18_convolutional_neural_nets/02_digit_classification_with_lenet5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Image Classification with Feedforward NN and LetNet5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All libraries we introduced in the last chapter provide support for convolutional layers. We are going to illustrate the LeNet5 architecture using the most basic MNIST handwritten digit dataset, and then use AlexNet on CIFAR10, a simplified version of the original ImageNet to demonstrate the use of data augmentation.\n",
    "LeNet5 and MNIST using Keras."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:17.309746Z",
     "start_time": "2021-02-23T05:53:15.972870Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "from random import randint\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import mnist\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping \n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import (Conv2D, \n",
    "                                     AveragePooling2D, \n",
    "                                     Dense, \n",
    "                                     Dropout, \n",
    "                                     Flatten)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:17.314389Z",
     "start_time": "2021-02-23T05:53:17.310942Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CPU\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpu_devices:\n",
    "    print('Using GPU')\n",
    "    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
    "else:\n",
    "    print('Using CPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:17.322715Z",
     "start_time": "2021-02-23T05:53:17.315741Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:17.330513Z",
     "start_time": "2021-02-23T05:53:17.323715Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('results')\n",
    "mnist_path = results_path / 'mnist'\n",
    "if not mnist_path.exists():\n",
    "    mnist_path.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load MNIST Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original MNIST dataset contains 60,000 images in 28x28 pixel resolution with a single grayscale containing handwritten digits from 0 to 9. A good alternative is the more challenging but structurally similar Fashion MNIST dataset that we encountered in Chapter 12 on Unsupervised Learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load it in keras out of the box:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:17.568292Z",
     "start_time": "2021-02-23T05:53:17.331429Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 0s 0us/step\n",
      "The MNIST database has a training set of 60000 examples.\n",
      "The MNIST database has a test set of 10000 examples.\n"
     ]
    }
   ],
   "source": [
    "# use Keras to import pre-shuffled MNIST database\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "print(\"The MNIST database has a training set of %d examples.\" % len(X_train))\n",
    "print(\"The MNIST database has a test set of %d examples.\" % len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:17.572961Z",
     "start_time": "2021-02-23T05:53:17.569363Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (10000, 28, 28))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize First 10 Training Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below figure shows the first ten images in the dataset and highlights significant variation among instances of the same digit. On the right, it shows how the pixel values for an indivual image range from 0 to 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:18.257312Z",
     "start_time": "2021-02-23T05:53:17.574022Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=5, nrows=2, figsize=(20, 8))\n",
    "axes = axes.flatten()\n",
    "for i, ax in enumerate(axes):\n",
    "    ax.imshow(X_train[i], cmap='gray')\n",
    "    ax.axis('off')\n",
    "    ax.set_title('Digit: {}'.format(y_train[i]), fontsize=16)\n",
    "fig.suptitle('First 10 Digits', fontsize=20)\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show random image in detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:21.054551Z",
     "start_time": "2021-02-23T05:53:18.259521Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (14, 14)) \n",
    "\n",
    "i = randint(0, len(X_train))\n",
    "img = X_train[i]\n",
    "\n",
    "ax.imshow(img, cmap='gray')\n",
    "ax.set_title('Digit: {}'.format(y_train[i]), fontsize=16)\n",
    "\n",
    "width, height = img.shape\n",
    "thresh = img.max()/2.5\n",
    "for x in range(width):\n",
    "    for y in range(height):\n",
    "        ax.annotate('{:2}'.format(img[x][y]), \n",
    "                    xy=(y,x),\n",
    "                    horizontalalignment='center',\n",
    "                    verticalalignment='center',\n",
    "                    color='white' if img[x][y]<thresh else 'black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rescale pixel values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We rescale the pixel values to the range [0, 1] to normalize the training data and faciliate the backpropagation process and convert the data to 32 bit floats that reduce memory requirements and computational cost while providing sufficient precision for our use case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:21.116077Z",
     "start_time": "2021-02-23T05:53:21.055852Z"
    }
   },
   "outputs": [],
   "source": [
    "# rescale [0,255] --> [0,1]\n",
    "X_train = X_train.astype('float32')/255\n",
    "X_test = X_test.astype('float32')/255 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Label Encoding using Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print first ten labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:21.119597Z",
     "start_time": "2021-02-23T05:53:21.117077Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integer-valued labels:\n",
      "[5 0 4 1 9 2 1 3 1 4]\n"
     ]
    }
   ],
   "source": [
    "print('Integer-valued labels:')\n",
    "print(y_train[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feed-Forward NN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:21.214077Z",
     "start_time": "2021-02-23T05:53:21.120994Z"
    }
   },
   "outputs": [],
   "source": [
    "ffnn = Sequential([\n",
    "    Flatten(input_shape=X_train.shape[1:]),\n",
    "    Dense(512, activation='relu'),\n",
    "    Dropout(0.2),\n",
    "    Dense(512, activation='relu'),\n",
    "    Dropout(0.2),\n",
    "    Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:21.220723Z",
     "start_time": "2021-02-23T05:53:21.215830Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 669,706\n",
      "Trainable params: 669,706\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "ffnn.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:21.234808Z",
     "start_time": "2021-02-23T05:53:21.221768Z"
    }
   },
   "outputs": [],
   "source": [
    "ffnn.compile(loss='sparse_categorical_crossentropy', \n",
    "              optimizer='rmsprop', \n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Baseline Classification Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:21.719309Z",
     "start_time": "2021-02-23T05:53:21.235838Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 13.07%\n"
     ]
    }
   ],
   "source": [
    "# evaluate test accuracy\n",
    "baseline_accuracy = ffnn.evaluate(X_test, y_test, verbose=0)[1]\n",
    "\n",
    "# print test accuracy\n",
    "print(f'Test accuracy: {baseline_accuracy:.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Callback for model persistence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:21.722525Z",
     "start_time": "2021-02-23T05:53:21.720422Z"
    }
   },
   "outputs": [],
   "source": [
    "ffn_path = mnist_path / 'ffn.best.hdf5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:21.737309Z",
     "start_time": "2021-02-23T05:53:21.723618Z"
    }
   },
   "outputs": [],
   "source": [
    "checkpointer = ModelCheckpoint(filepath=ffn_path.as_posix(), \n",
    "                               verbose=1, \n",
    "                               save_best_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Early Stopping Callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:21.745735Z",
     "start_time": "2021-02-23T05:53:21.738346Z"
    }
   },
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_loss', patience=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:53:21.753663Z",
     "start_time": "2021-02-23T05:53:21.746777Z"
    }
   },
   "outputs": [],
   "source": [
    "epochs = 100\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:55:37.420613Z",
     "start_time": "2021-02-23T05:53:21.754907Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.2358 - accuracy: 0.9301\n",
      "Epoch 00001: val_loss improved from inf to 0.13914, saving model to results/mnist/ffn.best.hdf5\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.2357 - accuracy: 0.9301 - val_loss: 0.1391 - val_accuracy: 0.9618\n",
      "Epoch 2/100\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.1322 - accuracy: 0.9652\n",
      "Epoch 00002: val_loss improved from 0.13914 to 0.12946, saving model to results/mnist/ffn.best.hdf5\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.1320 - accuracy: 0.9653 - val_loss: 0.1295 - val_accuracy: 0.9701\n",
      "Epoch 3/100\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.1153 - accuracy: 0.9724\n",
      "Epoch 00003: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.1162 - accuracy: 0.9722 - val_loss: 0.1344 - val_accuracy: 0.9731\n",
      "Epoch 4/100\n",
      "1493/1500 [============================>.] - ETA: 0s - loss: 0.1088 - accuracy: 0.9757\n",
      "Epoch 00004: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.1088 - accuracy: 0.9756 - val_loss: 0.1794 - val_accuracy: 0.9702\n",
      "Epoch 5/100\n",
      "1489/1500 [============================>.] - ETA: 0s - loss: 0.1047 - accuracy: 0.9778\n",
      "Epoch 00005: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.1047 - accuracy: 0.9778 - val_loss: 0.1506 - val_accuracy: 0.9722\n",
      "Epoch 6/100\n",
      "1492/1500 [============================>.] - ETA: 0s - loss: 0.0933 - accuracy: 0.9796\n",
      "Epoch 00006: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0930 - accuracy: 0.9797 - val_loss: 0.1600 - val_accuracy: 0.9752\n",
      "Epoch 7/100\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.0970 - accuracy: 0.9814\n",
      "Epoch 00007: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0969 - accuracy: 0.9814 - val_loss: 0.1674 - val_accuracy: 0.9764\n",
      "Epoch 8/100\n",
      "1489/1500 [============================>.] - ETA: 0s - loss: 0.0898 - accuracy: 0.9831\n",
      "Epoch 00008: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0901 - accuracy: 0.9831 - val_loss: 0.2152 - val_accuracy: 0.9743\n",
      "Epoch 9/100\n",
      "1491/1500 [============================>.] - ETA: 0s - loss: 0.0891 - accuracy: 0.9828\n",
      "Epoch 00009: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0887 - accuracy: 0.9829 - val_loss: 0.2123 - val_accuracy: 0.9770\n",
      "Epoch 10/100\n",
      "1488/1500 [============================>.] - ETA: 0s - loss: 0.0899 - accuracy: 0.9841\n",
      "Epoch 00010: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0903 - accuracy: 0.9840 - val_loss: 0.2225 - val_accuracy: 0.9724\n",
      "Epoch 11/100\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.0800 - accuracy: 0.9853\n",
      "Epoch 00011: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0799 - accuracy: 0.9853 - val_loss: 0.2246 - val_accuracy: 0.9770\n",
      "Epoch 12/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.0760 - accuracy: 0.9857\n",
      "Epoch 00012: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0760 - accuracy: 0.9857 - val_loss: 0.2523 - val_accuracy: 0.9718\n",
      "Epoch 13/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.0756 - accuracy: 0.9867\n",
      "Epoch 00013: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 7s 5ms/step - loss: 0.0756 - accuracy: 0.9867 - val_loss: 0.2186 - val_accuracy: 0.9778\n",
      "Epoch 14/100\n",
      "1491/1500 [============================>.] - ETA: 0s - loss: 0.0812 - accuracy: 0.9865\n",
      "Epoch 00014: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0813 - accuracy: 0.9865 - val_loss: 0.2346 - val_accuracy: 0.9778\n",
      "Epoch 15/100\n",
      "1491/1500 [============================>.] - ETA: 0s - loss: 0.0773 - accuracy: 0.9865\n",
      "Epoch 00015: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0783 - accuracy: 0.9865 - val_loss: 0.2618 - val_accuracy: 0.9772\n",
      "Epoch 16/100\n",
      "1490/1500 [============================>.] - ETA: 0s - loss: 0.0820 - accuracy: 0.9879\n",
      "Epoch 00016: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0821 - accuracy: 0.9880 - val_loss: 0.2316 - val_accuracy: 0.9787\n",
      "Epoch 17/100\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.0742 - accuracy: 0.9884\n",
      "Epoch 00017: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0742 - accuracy: 0.9884 - val_loss: 0.2217 - val_accuracy: 0.9790\n",
      "Epoch 18/100\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.0813 - accuracy: 0.9881\n",
      "Epoch 00018: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0814 - accuracy: 0.9881 - val_loss: 0.2616 - val_accuracy: 0.9774\n",
      "Epoch 19/100\n",
      "1492/1500 [============================>.] - ETA: 0s - loss: 0.0660 - accuracy: 0.9892\n",
      "Epoch 00019: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0665 - accuracy: 0.9891 - val_loss: 0.3383 - val_accuracy: 0.9768\n",
      "Epoch 20/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.0710 - accuracy: 0.9897\n",
      "Epoch 00020: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0710 - accuracy: 0.9897 - val_loss: 0.3035 - val_accuracy: 0.9790\n",
      "Epoch 21/100\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.0621 - accuracy: 0.9900\n",
      "Epoch 00021: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0620 - accuracy: 0.9900 - val_loss: 0.3206 - val_accuracy: 0.9803\n",
      "Epoch 22/100\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.0718 - accuracy: 0.9906\n",
      "Epoch 00022: val_loss did not improve from 0.12946\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0717 - accuracy: 0.9906 - val_loss: 0.3257 - val_accuracy: 0.9794\n"
     ]
    }
   ],
   "source": [
    "ffnn_history = ffnn.fit(X_train,\n",
    "                        y_train,\n",
    "                        batch_size=batch_size,\n",
    "                        epochs=epochs,\n",
    "                        validation_split=0.2,\n",
    "                        callbacks=[checkpointer, early_stopping],\n",
    "                        verbose=1,\n",
    "                        shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot CV Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:55:37.585446Z",
     "start_time": "2021-02-23T05:55:37.421681Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(ffnn_history.history)[['accuracy', 'val_accuracy']].plot(figsize=(14,4))\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:55:37.593140Z",
     "start_time": "2021-02-23T05:55:37.586879Z"
    }
   },
   "outputs": [],
   "source": [
    "# load the weights that yielded the best validation accuracy\n",
    "ffnn.load_weights(ffn_path.as_posix())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Classification Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:55:37.899530Z",
     "start_time": "2021-02-23T05:55:37.594340Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 97.29%\n"
     ]
    }
   ],
   "source": [
    "# evaluate test accuracy\n",
    "ffnn_accuracy = ffnn.evaluate(X_test, y_test, verbose=0)[1]\n",
    "\n",
    "print(f'Test accuracy: {ffnn_accuracy:.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LeNet5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:55:37.904234Z",
     "start_time": "2021-02-23T05:55:37.901579Z"
    }
   },
   "outputs": [],
   "source": [
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can define a simplified version of LeNet5 that omits the original final layer containing radial basis functions as follows, using the default ‘valid’ padding and single step strides unless defined otherwise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:55:37.945858Z",
     "start_time": "2021-02-23T05:55:37.905292Z"
    }
   },
   "outputs": [],
   "source": [
    "lenet5 = Sequential([\n",
    "    Conv2D(filters=6,\n",
    "           kernel_size=5,\n",
    "           activation='relu',\n",
    "           input_shape=(28, 28, 1),\n",
    "           name='CONV1'),\n",
    "    AveragePooling2D(pool_size=(2, 2),\n",
    "                     strides=(1, 1),\n",
    "                     padding='valid',\n",
    "                     name='POOL1'),\n",
    "    Conv2D(filters=16, kernel_size=(5, 5), activation='tanh', name='CONV2'),\n",
    "    AveragePooling2D(pool_size=(2, 2), strides=(2, 2), name='POOL2'),\n",
    "    Conv2D(filters=120, kernel_size=(5, 5), activation='tanh', name='CONV3'),\n",
    "    Flatten(name='FLAT'),\n",
    "    Dense(units=84, activation='tanh', name='FC6'),\n",
    "    Dense(units=10, activation='softmax', name='FC7')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary indicates that the model thus defined has over 300,000 parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:55:37.951648Z",
     "start_time": "2021-02-23T05:55:37.946792Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "CONV1 (Conv2D)               (None, 24, 24, 6)         156       \n",
      "_________________________________________________________________\n",
      "POOL1 (AveragePooling2D)     (None, 23, 23, 6)         0         \n",
      "_________________________________________________________________\n",
      "CONV2 (Conv2D)               (None, 19, 19, 16)        2416      \n",
      "_________________________________________________________________\n",
      "POOL2 (AveragePooling2D)     (None, 9, 9, 16)          0         \n",
      "_________________________________________________________________\n",
      "CONV3 (Conv2D)               (None, 5, 5, 120)         48120     \n",
      "_________________________________________________________________\n",
      "FLAT (Flatten)               (None, 3000)              0         \n",
      "_________________________________________________________________\n",
      "FC6 (Dense)                  (None, 84)                252084    \n",
      "_________________________________________________________________\n",
      "FC7 (Dense)                  (None, 10)                850       \n",
      "=================================================================\n",
      "Total params: 303,626\n",
      "Trainable params: 303,626\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "lenet5.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compile using crossentropy loss and the original stochastic gradient optimizer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:55:37.961785Z",
     "start_time": "2021-02-23T05:55:37.952680Z"
    }
   },
   "outputs": [],
   "source": [
    "lenet5.compile(loss='sparse_categorical_crossentropy',\n",
    "               optimizer='SGD',\n",
    "               metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define checkpoint callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:55:37.968992Z",
     "start_time": "2021-02-23T05:55:37.962780Z"
    }
   },
   "outputs": [],
   "source": [
    "lenet_path = mnist_path / 'lenet.best.hdf5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:55:37.977639Z",
     "start_time": "2021-02-23T05:55:37.970258Z"
    }
   },
   "outputs": [],
   "source": [
    "checkpointer = ModelCheckpoint(filepath=lenet_path.as_posix(),\n",
    "                               verbose=1,\n",
    "                               save_best_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to train the model. The model expects 4D input so we reshape accordingly. We use the standard batch size of 32, 80-20 train-validation split, use checkpointing to store the model weights if the validation error improves, and make sure the dataset is randomly shuffled:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:55:37.985637Z",
     "start_time": "2021-02-23T05:55:37.978569Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size=32\n",
    "epochs=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:07:00.129841Z",
     "start_time": "2021-02-23T05:55:37.987142Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.4736 - accuracy: 0.8673\n",
      "Epoch 00001: val_loss improved from inf to 0.23828, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.4736 - accuracy: 0.8673 - val_loss: 0.2383 - val_accuracy: 0.9317\n",
      "Epoch 2/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.2040 - accuracy: 0.9416\n",
      "Epoch 00002: val_loss improved from 0.23828 to 0.16032, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.2041 - accuracy: 0.9416 - val_loss: 0.1603 - val_accuracy: 0.9571\n",
      "Epoch 3/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.1415 - accuracy: 0.9598\n",
      "Epoch 00003: val_loss improved from 0.16032 to 0.12653, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.1415 - accuracy: 0.9598 - val_loss: 0.1265 - val_accuracy: 0.9634\n",
      "Epoch 4/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.1078 - accuracy: 0.9690\n",
      "Epoch 00004: val_loss improved from 0.12653 to 0.09780, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.1078 - accuracy: 0.9690 - val_loss: 0.0978 - val_accuracy: 0.9721\n",
      "Epoch 5/100\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.0882 - accuracy: 0.9745\n",
      "Epoch 00005: val_loss improved from 0.09780 to 0.08991, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.0883 - accuracy: 0.9745 - val_loss: 0.0899 - val_accuracy: 0.9732\n",
      "Epoch 6/100\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.0749 - accuracy: 0.9782\n",
      "Epoch 00006: val_loss improved from 0.08991 to 0.07613, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.0748 - accuracy: 0.9782 - val_loss: 0.0761 - val_accuracy: 0.9780\n",
      "Epoch 7/100\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.0659 - accuracy: 0.9808\n",
      "Epoch 00007: val_loss improved from 0.07613 to 0.07121, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0659 - accuracy: 0.9808 - val_loss: 0.0712 - val_accuracy: 0.9797\n",
      "Epoch 8/100\n",
      "1493/1500 [============================>.] - ETA: 0s - loss: 0.0590 - accuracy: 0.9830\n",
      "Epoch 00008: val_loss improved from 0.07121 to 0.06667, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0589 - accuracy: 0.9830 - val_loss: 0.0667 - val_accuracy: 0.9808\n",
      "Epoch 9/100\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.0529 - accuracy: 0.9850\n",
      "Epoch 00009: val_loss improved from 0.06667 to 0.06278, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.0529 - accuracy: 0.9850 - val_loss: 0.0628 - val_accuracy: 0.9812\n",
      "Epoch 10/100\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.0485 - accuracy: 0.9861\n",
      "Epoch 00010: val_loss improved from 0.06278 to 0.05955, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.0484 - accuracy: 0.9861 - val_loss: 0.0595 - val_accuracy: 0.9816\n",
      "Epoch 11/100\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.0446 - accuracy: 0.9868\n",
      "Epoch 00011: val_loss improved from 0.05955 to 0.05720, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.0445 - accuracy: 0.9868 - val_loss: 0.0572 - val_accuracy: 0.9827\n",
      "Epoch 12/100\n",
      "1495/1500 [============================>.] - ETA: 0s - loss: 0.0415 - accuracy: 0.9883\n",
      "Epoch 00012: val_loss improved from 0.05720 to 0.05526, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0415 - accuracy: 0.9883 - val_loss: 0.0553 - val_accuracy: 0.9833\n",
      "Epoch 13/100\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.0383 - accuracy: 0.9896\n",
      "Epoch 00013: val_loss improved from 0.05526 to 0.05236, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0382 - accuracy: 0.9896 - val_loss: 0.0524 - val_accuracy: 0.9846\n",
      "Epoch 14/100\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.0359 - accuracy: 0.9897\n",
      "Epoch 00014: val_loss improved from 0.05236 to 0.05134, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0359 - accuracy: 0.9897 - val_loss: 0.0513 - val_accuracy: 0.9844\n",
      "Epoch 15/100\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.0335 - accuracy: 0.9904\n",
      "Epoch 00015: val_loss improved from 0.05134 to 0.04950, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0335 - accuracy: 0.9904 - val_loss: 0.0495 - val_accuracy: 0.9852\n",
      "Epoch 16/100\n",
      "1493/1500 [============================>.] - ETA: 0s - loss: 0.0315 - accuracy: 0.9914\n",
      "Epoch 00016: val_loss did not improve from 0.04950\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0315 - accuracy: 0.9914 - val_loss: 0.0499 - val_accuracy: 0.9847\n",
      "Epoch 17/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.0296 - accuracy: 0.9918\n",
      "Epoch 00017: val_loss improved from 0.04950 to 0.04914, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0296 - accuracy: 0.9918 - val_loss: 0.0491 - val_accuracy: 0.9844\n",
      "Epoch 18/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.0280 - accuracy: 0.9924\n",
      "Epoch 00018: val_loss improved from 0.04914 to 0.04556, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0280 - accuracy: 0.9924 - val_loss: 0.0456 - val_accuracy: 0.9871\n",
      "Epoch 19/100\n",
      "1492/1500 [============================>.] - ETA: 0s - loss: 0.0261 - accuracy: 0.9931\n",
      "Epoch 00019: val_loss did not improve from 0.04556\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0262 - accuracy: 0.9931 - val_loss: 0.0458 - val_accuracy: 0.9862\n",
      "Epoch 20/100\n",
      "1493/1500 [============================>.] - ETA: 0s - loss: 0.0248 - accuracy: 0.9934\n",
      "Epoch 00020: val_loss improved from 0.04556 to 0.04541, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0248 - accuracy: 0.9934 - val_loss: 0.0454 - val_accuracy: 0.9862\n",
      "Epoch 21/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.0234 - accuracy: 0.9938\n",
      "Epoch 00021: val_loss did not improve from 0.04541\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0234 - accuracy: 0.9938 - val_loss: 0.0475 - val_accuracy: 0.9850\n",
      "Epoch 22/100\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.0223 - accuracy: 0.9941\n",
      "Epoch 00022: val_loss improved from 0.04541 to 0.04413, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0222 - accuracy: 0.9941 - val_loss: 0.0441 - val_accuracy: 0.9868\n",
      "Epoch 23/100\n",
      "1493/1500 [============================>.] - ETA: 0s - loss: 0.0210 - accuracy: 0.9949\n",
      "Epoch 00023: val_loss improved from 0.04413 to 0.04322, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0209 - accuracy: 0.9949 - val_loss: 0.0432 - val_accuracy: 0.9875\n",
      "Epoch 24/100\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.0202 - accuracy: 0.9950\n",
      "Epoch 00024: val_loss improved from 0.04322 to 0.04182, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0202 - accuracy: 0.9950 - val_loss: 0.0418 - val_accuracy: 0.9877\n",
      "Epoch 25/100\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.0187 - accuracy: 0.9959\n",
      "Epoch 00025: val_loss improved from 0.04182 to 0.04179, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0187 - accuracy: 0.9959 - val_loss: 0.0418 - val_accuracy: 0.9879\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26/100\n",
      "1492/1500 [============================>.] - ETA: 0s - loss: 0.0182 - accuracy: 0.9955\n",
      "Epoch 00026: val_loss did not improve from 0.04179\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0181 - accuracy: 0.9955 - val_loss: 0.0449 - val_accuracy: 0.9858\n",
      "Epoch 27/100\n",
      "1495/1500 [============================>.] - ETA: 0s - loss: 0.0174 - accuracy: 0.9958\n",
      "Epoch 00027: val_loss did not improve from 0.04179\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0174 - accuracy: 0.9958 - val_loss: 0.0418 - val_accuracy: 0.9872\n",
      "Epoch 28/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.0164 - accuracy: 0.9962\n",
      "Epoch 00028: val_loss did not improve from 0.04179\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0164 - accuracy: 0.9962 - val_loss: 0.0425 - val_accuracy: 0.9870\n",
      "Epoch 29/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.0155 - accuracy: 0.9967\n",
      "Epoch 00029: val_loss did not improve from 0.04179\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0155 - accuracy: 0.9967 - val_loss: 0.0425 - val_accuracy: 0.9877\n",
      "Epoch 30/100\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.0150 - accuracy: 0.9967\n",
      "Epoch 00030: val_loss improved from 0.04179 to 0.04099, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0150 - accuracy: 0.9967 - val_loss: 0.0410 - val_accuracy: 0.9874\n",
      "Epoch 31/100\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.0142 - accuracy: 0.9972\n",
      "Epoch 00031: val_loss improved from 0.04099 to 0.04070, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0142 - accuracy: 0.9972 - val_loss: 0.0407 - val_accuracy: 0.9873\n",
      "Epoch 32/100\n",
      "1493/1500 [============================>.] - ETA: 0s - loss: 0.0136 - accuracy: 0.9974\n",
      "Epoch 00032: val_loss did not improve from 0.04070\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0135 - accuracy: 0.9974 - val_loss: 0.0412 - val_accuracy: 0.9877\n",
      "Epoch 33/100\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.0129 - accuracy: 0.9974\n",
      "Epoch 00033: val_loss improved from 0.04070 to 0.03972, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0129 - accuracy: 0.9974 - val_loss: 0.0397 - val_accuracy: 0.9877\n",
      "Epoch 34/100\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.0123 - accuracy: 0.9976\n",
      "Epoch 00034: val_loss did not improve from 0.03972\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0123 - accuracy: 0.9976 - val_loss: 0.0398 - val_accuracy: 0.9881\n",
      "Epoch 35/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.0118 - accuracy: 0.9979\n",
      "Epoch 00035: val_loss did not improve from 0.03972\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0118 - accuracy: 0.9979 - val_loss: 0.0400 - val_accuracy: 0.9882\n",
      "Epoch 36/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.0113 - accuracy: 0.9980\n",
      "Epoch 00036: val_loss did not improve from 0.03972\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0113 - accuracy: 0.9980 - val_loss: 0.0401 - val_accuracy: 0.9874\n",
      "Epoch 37/100\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.0109 - accuracy: 0.9981\n",
      "Epoch 00037: val_loss improved from 0.03972 to 0.03957, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0109 - accuracy: 0.9981 - val_loss: 0.0396 - val_accuracy: 0.9883\n",
      "Epoch 38/100\n",
      "1492/1500 [============================>.] - ETA: 0s - loss: 0.0104 - accuracy: 0.9983\n",
      "Epoch 00038: val_loss improved from 0.03957 to 0.03874, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0104 - accuracy: 0.9983 - val_loss: 0.0387 - val_accuracy: 0.9882\n",
      "Epoch 39/100\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.0101 - accuracy: 0.9983\n",
      "Epoch 00039: val_loss did not improve from 0.03874\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0100 - accuracy: 0.9983 - val_loss: 0.0399 - val_accuracy: 0.9883\n",
      "Epoch 40/100\n",
      "1495/1500 [============================>.] - ETA: 0s - loss: 0.0096 - accuracy: 0.9986\n",
      "Epoch 00040: val_loss did not improve from 0.03874\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0096 - accuracy: 0.9986 - val_loss: 0.0398 - val_accuracy: 0.9885\n",
      "Epoch 41/100\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.0091 - accuracy: 0.9987\n",
      "Epoch 00041: val_loss did not improve from 0.03874\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0092 - accuracy: 0.9987 - val_loss: 0.0389 - val_accuracy: 0.9882\n",
      "Epoch 42/100\n",
      "1493/1500 [============================>.] - ETA: 0s - loss: 0.0087 - accuracy: 0.9988\n",
      "Epoch 00042: val_loss did not improve from 0.03874\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0088 - accuracy: 0.9987 - val_loss: 0.0390 - val_accuracy: 0.9885\n",
      "Epoch 43/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.0084 - accuracy: 0.9990\n",
      "Epoch 00043: val_loss did not improve from 0.03874\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0084 - accuracy: 0.9990 - val_loss: 0.0405 - val_accuracy: 0.9882\n",
      "Epoch 44/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.0082 - accuracy: 0.9988\n",
      "Epoch 00044: val_loss did not improve from 0.03874\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0082 - accuracy: 0.9988 - val_loss: 0.0391 - val_accuracy: 0.9879\n",
      "Epoch 45/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.0078 - accuracy: 0.9990\n",
      "Epoch 00045: val_loss improved from 0.03874 to 0.03856, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0078 - accuracy: 0.9990 - val_loss: 0.0386 - val_accuracy: 0.9892\n",
      "Epoch 46/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.0076 - accuracy: 0.9990\n",
      "Epoch 00046: val_loss improved from 0.03856 to 0.03762, saving model to results/mnist/lenet.best.hdf5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0076 - accuracy: 0.9990 - val_loss: 0.0376 - val_accuracy: 0.9897\n",
      "Epoch 47/100\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.0073 - accuracy: 0.9990\n",
      "Epoch 00047: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0073 - accuracy: 0.9990 - val_loss: 0.0387 - val_accuracy: 0.9891\n",
      "Epoch 48/100\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.0070 - accuracy: 0.9990\n",
      "Epoch 00048: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0070 - accuracy: 0.9990 - val_loss: 0.0399 - val_accuracy: 0.9880\n",
      "Epoch 49/100\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.0068 - accuracy: 0.9991\n",
      "Epoch 00049: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0068 - accuracy: 0.9991 - val_loss: 0.0377 - val_accuracy: 0.9889\n",
      "Epoch 50/100\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.0066 - accuracy: 0.9992\n",
      "Epoch 00050: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0066 - accuracy: 0.9992 - val_loss: 0.0400 - val_accuracy: 0.9875\n",
      "Epoch 51/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.0063 - accuracy: 0.9993\n",
      "Epoch 00051: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0063 - accuracy: 0.9993 - val_loss: 0.0406 - val_accuracy: 0.9881\n",
      "Epoch 52/100\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.0061 - accuracy: 0.9992\n",
      "Epoch 00052: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0061 - accuracy: 0.9992 - val_loss: 0.0382 - val_accuracy: 0.9887\n",
      "Epoch 53/100\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.0060 - accuracy: 0.9993\n",
      "Epoch 00053: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0060 - accuracy: 0.9993 - val_loss: 0.0387 - val_accuracy: 0.9885\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 54/100\n",
      "1495/1500 [============================>.] - ETA: 0s - loss: 0.0057 - accuracy: 0.9994\n",
      "Epoch 00054: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0057 - accuracy: 0.9994 - val_loss: 0.0388 - val_accuracy: 0.9877\n",
      "Epoch 55/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.0055 - accuracy: 0.9994\n",
      "Epoch 00055: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0055 - accuracy: 0.9994 - val_loss: 0.0381 - val_accuracy: 0.9892\n",
      "Epoch 56/100\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.0054 - accuracy: 0.9994\n",
      "Epoch 00056: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0054 - accuracy: 0.9994 - val_loss: 0.0391 - val_accuracy: 0.9890\n",
      "Epoch 57/100\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.0052 - accuracy: 0.9994\n",
      "Epoch 00057: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0052 - accuracy: 0.9994 - val_loss: 0.0384 - val_accuracy: 0.9882\n",
      "Epoch 58/100\n",
      "1492/1500 [============================>.] - ETA: 0s - loss: 0.0050 - accuracy: 0.9995\n",
      "Epoch 00058: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0051 - accuracy: 0.9995 - val_loss: 0.0384 - val_accuracy: 0.9888\n",
      "Epoch 59/100\n",
      "1495/1500 [============================>.] - ETA: 0s - loss: 0.0048 - accuracy: 0.9995\n",
      "Epoch 00059: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0048 - accuracy: 0.9995 - val_loss: 0.0380 - val_accuracy: 0.9884\n",
      "Epoch 60/100\n",
      "1495/1500 [============================>.] - ETA: 0s - loss: 0.0047 - accuracy: 0.9995\n",
      "Epoch 00060: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0047 - accuracy: 0.9995 - val_loss: 0.0385 - val_accuracy: 0.9891\n",
      "Epoch 61/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.0046 - accuracy: 0.9996\n",
      "Epoch 00061: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.0046 - accuracy: 0.9996 - val_loss: 0.0381 - val_accuracy: 0.9888\n",
      "Epoch 62/100\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.0044 - accuracy: 0.9996\n",
      "Epoch 00062: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.0044 - accuracy: 0.9996 - val_loss: 0.0379 - val_accuracy: 0.9898\n",
      "Epoch 63/100\n",
      "1495/1500 [============================>.] - ETA: 0s - loss: 0.0044 - accuracy: 0.9996\n",
      "Epoch 00063: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.0044 - accuracy: 0.9996 - val_loss: 0.0386 - val_accuracy: 0.9883\n",
      "Epoch 64/100\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.0042 - accuracy: 0.9996\n",
      "Epoch 00064: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.0042 - accuracy: 0.9996 - val_loss: 0.0383 - val_accuracy: 0.9893\n",
      "Epoch 65/100\n",
      "1495/1500 [============================>.] - ETA: 0s - loss: 0.0041 - accuracy: 0.9996\n",
      "Epoch 00065: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0041 - accuracy: 0.9996 - val_loss: 0.0380 - val_accuracy: 0.9897\n",
      "Epoch 66/100\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.0040 - accuracy: 0.9996\n",
      "Epoch 00066: val_loss did not improve from 0.03762\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0040 - accuracy: 0.9996 - val_loss: 0.0385 - val_accuracy: 0.9888\n"
     ]
    }
   ],
   "source": [
    "lenet_history = lenet5.fit(\n",
    "    X_train.reshape(-1, 28, 28, 1),\n",
    "    y_train,\n",
    "    batch_size=batch_size,\n",
    "    epochs=epochs,\n",
    "    validation_split=0.2,  # use 0 to train on all data\n",
    "    callbacks=[checkpointer, early_stopping],\n",
    "    verbose=1,\n",
    "    shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot CV Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a single GPU, 50 epochs take around 2.5 minutes, resulting in a test accuracy of 99.09%, slightly below the same result as for the original LeNet5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:07:00.258532Z",
     "start_time": "2021-02-23T06:07:00.130868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s7LHs637eiIAzA5xus7Hi6LF8xPocTz20vBB/VDfY8whvR/dRX4zoPtzRfWYesd/M+Pe0u/puWMcagi5zuc1jhpj66u5bw/vx4STndCiYAsUzIH2U2QOTPtpcTh8d/9jp7q7LQAd0NEVvjdBxyFxub+xe394ANc/DW7+Pr9ucEjP05PYIPqNLzX0c3msGsMMfRZf3mj8D/TUi00dD0Tlmw7Trlls2+F6kcDBW3gzPGxB812y4t0VvccsN5s/NgCzxDXJXFmQVQtZp5u9E1+9G1ljIHmsGDbur++XBTvD80wwxXYHG8x6E/ebzjkwoOtv8rJGQ+RnCAfMWCoC/zQyqXetDAbNRHWyPL6bV3t2b0NWozi42n2uPBo62huhyNNR0lWHAr8DWHXZcOeBymz/vbQ39f482F7gLzHAM/f9OHiEfzO82dzxMnAO5Z5jBJXe8eX9kL01ns/m97n8DDrwJ+16Hf27srrsxkyH/TLPOjxaYTjsXzvocjCoxw53THf/72vN32e7sfn2gA7wHzL9rLfvBG/0717IPmnbB7hch0GoGsm++M7jvO4EshtHX/EqJlUzTxSZTWWRwetZZRyDEewe8VO0zA03VvsPsbmyPzSpWmO1iSlE2U8ZmMaUoizMLsykryMRlV+/KyXZCftcMw/wn0XrQvLU1mP/4Yv9og/H/dI9c7q9BGeyEkL/7ebsLJvwrTJ4Hk+aY//wSLejrowHU459/rOGZdkSD84gGqc0Z3yA/spFu7Vpvo2bHe5Sdlm8Gjq7Q0RVA/K3Rx9FlI9zvfnqFgGB7j9f32F+g7cR9X/a03o26rCKzQZI+2vz+WvYf8c9+P/hbTlwZBmJz9X20+Mi6s1i7g0ls+EmPW3+Nv66jt70CTHQZzP0Ge4SengHoaL0qg+UuhIJyKPiYeZ9fbjbi0rKPf98D8bfCod1m462pJnrbBU07zSFvfZa3yGzg9bqVmg3gpl1w8K1oCKgyg1w4en6l0w2F0QCQP9n8Dtu7QljPANbU/8+Z1RFtbOeb3507HzILupfTcszgMJhw7POafx+80b+XfdVn10GBSBgad3QHgLRR0eB2jjmkqugcMxwOpecFzL/ZvsPxDequ37WugwjeA93focVqfvbMAvPzuguj30VB/DLE//3wtfT/t8mZGf1OC6L7OWLZlT3wwRDDiP8fEfbz/r5mpkydPrTv40htDWbYOfAmHHgDGneaf6dGjx84MA0HX0s0JLqH/72OkwLOAJKpLNK3QChCY5t5vktDq5+/v/8hDaF03tl/mF31bUSiP+EFWS7OKc5h6rhRnFOcw9njcsjPch195yNZyA87n4N3N5r/POK6oaP3WUVD/4d1jAb1u+ZvMxsO3v3mP+Suf8ytdd33gz2SZ4s25m327qETsWEGAzQoO5th52bzPbGYwwgmz4PJc81/9IPpGeg8HP+P68Bb5j9ye1o/R76PCCiRYI8hGQ3mP+u+pI0ytw91hbTeRxqHhbPrKGmWebPYevdS9HzcsxfGkW42KmKvzzYbbrHlHvu1OvoPTT2XMczGZOzn5UD0vq77Z+nII8lgNvB6Dp/p+fvRNZwGy+B6ZozI4Hp6eq4/EcKhWC/Nzh0fMOlj03ofvT0WoUB370+ws/eBgK6fubgDBT7zO+0KNCejUTZUhmGGjqZdZo9NRq4ZYHJKhn5OQigAjR90B56Db0PdO90/azanOQwos8f5QhnR84ei6z5qaKf0rPPNxvux9jwO9jP3+v2IPjYi0R6paK/UyR6C19EIWMy6OEn/k46X2o+JpSFqktRaOoPsbmjjo6YO6lt9sRDT0CPQNHf0Puo0xu1k6rgc5p09lnPG5TC1OIfC7NQ9We6EMQyz2/vt9Waw8R2O/tPMhd0v9D5CbrFFhwqM627QOTIG+WYW88hmQfSIbNcR4aFob4K928zbR38zGwg9hxA4Ms2hFFljoeQCM5Bl9xhukZnfPfa857hvq+34/zkbhjlEY8ez5u2vq+Gvq8wegMmXmoFnfIV5NNzfZm7bNfTgwBvmkeMuo8dDycfNRlQ40P9QpI5D3Q1Fq91sXI89p/8jj5n58UNMwGwshP19NEajt3Ag2iA3+mi0xweT/fVNjJtQ3h08uoZ+OLOS84TuMZOO/ry/1Qw6nYfM7y77tKGfl5GMbHaw5UBaDqHMlhMXKuxO85Y++sTsL1lYLGa4yMyD0y84vn3ZnVA01bx1HcyPhM0DE66s+OF1/eiorjb/hg6nnp+56Ozhfa+hslqTo5dcUooCjiRcKByhtrmT3Q1t7G5oZ3djGzX15n1jW/zUyWkOKwVZ5kn6E8a4uWB8HvlZLvPmNu/b6mu56Lyzdb5MT4d2Q9UfoOoxc9meDlPmw7QvwIRPmQ0gwzC7n3uOu+0aKuDdbzbM368cfA9JHIs5lr3gY5A/pfvo7ZhJ8Q3ww7XRMPOKeWv8wFxvc8G4f4FPfgtOnwmjzzBDzJEnXZ5MFkv32PqK75jDwnZuhh3PwDt/hH/81iz3qBLzO+86DyS72BwjPf3foiePnntyj2JbrWBNPyENd291NeMmjaAjlK4syE/gz5ScGqw2MzyLyLBRwJGTyjAMPmxsZ+uOBl7dfYhdDW181NQeN41ybqaTsvxMZk8pZEJ+JmX5bs4Yk0lhtgu3yz5gcKluOzg84SYSGfpR6VDAPFLXdU5Iz+EwsSFVgf5Pnu3rZNqeQ5es9v6P/nU2wz//BG8/BrWvAhY445Nw8a1QvqD3GHeLJfqeo6DwrKF9zr5EwuZsLg3V3TMj1b9vDovrOjHXYjPHbY8eT9n+t6GjzlzvyjZ7NKZdZc7Yctr05J+u0l0A068xb6EA7H3F7Nlp/gjOXmJ+htOmmyf1ioiIyLBRwJFh19IZZFtNIy/uaGTrjobY1MsluemUF2Xz6fLuIFOWn8mojOMcF368wkHzRD7Pu+atLnrf5jEb5P1Nf2ntsS7sN0NMR2Pv/Vsd3UOo8s80t/cdNs/JaP6o+/oOXUf8j8Zi7Wc6UIc52004AGPOhNl3wdTPm70JJ4vVBmMmmrfyBd3rQwFzbHtsKthqOLQbX245zku+BaUXmifipsg46z7ZneYkBBP+NdElEREROeUo4MgJF44YvL3vMC/taGTrzgbeqj1MOGLgdtm5qCyPr/1rGRWT8jk9b7DncgyTrpMqPe9EQ8w/zeWGD7pnbLE5zSFVZbPN80kioeg5CqE+ZuLqMUOXzQHjZvSedjNrrHm+y0A9QYZhng8Qd0G75u6TeY88d6Kvk3tnfMUcgjb23JN3Muhg2J1Q+DHz1sP+6mqydUKmiIiIHCcFHDkhmtsD/PWDerZU1/PyrkZaOoNYLHDOuBxuqCjjksn5TD99FA7bMJ94HGjH2bIbPmw8+nUQum49Z5RyF5lDs8pmQeFUc3nMJDOsnGyW6EXK0rKB0pP//iIiIiIpSgFHjtmHje385T0Pm6s9/H3PISKGORXzpR8r5OLJ+Xxy4hhyM4dhuFk4aA7latp1xK0GWg9Q1tdrXDnmidwZeebJnUVTzcdZY80T3oumdl/ES0RERERS1qACztatW1m1ahWRSIQrr7yS6667Lu75lpYWvvvd77J3715cLherV69m8uTJAPz2t79lw4YNWCwWJk+ezJo1a3C5TuFrj6SwcMTgzb3NbK728Jf3PNQ0mPP4TynK4sZPTeTT5YVMHZeD1TrE4VBB38AX4+o4BIeiF2Jr3hM/VXB6rnnF6QkVkFfG/k4X4yZNMwNLRp75/PFe70FEREREUsKAASccDrNy5UoefPBBCgsLWbJkCbNmzWLixImxbR544AHKy8u5//77qampYeXKlTz00EN4PB4efvhh/vznP5OWlsbNN99MZWUlixYtGtYPJSeOLxjmxR0NbH7Pw/Pv13OoPYDdauHCsjyunVnK7PJCSkanm7OBNb4Nb9SYoSTQ3uPq111X1u55le3olbD9rd3nuxyNIxNyJ5g9LWd9zgw0eRPNGbiOmGLXW11tXptDRERERE45AwacqqoqSktLKSkxZ1+aP38+W7ZsiQs4NTU1sV6dsrIy9u/fT2OjOXtUOBzG5/Nht9vx+XwUFOhiTckuFI6wbXcTT7x1gGffraPVHyI7zc7sM/O4vDTABVlNZLRsg4Yd8N4H5oxj/pbeO3K6zYtCOjPNZWeGeZ2JrCIzsDgzuy8M6MrufeVyV5Z59fITccVtERERETklDBhwPB4PRUVFsceFhYVUVVXFbTNlyhQ2b97MjBkzqKqq4sCBA9TV1XH22Wfzla98hU996lO4XC4+8YlP8MlPfvLEfwo5boZh8GbtYZ586wCbqg5wuK2DGa593D32ABe5PmRMx24su2rggx69Le5CGDMZzrnSnIp4zCTzlj7anLI4Ga9kLiIiIiIj2oABxzCMXuuOvIjiddddx6pVq1i4cCGTJ0+mvLwcu91OS0sLW7ZsYcuWLWRlZXHzzTfzxBNPsHDhwqO+p9/vp7q6eogfZXj4fL6kKctw+Kg5wF8/bOPN3Qcp7qzmfNtOfpe2i0kZu7BH/FAHwYxC2nImEph4Hv7sMwhkn4E/u5SI84gLRQaAA61AayI+SsxIr7ORSvWWelRnqUd1lnpUZ6lJ9XZylPdzeYkBA05RURF1dXWxxx6Pp9cwM7fbzZo1awAzEM2ePZvi4mJeeukliouLyc01z5G49NJLefPNNwcMOC6Xq98Cn2zV1dVJU5YTpc0f4qkXXuHgW5sZ11rF/7LtYKLlADjBsNqx5J8DJV8xryRfcgGOnHEkYKLkYzYS6+xUoHpLPaqz1KM6Sz2qs9SkekusAQPO1KlT2bNnD7W1tRQWFlJZWclPfvKTuG28Xi9paWk4nU42bNjAjBkzcLvdnHbaabz99tt0dnaSlpbGtm3bOPvss4ftw8jRtftDPPLyBzhfuod/N57EZjHwpedgKbkAxi+DkguwnHaeea6MiIiIiEgKGjDg2O127rrrLpYtW0Y4HGbx4sVMmjSJ9evXA7B06VJqamq4/fbbsVqtTJw4kVWrVgEwbdo05s6dy+c+9znsdjvl5eVcddVVw/uJpJfOQJjfvbqHV174M98L3U+Z9SBNZ36BvDm3kJY3SefKiIiIiMiIMajr4FRUVFBRURG3bunSpbHl6dOn89xzz/X52uXLl7N8+fLjKKIcK18wzO+37+U3f32PL/t/x2/szxDMOg0W/Ym8slmJLp6IiIiIyAk3qIAjqcUXDPM/r+3lFy/UcEbbW/y/zF9TZD8AM76Ka84PzemXRURERERGIAWcEcQfCvOHv+/j/ud30eI9zH25j/MZ15PgLoWFT8H4SxJdRBERERGRYaWAM0K8XXuY5f/zJh81dfDFsXtZ4byftLZauOBrMPsu86KaIiIiIiIjnAJOijMMg4de2cOqP1dTmhnhb2dvYtyu9ZA7Ab78NJRelOgiioiIiIicNAo4qcjfCk01dHh2sOWlV8hu2MmzmY2MZx+WXe1w4U3wqTs13bOIiIiInHIUcJLdwbdh94vQtAuaasz7NvPCqxnAAqA1owj3aVOw5H0Cpi2FkvMTWmQRERERkURRwElmH22Dhy+HcAAyxkDeRIyJs3mrPY9fV9toTjudW74wl3+ZOC7RJRURERERSQoKOMnq0Ifw2DUw6nT4UiVkFdHmD7Fi4zs89c4BLpmcz39/fhp5bleiSyoiIiIikjQUcJKRrwUevQqMCFz9B8gqovqglxt//wZ7mtq5be6Z3FBRhtVqSXRJRURERESSigJOsgmHYMOX4NBu+PfHMXIn8Nhre/n+k/8kJ93Bo/9rJjMn5CW6lCIiIiIiSUkBJ5kYBjz9Hah5Hhbej1H6Cb77p3dY/1otn5w4hp9edS75WRqSJiIiIiLSHwWcZPLaWvj7r+ETN8P0f+OXL+xi/Wu1fK2ijNvmnolNQ9JERERERI5KASdZ7NwMz9wBUy6D2T/ghQ/q+fGzH7Bg2mncPu9MLBaFGxERERGRgVgTXQABPO/Bhi9D4dmwaC0fNXeyfP2bnFmYxT2LpyrciIiIiIgMkgJOorU1mDOmOTNh6f/QgYvrf/cPLBYLa6+dQYZTnWwiIiIiIoOlgJNIQR/8z9XQ3gBL12Nkn8Ztf6xih6eVn189ndPzMhJdQhERERGRlKKAkyiGAU/eBPteg0W/gnHnsXbrbiqrDvKdeVO4eFJ+oksoIiIiIpJyFHASZeuP4Z0NMPsu+NhCXtrZwD3PvM/8c8Zy/SUTEl06EREREZGUpICTCO/8Ef66CqZdDZ+8hb1NHdz06JtMLszix0vO0aQCIiIiIiLHSAHnZHt9HWz8X3D6RbDgP+kIhrnud3/HMAx+de2/aFIBEREREZHjoNb0yRKJwF/ugld+BpPnweJfY9ic3L7hLT7wtPLgl86nNC8z0aUUEREREUlpg+rB2bp1K3PnzmXOnDmsXbu21/MtLS3ceOONLFiwgCVLlrBjx47Yc16vl+XLlzNv3jw+85nP8Oabb5640qeKYCf88UtmuPn4dfCFR8HlZt1LH/LU2we4be6Z/OuZBYkupYiIiIhIyhuwByccDrNy5UoefPBBCgsLWbJkCbNmzWLixImxbR544AHKy8u5//77qampYeXKlTz00EMArFq1iosvvpj//u//JhAI4PP5hu/TJKP2Rlj/Bdj3d5i7BmbeABYLL+9sZM3T1Xx2ahE3VJQlupQiIiIiIiPCgD04VVVVlJaWUlJSgtPpZP78+WzZsiVum5qaGmbOnAlAWVkZ+/fvp7Gxkba2Nl5//XWWLFkCgNPpJDs7exg+RpJq3AnrZkPdu3DV7+DCr4PFQu2hDr6x/g0mFrj58ZJpmlRAREREROQEGbAHx+PxUFRUFHtcWFhIVVVV3DZTpkxh8+bNzJgxg6qqKg4cOEBdXR02m43c3FxWrFjB+++/z1lnncWdd95JRsbRL2Dp9/uprq4+xo90Yvl8vmMqS3r9m5T87XYMi43af/05PiZCdD+3Pn2AYCjM7ReNZu/unSe6yKe8Y60zSSzVW+pRnaUe1VnqUZ2lJtXbyVFeXt7n+gEDjmEYvdYd2eNw3XXXsWrVKhYuXMjkyZMpLy/HbrcTDAZ57733+N73vse0adO4++67Wbt2Ld/85jeP+p4ul6vfAp9s1dXVQy9L1R9g680w+gy4ZgPjR58Re+rd/S38s34337vsY8y+YPwJLauYjqnOJOFUb6lHdZZ6VGepR3WWmlRviTVgwCkqKqKuri722OPxUFAQf0K82+1mzZo1gBmIZs+eTXFxMZ2dnRQVFTFt2jQA5s2b1+ckBSOGYcDWe+Gvd8MZF5vD0tJHx22y/rW9uOxWlpxXnKBCioiIiIiMXAOegzN16lT27NlDbW0tgUCAyspKZs2aFbeN1+slEAgAsGHDBmbMmIHb7SY/P5+ioiJ2794NwLZt2ygrG6En1IeD8MRNZrg55wvwbxt7hZt2f4gn3jrA/HPGkpPhSFBBRURERERGrgF7cOx2O3fddRfLli0jHA6zePFiJk2axPr16wFYunQpNTU13H777VitViZOnMiqVatir//e977HrbfeSjAYpKSkJNbTM+Js+ha89QhU3AH/egf0MXHAk28foM0f4poLTk9AAUVERERERr5BXeizoqKCioqKuHVLly6NLU+fPp3nnnuuz9eWl5ezcePG4yhiCgj54Z9/gunXwqdW9LvZo9v3cmZhFuedPrrfbURERERE5NgN6kKfMoC92yDQBlPm97vJO/taeGd/C1dfcLqmhRYRERERGSYKOCfCzs1gc8L4S/rd5NHXPiLNYeWK6eNOYsFERERERE4tCjgnws7n4IxPgjOzz6fbopMLXHbOaeSka3IBEREREZHhooBzvJr3QOMOmHRpv5s88dZ+OgJhrtbkAiIiIiIiw0oB53jt3GzeT5zT59OGYfDo9r1MKcpiesmok1cuEREREZFTkALO8dq5GUaPh7y+r+9Tta+Ffx7wco0mFxARERERGXYKOMcj6IMPt5rD0/oJL49u30u6w8ZCTS4gIiIiIjLsFHCOx0cvQ6gTJvU9PM3rC/Lk2we4fNppZKdpcgERERERkeGmgHM8dm4Ge5o5g1ofnnhzP53BMEs1uYCIiIiIyEmhgHM8dm42r33jSO/1lGEY/H77Xj42NptpxTkJKJyIiIiIyKlHAedYNdXAoZp+Z097q/Yw79e1crUmFxAREREROWkUcI5V1/TQ/Zx/8+j2vWQ4bSw897STWCgRERERkVObAs6x2rUZ8iZB7vheT7V0Bnmq6gALzz2NLE0uICIiIiJy0ijgHItAB3z4Ur+9N4+/uR9fMMLVHy89yQUTERERETm1KeAciz0vQdjfZ8AxDINHt+9l6rgcpmpyARERERGRk0oB51jsfA4cGVD6iV5PvbG3mQ88rSz9uKaGFhERERE52RRwhsowzIAzvgLsrl5P/377XjKdNi7X5AIiIiIiIiedAs5QNe6Ew3v7HJ7W0hGksuogC6ePw+2yJ6BwIiIiIiKnNgWcodr5nHnfR8DZ+OY+/KEIV2t4moiIiIhIQijgDNWuzZA/BUbFh5iuyQWmFedw9jhNLiAiIiIikggKOEPhb4M9f+uz9+bvHzWzs76Nqy9Q742IiIiISKIMKuBs3bqVuXPnMmfOHNauXdvr+ZaWFm688UYWLFjAkiVL2LFjR9zz4XCYK664guuvv/7ElDpRPnwRIkGYdGmvpx57vRa3y86CaZpcQEREREQkUQYMOOFwmJUrV7Ju3ToqKyvZtGkTu3btitvmgQceoLy8nKeeeop77rmHVatWxT3/8MMPU1ZWdmJLngg7N4MzC0pm9nqq+qCXGWeMJsOpyQVERERERBJlwIBTVVVFaWkpJSUlOJ1O5s+fz5YtW+K2qampYeZMs9FfVlbG/v37aWxsBKCuro4XXniBJUuWDEPxTyLDMAPOhAqwO3s97fH6KcxKS0DBRERERESky4DdDR6Ph6KiotjjwsJCqqqq4raZMmUKmzdvZsaMGVRVVXHgwAHq6uoYM2YMq1ev5rbbbqO9vX3QhfL7/VRXVw/hYwwfn89HdXU1rsM1TPDu4+Dkazl8RNnCEYOmNj/WQGvSlPtU1lVnklpUb6lHdZZ6VGepR3WWmlRvJ0d5eXmf6wcMOIZh9FpnsVjiHl933XWsWrWKhQsXMnnyZMrLy7Hb7fz1r38lNzeXs88+m+3btw+6sC6Xq98Cn2zV1dVmWV5+FoCxl/w7Y7Pjz7PxeH0YfMjHJhRTXl6aiGJKD7E6k5Sieks9qrPUozpLPaqz1KR6S6wBA05RURF1dXWxxx6Ph4KCgrht3G43a9asAcxANHv2bIqLi6msrOT5559n69at+P1+2trauPXWW7n33ntP8Mc4CXb9BQrPhuzekwjUe/0AFGS5TnapRERERESkhwHPwZk6dSp79uyhtraWQCBAZWUls2bNitvG6/USCAQA2LBhAzNmzMDtdvPtb3+brVu38vzzz3Pfffcxc+bM1Aw3vhbYu63P6aEB6lt9gAKOiIiIiEiiDdiDY7fbueuuu1i2bBnhcJjFixczadIk1q9fD8DSpUupqanh9ttvx2q1MnHixF6zqKW83S9AJNTn9NBgTjAAUJCtSQZERERERBJpUHMaV1RUUFFREbdu6dKlseXp06fz3HPPHXUfF1xwARdccMExFDEJ7NwMrhwo/nifT3f14OS71YMjIiIiIpJIg7rQ5ymta3rosk+Bre88WN/qJzfTidOur1NEREREJJHUIh+A6/BOaKvrd3gamJMM6PwbEREREZHEU8AZgPvgNnNh4qf73aah1Ue+Ao6IiIiISMIp4AzAffAVGDsNsgr73cbj9VOQpQkGREREREQSTQHnaDqbSW9656jD0yIRg8Y2P4XZ6sEREREREUk0BZyjqXkeixGBiX1f/wbgUEeAUMTQOTgiIiIiIklAAedo9m4n7MyG4hn9blKva+CIiIiIiCSNQV0H55Q148vUZp7LGVZbv5t0XQNHPTgiIiIiIomngHM0BeV0Nh19k1gPjiYZEBERERFJOA1RO06xHhxNMiAiIiIiknAKOMepvtVPdpqdNEf/w9hEREREROTkUMA5TvVevyYYEBERERFJEgo4x6m+1acJBkREREREkoQCznHyeP0KOCIiIiIiSUIB5zgYhkFDq59CDVETEREREUkKCjjHoaUzSCAcIV89OCIiIiIiSUEB5zjUt0avgaMeHBERERGRpKCAcxy6L/KpHhwRERERkWSggHMcPN7oRT4VcEREREREkoICznHQEDURERERkeSigHMc6lt9ZDptuF32RBdFREREREQYZMDZunUrc+fOZc6cOaxdu7bX8y0tLdx4440sWLCAJUuWsGPHDgAOHjzItddey2c+8xnmz5/PQw89dGJLn2D1rX713oiIiIiIJJEBA044HGblypWsW7eOyspKNm3axK5du+K2eeCBBygvL+epp57innvuYdWqVQDYbDbuuOMOnn76aR577DEeffTRXq9NZQ1ev6aIFhERERFJIgMGnKqqKkpLSykpKcHpdDJ//ny2bNkSt01NTQ0zZ84EoKysjP3799PY2EhBQQFnnXUWAG63mwkTJuDxeIbhYySGp9WnCQZERERERJLIgCePeDweioqKYo8LCwupqqqK22bKlCls3ryZGTNmUFVVxYEDB6irq2PMmDGxbfbt20d1dTXTpk0bsFB+v5/q6uqhfI5h4/P5+iyLYRjUtXRyXqEjacoqpv7qTJKb6i31qM5Sj+os9ajOUpPq7eQoLy/vc/2AAccwjF7rLBZL3OPrrruOVatWsXDhQiZPnkx5eTl2e/eu29vbWb58Od/97ndxu90DFtblcvVb4JOturq6z7K0+oL4Qx8ypXQs5eVlCSiZ9Ke/OpPkpnpLPaqz1KM6Sz2qs9SkekusAQNOUVERdXV1sccej4eCgoK4bdxuN2vWrAHMQDR79myKi4sBCAaDLF++nAULFnDppZeeyLInVPcU0RqiJiIiIiKSLAY8B2fq1Kns2bOH2tpaAoEAlZWVzJo1K24br9dLIBAAYMOGDcyYMQO3241hGNx5551MmDCBL3/5y8PzCRKk3hsNOFmaRU1EREREJFkM2INjt9u56667WLZsGeFwmMWLFzNp0iTWr18PwNKlS6mpqeH222/HarUyceLE2Cxq//jHP3jiiSeYPHkyCxcuBOCWW26hoqJiGD/SyVHf6gPQJAMiIiIiIklkUFeorKio6BVKli5dGluePn06zz33XK/XzZgxgw8++OA4i5ic1IMjIiIiIpJ8BnWhT+mtvtWHy24lO31QGVFERERERE4CBZxjVN/qpyDb1WtGORERERERSRwFnGNU7/VreJqIiIiISJJRwDlGnlafJhgQEREREUkyCjjHqMHrV8AREREREUkyCjjHoDMQptUfoiBbQ9RERERERJKJAs4x0DVwRERERESSkwLOMahvjV4DRz04IiIiIiJJRQHnGHi86sEREREREUlGCjjHoN4b7cFRwBERERERSSoKOMegvtWPw2ZhdIYz0UUREREREZEeFHCOQX2rj3y3C6vVkuiiiIiIiIhIDwo4x6Ch1U++JhgQEREREUk6CjjHwOP16fwbEREREZEkpIBzDOpb/Qo4IiIiIiJJSAFniPyhMIc7ghRqiJqIiIiISNJRwBmihlZNES0iIiIikqwUcIaovivgZCvgiIiIiIgkGwWcIar3+gAoyNIQNRERERGRZKOAM0T1GqImIiIiIpK0FHCGqN7rx2qBPLcCjoiIiIhIshlUwNm6dStz585lzpw5rF27ttfzLS0t3HjjjSxYsIAlS5awY8eOQb821dS3+hjjdmGzWhJdFBEREREROcKAASccDrNy5UrWrVtHZWUlmzZtYteuXXHbPPDAA5SXl/PUU09xzz33sGrVqkG/NtXUt/o1wYCIiIiISJIaMOBUVVVRWlpKSUkJTqeT+fPns2XLlrhtampqmDlzJgBlZWXs37+fxsbGQb021Xi8fk0wICIiIiKSpOwDbeDxeCgqKoo9LiwspKqqKm6bKVOmsHnzZmbMmEFVVRUHDhygrq5uUK/ti9/vp7q6eiifY9j4fL64shxsbqfUbSRN+aS3I+tMUoPqLfWozlKP6iz1qM5Sk+rt5CgvL+9z/YABxzCMXusslvjzT6677jpWrVrFwoULmTx5MuXl5djt9kG9ti8ul6vfAp9s1dXVsbKEwhFa/LuZfHoR5eWTE1wy6U/POpPUoXpLPaqz1KM6Sz2qs9SkekusAQNOUVERdXV1sccej4eCgoK4bdxuN2vWrAHMQDR79myKi4vp7Owc8LWppLEtgGFoimgRERERkWQ14Dk4U6dOZc+ePdTW1hIIBKisrGTWrFlx23i9XgKBAAAbNmxgxowZuN3uQb02ldS3dl3kUwFHRERERCQZDdiDY7fbueuuu1i2bBnhcJjFixczadIk1q9fD8DSpUupqanh9ttvx2q1MnHixNgsav29NlV5vNGLfGZrkgERERERkWQ0YMABqKiooKKiIm7d0qVLY8vTp0/nueeeG/RrU5V6cEREREREktugLvQppvpoD06+Ao6IiIiISFJSwBmC+lY/eZlOHDZ9bSIiIiIiyUgt9SFoaPWp90ZEREREJIkp4AyBx+vXBAMiIiIiIklMAWcI6lt9mmBARERERCSJKeAMUjhi0NgWoDBbAUdEREREJFkp4AzSofYA4YhBQZaGqImIiIiIJCsFnEHSNXBERERERJKfAs4gdV0Dp0BD1EREREREkpYCziB19+BoiJqIiIiISLJSwBmkrh4cXQdHRERERCR5KeAMUn2rn5x0B2kOW6KLIiIiIiIi/VDAGSRdA0dEREREJPkp4AxSfatfEwyIiIiIiCQ5e6ILkCrqvX4+Pj430cUQERERkWEWDAbZt28fPp/vmF9fXV19gkt16kpLS6O4uBiHwzGo7RVwBsEwDBrUgyMiIiJySti3bx9ZWVmcccYZWCyWIb++s7OT9PT0YSjZqccwDJqamti3bx/jx48f1Gs0RG0QDncECYQjmiJaRERE5BTg8/nIy8s7pnAjJ5bFYiEvL29IvWkKOINQ3xq9yKcmGRARERE5JSjcJI+h1oUCziB0X+RTAUdEREREJJkp4AyCJ3qRz4JsDVETEREREUlmCjiDoB4cERERERlpQqFQooswLAY1i9rWrVtZtWoVkUiEK6+8kuuuuy7u+dbWVm677TYOHDhAOBzmK1/5CosXLwbgt7/9LRs2bMBisTB58mTWrFmDy5VaQaHe68ftspPp0qRzIiIiIqeS//ePffzh77VDek0kEsFq7b8f4fMzSlj8L8VH3cfXv/516urq8Pv9/Pu//ztXXXUVW7du5ac//SnhcJjRo0fz0EMP0d7ezt133827774LwE033cTcuXOZPn06b775JgDPPPMML7zwAj/60Y+44447yMnJ4b333uOss87is5/9LKtXr8bn85GWlsbq1auZMGEC4XCYe++9l5dfftks8+c/z8SJE3nkkUe4//77Afjb3/7G+vXr+fnPfz6k72e4DdhiD4fDrFy5kgcffJDCwkKWLFnCrFmzmDhxYmyb3//+95SVlfHAAw9w6NAh5s2bx4IFC2hububhhx/mz3/+M2lpadx8881UVlayaNGiYf1QJ1pDq1+9NyIiIiJy0qxevZpRo0bh8/lYsmQJs2fP5nvf+x6PPPIIJSUlHD58GIBf/OIXuN1unnrqKQBaWloG3PeePXv47W9/i81mo62tjUceeQS73c4rr7zCT3/6U372s5/x2GOPsW/fPv70pz9ht9s5fPgwOTk5/PCHP+TQoUPk5uaycePGpGzXDxhwqqqqKC0tpaSkBID58+ezZcuWuIBjsVhob2/HMAza29vJycnBbjd3HQ6H8fl82O12fD4fBQUFw/RRhk99q498BRwRERGRU87ifykesLflSCfiOji/+93v2Lx5MwAHDx7kscceY8aMGbE2+ahRowDYtm0b9913X+x1OTk5A+573rx52Gw2wByJdfvtt/PRRx9hsVgIBoOx/X7hC1+Item73m/hwoU8+eSTLFq0iDfffJN77rnnuD7ncBgw4Hg8HoqKimKPCwsLqaqqitvmmmuu4YYbbuDiiy+mvb2dn/70p1itVgoLC/nKV77Cpz71KVwuF5/4xCf45Cc/OWCh/H5/0lz91efzUdvYypljXElTJjk6n8+nukpBqrfUozpLPaqz1KM6S4xgMEhnZ+cxv94wjON6/euvv87LL7/Mb3/7W9LT0/nqV7/KhAkT2LVrV6/9hsNh/H5/n+/Xta6trY1QKERnZyehUAibzRZ77ic/+QnnnXce9957L/v372fZsmWx7QKBQK/9fvazn+Xmm2/GYrHw6U9/mmAwGAtFwykYDPb6XSgvL+9z2wEDjmEYvdYdORf1yy+/THl5OQ8//DB79+7ly1/+MjNmzCAcDrNlyxa2bNlCVlYWN998M0888QQLFy486nu6XK5+C3yyvffeexz2R5hYXJA0ZZKjq66uVl2lINVb6lGdpR7VWepRnSVGdXX1cfXAHG8PTiAQYPTo0eTm5lJTU8M777wDwBtvvEFjY2NsiNqoUaO4+OKL+eMf/8idd94JmEPUcnJyGDNmDAcOHGD8+PG8+OKLZGZmkp6ejt1ux+l0xsrX2dlJcXEx6enpPP3001itVtLT07nkkkv405/+xMUXXxwbojZq1ChKS0spKipi3bp1PPjgg8fdUzVYDodj0L8LA86iVlRURF1dXeyxx+PpNcxs48aNXHrppVgsFkpLSykuLmb37t288sorFBcXk5ubi8Ph4NJLL42d7JQqOoIGvmCEwmwNURMRERGR4XfJJZcQCoVYsGAB//Vf/8W5555Lbm4uK1eu5Bvf+AaXX3453/rWtwC44YYb8Hq9XHbZZVx++eVs374dgG9/+9tcf/31fPGLXyQ/P7/f91q2bBn33XcfX/jCFwiHw7H1V155JWPHjuXyyy/n8ssvZ9OmTbHnFixYwNixY+NOWUkmA/bgTJ06lT179lBbW0thYSGVlZX85Cc/idtm7NixbNu2jRkzZtDY2MiHH35IcXExhmHw9ttv09nZSVpaGtu2bePss88etg8zHA51mtPnFWTpGjgiIiIiMvycTifr1q3r87mKioq4x5mZmX2eBzNv3jzmzZvXa/2PfvSjuMfTp0/n2WefjT3+5je/CYDdbmfFihWsWLGi1z7+8Y9/cOWVVw74ORJlwIBjt9u56667WLZsGeFwmMWLFzNp0iTWr18PwNKlS/n617/OihUrWLBgAYZhcOutt5Kbm0tubi5z587lc5/7HHa7nfLycq666qph/1An0qEOM8lqFjUREREROdUtWrSI9PR07rjjjkQXpV+DurBLRUVFr7S4dOnS2HJhYSG/+c1v+nzt8uXLWb58+XEUMbGaunpwNERNRERERE5xGzduTHQRBjTgOTinuq4enHwNURMRERERSXoKOANo7gyT5rCSnTaozi4REREREUkgBZwBHOoMUZCV1mtqbBERERERST4KOAM41BnWBAMiIiIiIilCAWcATR1hTTAgIiIiIpIiFHAG0DVETUREREQkGU2fPj3RRUgqOnP+KDoCITqDhnpwRERERE5Vb62HNx8Z0kuckTBYbf1vMP3f4Nyl/T+fokKhEHZ74uNF4kuQxOq9fgD14IiIiIjISfPjH/+Y0047jWuuuQaAn/3sZ1gsFl5//XW8Xi+hUIibb76ZT3/60wPuq729na9//et9vu7xxx/n17/+NRaLhTPPPJMf//jHNDY28v3vf5/a2loAfvCDH1BQUMDXvvY1Nm3aBMCvf/1rOjo6+MY3vsG1117L9OnTeeONN5g1axZnnHEGv/zlLwkGg4waNYp7772XMWPG0N7ezt133827774LwE033YTX62Xnzp1897vfBeAPf/gDNTU1rFix4ri+PwWco6hv7Qo46sEREREROSWdu3TIvS2Bzk7S09OP+S3nz5/P6tWrYwHn6aefZt26dXzpS1/C7XZz6NAhrrrqKmbPnj3gTL8ul4v777+/1+t27drFL3/5S9avX09ubi6HDx8G4O677+b888/n/vvvJxwO09HRQUtLy1Hfw+v18sgjZi9XS0sLf/jDH7BYLGzYsIF169Zxxx138Itf/AK3281TTz0V287hcHD55Zdz22234XA42LhxIz/84Q+P+XvrooBzFB6vD0BD1ERERETkpPnYxz5GU1MTHo+H5uZmsrOzyc/PZ82aNbz++utYrVY8Hg+NjY3k5+cfdV+GYXDffff1et2rr77KvHnzyM3NBWDUqFEAvPrqq/yf//N/ALDZbGRlZQ0YcD772c/Gluvq6vjWt75FQ0MDgUCA4uJiALZt28Z9990X2y4nJweAmTNn8sILLzBhwgSCwSBnnnnm0L6sPijgHEV3D46GqImIiIjIyTN37lyeffZZGhsbmT9/Pk899RSHDh1i48aNOBwOZs2ahd/vH3A//b3OMIxBl8VutxOJRGKPj3zfnr1Vd999N1/60peYPXs227dv5+c//zlgBq2+epuuvPJKHnjgASZMmMCiRYsGXaaj0SxqR1Hf6sNuhdEZjkQXRUREREROIfPnz+fPf/4zzz77LHPnzqW1tZW8vDwcDgevvvoq+/fvH9R++nvdhRdeyDPPPENzczNAbIjahRdeyKOPPgpAOBymra2NvLw8mpqaaG5uJhAI8MILLxz1/QoLCwHzHJ8un/jEJ2LD2IBYr9C0adOoq6tj06ZNXHbZZYP6TANRwDmKxtYAuen2Acc2ioiIiIicSJMmTaK9vZ2CggIKCgpYsGAB7777LosWLeKpp55iwoQJg9pPf6+bNGkSX/va17j22mu5/PLL+dGPfgTAnXfeyfbt21mwYAGLFi1i586dOBwObrzxRj7/+c9z/fXXH/W9b7rpJm6++Wauvvrq2LA3gBtuuAGv18tll13G5Zdfzvbt22PPfeYzn+G8886LDVs7XhZjKP1TJ0l1dTXl5eWJLgav7GrkzQ92c+P8jye6KDIEyfLzI0Ojeks9qrPUozpLPaqzxDje773zOCcZONVcf/31fOlLX+LCCy/sd5uh1Il6cI7iooljmDUhK9HFEBEREREZcbxeL3PnzsXlch013AyVJhkQEREREUlxH3zwAd/5znfi1jmdTjZs2JCgEg0sOzubZ5999oTvVwFHREREROQI/c36lazOPPNMnnjiiUQXY1gM9YwaDVETEREREekhLS2NpqamITes5cQzDIOmpibS0gZ/2Rb14IiIiIiI9FBcXMy+fftoaGg4ptcHg0EcDl1m5ERJS0uLXTB0MBRwRERERER6cDgcjB8//phfr9nvEktD1EREREREZMRQwBERERERkRFDAUdEREREREYMi5GE00O89dZbuFyuRBdDRERERESSlN1uZ9KkSb3WJ2XAERERERERORYaoiYiIiIiIiOGAo6IiIiIiIwYCjgiIiIiIjJiKOCIiIiIiMiIoYAjIiIiIiIjhgKOiIiIiIiMGPZEFyCZbd26lVWrVhGJRLjyyiu57rrrEl0kOcKKFSt44YUXyMvLY9OmTQAcPnyYb33rW+zfv59x48bxn//5n+Tk5CS4pNLl4MGDfOc736GxsRGr1crnP/95vvjFL6rekpjf7+eaa64hEAgQDoeZO3cuy5cvV52lgHA4zOLFiyksLORXv/qV6iwFzJo1i8zMTKxWKzabjY0bN6rekpzX6+V//+//zY4dO7BYLKxevZrx48erzhJIPTj9CIfDrFy5knXr1lFZWcmmTZvYtWtXooslR1i0aBHr1q2LW7d27VouvPBCnnvuOS688ELWrl2boNJJX2w2G3fccQdPP/00jz32GI8++ii7du1SvSUxp9PJQw89xJNPPsnjjz/OSy+9xFtvvaU6SwEPP/wwZWVlsceqs9Tw0EMP8cQTT7Bx40ZA9ZbsVq1axcUXX8wzzzzDE088QVlZmeoswRRw+lFVVUVpaSklJSU4nU7mz5/Pli1bEl0sOcL555/f64jIli1buOKKKwC44oor+Mtf/pKAkkl/CgoKOOusswBwu91MmDABj8ejektiFouFzMxMAEKhEKFQCIvFojpLcnV1dbzwwgssWbIktk51lppUb8mrra2N119/PfZ75nQ6yc7OVp0lmAJOPzweD0VFRbHHhYWFeDyeBJZIBqupqYmCggLAbEwfOnQowSWS/uzbt4/q6mqmTZumekty4XCYhQsXctFFF3HRRRepzlLA6tWrue2227Bau//Vq85Sw1e/+lUWLVrEY489BqjeklltbS25ubmsWLGCK664gjvvvJOOjg7VWYIp4PTDMIxe6ywWSwJKIjIytbe3s3z5cr773e/idrsTXRwZgM1m44knnuDFF1+kqqqKHTt2JLpIchR//etfyc3N5eyzz050UWSI1q9fz5/+9Cf+7//9v/z+97/n9ddfT3SR5ChCoRDvvfceS5cu5fHHHyc9PV3D0ZKAAk4/ioqKqKuriz32eDyxJC7JLS8vj/r6egDq6+vJzc1NcInkSMFgkOXLl7NgwQIuvfRSQPWWKrKzs7ngggt46aWXVGdJ7I033uD5559n1qxZ3HLLLbz66qvceuutqrMUUFhYCJh/E+fMmUNVVZXqLYkVFRVRVFTEtGnTAJg3bx7vvfee6izBFHD6MXXqVPbs2UNtbS2BQIDKykpmzZqV6GLJIMyaNYvHH38cgMcff5zZs2cntkASxzAM7rzzTiZMmMCXv/zl2HrVW/I6dOgQXq8XAJ/PxyuvvMKECRNUZ0ns29/+Nlu3buX555/nvvvuY+bMmdx7772qsyTX0dFBW1tbbPlvf/sbkyZNUr0lsfz8fIqKiti9ezcA27Zto6ysTHWWYBajr7FYAsCLL77I6tWrY9Ns3nDDDYkukhzhlltu4bXXXqO5uZm8vDy+8Y1v8OlPf5pvfvObHDx4kLFjx/Jf//VfjBo1KtFFlai///3vXHPNNUyePDl2bsAtt9zCOeeco3pLUu+//z533HEH4XAYwzCYN28eN910E83NzaqzFLB9+3Z+85vf8Ktf/Up1luRqa2u58cYbAfO8t8suu4wbbrhB9ZbkqqurufPOOwkGg5SUlLBmzRoikYjqLIEUcEREREREZMTQEDURERERERkxFHBERERERGTEUMAREREREZERQwFHRERERERGDAUcEREREREZMRRwRERERERkxFDAERERERGREeP/AwVvKFpffI4qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(lenet_history.history)[['accuracy', 'val_accuracy']].plot(figsize=(14,4))\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Classification Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:07:01.036227Z",
     "start_time": "2021-02-23T06:07:00.259583Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 98.97%\n"
     ]
    }
   ],
   "source": [
    "# evaluate test accuracy\n",
    "lenet_accuracy = lenet5.evaluate(X_test.reshape(-1, 28, 28, 1), y_test, verbose=0)[1]\n",
    "print('Test accuracy: {:.2%}'.format(lenet_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparison, a simple two-layer feedforward network achieves only 37.36% test accuracy. \n",
    "\n",
    "The LeNet5 improvement on MNIST is, in fact, modest. Non-neural methods have also achieved classification accuracies greater than or equal to 99%, including K-Nearest Neighbours or Support Vector Machines. CNNs really shine with more challenging datasets as we will see next."
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
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