092 - val_loss: 0.5691 - val_acc: 0.6820\n",
      "Epoch 32/100\n",
      " - 2s - loss: 0.5488 - acc: 0.7089 - val_loss: 0.5630 - val_acc: 0.6940\n",
      "Epoch 33/100\n",
      " - 2s - loss: 0.5438 - acc: 0.7099 - val_loss: 0.5658 - val_acc: 0.6995\n",
      "Epoch 34/100\n",
      " - 2s - loss: 0.5408 - acc: 0.7146 - val_loss: 0.5678 - val_acc: 0.6965\n",
      "Epoch 35/100\n",
      " - 2s - loss: 0.5365 - acc: 0.7187 - val_loss: 0.5651 - val_acc: 0.6930\n",
      "Epoch 36/100\n",
      " - 2s - loss: 0.5388 - acc: 0.7200 - val_loss: 0.5624 - val_acc: 0.6980\n",
      "Epoch 37/100\n",
      " - 2s - loss: 0.5282 - acc: 0.7215 - val_loss: 0.5642 - val_acc: 0.6930\n",
      "Epoch 38/100\n",
      " - 2s - loss: 0.5348 - acc: 0.7176 - val_loss: 0.5638 - val_acc: 0.6985\n",
      "Epoch 39/100\n",
      " - 2s - loss: 0.5279 - acc: 0.7241 - val_loss: 0.5625 - val_acc: 0.6980\n",
      "Epoch 40/100\n",
      " - 2s - loss: 0.5286 - acc: 0.7260 - val_loss: 0.5615 - val_acc: 0.6915\n",
      "Epoch 41/100\n",
      " - 2s - loss: 0.5229 - acc: 0.7267 - val_loss: 0.5621 - val_acc: 0.6925\n",
      "Epoch 42/100\n",
      " - 2s - loss: 0.5269 - acc: 0.7235 - val_loss: 0.5674 - val_acc: 0.6945\n",
      "Epoch 43/100\n",
      " - 2s - loss: 0.5206 - acc: 0.7249 - val_loss: 0.5578 - val_acc: 0.6975\n",
      "Epoch 44/100\n",
      " - 2s - loss: 0.5313 - acc: 0.7236 - val_loss: 0.5643 - val_acc: 0.6915\n",
      "Epoch 45/100\n",
      " - 2s - loss: 0.5251 - acc: 0.7218 - val_loss: 0.5612 - val_acc: 0.6965\n",
      "Epoch 46/100\n",
      " - 2s - loss: 0.5221 - acc: 0.7268 - val_loss: 0.5610 - val_acc: 0.6995\n",
      "Epoch 47/100\n",
      " - 2s - loss: 0.5189 - acc: 0.7335 - val_loss: 0.5620 - val_acc: 0.7015\n",
      "Epoch 48/100\n",
      " - 2s - loss: 0.5144 - acc: 0.7318 - val_loss: 0.5614 - val_acc: 0.6970\n",
      "Epoch 49/100\n",
      " - 2s - loss: 0.5202 - acc: 0.7251 - val_loss: 0.5620 - val_acc: 0.6950\n",
      "Epoch 50/100\n",
      " - 2s - loss: 0.5127 - acc: 0.7310 - val_loss: 0.5595 - val_acc: 0.6930\n",
      "Epoch 51/100\n",
      " - 2s - loss: 0.5138 - acc: 0.7342 - val_loss: 0.5602 - val_acc: 0.6965\n",
      "Epoch 52/100\n",
      " - 2s - loss: 0.5113 - acc: 0.7384 - val_loss: 0.5616 - val_acc: 0.6970\n",
      "Epoch 53/100\n",
      " - 2s - loss: 0.5029 - acc: 0.7412 - val_loss: 0.5624 - val_acc: 0.6920\n",
      "Epoch 54/100\n",
      " - 2s - loss: 0.5089 - acc: 0.7352 - val_loss: 0.5603 - val_acc: 0.6940\n",
      "Epoch 55/100\n",
      " - 2s - loss: 0.5067 - acc: 0.7386 - val_loss: 0.5567 - val_acc: 0.7090\n",
      "Epoch 56/100\n",
      " - 2s - loss: 0.5054 - acc: 0.7319 - val_loss: 0.5566 - val_acc: 0.7020\n",
      "Epoch 57/100\n",
      " - 2s - loss: 0.5109 - acc: 0.7349 - val_loss: 0.5618 - val_acc: 0.6935\n",
      "Epoch 58/100\n",
      " - 2s - loss: 0.5053 - acc: 0.7316 - val_loss: 0.5582 - val_acc: 0.7005\n",
      "Epoch 59/100\n",
      " - 2s - loss: 0.5046 - acc: 0.7396 - val_loss: 0.5606 - val_acc: 0.6940\n",
      "Epoch 60/100\n",
      " - 2s - loss: 0.5031 - acc: 0.7342 - val_loss: 0.5593 - val_acc: 0.6950\n",
      "Epoch 61/100\n",
      " - 2s - loss: 0.5066 - acc: 0.7356 - val_loss: 0.5606 - val_acc: 0.6960\n",
      "Epoch 62/100\n",
      " - 2s - loss: 0.5017 - acc: 0.7396 - val_loss: 0.5623 - val_acc: 0.7025\n",
      "Epoch 63/100\n",
      " - 2s - loss: 0.4964 - acc: 0.7434 - val_loss: 0.5586 - val_acc: 0.6980\n",
      "Epoch 64/100\n",
      " - 2s - loss: 0.4916 - acc: 0.7409 - val_loss: 0.5581 - val_acc: 0.6955\n",
      "Epoch 65/100\n",
      " - 2s - loss: 0.4979 - acc: 0.7386 - val_loss: 0.5577 - val_acc: 0.6995\n",
      "Epoch 66/100\n",
      " - 2s - loss: 0.4985 - acc: 0.7416 - val_loss: 0.5575 - val_acc: 0.6990\n",
      "Epoch 67/100\n",
      " - 2s - loss: 0.4991 - acc: 0.7438 - val_loss: 0.5615 - val_acc: 0.6930\n",
      "Epoch 68/100\n",
      " - 2s - loss: 0.4942 - acc: 0.7404 - val_loss: 0.5570 - val_acc: 0.7020\n",
      "Epoch 69/100\n",
      " - 2s - loss: 0.4951 - acc: 0.7402 - val_loss: 0.5606 - val_acc: 0.7025\n",
      "Epoch 70/100\n",
      " - 2s - loss: 0.4942 - acc: 0.7454 - val_loss: 0.5586 - val_acc: 0.7020\n",
      "Epoch 71/100\n",
      " - 2s - loss: 0.4966 - acc: 0.7408 - val_loss: 0.5642 - val_acc: 0.7060\n",
      "Epoch 72/100\n",
      " - 2s - loss: 0.4863 - acc: 0.7499 - val_loss: 0.5595 - val_acc: 0.7035\n",
      "Epoch 73/100\n",
      " - 2s - loss: 0.4863 - acc: 0.7526 - val_loss: 0.5565 - val_acc: 0.7000\n",
      "Epoch 74/100\n",
      " - 2s - loss: 0.4906 - acc: 0.7440 - val_loss: 0.5610 - val_acc: 0.6945\n",
      "Epoch 75/100\n",
      " - 2s - loss: 0.4925 - acc: 0.7509 - val_loss: 0.5558 - val_acc: 0.7010\n",
      "Epoch 76/100\n",
      " - 2s - loss: 0.4905 - acc: 0.7458 - val_loss: 0.5552 - val_acc: 0.7060\n",
      "Epoch 77/100\n",
      " - 2s - loss: 0.4864 - acc: 0.7478 - val_loss: 0.5562 - val_acc: 0.6995\n",
      "Epoch 78/100\n",
      " - 2s - loss: 0.4814 - acc: 0.7487 - val_loss: 0.5595 - val_acc: 0.6960\n",
      "Epoch 79/100\n",
      " - 2s - loss: 0.4814 - acc: 0.7511 - val_loss: 0.5565 - val_acc: 0.7035\n",
      "Epoch 80/100\n",
      " - 2s - loss: 0.4828 - acc: 0.7480 - val_loss: 0.5567 - val_acc: 0.6975\n",
      "Epoch 81/100\n",
      " - 2s - loss: 0.4817 - acc: 0.7501 - val_loss: 0.5538 - val_acc: 0.7050\n",
      "Epoch 82/100\n",
      " - 2s - loss: 0.4793 - acc: 0.7532 - val_loss: 0.5556 - val_acc: 0.6935\n",
      "Epoch 83/100\n",
      " - 2s - loss: 0.4848 - acc: 0.7496 - val_loss: 0.5541 - val_acc: 0.7045\n",
      "Epoch 84/100\n",
      " - 2s - loss: 0.4821 - acc: 0.7458 - val_loss: 0.5565 - val_acc: 0.7065\n",
      "Epoch 85/100\n",
      " - 2s - loss: 0.4831 - acc: 0.7538 - val_loss: 0.5575 - val_acc: 0.7030\n",
      "Epoch 86/100\n",
      " - 2s - loss: 0.4798 - acc: 0.7460 - val_loss: 0.5576 - val_acc: 0.7020\n",
      "Epoch 87/100\n",
      " - 2s - loss: 0.4756 - acc: 0.7507 - val_loss: 0.5604 - val_acc: 0.7025\n",
      "Epoch 88/100\n",
      " - 2s - loss: 0.4734 - acc: 0.7514 - val_loss: 0.5646 - val_acc: 0.7060\n",
      "Epoch 89/100\n",
      " - 2s - loss: 0.4770 - acc: 0.7518 - val_loss: 0.5675 - val_acc: 0.7055\n",
      "Epoch 90/100\n",
      " - 2s - loss: 0.4790 - acc: 0.7491 - val_loss: 0.5615 - val_acc: 0.7035\n",
      "Epoch 91/100\n",
      " - 2s - loss: 0.4799 - acc: 0.7500 - val_loss: 0.5617 - val_acc: 0.7045\n",
      "Epoch 92/100\n",
      " - 2s - loss: 0.4788 - acc: 0.7526 - val_loss: 0.5597 - val_acc: 0.6975\n",
      "Epoch 93/100\n",
      " - 2s - loss: 0.4783 - acc: 0.7516 - val_loss: 0.5620 - val_acc: 0.7050\n",
      "Epoch 94/100\n",
      " - 2s - loss: 0.4723 - acc: 0.7554 - val_loss: 0.5593 - val_acc: 0.7120\n",
      "Epoch 95/100\n",
      " - 2s - loss: 0.4728 - acc: 0.7511 - val_loss: 0.5679 - val_acc: 0.7045\n",
      "Epoch 96/100\n",
      " - 2s - loss: 0.4700 - acc: 0.7527 - val_loss: 0.5605 - val_acc: 0.7105\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/100\n",
      " - 2s - loss: 0.4689 - acc: 0.7548 - val_loss: 0.5594 - val_acc: 0.7120\n",
      "Epoch 98/100\n",
      " - 2s - loss: 0.4712 - acc: 0.7566 - val_loss: 0.5632 - val_acc: 0.7055\n",
      "Epoch 99/100\n",
      " - 2s - loss: 0.4680 - acc: 0.7521 - val_loss: 0.5605 - val_acc: 0.7055\n",
      "Epoch 100/100\n",
      " - 2s - loss: 0.4670 - acc: 0.7520 - val_loss: 0.5617 - val_acc: 0.7055\n"
     ]
    }
   ],
   "source": [
    "tbd_history = tbd_model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=2, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot('Time-based Decay', tbd_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step decay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define SGD optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "momentum = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = SGD(lr=0.0, momentum=momentum, decay=0.0, nesterov=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd_model = build_cnn(sgd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define step decay function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "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: {}'.format(step_decay(len(self.losses))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def step_decay(epoch):\n",
    "    initial_lr = 0.1\n",
    "    drop = 0.5\n",
    "    epochs_drop = 10.0\n",
    "    lr = initial_lr * math.pow(drop, math.floor(epoch/epochs_drop))\n",
    "    return lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn schedule callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_history = LossHistory()\n",
    "lr = LearningRateScheduler(step_decay)\n",
    "callbacks_list = [loss_history, lr]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      " - 2s - loss: 0.6934 - acc: 0.5091 - val_loss: 0.6909 - val_acc: 0.5050\n",
      "lr: 0.1\n",
      "Epoch 2/100\n",
      " - 2s - loss: 0.6820 - acc: 0.5634 - val_loss: 0.6864 - val_acc: 0.5345\n",
      "lr: 0.1\n",
      "Epoch 3/100\n",
      " - 2s - loss: 0.6693 - acc: 0.5930 - val_loss: 0.6668 - val_acc: 0.5950\n",
      "lr: 0.1\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.6606 - acc: 0.6040 - val_loss: 0.6475 - val_acc: 0.6190\n",
      "lr: 0.1\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.6492 - acc: 0.6178 - val_loss: 0.6520 - val_acc: 0.6230\n",
      "lr: 0.1\n",
      "Epoch 6/100\n",
      " - 2s - loss: 0.6394 - acc: 0.6290 - val_loss: 0.6242 - val_acc: 0.6465\n",
      "lr: 0.1\n",
      "Epoch 7/100\n",
      " - 2s - loss: 0.6299 - acc: 0.6413 - val_loss: 0.6199 - val_acc: 0.6435\n",
      "lr: 0.1\n",
      "Epoch 8/100\n",
      " - 2s - loss: 0.6153 - acc: 0.6629 - val_loss: 0.6385 - val_acc: 0.6365\n",
      "lr: 0.1\n",
      "Epoch 9/100\n",
      " - 2s - loss: 0.6128 - acc: 0.6601 - val_loss: 0.6186 - val_acc: 0.6470\n",
      "lr: 0.1\n",
      "Epoch 10/100\n",
      " - 2s - loss: 0.6072 - acc: 0.6641 - val_loss: 0.6228 - val_acc: 0.6680\n",
      "lr: 0.05\n",
      "Epoch 11/100\n",
      " - 2s - loss: 0.5861 - acc: 0.6856 - val_loss: 0.6013 - val_acc: 0.6705\n",
      "lr: 0.05\n",
      "Epoch 12/100\n",
      " - 2s - loss: 0.5781 - acc: 0.6918 - val_loss: 0.5854 - val_acc: 0.6830\n",
      "lr: 0.05\n",
      "Epoch 13/100\n",
      " - 2s - loss: 0.5782 - acc: 0.6930 - val_loss: 0.5841 - val_acc: 0.6920\n",
      "lr: 0.05\n",
      "Epoch 14/100\n",
      " - 2s - loss: 0.5710 - acc: 0.6984 - val_loss: 0.5863 - val_acc: 0.6775\n",
      "lr: 0.05\n",
      "Epoch 15/100\n",
      " - 2s - loss: 0.5675 - acc: 0.7039 - val_loss: 0.5935 - val_acc: 0.6640\n",
      "lr: 0.05\n",
      "Epoch 16/100\n",
      " - 2s - loss: 0.5630 - acc: 0.7052 - val_loss: 0.5874 - val_acc: 0.6695\n",
      "lr: 0.05\n",
      "Epoch 17/100\n",
      " - 2s - loss: 0.5567 - acc: 0.7095 - val_loss: 0.5797 - val_acc: 0.6795\n",
      "lr: 0.05\n",
      "Epoch 18/100\n",
      " - 2s - loss: 0.5596 - acc: 0.7097 - val_loss: 0.5918 - val_acc: 0.6720\n",
      "lr: 0.05\n",
      "Epoch 19/100\n",
      " - 2s - loss: 0.5518 - acc: 0.7124 - val_loss: 0.5872 - val_acc: 0.6755\n",
      "lr: 0.05\n",
      "Epoch 20/100\n",
      " - 2s - loss: 0.5476 - acc: 0.7126 - val_loss: 0.5735 - val_acc: 0.6810\n",
      "lr: 0.025\n",
      "Epoch 21/100\n",
      " - 2s - loss: 0.5347 - acc: 0.7224 - val_loss: 0.5719 - val_acc: 0.6875\n",
      "lr: 0.025\n",
      "Epoch 22/100\n",
      " - 2s - loss: 0.5308 - acc: 0.7219 - val_loss: 0.5710 - val_acc: 0.6885\n",
      "lr: 0.025\n",
      "Epoch 23/100\n",
      " - 2s - loss: 0.5260 - acc: 0.7253 - val_loss: 0.5785 - val_acc: 0.6840\n",
      "lr: 0.025\n",
      "Epoch 24/100\n",
      " - 2s - loss: 0.5275 - acc: 0.7235 - val_loss: 0.5701 - val_acc: 0.6820\n",
      "lr: 0.025\n",
      "Epoch 25/100\n",
      " - 2s - loss: 0.5230 - acc: 0.7290 - val_loss: 0.5783 - val_acc: 0.6870\n",
      "lr: 0.025\n",
      "Epoch 26/100\n",
      " - 2s - loss: 0.5236 - acc: 0.7258 - val_loss: 0.5806 - val_acc: 0.6890\n",
      "lr: 0.025\n",
      "Epoch 27/100\n",
      " - 2s - loss: 0.5234 - acc: 0.7309 - val_loss: 0.5716 - val_acc: 0.6890\n",
      "lr: 0.025\n",
      "Epoch 28/100\n",
      " - 2s - loss: 0.5140 - acc: 0.7333 - val_loss: 0.5826 - val_acc: 0.6895\n",
      "lr: 0.025\n",
      "Epoch 29/100\n",
      " - 2s - loss: 0.5181 - acc: 0.7331 - val_loss: 0.5679 - val_acc: 0.6910\n",
      "lr: 0.025\n",
      "Epoch 30/100\n",
      " - 2s - loss: 0.5133 - acc: 0.7316 - val_loss: 0.5697 - val_acc: 0.6940\n",
      "lr: 0.0125\n",
      "Epoch 31/100\n",
      " - 2s - loss: 0.5049 - acc: 0.7398 - val_loss: 0.5713 - val_acc: 0.6960\n",
      "lr: 0.0125\n",
      "Epoch 32/100\n",
      " - 2s - loss: 0.5075 - acc: 0.7411 - val_loss: 0.5678 - val_acc: 0.6940\n",
      "lr: 0.0125\n",
      "Epoch 33/100\n",
      " - 2s - loss: 0.5047 - acc: 0.7401 - val_loss: 0.5746 - val_acc: 0.6980\n",
      "lr: 0.0125\n",
      "Epoch 34/100\n",
      " - 2s - loss: 0.5075 - acc: 0.7424 - val_loss: 0.5646 - val_acc: 0.6870\n",
      "lr: 0.0125\n",
      "Epoch 35/100\n",
      " - 2s - loss: 0.5025 - acc: 0.7412 - val_loss: 0.5702 - val_acc: 0.6975\n",
      "lr: 0.0125\n",
      "Epoch 36/100\n",
      " - 2s - loss: 0.5071 - acc: 0.7464 - val_loss: 0.5675 - val_acc: 0.7000\n",
      "lr: 0.0125\n",
      "Epoch 37/100\n",
      " - 2s - loss: 0.4971 - acc: 0.7454 - val_loss: 0.5632 - val_acc: 0.7075\n",
      "lr: 0.0125\n",
      "Epoch 38/100\n",
      " - 2s - loss: 0.5025 - acc: 0.7355 - val_loss: 0.5646 - val_acc: 0.7055\n",
      "lr: 0.0125\n",
      "Epoch 39/100\n",
      " - 2s - loss: 0.4953 - acc: 0.7446 - val_loss: 0.5669 - val_acc: 0.7030\n",
      "lr: 0.0125\n",
      "Epoch 40/100\n",
      " - 2s - loss: 0.5013 - acc: 0.7453 - val_loss: 0.5675 - val_acc: 0.6975\n",
      "lr: 0.00625\n",
      "Epoch 41/100\n",
      " - 2s - loss: 0.4907 - acc: 0.7517 - val_loss: 0.5676 - val_acc: 0.6980\n",
      "lr: 0.00625\n",
      "Epoch 42/100\n",
      " - 2s - loss: 0.4871 - acc: 0.7494 - val_loss: 0.5679 - val_acc: 0.6970\n",
      "lr: 0.00625\n",
      "Epoch 43/100\n",
      " - 2s - loss: 0.4927 - acc: 0.7435 - val_loss: 0.5695 - val_acc: 0.6995\n",
      "lr: 0.00625\n",
      "Epoch 44/100\n",
      " - 2s - loss: 0.4941 - acc: 0.7443 - val_loss: 0.5646 - val_acc: 0.6985\n",
      "lr: 0.00625\n",
      "Epoch 45/100\n",
      " - 2s - loss: 0.4881 - acc: 0.7505 - val_loss: 0.5663 - val_acc: 0.6990\n",
      "lr: 0.00625\n",
      "Epoch 46/100\n",
      " - 2s - loss: 0.4889 - acc: 0.7517 - val_loss: 0.5642 - val_acc: 0.7030\n",
      "lr: 0.00625\n",
      "Epoch 47/100\n",
      " - 2s - loss: 0.4855 - acc: 0.7488 - val_loss: 0.5648 - val_acc: 0.7025\n",
      "lr: 0.00625\n",
      "Epoch 48/100\n",
      " - 2s - loss: 0.4865 - acc: 0.7533 - val_loss: 0.5659 - val_acc: 0.7040\n",
      "lr: 0.00625\n",
      "Epoch 49/100\n",
      " - 2s - loss: 0.4869 - acc: 0.7472 - val_loss: 0.5652 - val_acc: 0.7045\n",
      "lr: 0.00625\n",
      "Epoch 50/100\n",
      " - 2s - loss: 0.4949 - acc: 0.7492 - val_loss: 0.5622 - val_acc: 0.7025\n",
      "lr: 0.003125\n",
      "Epoch 51/100\n",
      " - 2s - loss: 0.4869 - acc: 0.7488 - val_loss: 0.5671 - val_acc: 0.6995\n",
      "lr: 0.003125\n",
      "Epoch 52/100\n",
      " - 2s - loss: 0.4878 - acc: 0.7502 - val_loss: 0.5648 - val_acc: 0.7020\n",
      "lr: 0.003125\n",
      "Epoch 53/100\n",
      " - 2s - loss: 0.4851 - acc: 0.7523 - val_loss: 0.5634 - val_acc: 0.6990\n",
      "lr: 0.003125\n",
      "Epoch 54/100\n",
      " - 2s - loss: 0.4773 - acc: 0.7530 - val_loss: 0.5666 - val_acc: 0.7000\n",
      "lr: 0.003125\n",
      "Epoch 55/100\n",
      " - 2s - loss: 0.4852 - acc: 0.7510 - val_loss: 0.5650 - val_acc: 0.7015\n",
      "lr: 0.003125\n",
      "Epoch 56/100\n",
      " - 2s - loss: 0.4782 - acc: 0.7518 - val_loss: 0.5676 - val_acc: 0.7020\n",
      "lr: 0.003125\n",
      "Epoch 57/100\n",
      " - 2s - loss: 0.4803 - acc: 0.7564 - val_loss: 0.5653 - val_acc: 0.6995\n",
      "lr: 0.003125\n",
      "Epoch 58/100\n",
      " - 2s - loss: 0.4807 - acc: 0.7544 - val_loss: 0.5672 - val_acc: 0.7015\n",
      "lr: 0.003125\n",
      "Epoch 59/100\n",
      " - 2s - loss: 0.4824 - acc: 0.7549 - val_loss: 0.5650 - val_acc: 0.7050\n",
      "lr: 0.003125\n",
      "Epoch 60/100\n",
      " - 2s - loss: 0.4802 - acc: 0.7543 - val_loss: 0.5643 - val_acc: 0.7025\n",
      "lr: 0.0015625\n",
      "Epoch 61/100\n",
      " - 2s - loss: 0.4816 - acc: 0.7509 - val_loss: 0.5640 - val_acc: 0.7040\n",
      "lr: 0.0015625\n",
      "Epoch 62/100\n",
      " - 2s - loss: 0.4818 - acc: 0.7560 - val_loss: 0.5643 - val_acc: 0.7015\n",
      "lr: 0.0015625\n",
      "Epoch 63/100\n",
      " - 2s - loss: 0.4791 - acc: 0.7533 - val_loss: 0.5651 - val_acc: 0.7025\n",
      "lr: 0.0015625\n",
      "Epoch 64/100\n",
      " - 2s - loss: 0.4788 - acc: 0.7526 - val_loss: 0.5654 - val_acc: 0.7015\n",
      "lr: 0.0015625\n",
      "Epoch 65/100\n",
      " - 2s - loss: 0.4761 - acc: 0.7613 - val_loss: 0.5645 - val_acc: 0.6995\n",
      "lr: 0.0015625\n",
      "Epoch 66/100\n",
      " - 2s - loss: 0.4922 - acc: 0.7474 - val_loss: 0.5658 - val_acc: 0.6975\n",
      "lr: 0.0015625\n",
      "Epoch 67/100\n",
      " - 2s - loss: 0.4737 - acc: 0.7591 - val_loss: 0.5679 - val_acc: 0.6975\n",
      "lr: 0.0015625\n",
      "Epoch 68/100\n",
      " - 2s - loss: 0.4787 - acc: 0.7524 - val_loss: 0.5653 - val_acc: 0.7010\n",
      "lr: 0.0015625\n",
      "Epoch 69/100\n",
      " - 2s - loss: 0.4812 - acc: 0.7494 - val_loss: 0.5655 - val_acc: 0.7030\n",
      "lr: 0.0015625\n",
      "Epoch 70/100\n",
      " - 2s - loss: 0.4792 - acc: 0.7547 - val_loss: 0.5647 - val_acc: 0.7060\n",
      "lr: 0.00078125\n",
      "Epoch 71/100\n",
      " - 2s - loss: 0.4784 - acc: 0.7507 - val_loss: 0.5649 - val_acc: 0.7000\n",
      "lr: 0.00078125\n",
      "Epoch 72/100\n",
      " - 2s - loss: 0.4773 - acc: 0.7538 - val_loss: 0.5657 - val_acc: 0.7010\n",
      "lr: 0.00078125\n",
      "Epoch 73/100\n",
      " - 2s - loss: 0.4805 - acc: 0.7517 - val_loss: 0.5642 - val_acc: 0.7005\n",
      "lr: 0.00078125\n",
      "Epoch 74/100\n",
      " - 2s - loss: 0.4790 - acc: 0.7599 - val_loss: 0.5659 - val_acc: 0.6985\n",
      "lr: 0.00078125\n",
      "Epoch 75/100\n",
      " - 2s - loss: 0.4746 - acc: 0.7594 - val_loss: 0.5683 - val_acc: 0.7015\n",
      "lr: 0.00078125\n",
      "Epoch 76/100\n",
      " - 2s - loss: 0.4730 - acc: 0.7556 - val_loss: 0.5662 - val_acc: 0.6990\n",
      "lr: 0.00078125\n",
      "Epoch 77/100\n",
      " - 2s - loss: 0.4806 - acc: 0.7558 - val_loss: 0.5648 - val_acc: 0.7045\n",
      "lr: 0.00078125\n",
      "Epoch 78/100\n",
      " - 2s - loss: 0.4821 - acc: 0.7517 - val_loss: 0.5651 - val_acc: 0.7010\n",
      "lr: 0.00078125\n",
      "Epoch 79/100\n",
      " - 2s - loss: 0.4742 - acc: 0.7602 - val_loss: 0.5653 - val_acc: 0.7040\n",
      "lr: 0.00078125\n",
      "Epoch 80/100\n",
      " - 2s - loss: 0.4817 - acc: 0.7524 - val_loss: 0.5642 - val_acc: 0.7035\n",
      "lr: 0.000390625\n",
      "Epoch 81/100\n",
      " - 2s - loss: 0.4822 - acc: 0.7535 - val_loss: 0.5641 - val_acc: 0.7035\n",
      "lr: 0.000390625\n",
      "Epoch 82/100\n",
      " - 2s - loss: 0.4813 - acc: 0.7503 - val_loss: 0.5643 - val_acc: 0.7030\n",
      "lr: 0.000390625\n",
      "Epoch 83/100\n",
      " - 2s - loss: 0.4771 - acc: 0.7573 - val_loss: 0.5649 - val_acc: 0.7025\n",
      "lr: 0.000390625\n",
      "Epoch 84/100\n",
      " - 2s - loss: 0.4747 - acc: 0.7553 - val_loss: 0.5645 - val_acc: 0.7015\n",
      "lr: 0.000390625\n",
      "Epoch 85/100\n",
      " - 2s - loss: 0.4790 - acc: 0.7478 - val_loss: 0.5651 - val_acc: 0.7015\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr: 0.000390625\n",
      "Epoch 86/100\n",
      " - 2s - loss: 0.4766 - acc: 0.7562 - val_loss: 0.5655 - val_acc: 0.7005\n",
      "lr: 0.000390625\n",
      "Epoch 87/100\n",
      " - 2s - loss: 0.4779 - acc: 0.7524 - val_loss: 0.5648 - val_acc: 0.7010\n",
      "lr: 0.000390625\n",
      "Epoch 88/100\n",
      " - 2s - loss: 0.4827 - acc: 0.7481 - val_loss: 0.5652 - val_acc: 0.7030\n",
      "lr: 0.000390625\n",
      "Epoch 89/100\n",
      " - 2s - loss: 0.4778 - acc: 0.7493 - val_loss: 0.5650 - val_acc: 0.6995\n",
      "lr: 0.000390625\n",
      "Epoch 90/100\n",
      " - 2s - loss: 0.4753 - acc: 0.7572 - val_loss: 0.5651 - val_acc: 0.7000\n",
      "lr: 0.0001953125\n",
      "Epoch 91/100\n",
      " - 2s - loss: 0.4783 - acc: 0.7545 - val_loss: 0.5648 - val_acc: 0.6985\n",
      "lr: 0.0001953125\n",
      "Epoch 92/100\n",
      " - 2s - loss: 0.4816 - acc: 0.7523 - val_loss: 0.5650 - val_acc: 0.7000\n",
      "lr: 0.0001953125\n",
      "Epoch 93/100\n",
      " - 2s - loss: 0.4783 - acc: 0.7503 - val_loss: 0.5648 - val_acc: 0.6995\n",
      "lr: 0.0001953125\n",
      "Epoch 94/100\n",
      " - 2s - loss: 0.4807 - acc: 0.7545 - val_loss: 0.5649 - val_acc: 0.7000\n",
      "lr: 0.0001953125\n",
      "Epoch 95/100\n",
      " - 2s - loss: 0.4763 - acc: 0.7583 - val_loss: 0.5650 - val_acc: 0.7000\n",
      "lr: 0.0001953125\n",
      "Epoch 96/100\n",
      " - 2s - loss: 0.4741 - acc: 0.7544 - val_loss: 0.5656 - val_acc: 0.7010\n",
      "lr: 0.0001953125\n",
      "Epoch 97/100\n",
      " - 2s - loss: 0.4736 - acc: 0.7617 - val_loss: 0.5653 - val_acc: 0.7005\n",
      "lr: 0.0001953125\n",
      "Epoch 98/100\n",
      " - 2s - loss: 0.4795 - acc: 0.7567 - val_loss: 0.5655 - val_acc: 0.7010\n",
      "lr: 0.0001953125\n",
      "Epoch 99/100\n",
      " - 2s - loss: 0.4747 - acc: 0.7578 - val_loss: 0.5650 - val_acc: 0.7005\n",
      "lr: 0.0001953125\n",
      "Epoch 100/100\n",
      " - 2s - loss: 0.4695 - acc: 0.7566 - val_loss: 0.5654 - val_acc: 0.6995\n",
      "lr: 9.765625e-05\n"
     ]
    }
   ],
   "source": [
    "sd_history = sd_model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=2, validation_data=(X_test, y_test), callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot('Step Decay', sd_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lr('Step Decay', loss_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exponential decay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define SGD optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "momentum = 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = SGD(lr=0.0, momentum=momentum, decay=0.0, nesterov=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ed_model = build_cnn(sgd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define exponential decay function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "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: {}'.format(exp_decay(len(self.losses))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exp_decay(epoch):\n",
    "    initial_lr = 0.1\n",
    "    k = 0.1\n",
    "    lr = initial_lr * np.exp(-k*epoch)\n",
    "    return lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn schedule callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_history_ = LossHistory_()\n",
    "lr_ = LearningRateScheduler(exp_decay)\n",
    "callbacks_list_ = [loss_history_, lr_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      " - 2s - loss: 0.6941 - acc: 0.5063 - val_loss: 0.6934 - val_acc: 0.5000\n",
      "lr: 0.09048374180359596\n",
      "Epoch 2/100\n",
      " - 2s - loss: 0.6909 - acc: 0.5317 - val_loss: 0.6879 - val_acc: 0.5670\n",
      "lr: 0.0818730753077982\n",
      "Epoch 3/100\n",
      " - 2s - loss: 0.6857 - acc: 0.5469 - val_loss: 0.6729 - val_acc: 0.5860\n",
      "lr: 0.0740818220681718\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.6764 - acc: 0.5732 - val_loss: 0.6656 - val_acc: 0.6070\n",
      "lr: 0.06703200460356394\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.6626 - acc: 0.5990 - val_loss: 0.6434 - val_acc: 0.6225\n",
      "lr: 0.06065306597126335\n",
      "Epoch 6/100\n",
      " - 2s - loss: 0.6556 - acc: 0.6138 - val_loss: 0.6455 - val_acc: 0.6270\n",
      "lr: 0.05488116360940264\n",
      "Epoch 7/100\n",
      " - 2s - loss: 0.6423 - acc: 0.6256 - val_loss: 0.6345 - val_acc: 0.6365\n",
      "lr: 0.04965853037914095\n",
      "Epoch 8/100\n",
      " - 2s - loss: 0.6339 - acc: 0.6335 - val_loss: 0.6302 - val_acc: 0.6310\n",
      "lr: 0.044932896411722156\n",
      "Epoch 9/100\n",
      " - 2s - loss: 0.6299 - acc: 0.6389 - val_loss: 0.6297 - val_acc: 0.6405\n",
      "lr: 0.04065696597405991\n",
      "Epoch 10/100\n",
      " - 2s - loss: 0.6233 - acc: 0.6476 - val_loss: 0.6184 - val_acc: 0.6470\n",
      "lr: 0.036787944117144235\n",
      "Epoch 11/100\n",
      " - 2s - loss: 0.6174 - acc: 0.6587 - val_loss: 0.6154 - val_acc: 0.6560\n",
      "lr: 0.03328710836980796\n",
      "Epoch 12/100\n",
      " - 2s - loss: 0.6135 - acc: 0.6533 - val_loss: 0.6140 - val_acc: 0.6630\n",
      "lr: 0.030119421191220203\n",
      "Epoch 13/100\n",
      " - 2s - loss: 0.6057 - acc: 0.6608 - val_loss: 0.6114 - val_acc: 0.6620\n",
      "lr: 0.02725317930340126\n",
      "Epoch 14/100\n",
      " - 2s - loss: 0.5942 - acc: 0.6752 - val_loss: 0.6092 - val_acc: 0.6610\n",
      "lr: 0.024659696394160643\n",
      "Epoch 15/100\n",
      " - 2s - loss: 0.5905 - acc: 0.6786 - val_loss: 0.5957 - val_acc: 0.6675\n",
      "lr: 0.022313016014842982\n",
      "Epoch 16/100\n",
      " - 2s - loss: 0.5934 - acc: 0.6770 - val_loss: 0.6046 - val_acc: 0.6575\n",
      "lr: 0.02018965179946554\n",
      "Epoch 17/100\n",
      " - 2s - loss: 0.5853 - acc: 0.6823 - val_loss: 0.5918 - val_acc: 0.6770\n",
      "lr: 0.018268352405273462\n",
      "Epoch 18/100\n",
      " - 2s - loss: 0.5784 - acc: 0.6888 - val_loss: 0.5955 - val_acc: 0.6690\n",
      "lr: 0.016529888822158653\n",
      "Epoch 19/100\n",
      " - 2s - loss: 0.5789 - acc: 0.6893 - val_loss: 0.5864 - val_acc: 0.6780\n",
      "lr: 0.014956861922263504\n",
      "Epoch 20/100\n",
      " - 2s - loss: 0.5728 - acc: 0.6945 - val_loss: 0.5818 - val_acc: 0.6850\n",
      "lr: 0.013533528323661271\n",
      "Epoch 21/100\n",
      " - 2s - loss: 0.5704 - acc: 0.6877 - val_loss: 0.5888 - val_acc: 0.6755\n",
      "lr: 0.012245642825298192\n",
      "Epoch 22/100\n",
      " - 2s - loss: 0.5623 - acc: 0.6946 - val_loss: 0.5810 - val_acc: 0.6865\n",
      "lr: 0.011080315836233388\n",
      "Epoch 23/100\n",
      " - 2s - loss: 0.5589 - acc: 0.7012 - val_loss: 0.5852 - val_acc: 0.6815\n",
      "lr: 0.010025884372280372\n",
      "Epoch 24/100\n",
      " - 2s - loss: 0.5611 - acc: 0.7048 - val_loss: 0.5877 - val_acc: 0.6750\n",
      "lr: 0.009071795328941248\n",
      "Epoch 25/100\n",
      " - 2s - loss: 0.5597 - acc: 0.7029 - val_loss: 0.5753 - val_acc: 0.6915\n",
      "lr: 0.008208499862389881\n",
      "Epoch 26/100\n",
      " - 2s - loss: 0.5528 - acc: 0.7073 - val_loss: 0.5805 - val_acc: 0.6880\n",
      "lr: 0.007427357821433388\n",
      "Epoch 27/100\n",
      " - 2s - loss: 0.5507 - acc: 0.7111 - val_loss: 0.5755 - val_acc: 0.6930\n",
      "lr: 0.006720551273974976\n",
      "Epoch 28/100\n",
      " - 2s - loss: 0.5500 - acc: 0.7050 - val_loss: 0.5741 - val_acc: 0.6940\n",
      "lr: 0.006081006262521796\n",
      "Epoch 29/100\n",
      " - 2s - loss: 0.5438 - acc: 0.7110 - val_loss: 0.5723 - val_acc: 0.6940\n",
      "lr: 0.005502322005640721\n",
      "Epoch 30/100\n",
      " - 2s - loss: 0.5461 - acc: 0.7152 - val_loss: 0.5732 - val_acc: 0.6970\n",
      "lr: 0.004978706836786395\n",
      "Epoch 31/100\n",
      " - 2s - loss: 0.5400 - acc: 0.7182 - val_loss: 0.5734 - val_acc: 0.6950\n",
      "lr: 0.00450492023935578\n",
      "Epoch 32/100\n",
      " - 2s - loss: 0.5385 - acc: 0.7147 - val_loss: 0.5746 - val_acc: 0.6890\n",
      "lr: 0.0040762203978366215\n",
      "Epoch 33/100\n",
      " - 2s - loss: 0.5404 - acc: 0.7145 - val_loss: 0.5716 - val_acc: 0.6985\n",
      "lr: 0.0036883167401239995\n",
      "Epoch 34/100\n",
      " - 2s - loss: 0.5344 - acc: 0.7172 - val_loss: 0.5710 - val_acc: 0.6930\n",
      "lr: 0.003337326996032607\n",
      "Epoch 35/100\n",
      " - 2s - loss: 0.5343 - acc: 0.7088 - val_loss: 0.5743 - val_acc: 0.6960\n",
      "lr: 0.00301973834223185\n",
      "Epoch 36/100\n",
      " - 2s - loss: 0.5365 - acc: 0.7156 - val_loss: 0.5700 - val_acc: 0.6980\n",
      "lr: 0.0027323722447292562\n",
      "Epoch 37/100\n",
      " - 2s - loss: 0.5344 - acc: 0.7190 - val_loss: 0.5684 - val_acc: 0.6965\n",
      "lr: 0.002472352647033939\n",
      "Epoch 38/100\n",
      " - 2s - loss: 0.5296 - acc: 0.7233 - val_loss: 0.5705 - val_acc: 0.7005\n",
      "lr: 0.002237077185616559\n",
      "Epoch 39/100\n",
      " - 2s - loss: 0.5299 - acc: 0.7247 - val_loss: 0.5686 - val_acc: 0.6910\n",
      "lr: 0.002024191144580438\n",
      "Epoch 40/100\n",
      " - 2s - loss: 0.5301 - acc: 0.7192 - val_loss: 0.5697 - val_acc: 0.7015\n",
      "lr: 0.001831563888873418\n",
      "Epoch 41/100\n",
      " - 2s - loss: 0.5286 - acc: 0.7198 - val_loss: 0.5688 - val_acc: 0.6920\n",
      "lr: 0.001657267540176124\n",
      "Epoch 42/100\n",
      " - 2s - loss: 0.5353 - acc: 0.7167 - val_loss: 0.5694 - val_acc: 0.6985\n",
      "lr: 0.0014995576820477704\n",
      "Epoch 43/100\n",
      " - 2s - loss: 0.5287 - acc: 0.7240 - val_loss: 0.5677 - val_acc: 0.6950\n",
      "lr: 0.0013568559012200935\n",
      "Epoch 44/100\n",
      " - 2s - loss: 0.5286 - acc: 0.7235 - val_loss: 0.5683 - val_acc: 0.6975\n",
      "lr: 0.0012277339903068436\n",
      "Epoch 45/100\n",
      " - 2s - loss: 0.5303 - acc: 0.7197 - val_loss: 0.5682 - val_acc: 0.6975\n",
      "lr: 0.0011108996538242307\n",
      "Epoch 46/100\n",
      " - 2s - loss: 0.5294 - acc: 0.7250 - val_loss: 0.5695 - val_acc: 0.7040\n",
      "lr: 0.0010051835744633575\n",
      "Epoch 47/100\n",
      " - 2s - loss: 0.5297 - acc: 0.7192 - val_loss: 0.5682 - val_acc: 0.6965\n",
      "lr: 0.0009095277101695816\n",
      "Epoch 48/100\n",
      " - 2s - loss: 0.5277 - acc: 0.7213 - val_loss: 0.5687 - val_acc: 0.7010\n",
      "lr: 0.0008229747049020024\n",
      "Epoch 49/100\n",
      " - 2s - loss: 0.5274 - acc: 0.7212 - val_loss: 0.5683 - val_acc: 0.6985\n",
      "lr: 0.0007446583070924339\n",
      "Epoch 50/100\n",
      " - 2s - loss: 0.5267 - acc: 0.7272 - val_loss: 0.5673 - val_acc: 0.6975\n",
      "lr: 0.0006737946999085467\n",
      "Epoch 51/100\n",
      " - 2s - loss: 0.5280 - acc: 0.7197 - val_loss: 0.5671 - val_acc: 0.7005\n",
      "lr: 0.0006096746565515633\n",
      "Epoch 52/100\n",
      " - 2s - loss: 0.5235 - acc: 0.7250 - val_loss: 0.5681 - val_acc: 0.7005\n",
      "lr: 0.0005516564420760772\n",
      "Epoch 53/100\n",
      " - 2s - loss: 0.5232 - acc: 0.7273 - val_loss: 0.5668 - val_acc: 0.6985\n",
      "lr: 0.0004991593906910213\n",
      "Epoch 54/100\n",
      " - 2s - loss: 0.5218 - acc: 0.7299 - val_loss: 0.5674 - val_acc: 0.6995\n",
      "lr: 0.0004516580942612666\n",
      "Epoch 55/100\n",
      " - 2s - loss: 0.5249 - acc: 0.7237 - val_loss: 0.5670 - val_acc: 0.7000\n",
      "lr: 0.00040867714384640666\n",
      "Epoch 56/100\n",
      " - 2s - loss: 0.5255 - acc: 0.7223 - val_loss: 0.5677 - val_acc: 0.7050\n",
      "lr: 0.0003697863716482929\n",
      "Epoch 57/100\n",
      " - 2s - loss: 0.5217 - acc: 0.7253 - val_loss: 0.5670 - val_acc: 0.6980\n",
      "lr: 0.0003345965457471272\n",
      "Epoch 58/100\n",
      " - 2s - loss: 0.5240 - acc: 0.7217 - val_loss: 0.5665 - val_acc: 0.6985\n",
      "lr: 0.0003027554745375813\n",
      "Epoch 59/100\n",
      " - 2s - loss: 0.5220 - acc: 0.7260 - val_loss: 0.5667 - val_acc: 0.7005\n",
      "lr: 0.00027394448187683686\n",
      "Epoch 60/100\n",
      " - 2s - loss: 0.5242 - acc: 0.7237 - val_loss: 0.5665 - val_acc: 0.7010\n",
      "lr: 0.00024787521766663585\n",
      "Epoch 61/100\n",
      " - 2s - loss: 0.5253 - acc: 0.7225 - val_loss: 0.5667 - val_acc: 0.6970\n",
      "lr: 0.00022428677194858012\n",
      "Epoch 62/100\n",
      " - 2s - loss: 0.5268 - acc: 0.7239 - val_loss: 0.5667 - val_acc: 0.6955\n",
      "lr: 0.00020294306362957342\n",
      "Epoch 63/100\n",
      " - 2s - loss: 0.5210 - acc: 0.7219 - val_loss: 0.5669 - val_acc: 0.6980\n",
      "lr: 0.00018363047770289057\n",
      "Epoch 64/100\n",
      " - 2s - loss: 0.5231 - acc: 0.7259 - val_loss: 0.5667 - val_acc: 0.6960\n",
      "lr: 0.0001661557273173934\n",
      "Epoch 65/100\n",
      " - 2s - loss: 0.5228 - acc: 0.7268 - val_loss: 0.5668 - val_acc: 0.6960\n",
      "lr: 0.00015034391929775724\n",
      "Epoch 66/100\n",
      " - 2s - loss: 0.5231 - acc: 0.7245 - val_loss: 0.5669 - val_acc: 0.6960\n",
      "lr: 0.00013603680375478928\n",
      "Epoch 67/100\n",
      " - 2s - loss: 0.5209 - acc: 0.7315 - val_loss: 0.5667 - val_acc: 0.6940\n",
      "lr: 0.0001230911902673481\n",
      "Epoch 68/100\n",
      " - 2s - loss: 0.5221 - acc: 0.7232 - val_loss: 0.5667 - val_acc: 0.6980\n",
      "lr: 0.00011137751478448025\n",
      "Epoch 69/100\n",
      " - 2s - loss: 0.5294 - acc: 0.7247 - val_loss: 0.5669 - val_acc: 0.6955\n",
      "lr: 0.00010077854290485106\n",
      "Epoch 70/100\n",
      " - 2s - loss: 0.5254 - acc: 0.7243 - val_loss: 0.5669 - val_acc: 0.6940\n",
      "lr: 9.118819655545162e-05\n",
      "Epoch 71/100\n",
      " - 2s - loss: 0.5325 - acc: 0.7230 - val_loss: 0.5668 - val_acc: 0.6960\n",
      "lr: 8.251049232659039e-05\n",
      "Epoch 72/100\n",
      " - 2s - loss: 0.5239 - acc: 0.7255 - val_loss: 0.5668 - val_acc: 0.6960\n",
      "lr: 7.465858083766792e-05\n",
      "Epoch 73/100\n",
      " - 2s - loss: 0.5249 - acc: 0.7252 - val_loss: 0.5669 - val_acc: 0.6985\n",
      "lr: 6.755387751938438e-05\n",
      "Epoch 74/100\n",
      " - 2s - loss: 0.5214 - acc: 0.7214 - val_loss: 0.5668 - val_acc: 0.6980\n",
      "lr: 6.112527611295723e-05\n",
      "Epoch 75/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 2s - loss: 0.5230 - acc: 0.7309 - val_loss: 0.5669 - val_acc: 0.6985\n",
      "lr: 5.530843701478336e-05\n",
      "Epoch 76/100\n",
      " - 2s - loss: 0.5219 - acc: 0.7248 - val_loss: 0.5669 - val_acc: 0.6980\n",
      "lr: 5.004514334406104e-05\n",
      "Epoch 77/100\n",
      " - 2s - loss: 0.5280 - acc: 0.7244 - val_loss: 0.5668 - val_acc: 0.6980\n",
      "lr: 4.52827182886797e-05\n",
      "Epoch 78/100\n",
      " - 2s - loss: 0.5228 - acc: 0.7280 - val_loss: 0.5668 - val_acc: 0.6980\n",
      "lr: 4.097349789797865e-05\n",
      "Epoch 79/100\n",
      " - 2s - loss: 0.5212 - acc: 0.7282 - val_loss: 0.5668 - val_acc: 0.6980\n",
      "lr: 3.7074354045908826e-05\n",
      "Epoch 80/100\n",
      " - 2s - loss: 0.5202 - acc: 0.7270 - val_loss: 0.5668 - val_acc: 0.6985\n",
      "lr: 3.354626279025119e-05\n",
      "Epoch 81/100\n",
      " - 2s - loss: 0.5222 - acc: 0.7275 - val_loss: 0.5668 - val_acc: 0.6990\n",
      "lr: 3.035391380788668e-05\n",
      "Epoch 82/100\n",
      " - 2s - loss: 0.5201 - acc: 0.7262 - val_loss: 0.5668 - val_acc: 0.6980\n",
      "lr: 2.7465356997214206e-05\n",
      "Epoch 83/100\n",
      " - 2s - loss: 0.5238 - acc: 0.7310 - val_loss: 0.5668 - val_acc: 0.6985\n",
      "lr: 2.4851682710795188e-05\n",
      "Epoch 84/100\n",
      " - 2s - loss: 0.5197 - acc: 0.7299 - val_loss: 0.5668 - val_acc: 0.6985\n",
      "lr: 2.248673241788482e-05\n",
      "Epoch 85/100\n",
      " - 2s - loss: 0.5188 - acc: 0.7259 - val_loss: 0.5668 - val_acc: 0.6985\n",
      "lr: 2.0346836901064418e-05\n",
      "Epoch 86/100\n",
      " - 2s - loss: 0.5197 - acc: 0.7289 - val_loss: 0.5668 - val_acc: 0.6985\n",
      "lr: 1.841057936675792e-05\n",
      "Epoch 87/100\n",
      " - 2s - loss: 0.5245 - acc: 0.7296 - val_loss: 0.5667 - val_acc: 0.6980\n",
      "lr: 1.6658581098763325e-05\n",
      "Epoch 88/100\n",
      " - 2s - loss: 0.5215 - acc: 0.7288 - val_loss: 0.5667 - val_acc: 0.6980\n",
      "lr: 1.5073307509547651e-05\n",
      "Epoch 89/100\n",
      " - 2s - loss: 0.5262 - acc: 0.7306 - val_loss: 0.5668 - val_acc: 0.6975\n",
      "lr: 1.363889264820114e-05\n",
      "Epoch 90/100\n",
      " - 2s - loss: 0.5258 - acc: 0.7209 - val_loss: 0.5667 - val_acc: 0.6975\n",
      "lr: 1.2340980408667957e-05\n",
      "Epoch 91/100\n",
      " - 2s - loss: 0.5234 - acc: 0.7234 - val_loss: 0.5667 - val_acc: 0.6975\n",
      "lr: 1.1166580849011478e-05\n",
      "Epoch 92/100\n",
      " - 2s - loss: 0.5270 - acc: 0.7203 - val_loss: 0.5667 - val_acc: 0.6975\n",
      "lr: 1.0103940183709325e-05\n",
      "Epoch 93/100\n",
      " - 2s - loss: 0.5183 - acc: 0.7334 - val_loss: 0.5667 - val_acc: 0.6975\n",
      "lr: 9.142423147817328e-06\n",
      "Epoch 94/100\n",
      " - 2s - loss: 0.5253 - acc: 0.7235 - val_loss: 0.5667 - val_acc: 0.6980\n",
      "lr: 8.272406555663223e-06\n",
      "Epoch 95/100\n",
      " - 2s - loss: 0.5261 - acc: 0.7214 - val_loss: 0.5667 - val_acc: 0.6980\n",
      "lr: 7.48518298877006e-06\n",
      "Epoch 96/100\n",
      " - 2s - loss: 0.5198 - acc: 0.7293 - val_loss: 0.5667 - val_acc: 0.6980\n",
      "lr: 6.772873649085378e-06\n",
      "Epoch 97/100\n",
      " - 2s - loss: 0.5210 - acc: 0.7275 - val_loss: 0.5667 - val_acc: 0.6980\n",
      "lr: 6.128349505322203e-06\n",
      "Epoch 98/100\n",
      " - 2s - loss: 0.5233 - acc: 0.7218 - val_loss: 0.5667 - val_acc: 0.6980\n",
      "lr: 5.545159943217695e-06\n",
      "Epoch 99/100\n",
      " - 2s - loss: 0.5184 - acc: 0.7258 - val_loss: 0.5667 - val_acc: 0.6980\n",
      "lr: 5.017468205617529e-06\n",
      "Epoch 100/100\n",
      " - 2s - loss: 0.5235 - acc: 0.7197 - val_loss: 0.5667 - val_acc: 0.6980\n",
      "lr: 4.539992976248485e-06\n"
     ]
    }
   ],
   "source": [
    "ed_history = ed_model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=2, validation_data=(X_test, y_test), callbacks=callbacks_list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot('Exponential Decay', ed_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lr('Exponential Decay', loss_history_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparison of constant learning rate, time-based decay, step-based decay, and exponential decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(range(1, EPOCHS+1), clr_history.history['val_acc'], label='Constant lr')\n",
    "plt.plot(range(1, EPOCHS+1), tbd_history.history['val_acc'], label='Time-based decay')\n",
    "plt.plot(range(1, EPOCHS+1), sd_history.history['val_acc'], label='Step decay')\n",
    "plt.plot(range(1, EPOCHS+1), ed_history.history['val_acc'], label='Exponential decay')\n",
    "plt.legend()\n",
    "    \n",
    "plt.xlim([1, EPOCHS])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy on validation set')\n",
    "    \n",
    "plt.title('Methods Comparison')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adagrad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "adagrad = Adagrad(lr=0.01, epsilon=1e-08, decay=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "ag_model = build_cnn(adagrad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      " - 2s - loss: 0.6853 - acc: 0.5424 - val_loss: 0.6638 - val_acc: 0.6205\n",
      "Epoch 2/100\n",
      " - 2s - loss: 0.6591 - acc: 0.6098 - val_loss: 0.6401 - val_acc: 0.6445\n",
      "Epoch 3/100\n",
      " - 2s - loss: 0.6410 - acc: 0.6351 - val_loss: 0.6274 - val_acc: 0.6435\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.6270 - acc: 0.6452 - val_loss: 0.6196 - val_acc: 0.6555\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.6225 - acc: 0.6573 - val_loss: 0.6111 - val_acc: 0.6535\n",
      "Epoch 6/100\n",
      " - 2s - loss: 0.6145 - acc: 0.6641 - val_loss: 0.6108 - val_acc: 0.6595\n",
      "Epoch 7/100\n",
      " - 2s - loss: 0.6065 - acc: 0.6703 - val_loss: 0.6082 - val_acc: 0.6590\n",
      "Epoch 8/100\n",
      " - 2s - loss: 0.6033 - acc: 0.6735 - val_loss: 0.5977 - val_acc: 0.6695\n",
      "Epoch 9/100\n",
      " - 2s - loss: 0.5988 - acc: 0.6809 - val_loss: 0.6045 - val_acc: 0.6640\n",
      "Epoch 10/100\n",
      " - 2s - loss: 0.5967 - acc: 0.6794 - val_loss: 0.5906 - val_acc: 0.6805\n",
      "Epoch 11/100\n",
      " - 2s - loss: 0.5937 - acc: 0.6821 - val_loss: 0.5870 - val_acc: 0.6780\n",
      "Epoch 12/100\n",
      " - 2s - loss: 0.5916 - acc: 0.6836 - val_loss: 0.5848 - val_acc: 0.6840\n",
      "Epoch 13/100\n",
      " - 2s - loss: 0.5878 - acc: 0.6879 - val_loss: 0.5866 - val_acc: 0.6820\n",
      "Epoch 14/100\n",
      " - 2s - loss: 0.5851 - acc: 0.6940 - val_loss: 0.5900 - val_acc: 0.6800\n",
      "Epoch 15/100\n",
      " - 2s - loss: 0.5804 - acc: 0.6978 - val_loss: 0.5807 - val_acc: 0.6860\n",
      "Epoch 16/100\n",
      " - 2s - loss: 0.5824 - acc: 0.6944 - val_loss: 0.5802 - val_acc: 0.6865\n",
      "Epoch 17/100\n",
      " - 2s - loss: 0.5761 - acc: 0.6967 - val_loss: 0.5781 - val_acc: 0.6880\n",
      "Epoch 18/100\n",
      " - 2s - loss: 0.5728 - acc: 0.7052 - val_loss: 0.5842 - val_acc: 0.6860\n",
      "Epoch 19/100\n",
      " - 2s - loss: 0.5717 - acc: 0.7015 - val_loss: 0.5783 - val_acc: 0.6895\n",
      "Epoch 20/100\n",
      " - 2s - loss: 0.5713 - acc: 0.7015 - val_loss: 0.5730 - val_acc: 0.6930\n",
      "Epoch 21/100\n",
      " - 2s - loss: 0.5659 - acc: 0.7012 - val_loss: 0.5713 - val_acc: 0.6885\n",
      "Epoch 22/100\n",
      " - 2s - loss: 0.5667 - acc: 0.7070 - val_loss: 0.5723 - val_acc: 0.6950\n",
      "Epoch 23/100\n",
      " - 2s - loss: 0.5678 - acc: 0.7039 - val_loss: 0.5710 - val_acc: 0.6915\n",
      "Epoch 24/100\n",
      " - 2s - loss: 0.5610 - acc: 0.7121 - val_loss: 0.5706 - val_acc: 0.6985\n",
      "Epoch 25/100\n",
      " - 2s - loss: 0.5621 - acc: 0.7128 - val_loss: 0.5694 - val_acc: 0.6950\n",
      "Epoch 26/100\n",
      " - 2s - loss: 0.5583 - acc: 0.7089 - val_loss: 0.5682 - val_acc: 0.6925\n",
      "Epoch 27/100\n",
      " - 2s - loss: 0.5560 - acc: 0.7120 - val_loss: 0.5665 - val_acc: 0.6905\n",
      "Epoch 28/100\n",
      " - 2s - loss: 0.5593 - acc: 0.7116 - val_loss: 0.5660 - val_acc: 0.6970\n",
      "Epoch 29/100\n",
      " - 2s - loss: 0.5552 - acc: 0.7167 - val_loss: 0.5677 - val_acc: 0.7015\n",
      "Epoch 30/100\n",
      " - 2s - loss: 0.5548 - acc: 0.7166 - val_loss: 0.5661 - val_acc: 0.6985\n",
      "Epoch 31/100\n",
      " - 2s - loss: 0.5550 - acc: 0.7119 - val_loss: 0.5645 - val_acc: 0.6995\n",
      "Epoch 32/100\n",
      " - 2s - loss: 0.5500 - acc: 0.7193 - val_loss: 0.5625 - val_acc: 0.7030\n",
      "Epoch 33/100\n",
      " - 2s - loss: 0.5490 - acc: 0.7184 - val_loss: 0.5614 - val_acc: 0.6970\n",
      "Epoch 34/100\n",
      " - 2s - loss: 0.5510 - acc: 0.7174 - val_loss: 0.5623 - val_acc: 0.7025\n",
      "Epoch 35/100\n",
      " - 2s - loss: 0.5510 - acc: 0.7222 - val_loss: 0.5596 - val_acc: 0.6990\n",
      "Epoch 36/100\n",
      " - 2s - loss: 0.5504 - acc: 0.7207 - val_loss: 0.5608 - val_acc: 0.7005\n",
      "Epoch 37/100\n",
      " - 2s - loss: 0.5482 - acc: 0.7198 - val_loss: 0.5636 - val_acc: 0.6985\n",
      "Epoch 38/100\n",
      " - 2s - loss: 0.5413 - acc: 0.7260 - val_loss: 0.5580 - val_acc: 0.7025\n",
      "Epoch 39/100\n",
      " - 2s - loss: 0.5465 - acc: 0.7237 - val_loss: 0.5611 - val_acc: 0.7060\n",
      "Epoch 40/100\n",
      " - 2s - loss: 0.5415 - acc: 0.7252 - val_loss: 0.5569 - val_acc: 0.7045\n",
      "Epoch 41/100\n",
      " - 2s - loss: 0.5395 - acc: 0.7256 - val_loss: 0.5625 - val_acc: 0.6980\n",
      "Epoch 42/100\n",
      " - 2s - loss: 0.5430 - acc: 0.7202 - val_loss: 0.5570 - val_acc: 0.7060\n",
      "Epoch 43/100\n",
      " - 2s - loss: 0.5379 - acc: 0.7273 - val_loss: 0.5596 - val_acc: 0.7035\n",
      "Epoch 44/100\n",
      " - 2s - loss: 0.5371 - acc: 0.7275 - val_loss: 0.5561 - val_acc: 0.7055\n",
      "Epoch 45/100\n",
      " - 2s - loss: 0.5337 - acc: 0.7269 - val_loss: 0.5560 - val_acc: 0.7080\n",
      "Epoch 46/100\n",
      " - 2s - loss: 0.5396 - acc: 0.7275 - val_loss: 0.5538 - val_acc: 0.7125\n",
      "Epoch 47/100\n",
      " - 2s - loss: 0.5323 - acc: 0.7337 - val_loss: 0.5563 - val_acc: 0.7060\n",
      "Epoch 48/100\n",
      " - 2s - loss: 0.5364 - acc: 0.7276 - val_loss: 0.5567 - val_acc: 0.7055\n",
      "Epoch 49/100\n",
      " - 2s - loss: 0.5357 - acc: 0.7260 - val_loss: 0.5527 - val_acc: 0.7120\n",
      "Epoch 50/100\n",
      " - 2s - loss: 0.5319 - acc: 0.7320 - val_loss: 0.5546 - val_acc: 0.7100\n",
      "Epoch 51/100\n",
      " - 2s - loss: 0.5334 - acc: 0.7323 - val_loss: 0.5520 - val_acc: 0.7100\n",
      "Epoch 52/100\n",
      " - 2s - loss: 0.5367 - acc: 0.7278 - val_loss: 0.5525 - val_acc: 0.7070\n",
      "Epoch 53/100\n",
      " - 2s - loss: 0.5298 - acc: 0.7364 - val_loss: 0.5522 - val_acc: 0.7075\n",
      "Epoch 54/100\n",
      " - 2s - loss: 0.5245 - acc: 0.7345 - val_loss: 0.5529 - val_acc: 0.7075\n",
      "Epoch 55/100\n",
      " - 2s - loss: 0.5313 - acc: 0.7333 - val_loss: 0.5504 - val_acc: 0.7110\n",
      "Epoch 56/100\n",
      " - 2s - loss: 0.5279 - acc: 0.7342 - val_loss: 0.5517 - val_acc: 0.7090\n",
      "Epoch 57/100\n",
      " - 2s - loss: 0.5233 - acc: 0.7368 - val_loss: 0.5485 - val_acc: 0.7150\n",
      "Epoch 58/100\n",
      " - 2s - loss: 0.5287 - acc: 0.7305 - val_loss: 0.5550 - val_acc: 0.7090\n",
      "Epoch 59/100\n",
      " - 2s - loss: 0.5219 - acc: 0.7426 - val_loss: 0.5502 - val_acc: 0.7100\n",
      "Epoch 60/100\n",
      " - 2s - loss: 0.5236 - acc: 0.7412 - val_loss: 0.5483 - val_acc: 0.7150\n",
      "Epoch 61/100\n",
      " - 2s - loss: 0.5231 - acc: 0.7387 - val_loss: 0.5484 - val_acc: 0.7130\n",
      "Epoch 62/100\n",
      " - 2s - loss: 0.5221 - acc: 0.7370 - val_loss: 0.5499 - val_acc: 0.7075\n",
      "Epoch 63/100\n",
      " - 2s - loss: 0.5207 - acc: 0.7341 - val_loss: 0.5519 - val_acc: 0.7075\n",
      "Epoch 64/100\n",
      " - 2s - loss: 0.5196 - acc: 0.7415 - val_loss: 0.5469 - val_acc: 0.7110\n",
      "Epoch 65/100\n",
      " - 2s - loss: 0.5203 - acc: 0.7408 - val_loss: 0.5482 - val_acc: 0.7130\n",
      "Epoch 66/100\n",
      " - 2s - loss: 0.5212 - acc: 0.7355 - val_loss: 0.5454 - val_acc: 0.7145\n",
      "Epoch 67/100\n",
      " - 2s - loss: 0.5189 - acc: 0.7448 - val_loss: 0.5481 - val_acc: 0.7130\n",
      "Epoch 68/100\n",
      " - 2s - loss: 0.5225 - acc: 0.7390 - val_loss: 0.5454 - val_acc: 0.7160\n",
      "Epoch 69/100\n",
      " - 2s - loss: 0.5151 - acc: 0.7374 - val_loss: 0.5469 - val_acc: 0.7160\n",
      "Epoch 70/100\n",
      " - 2s - loss: 0.5132 - acc: 0.7425 - val_loss: 0.5441 - val_acc: 0.7185\n",
      "Epoch 71/100\n",
      " - 2s - loss: 0.5178 - acc: 0.7364 - val_loss: 0.5449 - val_acc: 0.7135\n",
      "Epoch 72/100\n",
      " - 2s - loss: 0.5144 - acc: 0.7428 - val_loss: 0.5447 - val_acc: 0.7165\n",
      "Epoch 73/100\n",
      " - 2s - loss: 0.5177 - acc: 0.7430 - val_loss: 0.5460 - val_acc: 0.7150\n",
      "Epoch 74/100\n",
      " - 2s - loss: 0.5164 - acc: 0.7421 - val_loss: 0.5445 - val_acc: 0.7185\n",
      "Epoch 75/100\n",
      " - 2s - loss: 0.5182 - acc: 0.7413 - val_loss: 0.5433 - val_acc: 0.7165\n",
      "Epoch 76/100\n",
      " - 2s - loss: 0.5130 - acc: 0.7464 - val_loss: 0.5431 - val_acc: 0.7155\n",
      "Epoch 77/100\n",
      " - 2s - loss: 0.5132 - acc: 0.7479 - val_loss: 0.5435 - val_acc: 0.7145\n",
      "Epoch 78/100\n",
      " - 2s - loss: 0.5134 - acc: 0.7468 - val_loss: 0.5450 - val_acc: 0.7145\n",
      "Epoch 79/100\n",
      " - 2s - loss: 0.5148 - acc: 0.7440 - val_loss: 0.5435 - val_acc: 0.7145\n",
      "Epoch 80/100\n",
      " - 2s - loss: 0.5148 - acc: 0.7428 - val_loss: 0.5448 - val_acc: 0.7145\n",
      "Epoch 81/100\n",
      " - 2s - loss: 0.5066 - acc: 0.7503 - val_loss: 0.5420 - val_acc: 0.7180\n",
      "Epoch 82/100\n",
      " - 2s - loss: 0.5036 - acc: 0.7530 - val_loss: 0.5431 - val_acc: 0.7155\n",
      "Epoch 83/100\n",
      " - 2s - loss: 0.5074 - acc: 0.7478 - val_loss: 0.5434 - val_acc: 0.7185\n",
      "Epoch 84/100\n",
      " - 2s - loss: 0.5128 - acc: 0.7413 - val_loss: 0.5415 - val_acc: 0.7180\n",
      "Epoch 85/100\n",
      " - 2s - loss: 0.5094 - acc: 0.7496 - val_loss: 0.5419 - val_acc: 0.7175\n",
      "Epoch 86/100\n",
      " - 2s - loss: 0.5065 - acc: 0.7492 - val_loss: 0.5419 - val_acc: 0.7175\n",
      "Epoch 87/100\n",
      " - 2s - loss: 0.5073 - acc: 0.7451 - val_loss: 0.5419 - val_acc: 0.7180\n",
      "Epoch 88/100\n",
      " - 2s - loss: 0.5011 - acc: 0.7505 - val_loss: 0.5419 - val_acc: 0.7155\n",
      "Epoch 89/100\n",
      " - 2s - loss: 0.5110 - acc: 0.7447 - val_loss: 0.5416 - val_acc: 0.7190\n",
      "Epoch 90/100\n",
      " - 2s - loss: 0.5052 - acc: 0.7476 - val_loss: 0.5408 - val_acc: 0.7215\n",
      "Epoch 91/100\n",
      " - 2s - loss: 0.5081 - acc: 0.7493 - val_loss: 0.5420 - val_acc: 0.7230\n",
      "Epoch 92/100\n",
      " - 2s - loss: 0.5077 - acc: 0.7477 - val_loss: 0.5408 - val_acc: 0.7170\n",
      "Epoch 93/100\n",
      " - 2s - loss: 0.5028 - acc: 0.7466 - val_loss: 0.5415 - val_acc: 0.7175\n",
      "Epoch 94/100\n",
      " - 2s - loss: 0.5047 - acc: 0.7523 - val_loss: 0.5408 - val_acc: 0.7190\n",
      "Epoch 95/100\n",
      " - 2s - loss: 0.5051 - acc: 0.7472 - val_loss: 0.5410 - val_acc: 0.7205\n",
      "Epoch 96/100\n",
      " - 2s - loss: 0.5023 - acc: 0.7477 - val_loss: 0.5418 - val_acc: 0.7160\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/100\n",
      " - 2s - loss: 0.5029 - acc: 0.7508 - val_loss: 0.5403 - val_acc: 0.7190\n",
      "Epoch 98/100\n",
      " - 2s - loss: 0.4989 - acc: 0.7542 - val_loss: 0.5394 - val_acc: 0.7220\n",
      "Epoch 99/100\n",
      " - 2s - loss: 0.5043 - acc: 0.7523 - val_loss: 0.5390 - val_acc: 0.7235\n",
      "Epoch 100/100\n",
      " - 2s - loss: 0.5004 - acc: 0.7492 - val_loss: 0.5395 - val_acc: 0.7215\n"
     ]
    }
   ],
   "source": [
    "ag_history = ag_model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=2, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot('Adagrad', ag_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adadelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "adadelta = Adadelta(lr=1.0, rho=0.95, epsilon=1e-08, decay=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad_model = build_cnn(adadelta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      " - 2s - loss: 0.6870 - acc: 0.5459 - val_loss: 0.6733 - val_acc: 0.5820\n",
      "Epoch 2/100\n",
      " - 2s - loss: 0.6707 - acc: 0.5845 - val_loss: 0.6584 - val_acc: 0.6050\n",
      "Epoch 3/100\n",
      " - 2s - loss: 0.6584 - acc: 0.6062 - val_loss: 0.6473 - val_acc: 0.6215\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.6439 - acc: 0.6349 - val_loss: 0.6314 - val_acc: 0.6500\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.6302 - acc: 0.6454 - val_loss: 0.6417 - val_acc: 0.6240\n",
      "Epoch 6/100\n",
      " - 2s - loss: 0.6211 - acc: 0.6572 - val_loss: 0.6126 - val_acc: 0.6670\n",
      "Epoch 7/100\n",
      " - 2s - loss: 0.6152 - acc: 0.6627 - val_loss: 0.6139 - val_acc: 0.6560\n",
      "Epoch 8/100\n",
      " - 2s - loss: 0.6075 - acc: 0.6693 - val_loss: 0.6011 - val_acc: 0.6670\n",
      "Epoch 9/100\n",
      " - 2s - loss: 0.6010 - acc: 0.6802 - val_loss: 0.6017 - val_acc: 0.6695\n",
      "Epoch 10/100\n",
      " - 2s - loss: 0.5995 - acc: 0.6827 - val_loss: 0.5947 - val_acc: 0.6675\n",
      "Epoch 11/100\n",
      " - 2s - loss: 0.5926 - acc: 0.6846 - val_loss: 0.6021 - val_acc: 0.6625\n",
      "Epoch 12/100\n",
      " - 2s - loss: 0.5851 - acc: 0.6917 - val_loss: 0.5942 - val_acc: 0.6645\n",
      "Epoch 13/100\n",
      " - 2s - loss: 0.5889 - acc: 0.6877 - val_loss: 0.5861 - val_acc: 0.6870\n",
      "Epoch 14/100\n",
      " - 2s - loss: 0.5809 - acc: 0.6924 - val_loss: 0.5825 - val_acc: 0.6820\n",
      "Epoch 15/100\n",
      " - 2s - loss: 0.5777 - acc: 0.6962 - val_loss: 0.5889 - val_acc: 0.6865\n",
      "Epoch 16/100\n",
      " - 2s - loss: 0.5751 - acc: 0.6980 - val_loss: 0.5841 - val_acc: 0.6905\n",
      "Epoch 17/100\n",
      " - 2s - loss: 0.5767 - acc: 0.6934 - val_loss: 0.5801 - val_acc: 0.6810\n",
      "Epoch 18/100\n",
      " - 2s - loss: 0.5740 - acc: 0.7008 - val_loss: 0.5865 - val_acc: 0.6920\n",
      "Epoch 19/100\n",
      " - 2s - loss: 0.5707 - acc: 0.6985 - val_loss: 0.5810 - val_acc: 0.6855\n",
      "Epoch 20/100\n",
      " - 2s - loss: 0.5678 - acc: 0.7034 - val_loss: 0.5729 - val_acc: 0.6980\n",
      "Epoch 21/100\n",
      " - 2s - loss: 0.5633 - acc: 0.7100 - val_loss: 0.5717 - val_acc: 0.7030\n",
      "Epoch 22/100\n",
      " - 2s - loss: 0.5609 - acc: 0.7115 - val_loss: 0.5856 - val_acc: 0.6945\n",
      "Epoch 23/100\n",
      " - 2s - loss: 0.5588 - acc: 0.7128 - val_loss: 0.5754 - val_acc: 0.7005\n",
      "Epoch 24/100\n",
      " - 2s - loss: 0.5600 - acc: 0.7107 - val_loss: 0.5707 - val_acc: 0.6975\n",
      "Epoch 25/100\n",
      " - 2s - loss: 0.5578 - acc: 0.7096 - val_loss: 0.5793 - val_acc: 0.6995\n",
      "Epoch 26/100\n",
      " - 2s - loss: 0.5566 - acc: 0.7183 - val_loss: 0.5698 - val_acc: 0.7020\n",
      "Epoch 27/100\n",
      " - 2s - loss: 0.5507 - acc: 0.7155 - val_loss: 0.5667 - val_acc: 0.6980\n",
      "Epoch 28/100\n",
      " - 2s - loss: 0.5477 - acc: 0.7189 - val_loss: 0.5689 - val_acc: 0.6955\n",
      "Epoch 29/100\n",
      " - 2s - loss: 0.5523 - acc: 0.7177 - val_loss: 0.5662 - val_acc: 0.7035\n",
      "Epoch 30/100\n",
      " - 2s - loss: 0.5495 - acc: 0.7195 - val_loss: 0.5986 - val_acc: 0.6720\n",
      "Epoch 31/100\n",
      " - 2s - loss: 0.5447 - acc: 0.7208 - val_loss: 0.5706 - val_acc: 0.7005\n",
      "Epoch 32/100\n",
      " - 2s - loss: 0.5447 - acc: 0.7222 - val_loss: 0.5735 - val_acc: 0.6990\n",
      "Epoch 33/100\n",
      " - 2s - loss: 0.5418 - acc: 0.7243 - val_loss: 0.5631 - val_acc: 0.7050\n",
      "Epoch 34/100\n",
      " - 2s - loss: 0.5413 - acc: 0.7225 - val_loss: 0.5648 - val_acc: 0.7000\n",
      "Epoch 35/100\n",
      " - 2s - loss: 0.5391 - acc: 0.7302 - val_loss: 0.5674 - val_acc: 0.7025\n",
      "Epoch 36/100\n",
      " - 2s - loss: 0.5339 - acc: 0.7282 - val_loss: 0.5706 - val_acc: 0.6920\n",
      "Epoch 37/100\n",
      " - 2s - loss: 0.5373 - acc: 0.7298 - val_loss: 0.5678 - val_acc: 0.7025\n",
      "Epoch 38/100\n",
      " - 2s - loss: 0.5311 - acc: 0.7336 - val_loss: 0.5608 - val_acc: 0.7025\n",
      "Epoch 39/100\n",
      " - 2s - loss: 0.5325 - acc: 0.7258 - val_loss: 0.5633 - val_acc: 0.7100\n",
      "Epoch 40/100\n",
      " - 2s - loss: 0.5287 - acc: 0.7354 - val_loss: 0.5631 - val_acc: 0.7115\n",
      "Epoch 41/100\n",
      " - 2s - loss: 0.5308 - acc: 0.7326 - val_loss: 0.5605 - val_acc: 0.7080\n",
      "Epoch 42/100\n",
      " - 2s - loss: 0.5268 - acc: 0.7357 - val_loss: 0.5695 - val_acc: 0.7010\n",
      "Epoch 43/100\n",
      " - 2s - loss: 0.5244 - acc: 0.7386 - val_loss: 0.5582 - val_acc: 0.7095\n",
      "Epoch 44/100\n",
      " - 2s - loss: 0.5216 - acc: 0.7432 - val_loss: 0.5640 - val_acc: 0.7005\n",
      "Epoch 45/100\n",
      " - 2s - loss: 0.5223 - acc: 0.7413 - val_loss: 0.5649 - val_acc: 0.7055\n",
      "Epoch 46/100\n",
      " - 2s - loss: 0.5185 - acc: 0.7425 - val_loss: 0.5593 - val_acc: 0.7065\n",
      "Epoch 47/100\n",
      " - 2s - loss: 0.5218 - acc: 0.7382 - val_loss: 0.5645 - val_acc: 0.7025\n",
      "Epoch 48/100\n",
      " - 2s - loss: 0.5154 - acc: 0.7440 - val_loss: 0.5594 - val_acc: 0.7075\n",
      "Epoch 49/100\n",
      " - 2s - loss: 0.5198 - acc: 0.7365 - val_loss: 0.5787 - val_acc: 0.6930\n",
      "Epoch 50/100\n",
      " - 2s - loss: 0.5209 - acc: 0.7407 - val_loss: 0.5579 - val_acc: 0.7075\n",
      "Epoch 51/100\n",
      " - 2s - loss: 0.5152 - acc: 0.7419 - val_loss: 0.5597 - val_acc: 0.7065\n",
      "Epoch 52/100\n",
      " - 2s - loss: 0.5134 - acc: 0.7422 - val_loss: 0.5595 - val_acc: 0.7110\n",
      "Epoch 53/100\n",
      " - 2s - loss: 0.5123 - acc: 0.7456 - val_loss: 0.5784 - val_acc: 0.6975\n",
      "Epoch 54/100\n",
      " - 2s - loss: 0.5035 - acc: 0.7458 - val_loss: 0.5574 - val_acc: 0.7150\n",
      "Epoch 55/100\n",
      " - 2s - loss: 0.5126 - acc: 0.7469 - val_loss: 0.5526 - val_acc: 0.7145\n",
      "Epoch 56/100\n",
      " - 2s - loss: 0.5070 - acc: 0.7483 - val_loss: 0.6009 - val_acc: 0.6875\n",
      "Epoch 57/100\n",
      " - 2s - loss: 0.5052 - acc: 0.7558 - val_loss: 0.5598 - val_acc: 0.7095\n",
      "Epoch 58/100\n",
      " - 2s - loss: 0.5074 - acc: 0.7480 - val_loss: 0.5556 - val_acc: 0.7120\n",
      "Epoch 59/100\n",
      " - 2s - loss: 0.5052 - acc: 0.7503 - val_loss: 0.5646 - val_acc: 0.7055\n",
      "Epoch 60/100\n",
      " - 2s - loss: 0.5058 - acc: 0.7548 - val_loss: 0.5591 - val_acc: 0.7100\n",
      "Epoch 61/100\n",
      " - 2s - loss: 0.5030 - acc: 0.7484 - val_loss: 0.5575 - val_acc: 0.7135\n",
      "Epoch 62/100\n",
      " - 2s - loss: 0.4993 - acc: 0.7532 - val_loss: 0.5662 - val_acc: 0.7130\n",
      "Epoch 63/100\n",
      " - 2s - loss: 0.5037 - acc: 0.7535 - val_loss: 0.5538 - val_acc: 0.7165\n",
      "Epoch 64/100\n",
      " - 2s - loss: 0.5057 - acc: 0.7519 - val_loss: 0.5593 - val_acc: 0.7115\n",
      "Epoch 65/100\n",
      " - 2s - loss: 0.4960 - acc: 0.7565 - val_loss: 0.5543 - val_acc: 0.7155\n",
      "Epoch 66/100\n",
      " - 2s - loss: 0.4966 - acc: 0.7499 - val_loss: 0.5496 - val_acc: 0.7130\n",
      "Epoch 67/100\n",
      " - 2s - loss: 0.4975 - acc: 0.7573 - val_loss: 0.5538 - val_acc: 0.7115\n",
      "Epoch 68/100\n",
      " - 2s - loss: 0.4990 - acc: 0.7542 - val_loss: 0.5527 - val_acc: 0.7100\n",
      "Epoch 69/100\n",
      " - 2s - loss: 0.4935 - acc: 0.7550 - val_loss: 0.5500 - val_acc: 0.7150\n",
      "Epoch 70/100\n",
      " - 2s - loss: 0.4904 - acc: 0.7612 - val_loss: 0.5503 - val_acc: 0.7200\n",
      "Epoch 71/100\n",
      " - 2s - loss: 0.4851 - acc: 0.7618 - val_loss: 0.5585 - val_acc: 0.7155\n",
      "Epoch 72/100\n",
      " - 2s - loss: 0.4935 - acc: 0.7546 - val_loss: 0.5535 - val_acc: 0.7185\n",
      "Epoch 73/100\n",
      " - 2s - loss: 0.4942 - acc: 0.7570 - val_loss: 0.5519 - val_acc: 0.7135\n",
      "Epoch 74/100\n",
      " - 2s - loss: 0.4934 - acc: 0.7585 - val_loss: 0.5514 - val_acc: 0.7140\n",
      "Epoch 75/100\n",
      " - 2s - loss: 0.4826 - acc: 0.7544 - val_loss: 0.5523 - val_acc: 0.7140\n",
      "Epoch 76/100\n",
      " - 2s - loss: 0.4839 - acc: 0.7599 - val_loss: 0.5699 - val_acc: 0.7055\n",
      "Epoch 77/100\n",
      " - 2s - loss: 0.4891 - acc: 0.7568 - val_loss: 0.5512 - val_acc: 0.7160\n",
      "Epoch 78/100\n",
      " - 2s - loss: 0.4824 - acc: 0.7624 - val_loss: 0.5666 - val_acc: 0.7100\n",
      "Epoch 79/100\n",
      " - 2s - loss: 0.4883 - acc: 0.7579 - val_loss: 0.5545 - val_acc: 0.7190\n",
      "Epoch 80/100\n",
      " - 2s - loss: 0.4857 - acc: 0.7622 - val_loss: 0.5591 - val_acc: 0.7100\n",
      "Epoch 81/100\n",
      " - 2s - loss: 0.4748 - acc: 0.7651 - val_loss: 0.5610 - val_acc: 0.7130\n",
      "Epoch 82/100\n",
      " - 2s - loss: 0.4798 - acc: 0.7661 - val_loss: 0.5579 - val_acc: 0.7120\n",
      "Epoch 83/100\n",
      " - 2s - loss: 0.4777 - acc: 0.7676 - val_loss: 0.5653 - val_acc: 0.7080\n",
      "Epoch 84/100\n",
      " - 2s - loss: 0.4806 - acc: 0.7641 - val_loss: 0.5521 - val_acc: 0.7130\n",
      "Epoch 85/100\n",
      " - 2s - loss: 0.4760 - acc: 0.7700 - val_loss: 0.5577 - val_acc: 0.7095\n",
      "Epoch 86/100\n",
      " - 2s - loss: 0.4774 - acc: 0.7661 - val_loss: 0.5661 - val_acc: 0.7090\n",
      "Epoch 87/100\n",
      " - 2s - loss: 0.4781 - acc: 0.7657 - val_loss: 0.5576 - val_acc: 0.7095\n",
      "Epoch 88/100\n",
      " - 2s - loss: 0.4754 - acc: 0.7676 - val_loss: 0.5595 - val_acc: 0.7050\n",
      "Epoch 89/100\n",
      " - 2s - loss: 0.4742 - acc: 0.7647 - val_loss: 0.5729 - val_acc: 0.7055\n",
      "Epoch 90/100\n",
      " - 2s - loss: 0.4750 - acc: 0.7679 - val_loss: 0.5634 - val_acc: 0.7100\n",
      "Epoch 91/100\n",
      " - 2s - loss: 0.4761 - acc: 0.7733 - val_loss: 0.5520 - val_acc: 0.7170\n",
      "Epoch 92/100\n",
      " - 2s - loss: 0.4717 - acc: 0.7638 - val_loss: 0.5552 - val_acc: 0.7170\n",
      "Epoch 93/100\n",
      " - 2s - loss: 0.4718 - acc: 0.7644 - val_loss: 0.5630 - val_acc: 0.7100\n",
      "Epoch 94/100\n",
      " - 2s - loss: 0.4737 - acc: 0.7720 - val_loss: 0.5540 - val_acc: 0.7165\n",
      "Epoch 95/100\n",
      " - 2s - loss: 0.4638 - acc: 0.7726 - val_loss: 0.5636 - val_acc: 0.7080\n",
      "Epoch 96/100\n",
      " - 2s - loss: 0.4697 - acc: 0.7725 - val_loss: 0.5599 - val_acc: 0.7120\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/100\n",
      " - 2s - loss: 0.4634 - acc: 0.7710 - val_loss: 0.5571 - val_acc: 0.7095\n",
      "Epoch 98/100\n",
      " - 2s - loss: 0.4622 - acc: 0.7753 - val_loss: 0.5493 - val_acc: 0.7235\n",
      "Epoch 99/100\n",
      " - 2s - loss: 0.4705 - acc: 0.7653 - val_loss: 0.5549 - val_acc: 0.7175\n",
      "Epoch 100/100\n",
      " - 2s - loss: 0.4582 - acc: 0.7726 - val_loss: 0.5817 - val_acc: 0.7075\n"
     ]
    }
   ],
   "source": [
    "ad_history = ad_model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=2, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lq6srJ06c4NSpU4wcOZJz586RnZ3Nc889x9SpUwEKU7enp6dfMUX7Qw89xLBhwxg9ejS+vr6MHTuWtWvXkpeXx5IlS/D39yc+Pp7x48cTGxtLz549WbduHSEhIaWy+2puMcI2w+bpSkXzzH6wtC67n8kEa16n6YVLQPD1X+dSjAp+EwalUjo4v8hzKPEs7J8HQVNVXMRfL8IPwdDtYRVlHbtfeWINev/KRmzPthAwHnZ9o44bd4ehn5ZMV+7qCw/+ruI6Zg8GgxWMXwTW9kV9AieqL81Np+4IkVWvKg+RysSrA9w5/apdpk+fztGjRzl48CCbNm1i6NChHD16FD8/PwBmz56Nm5sbWVlZdOvWjXvvvRd395Jb7vKmaHd3d2f//v18++23fPLJJ8ycOZN3332XgQMH8tprr7F69Wpmzbrx1M+aakZK5eVk5aBSchycr1Q7ZbH+bdj9HX4AUQ9Bk+4lzx/9VRmfM5NUsJydqxISPt0gNx1+HqO8mx5eq4TE+ndU9Ladq7JLWNpCv5eUu65nW1j0oLqmYyNlxO46BYIeu/rnGfCmUou1G6VqYRjKUJB4tFSC5JdxKjCwYafrv2+aKqFKhYgQYgjwBWABzJRSTr/s/GfAAPOhPdBASuliPmcECp76UVLKu6tyrTeLoKCgQgEC8OWXX/Lbb78BcO7cOU6fPl1KiJQ3Rfvdd99d2OfXX38FVOr3gvmHDBmCq2slRL1qqpdTq1Wk9bDPVcrvLf9VD19Lm5L99sxQtoPAB8gOXYntXy/A1M1gYf633zsL/npBpQO3d1fV7y5Fq5oTnh2UCuliKExYDJ7tlJH6+34qhXjABDj2K/T7hxIgAN5dVCqNnNSitvLg1FDZK66FZzuYdrj882puClUmRIQQFsA3wB1ANLBXCLFCShla0EdK+Xyx/s8AxXMBZEkpAyptQdfYMdwsCup4gMrfs379enbu3Im9vT3BwcFlpnIvb4r2gn4WFhbXtLloblFMJtjwLxVBHTgRXJooW8SB+UqNVMDJVbDqH6p40bDPOZPrTftj02H3/6DX0yoX06p/qDQb4xYWBfzlpMGRpbBvFpzbpWwZLcx2N68OylC9dyZE7lS7kV5Pl1yflW2lpdPQ3BpUpWE9CDgjpQyTUuYCC4ERV+k/DqiaSvLVyJVSsgNcunQJV1dX7O3tOXHiBLt27ar06/fu3ZvFixcDsHbtWpKTkyv9GpqbyLFfVSryAW+o6OfmA5UdYet/VexEfq5KY75kMnh1hNGzwcKSBI8eSmBs+lAJgMUPqmJI984sGTFu46hSkj+2FV46o4RGcQaYczhdOKIyzdo639zPr6lxVKU6yxs4V+w4GuheVkchRFPAD/i7WLOtEGIfkA9Ml1IuL2PcVGAqgKenJ5s2bSpx3tnZ+YoP8JuFtbU1QUFBtG3bFltbWxo0aFC4pt69e/P111/TunVrWrZsSbdu3cjMzCQtLQ0pJenp6aSnp2MymQrH5OTkkJOTQ1paGnl5eWRlZRX2NxqNpKWlkZGRUfjziy++yJQpU5g7dy5BQUF4eio//Ru5L9nZ2aXudU0jPT29xq/xehGmPLrtfROTgy/7EtzA/PlcXe+i07m3iZ31AM6XjuGQGU2iW2dO+D1H3o69AKRnZLDL7V66nd2EYc5dGC3sCGn/PFm7Dlz3Ojz8puIVt57QrFaYbtF7XBv/PqoNKWWVfAGjUXaQguMHgK+v0PcV4KvL2rzN35sBEUDzq12vS5cu8nJCQ0NLtdVmUlNTS7VlZ2fLvLw8KaWUO3bskJ06dbrh69wK93Xjxo3VvYTKwWiUMmK7lCuek/LDJlK+7STlydUl+5hMUs4cpM593knKE6tUWzEK78fWz6R8103K0+tuzvprKLXm76MSAPbJG3jWV+VOJAYo7pjtY24ri7HAU8UbpJQx5u9hQohNKHvJ2cpfZu0mKiqK+++/H5PJhLW1NTNmzKjuJWmuhJRKJZWVDJHb4cwGOLsB0i+Alb0qwxowAZoPKDlOCBj5rbJzdBpb2sBenD7ToMtDlVKMSKOBqlVn7QVaCiH8UMJjLDD+8k5CCH/AFdhZrM0VyJRS5gghPIDewEdVuNZaS8uWLTlw4PpVFpqbhJTKg2r7F6rAUfHCRnauyubRagi0vuvq6Trcm6uv8qAFiKYSqTIhIqXMF0I8DaxBufjOllIeE0K8h9o+rTB3HQssNG+rCmgDfC+EMKGM/9NlMa+u61wHoqxIWU2FKPlr0pCRCCf/gk7ji1xny4sxH1a+qMqhNr9NxT5YO4CNE3h3VnEWlZ0mXaOpZKo0TkRKuRJYeVnbW5cdv1PGuB1Ah8vbrxdbW1sSExNxd3fXgqQSkFKSmJiIrW0tdOHMz4HlT6rguT7TVHBbAcZ8iD8BDdqUfKhLCcsfh9Nr1fH11IDITlVlVc9ugD4vwMB/lh1kp9HUcGp1xLqPjw/R0dHEx8dX91JuCtnZ2VX+gLe1tcXHx6dKr3HTkRL+eA6OLlVC5OACVe2u1RAI2win1kBWEnS4H+75vuhhf2C+EiA2zrBpujpfVoxEUpiK2wjbpGpcZyWrOtvGPBj+JXSZdFM/rkZTmdRqIWJlZVUiOry2s2nTJgIDr1C7WXNltn4Ch35Rday7TlF5nPbMVDEZti7QarBSM+2bDQ71Vd3tS+dg9WuqfGnfF1VywH2zoeeTRfOG/q6iu+NPqOP6/uDSFDzbg72bMpQ37VU9n1mjqSRqtRDRaK7J0WUqB1THMaq2tRAqs2zv59QOwquTsnVICRbWSsA4uKsEiEgY8bVKEOjXXwX8dX5ABeydWKkC/hq0gSHTVXlVXUlPUwvRQkRTdzm6DH57Apr0hLu/Kpmq3M5V5YIqQAgY/CFkJKj6GADDPisSDLe9BTNvg13/U0kOlzykDOWTViihotHUUrQQ0dQ9cjNh9auwfy74BMGYBVePrSjAYICR34E0qtToXSYXnfPpCq2HKlfd7RLc/GDiMi1ANLUeLUQ0tRKDMbfsExdPqF1C/HHo83xRDqryYmkN9/1Y9rmBb8B3K1VSxAeWK7uHRlPL0UJEU/s4MJ8+254F6zeVbaPALffYb7D8KVXMaOKv0OK2yr2uZztVhc+9BTh6Ve7cGk0NRTuma25NctKU6qigNncBUsKOr5HCAja8Cz+NgORIWPeW2oF4tlMZaitbgBTg20cLEE2dQu9ENLceWSmwYLQqzJRyDoZ+UnQuaifEH+d062fwb9MWVr4MXwaANEHXh5Wn1JVKyWo0mutG70Q0txaZSfDT3RB7UBnF989V1fgK2DsLbJy52KCvqt392BYVjzHyOxj2qRYgGk0lo4WIpmaRFgerX4fYMpJGXoqGH4cp4/jYn2H0LKW+2vaZOp8erwL8AsZhsjB7W7k3hzHzlUDRaDSVjlZnaWoWG/4FB+eroD7/YdD/FchMVNHgJ1eCwQrGLypKhx44Efb/pDytjiwBU56KOj92vno/h0ZTR9BCRFNzSDyr0o90ngRO3rDzGzjxpzpn5wY9nlB2DbdiqWz6vqhyWG35GM5uVGlI6rcGtBDRaG4GWohoag6b/6NSiwx4Axw9oftUOLBA5atqO6Ls5IYujVX23H2z1PHtb9/cNWs0dRwtRDQ1g/iTSh3V8yklQEClHun19LXH9n0BDsxTyRL9h1ftOjUaTQm0ENHcfNIvKrVVo0ClfhJCpVK3tIPe065/PmcflVLdxlF7X2k0NxktRDQ3j+xU2Pk17Pga8jJUW8MA6HCfiibv8zw4eFRs7oBxlbdOjaYWcS4pk+cWHuCj0Z1o0eAqJZYriHbx1VQ9F0+ozLdfBii7R8s74PHtMPwLyE2HtW+AdT3o9Ux1r1SjqXX8cTiW/VEpvLz0EEZT5Ze31jsRTdWQHAmhy5WdI+6Iynrb4nYIfrUoxbpXewh8EE6vUaoonbBQo6l0Np+Mx97aggNRKczZHs4jfZtV6vxaiGgqj8wkZeA+9ltRsGCjzirVSLtRRQbz4hgMqmCTplaTlWvk+UUH6dvKgwndm1b3cuoM6Tn5hEQm80jfZpy+kMYna09yR1tPmro7VNo1tDpLUzYJZ+Di8dLtiWdhzlCYNwr2zoRLMZAcoXJUfdpWJTpEwB3vwbMHYepGFd9RlgDR1AmklLzx2xFWH4vjzeVHWXWkZAxPckYu/9t8lqSMK6Tvv0Hm7YxgyOdbyMk3Vsn8NZkdZxLIN0n6t6rPB/d0wMpg4JVlhzFVolpL70Q0pTHmq5rhqbFK/dT3RZVOPWo3/DIWkMr99q8X1ZcwgLBQJWZ7Pa1Kwmo0ZubtiuTXAzE8PaAFO8MSmbboIA2cbOjS1I2D51J4asF+YlKy2HwynnkPB2FpUXnvtrn5Jr7eeIYLqTn8ceg8o7v4VNrclUlsShZn49MLj5vVr4e3i90Nz7v5VDwO1hZ0aeqKtaWBN4a24dVfj7Bo3znGBTW54flBCxFNWYQuh0vnoHF32PgBnP0b2t8La95Q7rQTloBbM0g4pVKR5OeogD+nRtW9ck0FMZkkX2w4TUxKFtNHdai0B3lIZBLv/RHK7W0a8MIdrUjJymPUt9t5ZO4+JvXy5ZuNZ2jgaMvTA1rw9cYzfLTmJK/fdf0vIXlGE3+fuMht/g1KrH3V0fNcSM2hno0ls7eFc29nb0TxMsg1gLhL2dzx6WYycot2SvbWFmx4sT8NnUsKkvf/DMW9ng1PBDe/5rxSSjafiqdXCw+sLdU9GdOtMQv3nmPGljDGdmtcKfdCq7M0JZESdnylCitNXg2jZkDcUVj5Enh3hkfWq6SGQqj0In2eV7sVLUAqBSklc3dEMGNLGKuOnOdozCWy86pWDZObb+L5xQf5YsNploZE8/HakyXOJ2XkMvKb7Tw0Zw9RiZnlnvdiWjZPzN+Pt6sd/70/AINB4OZgzY+TgxBC8Pn60/RrWZ+/nu3DS4NbM7FHE37YEsZfh68/Zc3SkGgemxfCD1vDCtuklMzeFk4zDwdeu8uf0POp7AlPusos1cMna0+SZ5TMeagbSx/vyZzJ3cg3ST5eU/L3sPlUPDO3hfPZ+lPEp+Vcc97whAyik7Po36p+YZsQgok9mhKWkFFp90LvRDQlidwO5w/CsM+U0bvj/dA4CE6tUTmtyko9oqk0whIyeHvFsRJt3f3cWPRYz0qZP99o4vj5NNzqWePlZEtmbj6Pzw9h+5lEXh7cmvOXsvh+cxgBPi7c2aEhKZm5TJi5m7D4dKwsDAz6fDMv3NGKKb39rrpbyTOaeHrBAVKz85g7JQhnu6ISxL4eDvz8aHeOxqQyKtAbg0G9Db81rB3HYlN5eekhMnLyScrM5VxSJgnpOUizCt/CIHhqQAvaezuXuN7SEFUO4PP1pxnU1osWDeqxPyqFQ9GXeG9EO+7t7MMna04ye3s43Zu5V8q9rAyOxlxi2f5opvZtxgD/BoXtk3v78v3mMKb09qO9tzM5+Ube/v0ojZxtOZ+azdwdEbw0uPVV5958Kh6ghBABGNqhIe+uOMaivecq5V5oIVJXMBkhcgecWq12GYETy64tvuMrsHeHTsWC91x9oftjN22pdZmQyGQAlj3RC1srA19tOMPW0/FIKW9Y9ZCek88T80PYejoBAEuDwM7agqxcI/+9rxP3dvEhN9/EsdhUXlpyiAZOtry94ihn49OZ+WBXWnrW463fj/HvlSf4dX8ME3s0ZXinRiUERAH/XnmcPRFJfDE2gDYNnUqd9/dywt+rZLu1pYHvJnRh2Ffb+MeywwC42FvRwNEGg/mzRyVlkpadz/xHuheOC4tPJyQymYf7+LE0JJpXlh1m8WM9mbM9HEdbS+7t7IOtlQXjgprw3eaznEtSu6mMnHymrzpBVFIm3Zu50au5B+0bOZUpHOftimRfRBLvj2yPo20Z/zcVQErJB38dx8XOiicHtChx7qkBLViyL5r3/wrll0d7MGNLGBGJmfw0JYhf9kTx084IHg9uTj2bKz/CN5+Kp5mHA43d7Eu021lbMCKwEUv2RfP28HY3/Dm0EKnNSAnR+1SKkeN/QMZFZQCXRiUsBr4J7e5RqimA+FNKyPR/Faxu3KhXHZhMkp1hiayJyKNXvqlQF1zTMJkkEvVmXZwDUck42VoS2Ng6+klwAAAgAElEQVQFg0HQs7k7q4/FEZ+eQwPHsneBCek5vP9nKOGJmSx8tAd21hal+qTkmBjz/U5OxKXx2p3+ONpacS45kwup2dzb2YfeLVSmAGtLA99O6Mzwr7Zx73c7sLYw8P0DXehnfpv94YEurD4axxcbTvPm8qP8689Q7mzvxf1dG9OjmTsGg+D3gzHM2R7BQ718GRHgfV33xcvZlg0v9CcmJYvGbnalHtj/23yW6atOcDTmUuFu5Nf9MRgETO3XjHaNnHhh8SE+Wn2CVUfjmNLbFwfzg/aBnk35YUsYc3dE0AwTI77ZTlh8Or4eDmxeHQ+cxKOeNTMndSOgsUvhNTefiuet348iJZy6kM6Pk7vh6XTjO/INxy+yMyyR90a0KyWInWyteP72lvzz92PM2R7B1xvPcFcHL/q1qo+jrSWrjsaxcE9UYczH2fh0HpsXQjdfV14d0gYbKwO7whIZ261s4/nYbk2YvyuK3w/F3PDn0EKkNpKVouI1DsyH+BMqJ1WrQdB2JLQcBBFbYf27sHSySqHe+i4VCHhwPljaQrdHqvsTXDcXU7NZsDuKpSHRxKRkAXDux718N7Fzpb05VhYJ6TmM/WEXPZq58f7IDiXO7Y9MIbCJa6GKx9dD+fOHx2eUEiJSSpaGRPPByuNk5OSTZ5R8vfE0Lw/2L9EvLD6d93dlk2nMZeakrgxo3YCr0dDZjq/Hd+aVZYd5c2jbEmoWIQR3dmjIkPZeHIm5xJJ90Sw/GMPyg7H4uNoxtGNDftoRSTdfV94YWjEvPWd7K5zty/6dje/ehG/+PsP3W8L4alwgRpNk2f5o+rasj6eTLfcEerPiUCzfbwnDIODBnr4lPtddHRry854ojEYj9Wytmfdwd3q38CA+LYddYYl8tOYED87azc+P9qC9t3NhypDWno5Mu70VLyw+yKhvdzB3StANpRDJM5r498rjNKvvcEUvqXFBTfhxRwTv/RmKvbUF/xzWFoDAJq5093Nj1rZwJvXyJSY5i/EzdpGZa2TxvmjWhV5kZEAjsvNMpVRZBbT3dqa9txO/7DlX4c9QQM18TdNUHJMJFtwHa98EGyeVmPClU3D/T9B+FNjUU8F9T2yHkf9TfbZ9BnOGKKHTaRzUK/sPr6aSm2/i/u938uXfp2lW34GvxgUyub01u8ISGfP9Li6mZpc57lxSJpPn7CHuUtnnq4KsXCOPzN3HmYvp/H4wlnyjqfBcanYepy6m0bmJa2GbnzkoLCIxo9Rc0xYd5OWlh2lRvx6rnuvLqEBvftgSRlgxV9GYlCzG/rCLHKPkl0d7XFOAFNCjmTubXx7AHW3Lju8RQtDRx4V/jWzP3jdu54uxAfh5OPDDljDq2VryzfjOWFWiq24BTrZWjO/RhL8Ox3IuKZOdZxM5fym70HVXCMG/7+mAo40ld3ZoWEqV83AfP7LyjDR1MvDXs30Ld2D1HW0Y3qkRPz/SA0dbKybO2s2BqGQenx+C0ST538QuDGnvxaKpPcnJNxZ6mL37xzFmbQvnf5vP8sZvR5g0ew9PLgjhUmbeVT/H9FUnCEvI4I272lzxPllaGHhzqBIc025vWcJT6/H+zTl/KZtvN55l/Ixd5OabWPp4L35/qjcNnW2ZuS0ca0sD3ZtdOQvEmG5NOH4+9do3/RronUht49AvEL0H7v5Kud1eCYOFSloYME7tXMI3K9VXjydv3loricX7zhGRmMmMB7sWPvQck0/Rv1snnlywn3u+3cHPj3YvFaX77aYzbDwZz6xtYbxh/metCHlGU5kPAikl+SZZeM5okkxbdIBD0SmM7uLD0pBoQiKTC42bB6NSkBK6NC0SIo1cbLGyEIQnlPSKyszNZ8WhWMYFNeGDke0xGASv3uXPutALvL3iGD9NCSItJ58pc/aSlWvklW52dCqmoqlMbK0sGBHgzYgAb85fysJCCBpUgrrnSkzp7cfsbeHM3BpGanY+jraWJYRdIxc7/n4pGCe70o+3To1d2PRSMKcP7cHLufQaG7vZ88ujPRjzw05GfbcDKWHWpK6FO8IOPs78+kRvpq8+ztmLGew4m0Cm2TXXxd4KH1c7dpxNIzkjhLlTgspUp87ZHs6sbeFM7u3LbW2uHoQ7wL8B214ZUCpmJLh1fVp7OvLZ+lM421nx86Pdae3lCMBvT/Ziwe4ohAB76ys/4kcENOKDv0Kvev3yoIVILcIyL11FjPsEQcDE8g+0c1FFn9qOqLrFVRHZeUa+3HCark1dub1Nybfs4NYNWDS1JxNm7uKN344y7+GgQuP0xbRsloXEYGkQLNx7jmm3tyrUnV9OvtFEbEo2jd3sShm3f9oZwYcrT7Dhxf40uuwf/ZO1J/l+cxidGrvQs5k7F1KzWXPsAm8Pb8voLj4sPxDDxpPxhUIkJDIZIaBT4yLPI0sLA43d7IlIKLkTOXUhHSnVw6RA9dXA0ZYXB7XinT9CWXEolsX7znE2Pp25U4LIiz56/Te3Alwe11AVFKitFu1TqphRZsN5ceo72lxxfFN3B8INV3ZSaOJuz8+P9uDRn/YxqrN3qQd9E3d7vp2g8r9JKUnOzMPKQhSqTX87EM3ziw7x6rLD/Pf+TiX+ZtYci+O9P0MZ3M6zcJdxLXxc7Uu1CSF4aXBrPvgrlK/GdaZdo5J/M5N6+V5zXidbK4Z2aMTJa/a8OlqdVYvwjfhF1SMf+olyz60D/LQzgotpObw8uHWZ3ksdfJx5/o5WbDuTwKaT8YXtP26PIM9k4uP7OpKWnc+y/dElxl3KzGPGljAmz9lDwHvr6PfxRqYtOlgiC+qecBVIl5VnLDF3AauOxOHtaofRJPlu81mWhEQzpbcfk3v74WhrRTdfNzaeuFjYf39UMq09HUvZcJp5OBB+mRA5GafUEP7mt88CJvZoStuGTkxbdJDtZxKZfm/HQpVNbWJqv2Zk55nIzjNVSRS6n4cD61/oz5PBLa7aTwgV+1L8d3ZPoA8v3tGKXw/E8Nm6U2Tk5HMyLo3fD8bw3MIDdPJx4fMxgaWcKq6XO9p6sunlAXTwcb525yswubfvDa0B9E6k9hB3FO+YldB1CjTsVN2ruSoX07J59KcQHu7jx92dKh6kmJadx7ebztKvVf2r+rtP6N6Un3ZG8sHK4/Rt6UF2vol5uyK5s70X9wT6MHdHJHO2RzCxe1MMBkFuvonJP+5hf1QKzTwcGBHQCCsLAz/uiMDCIPh4dCcS0nN4csF+GrvZk5mbz9bT8YzvXmQgjU7OJCwhgzeHtuGRvs1Iy87j9MV0AnyKVEoD/RvwwcrjxKRk0dDJloNRKQwPKH0/fN0d2Ho6AZNJFu46jp9Pw97agsaXvaVaWhj418j2jJuxi6cHtKixaT5ulBYNHBnaoSFhCRkEVpGa7kZ4emALopOz+PLvM3z595nC9qbu9syc1LVMD7rq4PJ4m4qghUhtwGSCVf8g39IBq4FvkptvIjvfiNNN8EraHZZIeEIGY8uZh0dKyT+XH+XQuRReXnKIZh4OFf5DnrUtnJTMPF4edPWgK2tLA6/d6c/UeSH8svcc2blG0rLzeayfSh0xubcvzy08yKZTFxno78n7f4WyPyqFr8YFMryYkHNzsObTdaewNAjOxmeQkZPPgke6M3NrGGtDL2A0ycK3y23mWIwC11hHW6sSBnOAAf71+WDlcTadvEjXpm6k5eSX6gPKQysn30RcanahyuxEXCqtvRwLhUpxujR15dBbg2rMg6qq+GxMAKZKiJ+pCoQQvH9Pe1o0qEeu0URjN3sau9rh7+VU634vVSpEhBBDgC8AC2CmlHL6Zec/AwaYD+2BBlJKF/O5ScCb5nPvSynnVuVab2k2T4fI7Zxt/Qy+Vs7c990O0nPyWft8vwp7yJy5mI6lQRQaFMsiz2jixSWHSM7IZUw58/CsPBLHmmMXeKxfM1YciuWJBSH88XQfXOytkVKy+mgcfx05zwf3dCjlO78u9AL/23wWaQ5fDj2fyp3tvcq1nb+jrSfd/dz4bN0prC0M9GjmVmhovqtDQ/698jhztkeQkpnHTzsjebSvXwkBAvDsbS3JN5oK3yy/GhdIay9H+rT0YElINEdiLhXGF2w9k4Cnkw0tr+IG2rx+PXxc7dh44mJhMF1xo3oBfubfQURCBo1c7JBScjIujSHtva44d217UJVFTY0BKsDKwsCj/Sq3dkdNpMqEiBDCAvgGuAOIBvYKIVZIKQvdAaSUzxfr/wwQaP7ZDXgb6ApIIMQ8Nrmq1ntLkBKlMuY6F1NRnPhLVQsMmMB5p4HMXH6UIzGXAFh55HypYK/iKpGyyMo18vmGU8zcGo69lQULHu1OR5+y1QW/HYghOlnFZFzKysPFvnR98+KR1kkZuby94igdvJ15eXBrhrT3Ysz3u3hu4UH+PaoD76w4xrrQCwB0berKQ739Ssz1+fpTXEjNLoyA7tXcg1fvLBkTcSWEEPxzWFuGf70NKeHDe4viM6wsDDzY05eP15xkd3gSPZq58cqQsud9/o5WONtbI6BQyPQx2xy2nY4noLELRpNk+5kEBvo3uKpgFUIw0L8BS/ZFY29tiZuDNb7upY2oBUIkLCGDXi08uJiWQ3JmXqmIb42mOqhKUR4EnJFShkkpc4GFwNXcf8YBv5h/Hgysk1ImmQXHOmBIFa615pObATPvgC87q1KzOekqwvzXx1Thp6GfsjHayNKQaJ4d2IIWDerxv81hhW/toNI8DPp8Cw/M2l1mArdtpxMY/PkWvt8cxj2B3rg4WDFx5m6OmoVScfKNJr7deAZr806nQJgU58HZe+jzn418vv4U0cmZvPfHMVIy8/hodEcsLQwENnHl7bvbsvlUPP0/2sjW0/G8dqc/7Ro5sfQyQ3dobCrHYlN5ekAL5j3cnXkPd2f2Q92uq7hOe29nJnZvSpCvG8GXBWGNC2qCjaUBN3trvh7f+Yp5oYQQPNzHjyl9igScez0b2jVyYotZhXUs9hIpmXn0a3nteJsBrRuQlWfkryPn6dzEpUyh4+Vki42lodBDq8C3/3KjukZTHYjiD5lKnViI0cAQKeUj5uMHgO5SyqfL6NsU2AX4SCmNQoiXAFsp5fvm8/8EsqSUn1w2biowFcDT07PLwoULq+Sz1ASaRC6lWfg8Et264J4UQo61OyaDFRbGLEK6fMqxbFc+3J1FO3dLpnWxYXtMPrOO5vJiFxs61FcbznmhOfwdlY+lARysBE90sqG1mwVhKUZ+PZPH0QQjnvaCh9rZ0MbdgvhME9P3ZJNtlLwaZEdjx6IH647YfH44nMPdza1YcTaPZwJt6OJZtLE1Sclj6zKxtYR0c60hCYxobsU9LYt2LFJKFp3M40KmiXH+1jSwN7AuMo8Fx3P5V++ia/5yPIf1Ufl8PsAeR+trq83S09OpV+/6IorPJBtxthHUt7/+d6vFJ3NZE5HHN7fZsz4yj6Wn8/higD3ONldfa65R8tSGTPJMMLqlFcOal97NAby5LRMPOwPTutiyMjyXxSfVtRysymcPqMj9qM3o+1HEgAEDQqSUXSs6vqYY1scCS6WU15XzWkr5A/ADQNeuXWVwcHAVLO0mEhOiclplpcCYearuOKiyszsfhFZDcB+/CKJ2YbPqH3AhFCatoLN3d174eBNutgZ+enIgLvbW9M438udHG9mZUo9n7uvBnvAkNqzeyUO9fBnTrTFPLtjPR/sy6dLElT0RSbjaW/HanS2Z1Mu3hM99UPcMxny/i08P5PPhqA4Mad8Qo0ny3meb8fey5p3xPVjxr3U4N2pGcLHazXGXsslbs4G37mzHAP8GLAuJITYli/dGtsPGsqS+fsCAEod0zMhl8b/XE2Xw4oHgtuQZTby0bQN3tPVi+KAu5bqVmzZt4nr/Hq6vd0msfBJYOXM31j5tiQkLx98rlxGD+5VrbJ+oPWw8Gc/oAV3ocQUvs3bnVJR7cHAwv184SEPnRIbeMaDMvmVRkftRm9H3o/KoSnVWDNC42LGPua0sxlKkyrresbc+YZvgx2EwYyCc+RvCt8DSh1WFQYDtn0NOKgz8pzpu0gMe3QgvnoSmvdhxRqV+mNDGutAuYWNpwcN9/NhxNpG9EUm8suwwPq52vDy4NW0aOrHi6d4Mae/FyQtpvDSoFVtfGchj/ZuXCtpq6u7Awqk9aORix+Pz9zP1p338uCOCsPgMnhnYEld7K+rZWJZSZ0Wa03Q0cXfAx9We525vyX9GdywlQMrCzcGagf4N+O1ALHlGE5tPxpOQnsu9NdhdtUtTV2wsDawLvUBIZHKhV1Z5GNXZB28XOzpdwfYEykMrKimTfKOJE3FpWpWlqTFU5U5kL9BSCOGHEgBjgfGXdxJC+AOuwM5izWuAfwshClxVBgGvVeFaq499c+DP51VRp0EfQJdJcGSJalvzOvSZBru/V3U9vNoXjTNYgIN6a10bGkc9G0vaeZR8QI8LasJXG84wZc5e0szuqAVR2Y62VnwzvnO5Uoz7ejjw+1O9mbUtnE/XnWJt6AVaNKjHne29EELg42pXWoiY022XZSguD/d29mHNsQtsORXP0pBo3B2sCW5dc3N62VpZEOTnxpJ90eSbZKGxvTwM79SolCfY5TTzcCDPKIlMyuTMxbQrJtbTaG42VSZEpJT5QoinUQLBApgtpTwmhHgP2CelXGHuOhZYKIsZZ6SUSUKIf6EEEcB7UsqaV5LsRtn9Paz6h8qse/9PRenXu06BxLOw82u1SzEZIbhsGWo0SdaFXiC4dX2sDCWTqTnaWjGhR1P+t/ksY7s1LjNyubw+9pYWBh7r35wh7b34Yv1pRnf1KfTyUkKkZG6nyMQMLAyiVCqQ8jLAvwHuDtbM2hbO3ogkHuzpWyUJ/SqTfi3rs/V0AtaWBoL8rpz4riL4mh0I/j5+kTyjpE1DvRPR1Ayq1CYipVwJrLys7a3Ljt+5wtjZwOwqW1x1s/1LWPdP8B8Go2eD5WW5fu54D5LCVA3zoKng5lfmNAeikklIz2VQOy9ILp2R84ng5jjaWvJgz6aVsuym7g58OiagRJu3ix27w5JK7GoiEzPxdrGr8IPfysLAiABvZm8PB7glIq/7tFRCOsjXrZRa8EYpcPNddVSVjtXuvZqaQs1+tbuVkRKOLYeMhNLn9sxQAqTdKLjvx9ICBJS6atQMGPwhDHgDUBHaS/aVzP+/NvQCVhbiiqoeZzsrnhrQokpravi42pOWk09qVn5hW1RSJk0rqMoq4N4uKsalbUOnMqvj1TT8vRwJbl2fMd0aX7vzdVLf0QYHawv2R6VgZSFoVr/8rs0aTVVSU7yzah+hy2HJQ1DfHyb9WVSj4+zfsOoVaHWnEhIWJX8F0cmZxF3Kpquvm6r90VOlZr+Ulcd/Vp0AAUF+bjR1d0BKyZpjcfRq7nFTUpxcCR9XpbKKTsnE2V5Fj0cmZjK8U8MbmrddI2cm9/a9LvtCdSKE4MfJQVU2t6+HA8diU2lev16NV+1p6g76L7EqyM+B9e+ASxNIjoSfRkBGIiSchsUPKcFyb2kBcuZiOiO/2cG4GbtKBQOuPnqeXKMJKSXvrDiGlJJTF9KJTMxkULur1ySoagpSVRcY11Myc7mUlUdTtxt/W357eLtr1lyoKxSkoLkVdmWauoPeiVQFe2dBcgRMWKYExc9jYN4IyM0ES2sYv7AoBsRMeEIG42fswmgykWeULN53jqcGFKWhXn4gFl93eyb2aMr7fx1nbegFTsWlIQTcUc0P2cKdiFmIRCYqI3uTG1RnaUpSUOWwtXbv1dQg9E6ksslKhi0fQbMB0OI2aBYMYxcgL57ElHIOxixQO5RiRCVmMn7GLvJNkkWP9aR3C3cW7IosrF1x/lIWu8ITGRnozaRevrT2dOS9P0L58/B5Ahu7VGkVufLgYm+Fg7VFoYdWgXvvjdpENCUp2InoGBFNTUILkcpm639VxPmgf0GB+2yL23nd9WMezH+DUzYlq5ldTM1m3IxdZOUZmf9wd1p5OvJAj6bEXsrmb3PBohUHY5ESRgZ4Y2WuFxGTksXJC2nKK6uaUbEi9oU7kaiCQEM3LUQqkzvaePJ4/+ZXjGrXaKoDLUQqk4QzKvYjYDx4FWWJlVLyZ2JDtuW25PF5IaRm5wEqIeKUuXtJzszlpylBtG2kdN23t/HE08mG+bsiAVh+MJZOjV0K30SD/NwYFag8lwa1rRn2guIBhxGJmTRwtLlqfWfN9eNsb8Wrd/pXuvuwRnMjaCFSGaREwV8vwne9wMK60CW3gLjUbNKy8xkR0IjIpExeWnyIPKOJZ345QGhsKt+M71wi3bqlhYFxQU3YfCqetcfiOH4+lXsuq3j3/j3tWTi1B83q14wkcsUDDqMSb9y9V6PR3BpcU4gIIZ4pln5EU5zMJPhjGnwZCCFzodNYeHwbOJes4XEiLg1QZVpfv6sNa0MvMOzLbfx94iLvjWjPAP8GpaYeF9QEC4PgxSWHsDAIhl2WFsPe2rJGqTW8Xe1Iy87nUlYekUkZNKkEzyyNRlPzKY++wRNVUGo/KoJ8jayq/PG3ClLCsd9UypLMJJWmpM+0ksWiinHSLERaezrSzdeVg+dS+ONQLI/3b87EHmVHkns62TK4nScrj8QR3Lo+HvXKCEisQRS4+Z65mM6F1JwK58zSaDS3FtcUIlLKN831PAYBk4GvhRCLgVlSyrNVvcAaR24mLJ0Cp1ZBwwCYuAwadrrqkFNxaXg52eJsrwICPx7dkfu6+FwziO7Bnr6sPBLHvZ1rfsqPAjffnWdVhL5279Vo6gblsnxKKaUQIg6IA/JRWXeXCiHWSSn/UZULrHEcXqgEyO3vQM9nSgUMlsWJuLQSvv22VhblShXeo5k7f7/YvzBvUk2mYCey/UwiwHVVHNRoNLcu5bGJPCeECAE+ArYDHaSUTwBdgHureH01j0OLVMR572nlEiD5RhNn4tMrHCDWrH69cmfarU5c7a2wt7YgJCoZqHgKeI1Gc2tRnp2IGzBKShlZvFFKaRJCDKuaZdVQksLh3C647e2iGJBrEJGYSW6+idaetTtArKCuyKkL6TjZWhYWx9JoNLWb8rj4rgIKa3kIIZyEEN0BpJTHq2phNZIjS9X3DveVe0ihUb0ORBkXqLS0KkujqTuUR4h8B6QXO043t9UtpITDi6BpH3Apf6rvkxfSMAho0aBmxHNUJQXGdW1U12jqDuURIuKyqoMm6mLixtj9kHhalam9Dk7GpeLr4VAnoowLhIi2h2g0dYfyCJEwIcSzQggr89dzQFhVL6zGcXgxWNhA2xHXNexkXFqtt4cU4O1iVmfpQEONps5QHiHyONALiAGige7A1KpcVI3DmKfsIa2HgJ3Ltfubyco1EpmUWSfsIQAdfZzxcrKlc1Od4ECjqSuUJ9jwIjD2Jqyl5hK2CTIToOMYcvNNHIpOoYO38zVVVKcvpiFl3Und3djNnl2v31bdy9BoNDeRawoRIYQt8DDQDigsXCGlnFKF66pZ7P4e7Fw5aNOVV77axskLaXg62fDUgBaM6dYYG8uyhUlBzqxWdUSdpdFo6h7lUWfNA7yAwcBmwAdIq8pFVQcmkyQpI7f0iZOr4cw61rhN5J4f9pGance7d7ejiZs9b/1+jAEfb2LjyYtlznkqLg0bS4N2edVoNLWW8giRFlLKfwIZUsq5wFCUXaRWsfpYHD0+3ECUubQrAHnZsPoV4qyb8nRYNx7s0ZS1z/djUi9fFj/Wk3kPB+Foa8WT8/cXxoMU5+SFNFp61sPCUPMjzjUajaYilEeI5Jm/pwgh2gPOQOnc5bc4YfHp5OabWH4wpqhxx1eQHMGrWRO5P6gZ745oj6OtSqIohKBvy/rMeziIeraWPD6/qNhUASfi0mjt6XQzP4ZGo9HcVMojRH4w1xN5E1gBhAL/qdJVVQPxaTkALD8Qg5RSFZra+l+iGw5mU147RnX2LnNcAydbvp3QmXNJmbyw6FChWuzDlceJT8uhTUNtD9FoNLWXqxrWhRAGIFVKmQxsAZrdlFVVAwnpyh4SlpDBkZhLdNz+OgjBJzxAYzc7Oje5sttqN1833hjahnf/CGXyj3vZF5FEVp6RewK9GdOt/NHtGo1Gc6tx1Z2IOTq9TqR6j0/Pwd/LEWsLA6c3/AjH/yC9+zRWRBgY0cn7mpl0H+rlyz2B3mw5Hc/ANp6sfb4fn40JKFR/aTQaTW2kPOlL1gshXgIWARkFjVLKpCsPufVISMuhTSMnOjulMjj8P0ifIBZbj8IkTzEysNE1xwsh+OS+Trx6pz+eTrbX7K/RaDS1gfIIkTHm708Va5PUMtVWfHoOwQ4WPJ34KVJK9nb+D8t3XKC9txMtGpTPrmFhEFqAaDSaOkV5Itb9bsZCqpPsPCNp2fkMSvoZt8QQXhNPc3p3NoejL/Hm0DbVvTyNRqOpsZQnYv3BstqllD9V/nKqh4T0HNqJcIKiZkCH+zDJMezbdw4hYHina6uyNBqNpq5SHnVWt2I/2wK3AfuBWiREcplgsQGThQ2Guz5hRGw+i/ado1dzd62e0mg0mqtQHnXWM8WPhRAuwMIqW1E1kHApg8EWe0lvcjsudi708JOM6uzN6M4+1b00jUajqdFUpLhUBlCr7CSGqO24izSSzLVCDAbBp/cHVPOqNBqNpuZTHpvIHyhvLFBxJW2BxeWZXAgxBPgCsABmSimnl9HnfuAd8zUOSSnHm9uNwBFztygp5d3luWZFqB+1igxpg0O7IVV1CY1Go6mVlGcn8kmxn/OBSCll9LUGCSEsgG+AO1DFrPYKIVZIKUOL9WkJvAb0llImCyGK5+TKklJW/XbAmI9fwt9sFV0YYlf766BrNBpNZVKe3FlRwG4p5WYp5XYgUQjhW45xQcAZKWWYlDIXZUe5vLbso8A35rQqBQWwqgwpJfN3RZKek1/UGLmNevkp7LLtW5WX1mg0mlpJeXYiS1DlcQswmtu6ld29ECpjydEAABKWSURBVG/gXLHjgtK6xWkFIITYjlJ5vSOlXG0+ZyuE2Ifa/UyXUi6//AJCiKmYS/V6enqyadOmqy4o/JKRd3dmc+b0KYIbq3QkrU5+iys27KftNcfXdNLT02/5z1BZ6HtREn0/SqLvR+VRHiFiad5JACClzBVCWFfi9VsCwahiV1uEEB2klClAUylljBCiGfC3EOKIlPJs8cFSyh+AHwC6du0qg4ODr3qx1EOxsPMAVm7eBAe3BWM+7HmYvy270aRJE4KDO1fSx6oeNm3axLXuQV1B34uS6PtREn0/Ko/yqLPihRCFRm0hxAggoRzjYoDiKWx9zG3FiQZWSCnzpJThwCmUUEFKGWP+HgZsAgLLcc2rEpWoUn+FxZtTgEVuh8wE/sgLwqOezY1Or9FoNHWO8giRx4HXhRBRQogo4BXgsXKM2wu0FEL4mXcuY1H1SIqzHLULQQjhgVJvhQkhXIUQNsXae6PqmNwQkeaqheEJZiESuhxpZc+qnA7Ud9RCRKPRaK6X8gQbngV6CCHqmY/TyzOxlDJfCPE0sAZl75gtpTwmhHgP2CelXGE+N0gIEYqytbwspUwUQvQCvhdCmFCCbnpxr66KEpmkhEhUUiZ5OVlYHfuNTL9BZB+24f/t3X1wXNV9xvHvI1k2kiXbkoVlYxvbgF9CgAA1JA1MKiChpEkDk7SENJ0Ck5Q2LSVvTQOdDGlpp5NkOmkgZZg6hJROE0hLU+p0XFIGUMPkBQyBALYDGL+/ybIlv8gvsrT69Y97Za+EbcmLdle6+3xmdrT37F3t2TPXenzOuffc090TMTM7ZSO5TuTvgK+l8xSkdzn8fER8abj3RsQKYMWQsjvzngfwufSRv89PgfNH8gVOxcbdBzitporDvf3sfuGHzDzUxfb518FL0NwwWtM8ZmaVYyTDWe8fCBCA9HTc3ypelYrjcG+O9n09vPvsZgCqX3oYGmaxbsqlAJxe7zWyzMxO1UhCpHpgfgJAUi0w7sZ+NqVDWVcsPp1m9jJ9extccD27DuQA90TMzAoxklN8vws8Iek7gICbgAeLWaliGJhUP3/OND5W+3OqIgfv+D06XuoBYPrkcZeLZmZlN5KJ9a9K+iXwXpL1rX4EzCt2xUbbxvT03nlNdXy4+mnWahHnzFjCru5XmFZXw8QJI+mUmZlZvpH+5WwnCZDfBa4E1hStRkWycfdBppw2gWn71rCgbx2P5H4DSG5I5WtEzMwKc8KeiKRFwMfSxy7g+4Ai4ooS1W1Ubew8yLzpk9GLD5FTDQ8dvIQ/OdxLx/4en95rZlagk/VEfkXS6/hgRFweEd8kuZZjXNq0+wDzmybCy/9Ox+wr2Us96zsOJD0RX2hoZlaQk4XIh4HtwFOSviXpKpKJ9XGnL9fPlq5DXDJpKxzcBedeB8C6Xd107O+hud5nZpmZFeKEIRIRj0bEDcAS4CngM8AMSfdJurpUFRwN2/Ycpq8/WFjTAUDTvPOoEqzeto8DR3Je8sTMrEDDTqxHxIGI+F5E/DbJIoovkKyfNW5s7EzOzJrNDgAmNp/F3KY6nl3fCeCJdTOzAp3Sea0R0RURyyLiqmJVqBgGrhFpPrINJs+ASfUsaJ7MK9v2AbgnYmZWoIq4OGLj7gNMnFBFbfcmaFoAwFnN9eT6k1vH++wsM7PCVEiIHOTMpjrUtQEa0xA5ffLR1z2cZWZWmIoIkU2dBzmncQLs25bXEzkWItN9dpaZWUEyHyIRwabOg1wweS8QeT2RegAa62qoqc58M5iZFcVIFmAc1zq6ezh4JMfiSekdfdOeSMuUSdRNrPZQlpnZW5D5EBk4M+tMtScFaU9EEotaGphaW1OuqpmZjXsVEyIz+rbDxHqY3Hz0tbtvuJDqqnF5Eb6Z2ZiQ+RDZsOsAVYKGg5uToSwdC4150yef5J1mZjaczM8ov7C5iyUzp1CVd3qvmZmNjkyHSG+un19s3MM750+FPRuPTqqbmdnoyHSIrNq2j0O9OS5v6YXcEfdEzMxGWaZD5LkNyQKLF9d3JQXuiZiZjapMh8iz6zuZN72Oxp6tSYF7ImZmoyqzIRIRPLexi0vmN0HXeqiqgalzyl0tM7NMyWyIvNHRTeeBI1w6vwk618O0M6GqutzVMjPLlMyGyLPrk3mQSxakPRHPh5iZjbrMhsjKDZ00109iflMtdG7wfIiZWRFkNkSeXd/JpQsa0eE90LPXPREzsyLIZIhs23OIrXsOsXReOh8C7omYmRVBJkNkZXp9yKUD8yHgnoiZWRFkNkTqJ03gbbOm5PVE5pe1TmZmWZTNEFnfxcXzGpNl3jt+BVPnQk1tuatlZpY5mQuRw705Xt+5nwvnTE0K2ldBy3nlrZSZWUYVNUQkXSPpVUlrJd1+gn2ul7Ra0ipJ38srv1HS6+njxpF+5hsd3fQHLJ45BXoPw67XYKZDxMysGIp2UypJ1cC9wPuALcBKScsjYnXePguBO4DLIqJL0oy0vAn4MrAUCOD59L1dw33ua+37AVjUUg+7XoXIQcvbR/nbmZkZFLcncimwNiLWRcQR4GHg2iH7/CFw70A4RMTOtPw3gccjojN97XHgmpF86Ks7uqmpFvObJydDWeDhLDOzIinm7XFnA5vztrcA7xyyzyIAST8BqoG/iojHTvDe2UM/QNItwC0ALS0ttLW18bPVh2mphZ88/WPOXvs/nFE1kadf3gTaOmpfbKzq7u6mra2t3NUYE9wWg7k9BnN7jJ5y32N9ArAQaAXmAD+WdP5I3xwRy4BlAEuXLo3W1la+9MyTXHR2I62tF8HGr8PM82i94qpi1H3MaWtro7W1tdzVGBPcFoO5PQZze4yeYg5nbQXm5m3PScvybQGWR0RvRKwHXiMJlZG8900O9PSxpesQi1vqIQLaX/F8iJlZERUzRFYCCyUtkDQRuAFYPmSfR0l6IUhqJhneWgf8CLhaUqOkRuDqtOykXt/ZDcCilgbo3gkHd3s+xMysiIo2nBURfZJuJfnjXw08EBGrJN0FPBcRyzkWFquBHPCFiNgNIOlvSIII4K6I6BzuM1/bMXBmVgO0/ywp9Om9ZmZFU9Q5kYhYAawYUnZn3vMAPpc+hr73AeCBU/m8V9v3c1pNFXOb6uDV9MysGeeeesXNzGxEMnXF+mvt+1k4oyFZ7qR9FUyZDXVN5a6WmVlmZS5EFrU0JBs7XvF8iJlZkWUmRHL9Qfu+nuRK9b4jydXqPjPLzKyoMhMih3tzACya2ZCsl9Xf5xAxMyuyzIRIT18/AItbGrzciZlZiWQmRA735miYNIFZU0+D9pehehJMP6fc1TIzy7QMhUg/C1vqkdIzs2Ysgepyr+piZpZt2QmRvhyLZ6ZnZu1cAzM8H2JmVmyZCZFcf7BwRgPkemH/DmicV+4qmZllXmZCBEh6It07gYCGmeWujplZ5mUmRKZPnsiSmQ1JLwSgYVZ5K2RmVgEyEyJnTKtlev0k2L89KXBPxMys6DITIkcdDRH3RMzMii2DIbIDVA11zeWuiZlZ5mUzRBpmQlX2vpqZ2ViTvb+0+7d5PsTMrEQyGCI7PB9iZlYiGQyR7e6JmJmVSLZCpPcwHOpyiJiZlUi2QqTbFxqamZVStkLk6NXq7omYmZVCxkLEFxqamZVSxkLEw1lmZqWUsRDZDtUTobax3DUxM6sIGQuR9Gp1qdw1MTOrCBkLke0eyjIzK6GMhcgOn5llZlZCGQwR90TMzEolOyES/dCzzz0RM7MSyk6I5HqTn+6JmJmVTHZCpH8gRNwTMTMrleyEiHsiZmYll8EQcU/EzKxUshMi/b1QUweTppS7JmZmFaOoISLpGkmvSlor6fbjvH6TpA5JL6aPT+a9lssrXz7sh+V6fbW6mVmJTSjWL5ZUDdwLvA/YAqyUtDwiVg/Z9fsRcetxfsWhiLhwxB+Y6/V8iJlZiRWzJ3IpsDYi1kXEEeBh4NqifVp/r+dDzMxKrGg9EWA2sDlvewvwzuPs9xFJ7wFeAz4bEQPvOU3Sc0Af8JWIeHToGyXdAtwCcPGsajbv6eONtrZR/ArjS3d3N20V/P3zuS0Gc3sM5vYYPcUMkZH4IfBQRPRI+iPgQeDK9LV5EbFV0lnAk5Jejog38t8cEcuAZQBLz6iOuedewtx3t5aw+mNLW1sbra2t5a7GmOC2GMztMZjbY/QUczhrKzA3b3tOWnZUROyOiJ50837g1/Je25r+XAe0ARcN+4meEzEzK6lihshKYKGkBZImAjcAg86ykpT/V/9DwJq0vFHSpPR5M3AZMHRC/s08J2JmVlJFG86KiD5JtwI/AqqBByJilaS7gOciYjlwm6QPkcx7dAI3pW9/G/BPkvpJgu4rxzmra7DaaTBtXnG+jJmZHVdR50QiYgWwYkjZnXnP7wDuOM77fgqcf0of1rgAps0dfj8zMxs12bli3czMSs4hYmZmBXOImJlZwRwiZmZWMIeImZkVzCFiZmYFc4iYmVnBHCJmZlYwRUS56zAqJHUAG8tdjzJrBnaVuxJjhNtiMLfHYG6PYxZHREOhby73Kr6jJiJOL3cdyk3ScxGxtNz1GAvcFoO5PQZzexyT3nKjYB7OMjOzgjlEzMysYA6RbFlW7gqMIW6Lwdweg7k9jnlLbZGZiXUzMys990TMzKxgDhEzMyuYQ2QckjRX0lOSVktaJenTaXmTpMclvZ7+bCx3XUtJUrWkFyT9d7q9QNIzktZK+n56m+bMkzRN0iOSfiVpjaRfr+RjQ9Jn038nr0h6SNJplXRsSHpA0k5Jr+SVHfd4UOKetF1eknTxcL/fITI+9QGfj4hzgXcBfyrpXOB24ImIWAg8kW5Xkk8Da/K2vwr8Q0ScA3QBnyhLrUrvbuCxiFgCvIOkTSry2JA0G7gNWBoR55HcqvsGKuvY+GfgmiFlJzoe3g8sTB+3APcN98sdIuNQRGyPiF+kz/eT/JGYDVwLPJju9iBwXXlqWHqS5gAfAO5PtwVcCTyS7lIR7SFpKvAe4NsAEXEkIvZQwccGyUXVtZImAHXAdiro2IiIHwOdQ4pPdDxcC/xLJH4OTJM062S/3yEyzkmaD1wEPAO0RMT29KUdQEuZqlUO3wD+AuhPt6cDeyKiL93eQhK0WbcA6AC+kw7t3S9pMhV6bETEVuDvgU0k4bEXeJ7KPDbyneh4mA1szttv2LZxiIxjkuqB/wA+ExH78l+L5Nztijh/W9IHgZ0R8Xy56zIGTAAuBu6LiIuAAwwZuqqwY6OR5H/XC4AzgMm8eWinor3V48EhMk5JqiEJkO9GxA/S4vaBrmf6c2e56ldilwEfkrQBeJhkqOJukq74wPpwc4Ct5aleSW0BtkTEM+n2IyShUqnHxnuB9RHRERG9wA9IjpdKPDbyneh42ArMzdtv2LZxiIxD6Xj/t4E1EfH1vJeWAzemz28E/qvUdSuHiLgjIuZExHySSdMnI+LjwFPA76S7VUR7RMQOYLOkxWnRVcBqKvTYIBnGepekuvTfzUB7VNyxMcSJjoflwB+kZ2m9C9ibN+x1XL5ifRySdDnwNPAyx+YA/pJkXuTfgDNJlsW/PiKGTqhlmqRW4M8j4oOSziLpmTQBLwC/HxE95axfKUi6kOQEg4nAOuBmkv8wVuSxIemvgY+SnNX4AvBJknH+ijg2JD0EtJIsf98OfBl4lOMcD2nQ/iPJkN9B4OaIOOkqvw4RMzMrmIezzMysYA4RMzMrmEPEzMwK5hAxM7OCOUTMzKxgDhGzYUjKSXox7zFqixdKmp+/uqrZeDNh+F3MKt6hiLiw3JUwG4vcEzErkKQNkr4m6WVJz0o6Jy2fL+nJ9H4MT0g6My1vkfSfkn6ZPt6d/qpqSd9K73nxv5Jq0/1vS+8Z85Kkh8v0Nc1OyiFiNrzaIcNZH817bW9EnE9yle830rJvAg9GxAXAd4F70vJ7gP+LiHeQrGe1Ki1fCNwbEW8H9gAfSctvBy5Kf88fF+vLmb0VvmLdbBiSuiOi/jjlG4ArI2JduiDmjoiYLmkXMCsietPy7RHRLKkDmJO/vEa6lP/j6c2BkPRFoCYi/lbSY0A3yRIVj0ZEd5G/qtkpc0/E7K2JEzw/FflrNuU4Nlf5AeBekl7LyrxVZ83GDIeI2Vvz0byfP0uf/5RkNWGAj5MslgnJbUg/BUfvBz/1RL9UUhUwNyKeAr4ITAXe1BsyKzf/z8ZseLWSXszbfiwiBk7zbZT0Eklv4mNp2Z+R3FnwCyR3Gbw5Lf80sEzSJ0h6HJ8iudve8VQD/5oGjYB70tvcmo0pnhMxK1A6J7I0InaVuy5m5eLhLDMzK5h7ImZmVjD3RMzMrGAOETMzK5hDxMzMCuYQMTOzgjlEzMysYP8PFsG4MZpVpLQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot('Adadelta', ad_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RMSprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "rms = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buld the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "rms_model = build_cnn(rms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      " - 2s - loss: 0.6883 - acc: 0.5380 - val_loss: 0.6706 - val_acc: 0.6065\n",
      "Epoch 2/100\n",
      " - 2s - loss: 0.6600 - acc: 0.6077 - val_loss: 0.6346 - val_acc: 0.6605\n",
      "Epoch 3/100\n",
      " - 2s - loss: 0.6427 - acc: 0.6331 - val_loss: 0.6262 - val_acc: 0.6450\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.6233 - acc: 0.6571 - val_loss: 0.6003 - val_acc: 0.6765\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.6124 - acc: 0.6670 - val_loss: 0.5978 - val_acc: 0.6720\n",
      "Epoch 6/100\n",
      " - 2s - loss: 0.6007 - acc: 0.6792 - val_loss: 0.5799 - val_acc: 0.6895\n",
      "Epoch 7/100\n",
      " - 2s - loss: 0.5913 - acc: 0.6797 - val_loss: 0.5833 - val_acc: 0.6815\n",
      "Epoch 8/100\n",
      " - 2s - loss: 0.5864 - acc: 0.6985 - val_loss: 0.5710 - val_acc: 0.6990\n",
      "Epoch 9/100\n",
      " - 2s - loss: 0.5761 - acc: 0.6990 - val_loss: 0.5797 - val_acc: 0.6875\n",
      "Epoch 10/100\n",
      " - 2s - loss: 0.5710 - acc: 0.7047 - val_loss: 0.5684 - val_acc: 0.7015\n",
      "Epoch 11/100\n",
      " - 2s - loss: 0.5649 - acc: 0.7002 - val_loss: 0.5782 - val_acc: 0.6970\n",
      "Epoch 12/100\n",
      " - 2s - loss: 0.5612 - acc: 0.7116 - val_loss: 0.5690 - val_acc: 0.6945\n",
      "Epoch 13/100\n",
      " - 2s - loss: 0.5598 - acc: 0.7165 - val_loss: 0.5622 - val_acc: 0.7115\n",
      "Epoch 14/100\n",
      " - 2s - loss: 0.5548 - acc: 0.7133 - val_loss: 0.5695 - val_acc: 0.6925\n",
      "Epoch 15/100\n",
      " - 2s - loss: 0.5493 - acc: 0.7189 - val_loss: 0.5671 - val_acc: 0.6990\n",
      "Epoch 16/100\n",
      " - 2s - loss: 0.5469 - acc: 0.7208 - val_loss: 0.5777 - val_acc: 0.6845\n",
      "Epoch 17/100\n",
      " - 2s - loss: 0.5399 - acc: 0.7274 - val_loss: 0.5624 - val_acc: 0.7145\n",
      "Epoch 18/100\n",
      " - 2s - loss: 0.5349 - acc: 0.7311 - val_loss: 0.5570 - val_acc: 0.7170\n",
      "Epoch 19/100\n",
      " - 2s - loss: 0.5274 - acc: 0.7383 - val_loss: 0.5581 - val_acc: 0.7070\n",
      "Epoch 20/100\n",
      " - 2s - loss: 0.5269 - acc: 0.7389 - val_loss: 0.5506 - val_acc: 0.7160\n",
      "Epoch 21/100\n",
      " - 2s - loss: 0.5239 - acc: 0.7395 - val_loss: 0.5587 - val_acc: 0.7200\n",
      "Epoch 22/100\n",
      " - 2s - loss: 0.5217 - acc: 0.7407 - val_loss: 0.5668 - val_acc: 0.7145\n",
      "Epoch 23/100\n",
      " - 2s - loss: 0.5182 - acc: 0.7471 - val_loss: 0.5700 - val_acc: 0.7125\n",
      "Epoch 24/100\n",
      " - 2s - loss: 0.5142 - acc: 0.7411 - val_loss: 0.5557 - val_acc: 0.7100\n",
      "Epoch 25/100\n",
      " - 2s - loss: 0.5059 - acc: 0.7508 - val_loss: 0.5744 - val_acc: 0.7095\n",
      "Epoch 26/100\n",
      " - 2s - loss: 0.5182 - acc: 0.7474 - val_loss: 0.5702 - val_acc: 0.7095\n",
      "Epoch 27/100\n",
      " - 2s - loss: 0.5076 - acc: 0.7463 - val_loss: 0.5629 - val_acc: 0.7205\n",
      "Epoch 28/100\n",
      " - 2s - loss: 0.5042 - acc: 0.7495 - val_loss: 0.5621 - val_acc: 0.6975\n",
      "Epoch 29/100\n",
      " - 2s - loss: 0.5043 - acc: 0.7580 - val_loss: 0.5541 - val_acc: 0.7060\n",
      "Epoch 30/100\n",
      " - 2s - loss: 0.4976 - acc: 0.7558 - val_loss: 0.5697 - val_acc: 0.7110\n",
      "Epoch 31/100\n",
      " - 2s - loss: 0.4943 - acc: 0.7550 - val_loss: 0.5819 - val_acc: 0.7055\n",
      "Epoch 32/100\n",
      " - 2s - loss: 0.4939 - acc: 0.7552 - val_loss: 0.5630 - val_acc: 0.7085\n",
      "Epoch 33/100\n",
      " - 2s - loss: 0.4998 - acc: 0.7559 - val_loss: 0.5679 - val_acc: 0.7050\n",
      "Epoch 34/100\n",
      " - 2s - loss: 0.4929 - acc: 0.7572 - val_loss: 0.5589 - val_acc: 0.7190\n",
      "Epoch 35/100\n",
      " - 2s - loss: 0.4906 - acc: 0.7602 - val_loss: 0.5605 - val_acc: 0.7165\n",
      "Epoch 36/100\n",
      " - 2s - loss: 0.4870 - acc: 0.7625 - val_loss: 0.5701 - val_acc: 0.7095\n",
      "Epoch 37/100\n",
      " - 2s - loss: 0.4877 - acc: 0.7619 - val_loss: 0.5595 - val_acc: 0.7155\n",
      "Epoch 38/100\n",
      " - 2s - loss: 0.4869 - acc: 0.7641 - val_loss: 0.5661 - val_acc: 0.7220\n",
      "Epoch 39/100\n",
      " - 2s - loss: 0.4854 - acc: 0.7593 - val_loss: 0.5655 - val_acc: 0.7215\n",
      "Epoch 40/100\n",
      " - 2s - loss: 0.4776 - acc: 0.7680 - val_loss: 0.5576 - val_acc: 0.7225\n",
      "Epoch 41/100\n",
      " - 2s - loss: 0.4792 - acc: 0.7635 - val_loss: 0.5574 - val_acc: 0.7215\n",
      "Epoch 42/100\n",
      " - 2s - loss: 0.4750 - acc: 0.7647 - val_loss: 0.5576 - val_acc: 0.7215\n",
      "Epoch 43/100\n",
      " - 2s - loss: 0.4742 - acc: 0.7682 - val_loss: 0.5796 - val_acc: 0.7235\n",
      "Epoch 44/100\n",
      " - 2s - loss: 0.4769 - acc: 0.7632 - val_loss: 0.5973 - val_acc: 0.6930\n",
      "Epoch 45/100\n",
      " - 2s - loss: 0.4768 - acc: 0.7679 - val_loss: 0.5698 - val_acc: 0.7225\n",
      "Epoch 46/100\n",
      " - 2s - loss: 0.4675 - acc: 0.7767 - val_loss: 0.5623 - val_acc: 0.7180\n",
      "Epoch 47/100\n",
      " - 2s - loss: 0.4651 - acc: 0.7740 - val_loss: 0.5738 - val_acc: 0.7215\n",
      "Epoch 48/100\n",
      " - 2s - loss: 0.4668 - acc: 0.7731 - val_loss: 0.5604 - val_acc: 0.7290\n",
      "Epoch 49/100\n",
      " - 2s - loss: 0.4636 - acc: 0.7735 - val_loss: 0.5656 - val_acc: 0.7245\n",
      "Epoch 50/100\n",
      " - 2s - loss: 0.4607 - acc: 0.7730 - val_loss: 0.5719 - val_acc: 0.7175\n",
      "Epoch 51/100\n",
      " - 2s - loss: 0.4622 - acc: 0.7741 - val_loss: 0.5694 - val_acc: 0.7115\n",
      "Epoch 52/100\n",
      " - 2s - loss: 0.4650 - acc: 0.7727 - val_loss: 0.5679 - val_acc: 0.7180\n",
      "Epoch 53/100\n",
      " - 2s - loss: 0.4673 - acc: 0.7727 - val_loss: 0.5639 - val_acc: 0.7185\n",
      "Epoch 54/100\n",
      " - 2s - loss: 0.4587 - acc: 0.7722 - val_loss: 0.5825 - val_acc: 0.7180\n",
      "Epoch 55/100\n",
      " - 2s - loss: 0.4535 - acc: 0.7757 - val_loss: 0.5672 - val_acc: 0.7225\n",
      "Epoch 56/100\n",
      " - 2s - loss: 0.4519 - acc: 0.7830 - val_loss: 0.5705 - val_acc: 0.6940\n",
      "Epoch 57/100\n",
      " - 2s - loss: 0.4442 - acc: 0.7838 - val_loss: 0.5819 - val_acc: 0.7035\n",
      "Epoch 58/100\n",
      " - 2s - loss: 0.4482 - acc: 0.7814 - val_loss: 0.5950 - val_acc: 0.7275\n",
      "Epoch 59/100\n",
      " - 2s - loss: 0.4494 - acc: 0.7816 - val_loss: 0.5753 - val_acc: 0.7090\n",
      "Epoch 60/100\n",
      " - 2s - loss: 0.4524 - acc: 0.7816 - val_loss: 0.5832 - val_acc: 0.7295\n",
      "Epoch 61/100\n",
      " - 2s - loss: 0.4500 - acc: 0.7810 - val_loss: 0.5903 - val_acc: 0.7260\n",
      "Epoch 62/100\n",
      " - 2s - loss: 0.4476 - acc: 0.7823 - val_loss: 0.5995 - val_acc: 0.7295\n",
      "Epoch 63/100\n",
      " - 2s - loss: 0.4448 - acc: 0.7858 - val_loss: 0.5725 - val_acc: 0.7220\n",
      "Epoch 64/100\n",
      " - 2s - loss: 0.4442 - acc: 0.7904 - val_loss: 0.5725 - val_acc: 0.7310\n",
      "Epoch 65/100\n",
      " - 2s - loss: 0.4365 - acc: 0.7867 - val_loss: 0.5866 - val_acc: 0.7200\n",
      "Epoch 66/100\n",
      " - 2s - loss: 0.4460 - acc: 0.7806 - val_loss: 0.5973 - val_acc: 0.7190\n",
      "Epoch 67/100\n",
      " - 2s - loss: 0.4430 - acc: 0.7847 - val_loss: 0.5872 - val_acc: 0.7155\n",
      "Epoch 68/100\n",
      " - 2s - loss: 0.4492 - acc: 0.7806 - val_loss: 0.5742 - val_acc: 0.7190\n",
      "Epoch 69/100\n",
      " - 2s - loss: 0.4495 - acc: 0.7774 - val_loss: 0.6062 - val_acc: 0.7230\n",
      "Epoch 70/100\n",
      " - 2s - loss: 0.4460 - acc: 0.7831 - val_loss: 0.5739 - val_acc: 0.7010\n",
      "Epoch 71/100\n",
      " - 2s - loss: 0.4411 - acc: 0.7826 - val_loss: 0.5842 - val_acc: 0.7245\n",
      "Epoch 72/100\n",
      " - 2s - loss: 0.4462 - acc: 0.7825 - val_loss: 0.5725 - val_acc: 0.7295\n",
      "Epoch 73/100\n",
      " - 2s - loss: 0.4442 - acc: 0.7844 - val_loss: 0.5792 - val_acc: 0.7095\n",
      "Epoch 74/100\n",
      " - 2s - loss: 0.4436 - acc: 0.7816 - val_loss: 0.5818 - val_acc: 0.7185\n",
      "Epoch 75/100\n",
      " - 2s - loss: 0.4390 - acc: 0.7861 - val_loss: 0.5719 - val_acc: 0.7050\n",
      "Epoch 76/100\n",
      " - 2s - loss: 0.4361 - acc: 0.7858 - val_loss: 0.5761 - val_acc: 0.7320\n",
      "Epoch 77/100\n",
      " - 2s - loss: 0.4375 - acc: 0.7850 - val_loss: 0.5904 - val_acc: 0.7210\n",
      "Epoch 78/100\n",
      " - 2s - loss: 0.4453 - acc: 0.7793 - val_loss: 0.6002 - val_acc: 0.7065\n",
      "Epoch 79/100\n",
      " - 2s - loss: 0.4404 - acc: 0.7833 - val_loss: 0.5854 - val_acc: 0.7205\n",
      "Epoch 80/100\n",
      " - 2s - loss: 0.4403 - acc: 0.7818 - val_loss: 0.5856 - val_acc: 0.7180\n",
      "Epoch 81/100\n",
      " - 2s - loss: 0.4417 - acc: 0.7792 - val_loss: 0.5657 - val_acc: 0.7070\n",
      "Epoch 82/100\n",
      " - 2s - loss: 0.4487 - acc: 0.7791 - val_loss: 0.6210 - val_acc: 0.7290\n",
      "Epoch 83/100\n",
      " - 2s - loss: 0.4402 - acc: 0.7841 - val_loss: 0.5912 - val_acc: 0.7155\n",
      "Epoch 84/100\n",
      " - 2s - loss: 0.4348 - acc: 0.7857 - val_loss: 0.6177 - val_acc: 0.7045\n",
      "Epoch 85/100\n",
      " - 2s - loss: 0.4440 - acc: 0.7844 - val_loss: 0.5699 - val_acc: 0.7320\n",
      "Epoch 86/100\n",
      " - 2s - loss: 0.4400 - acc: 0.7836 - val_loss: 0.5587 - val_acc: 0.7175\n",
      "Epoch 87/100\n",
      " - 2s - loss: 0.4404 - acc: 0.7824 - val_loss: 0.5875 - val_acc: 0.6955\n",
      "Epoch 88/100\n",
      " - 2s - loss: 0.4303 - acc: 0.7843 - val_loss: 0.6011 - val_acc: 0.7145\n",
      "Epoch 89/100\n",
      " - 2s - loss: 0.4421 - acc: 0.7781 - val_loss: 0.5700 - val_acc: 0.7175\n",
      "Epoch 90/100\n",
      " - 2s - loss: 0.4405 - acc: 0.7844 - val_loss: 0.6216 - val_acc: 0.7305\n",
      "Epoch 91/100\n",
      " - 2s - loss: 0.4389 - acc: 0.7836 - val_loss: 0.5855 - val_acc: 0.7220\n",
      "Epoch 92/100\n",
      " - 2s - loss: 0.4424 - acc: 0.7865 - val_loss: 0.5756 - val_acc: 0.7235\n",
      "Epoch 93/100\n",
      " - 2s - loss: 0.4438 - acc: 0.7800 - val_loss: 0.6190 - val_acc: 0.7335\n",
      "Epoch 94/100\n",
      " - 2s - loss: 0.4412 - acc: 0.7847 - val_loss: 0.5812 - val_acc: 0.7135\n",
      "Epoch 95/100\n",
      " - 2s - loss: 0.4384 - acc: 0.7875 - val_loss: 0.6110 - val_acc: 0.7235\n",
      "Epoch 96/100\n",
      " - 2s - loss: 0.4358 - acc: 0.7846 - val_loss: 0.5858 - val_acc: 0.7270\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/100\n",
      " - 2s - loss: 0.4435 - acc: 0.7859 - val_loss: 0.6009 - val_acc: 0.7240\n",
      "Epoch 98/100\n",
      " - 2s - loss: 0.4361 - acc: 0.7828 - val_loss: 0.5860 - val_acc: 0.6955\n",
      "Epoch 99/100\n",
      " - 2s - loss: 0.4308 - acc: 0.7896 - val_loss: 0.5965 - val_acc: 0.7300\n",
      "Epoch 100/100\n",
      " - 2s - loss: 0.4368 - acc: 0.7860 - val_loss: 0.6170 - val_acc: 0.7365\n"
     ]
    }
   ],
   "source": [
    "rms_history = rms_model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=2, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot('RMSprop', rms_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam_model = build_cnn(adam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      " - 2s - loss: 0.7038 - acc: 0.5068 - val_loss: 0.6911 - val_acc: 0.5855\n",
      "Epoch 2/100\n",
      " - 2s - loss: 0.6890 - acc: 0.5257 - val_loss: 0.6892 - val_acc: 0.5550\n",
      "Epoch 3/100\n",
      " - 2s - loss: 0.6840 - acc: 0.5429 - val_loss: 0.6808 - val_acc: 0.5945\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.6808 - acc: 0.5489 - val_loss: 0.6752 - val_acc: 0.6235\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.6740 - acc: 0.5631 - val_loss: 0.6612 - val_acc: 0.6300\n",
      "Epoch 6/100\n",
      " - 2s - loss: 0.6677 - acc: 0.5634 - val_loss: 0.6516 - val_acc: 0.6305\n",
      "Epoch 7/100\n",
      " - 2s - loss: 0.6568 - acc: 0.5697 - val_loss: 0.6278 - val_acc: 0.6610\n",
      "Epoch 8/100\n",
      " - 2s - loss: 0.6494 - acc: 0.5898 - val_loss: 0.6288 - val_acc: 0.6730\n",
      "Epoch 9/100\n",
      " - 2s - loss: 0.6373 - acc: 0.6122 - val_loss: 0.6016 - val_acc: 0.6910\n",
      "Epoch 10/100\n",
      " - 2s - loss: 0.6328 - acc: 0.6226 - val_loss: 0.6056 - val_acc: 0.6810\n",
      "Epoch 11/100\n",
      " - 2s - loss: 0.6170 - acc: 0.6469 - val_loss: 0.5920 - val_acc: 0.6705\n",
      "Epoch 12/100\n",
      " - 2s - loss: 0.6068 - acc: 0.6573 - val_loss: 0.5834 - val_acc: 0.6840\n",
      "Epoch 13/100\n",
      " - 2s - loss: 0.6013 - acc: 0.6614 - val_loss: 0.5752 - val_acc: 0.6850\n",
      "Epoch 14/100\n",
      " - 2s - loss: 0.6005 - acc: 0.6638 - val_loss: 0.5754 - val_acc: 0.6915\n",
      "Epoch 15/100\n",
      " - 2s - loss: 0.5929 - acc: 0.6666 - val_loss: 0.5722 - val_acc: 0.6845\n",
      "Epoch 16/100\n",
      " - 2s - loss: 0.5876 - acc: 0.6704 - val_loss: 0.5728 - val_acc: 0.6870\n",
      "Epoch 17/100\n",
      " - 2s - loss: 0.5808 - acc: 0.6718 - val_loss: 0.5649 - val_acc: 0.6835\n",
      "Epoch 18/100\n",
      " - 2s - loss: 0.5789 - acc: 0.6863 - val_loss: 0.5625 - val_acc: 0.6920\n",
      "Epoch 19/100\n",
      " - 2s - loss: 0.5757 - acc: 0.6786 - val_loss: 0.5598 - val_acc: 0.6830\n",
      "Epoch 20/100\n",
      " - 2s - loss: 0.5680 - acc: 0.6873 - val_loss: 0.5595 - val_acc: 0.6870\n",
      "Epoch 21/100\n",
      " - 2s - loss: 0.5706 - acc: 0.6867 - val_loss: 0.5541 - val_acc: 0.6840\n",
      "Epoch 22/100\n",
      " - 2s - loss: 0.5613 - acc: 0.6954 - val_loss: 0.5567 - val_acc: 0.6980\n",
      "Epoch 23/100\n",
      " - 2s - loss: 0.5609 - acc: 0.6960 - val_loss: 0.5516 - val_acc: 0.6900\n",
      "Epoch 24/100\n",
      " - 2s - loss: 0.5566 - acc: 0.7051 - val_loss: 0.5580 - val_acc: 0.6965\n",
      "Epoch 25/100\n",
      " - 2s - loss: 0.5506 - acc: 0.7016 - val_loss: 0.5487 - val_acc: 0.6935\n",
      "Epoch 26/100\n",
      " - 2s - loss: 0.5518 - acc: 0.7045 - val_loss: 0.5509 - val_acc: 0.6900\n",
      "Epoch 27/100\n",
      " - 2s - loss: 0.5424 - acc: 0.7072 - val_loss: 0.5509 - val_acc: 0.6980\n",
      "Epoch 28/100\n",
      " - 2s - loss: 0.5435 - acc: 0.7060 - val_loss: 0.5482 - val_acc: 0.6940\n",
      "Epoch 29/100\n",
      " - 2s - loss: 0.5347 - acc: 0.7133 - val_loss: 0.5491 - val_acc: 0.6940\n",
      "Epoch 30/100\n",
      " - 2s - loss: 0.5345 - acc: 0.7171 - val_loss: 0.5530 - val_acc: 0.6980\n",
      "Epoch 31/100\n",
      " - 2s - loss: 0.5287 - acc: 0.7155 - val_loss: 0.5451 - val_acc: 0.7010\n",
      "Epoch 32/100\n",
      " - 2s - loss: 0.5280 - acc: 0.7190 - val_loss: 0.5455 - val_acc: 0.7085\n",
      "Epoch 33/100\n",
      " - 2s - loss: 0.5250 - acc: 0.7245 - val_loss: 0.5479 - val_acc: 0.7015\n",
      "Epoch 34/100\n",
      " - 2s - loss: 0.5183 - acc: 0.7299 - val_loss: 0.5433 - val_acc: 0.7045\n",
      "Epoch 35/100\n",
      " - 2s - loss: 0.5178 - acc: 0.7278 - val_loss: 0.5382 - val_acc: 0.7015\n",
      "Epoch 36/100\n",
      " - 2s - loss: 0.5110 - acc: 0.7317 - val_loss: 0.5380 - val_acc: 0.7105\n",
      "Epoch 37/100\n",
      " - 2s - loss: 0.5118 - acc: 0.7268 - val_loss: 0.5417 - val_acc: 0.7070\n",
      "Epoch 38/100\n",
      " - 2s - loss: 0.4998 - acc: 0.7365 - val_loss: 0.5427 - val_acc: 0.7140\n",
      "Epoch 39/100\n",
      " - 2s - loss: 0.5048 - acc: 0.7316 - val_loss: 0.5429 - val_acc: 0.7040\n",
      "Epoch 40/100\n",
      " - 2s - loss: 0.4995 - acc: 0.7351 - val_loss: 0.5440 - val_acc: 0.7220\n",
      "Epoch 41/100\n",
      " - 2s - loss: 0.4985 - acc: 0.7353 - val_loss: 0.5439 - val_acc: 0.7055\n",
      "Epoch 42/100\n",
      " - 2s - loss: 0.4941 - acc: 0.7438 - val_loss: 0.5406 - val_acc: 0.7105\n",
      "Epoch 43/100\n",
      " - 2s - loss: 0.4960 - acc: 0.7402 - val_loss: 0.5375 - val_acc: 0.7155\n",
      "Epoch 44/100\n",
      " - 2s - loss: 0.4820 - acc: 0.7454 - val_loss: 0.5425 - val_acc: 0.7100\n",
      "Epoch 45/100\n",
      " - 2s - loss: 0.4805 - acc: 0.7453 - val_loss: 0.5397 - val_acc: 0.7145\n",
      "Epoch 46/100\n",
      " - 2s - loss: 0.4824 - acc: 0.7458 - val_loss: 0.5370 - val_acc: 0.7150\n",
      "Epoch 47/100\n",
      " - 2s - loss: 0.4745 - acc: 0.7494 - val_loss: 0.5359 - val_acc: 0.7135\n",
      "Epoch 48/100\n",
      " - 2s - loss: 0.4778 - acc: 0.7446 - val_loss: 0.5406 - val_acc: 0.7165\n",
      "Epoch 49/100\n",
      " - 2s - loss: 0.4727 - acc: 0.7480 - val_loss: 0.5389 - val_acc: 0.7160\n",
      "Epoch 50/100\n",
      " - 2s - loss: 0.4676 - acc: 0.7582 - val_loss: 0.5394 - val_acc: 0.7160\n",
      "Epoch 51/100\n",
      " - 2s - loss: 0.4733 - acc: 0.7452 - val_loss: 0.5402 - val_acc: 0.7145\n",
      "Epoch 52/100\n",
      " - 2s - loss: 0.4729 - acc: 0.7516 - val_loss: 0.5417 - val_acc: 0.7190\n",
      "Epoch 53/100\n",
      " - 2s - loss: 0.4688 - acc: 0.7520 - val_loss: 0.5415 - val_acc: 0.7170\n",
      "Epoch 54/100\n",
      " - 2s - loss: 0.4638 - acc: 0.7515 - val_loss: 0.5400 - val_acc: 0.7150\n",
      "Epoch 55/100\n",
      " - 2s - loss: 0.4659 - acc: 0.7528 - val_loss: 0.5385 - val_acc: 0.7060\n",
      "Epoch 56/100\n",
      " - 2s - loss: 0.4603 - acc: 0.7535 - val_loss: 0.5487 - val_acc: 0.7160\n",
      "Epoch 57/100\n",
      " - 2s - loss: 0.4607 - acc: 0.7542 - val_loss: 0.5522 - val_acc: 0.7110\n",
      "Epoch 58/100\n",
      " - 2s - loss: 0.4603 - acc: 0.7555 - val_loss: 0.5425 - val_acc: 0.7095\n",
      "Epoch 59/100\n",
      " - 2s - loss: 0.4576 - acc: 0.7583 - val_loss: 0.5484 - val_acc: 0.7115\n",
      "Epoch 60/100\n",
      " - 2s - loss: 0.4527 - acc: 0.7585 - val_loss: 0.5450 - val_acc: 0.7115\n",
      "Epoch 61/100\n",
      " - 2s - loss: 0.4551 - acc: 0.7584 - val_loss: 0.5497 - val_acc: 0.7040\n",
      "Epoch 62/100\n",
      " - 2s - loss: 0.4458 - acc: 0.7574 - val_loss: 0.5394 - val_acc: 0.7110\n",
      "Epoch 63/100\n",
      " - 2s - loss: 0.4446 - acc: 0.7672 - val_loss: 0.5456 - val_acc: 0.7070\n",
      "Epoch 64/100\n",
      " - 2s - loss: 0.4430 - acc: 0.7633 - val_loss: 0.5438 - val_acc: 0.7150\n",
      "Epoch 65/100\n",
      " - 2s - loss: 0.4363 - acc: 0.7652 - val_loss: 0.5455 - val_acc: 0.7135\n",
      "Epoch 66/100\n",
      " - 2s - loss: 0.4452 - acc: 0.7644 - val_loss: 0.5444 - val_acc: 0.7185\n",
      "Epoch 67/100\n",
      " - 2s - loss: 0.4438 - acc: 0.7652 - val_loss: 0.5527 - val_acc: 0.7120\n",
      "Epoch 68/100\n",
      " - 2s - loss: 0.4369 - acc: 0.7647 - val_loss: 0.5512 - val_acc: 0.7160\n",
      "Epoch 69/100\n",
      " - 2s - loss: 0.4337 - acc: 0.7685 - val_loss: 0.5477 - val_acc: 0.7185\n",
      "Epoch 70/100\n",
      " - 2s - loss: 0.4372 - acc: 0.7669 - val_loss: 0.5453 - val_acc: 0.7160\n",
      "Epoch 71/100\n",
      " - 2s - loss: 0.4340 - acc: 0.7617 - val_loss: 0.5471 - val_acc: 0.7185\n",
      "Epoch 72/100\n",
      " - 2s - loss: 0.4271 - acc: 0.7694 - val_loss: 0.5382 - val_acc: 0.7180\n",
      "Epoch 73/100\n",
      " - 2s - loss: 0.4192 - acc: 0.7814 - val_loss: 0.5478 - val_acc: 0.7185\n",
      "Epoch 74/100\n",
      " - 2s - loss: 0.4230 - acc: 0.7715 - val_loss: 0.5433 - val_acc: 0.7185\n",
      "Epoch 75/100\n",
      " - 2s - loss: 0.4257 - acc: 0.7734 - val_loss: 0.5518 - val_acc: 0.7185\n",
      "Epoch 76/100\n",
      " - 2s - loss: 0.4255 - acc: 0.7710 - val_loss: 0.5477 - val_acc: 0.7170\n",
      "Epoch 77/100\n",
      " - 2s - loss: 0.4207 - acc: 0.7740 - val_loss: 0.5535 - val_acc: 0.7120\n",
      "Epoch 78/100\n",
      " - 2s - loss: 0.4190 - acc: 0.7719 - val_loss: 0.5467 - val_acc: 0.7165\n",
      "Epoch 79/100\n",
      " - 2s - loss: 0.4160 - acc: 0.7796 - val_loss: 0.5529 - val_acc: 0.7125\n",
      "Epoch 80/100\n",
      " - 2s - loss: 0.4197 - acc: 0.7795 - val_loss: 0.5560 - val_acc: 0.7155\n",
      "Epoch 81/100\n",
      " - 2s - loss: 0.4198 - acc: 0.7739 - val_loss: 0.5449 - val_acc: 0.7185\n",
      "Epoch 82/100\n",
      " - 2s - loss: 0.4104 - acc: 0.7798 - val_loss: 0.5564 - val_acc: 0.7125\n",
      "Epoch 83/100\n",
      " - 2s - loss: 0.4062 - acc: 0.7852 - val_loss: 0.5524 - val_acc: 0.7140\n",
      "Epoch 84/100\n",
      " - 2s - loss: 0.4096 - acc: 0.7866 - val_loss: 0.5575 - val_acc: 0.7200\n",
      "Epoch 85/100\n",
      " - 2s - loss: 0.4002 - acc: 0.7863 - val_loss: 0.5626 - val_acc: 0.7225\n",
      "Epoch 86/100\n",
      " - 2s - loss: 0.4063 - acc: 0.7821 - val_loss: 0.5579 - val_acc: 0.7175\n",
      "Epoch 87/100\n",
      " - 2s - loss: 0.3999 - acc: 0.7861 - val_loss: 0.5533 - val_acc: 0.7070\n",
      "Epoch 88/100\n",
      " - 2s - loss: 0.4082 - acc: 0.7840 - val_loss: 0.5527 - val_acc: 0.7115\n",
      "Epoch 89/100\n",
      " - 2s - loss: 0.4097 - acc: 0.7787 - val_loss: 0.5584 - val_acc: 0.7160\n",
      "Epoch 90/100\n",
      " - 2s - loss: 0.4008 - acc: 0.7880 - val_loss: 0.5624 - val_acc: 0.7155\n",
      "Epoch 91/100\n",
      " - 2s - loss: 0.3926 - acc: 0.7900 - val_loss: 0.5655 - val_acc: 0.7170\n",
      "Epoch 92/100\n",
      " - 2s - loss: 0.4077 - acc: 0.7871 - val_loss: 0.5543 - val_acc: 0.7115\n",
      "Epoch 93/100\n",
      " - 2s - loss: 0.4066 - acc: 0.7794 - val_loss: 0.5584 - val_acc: 0.7115\n",
      "Epoch 94/100\n",
      " - 2s - loss: 0.3973 - acc: 0.7885 - val_loss: 0.5720 - val_acc: 0.7010\n",
      "Epoch 95/100\n",
      " - 2s - loss: 0.3959 - acc: 0.7807 - val_loss: 0.5674 - val_acc: 0.7130\n",
      "Epoch 96/100\n",
      " - 2s - loss: 0.3956 - acc: 0.7875 - val_loss: 0.5568 - val_acc: 0.7275\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/100\n",
      " - 2s - loss: 0.3990 - acc: 0.7853 - val_loss: 0.5667 - val_acc: 0.7105\n",
      "Epoch 98/100\n",
      " - 2s - loss: 0.3990 - acc: 0.7774 - val_loss: 0.5543 - val_acc: 0.7225\n",
      "Epoch 99/100\n",
      " - 2s - loss: 0.3943 - acc: 0.7868 - val_loss: 0.5576 - val_acc: 0.7220\n",
      "Epoch 100/100\n",
      " - 2s - loss: 0.3903 - acc: 0.7998 - val_loss: 0.5593 - val_acc: 0.7190\n"
     ]
    }
   ],
   "source": [
    "adam_history = adam_model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=2, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot('Adam', adam_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparison of Adagrad, Adadelta, RMSprop, and Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(range(1, EPOCHS+1), ag_history.history['val_acc'], label='Adagrad')\n",
    "plt.plot(range(1, EPOCHS+1), ad_history.history['val_acc'], label='Adadelta')\n",
    "plt.plot(range(1, EPOCHS+1), rms_history.history['val_acc'], label='RMSprop')\n",
    "plt.plot(range(1, EPOCHS+1), adam_history.history['val_acc'], label='Adam')\n",
    "plt.legend()\n",
    "    \n",
    "plt.xlim([1, EPOCHS])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy on validation set')\n",
    "    \n",
    "plt.title('Methods Comparison')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following plot shows the comparison of:\n",
    "1. Constant Learning Rate,\n",
    "2. Time-based Decay,\n",
    "3. Step Decay,\n",
    "4. Exponential Decay\n",
    "5. Adagrad,\n",
    "6. Adadelta,\n",
    "7. RMSprop,\n",
    "8. Adam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 10))\n",
    "\n",
    "plt.plot(range(1, EPOCHS+1), clr_history.history['val_acc'], label='Constant lr')\n",
    "plt.plot(range(1, EPOCHS+1), tbd_history.history['val_acc'], label='Time-based decay')\n",
    "plt.plot(range(1, EPOCHS+1), sd_history.history['val_acc'], label='Step decay')\n",
    "plt.plot(range(1, EPOCHS+1), ed_history.history['val_acc'], label='Exponential decay')\n",
    "plt.plot(range(1, EPOCHS+1), ag_history.history['val_acc'], label='Adagrad')\n",
    "plt.plot(range(1, EPOCHS+1), ad_history.history['val_acc'], label='Adadelta')\n",
    "plt.plot(range(1, EPOCHS+1), rms_history.history['val_acc'], label='RMSprop')\n",
    "plt.plot(range(1, EPOCHS+1), adam_history.history['val_acc'], label='Adam')\n",
    "plt.legend()\n",
    "    \n",
    "plt.xlim([1, EPOCHS])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy on validation set')\n",
    "    \n",
    "plt.title('Methods Comparison')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
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