notebook/CNN_Eng_Drawings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Symbols in Engineering Drawings\n",
    "* Convolutional Neural Network for Multiclass\n",
    "Imbalanced Dataset Classi\f",
    "cation of Symbols in\n",
    "Engineering Drawings\n",
    "* EANN 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading symbols files \n",
    "\n",
    "* Load necessary libraries \n",
    "* Read the file (pixels of symbols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd #CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt #for plotting\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "import seaborn as sns\n",
    "from random import randint\n",
    "# imports for array-handling and plotting\n",
    "import matplotlib\n",
    "matplotlib.use('agg')\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.utils import np_utils\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "\n",
    "\n",
    "from subprocess import check_output\n",
    "%matplotlib inline\n",
    "# hide warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# solve cudNN initialisation \n",
    "from tensorflow.compat.v1 import InteractiveSession\n",
    "config = tf.compat.v1.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "session = InteractiveSession(config=config)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quick Exploratory Data Analysis (EDA)\n",
    "* Show first few rows\n",
    "* Column names \n",
    "* Shape (dimension of the dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2431, 10001)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =pd.read_csv('../data/Symbols_pixel.csv')\n",
    "\n",
    "df.head()\n",
    "df.columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate headers names \n",
    "* This might not be necessary\n",
    "* Just for clarity \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['p_1', 'p_2', 'p_3', 'p_4', 'p_5', 'p_6', 'p_7', 'p_8', 'p_9', 'p_10',\n",
       "       ...\n",
       "       'p_9992', 'p_9993', 'p_9994', 'p_9995', 'p_9996', 'p_9997', 'p_9998',\n",
       "       'p_9999', 'p_10000', 'label'],\n",
       "      dtype='object', length=10001)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# get the number df's columns\n",
    "num_cols = df.shape[1]\n",
    "\n",
    "# generate range of ints for suffixes\n",
    "# with length exactly half that of num_cols;\n",
    "# if num_cols is even, truncate concatenated list later\n",
    "# to get to original list length\n",
    "rng = range(1, (num_cols))\n",
    "\n",
    "new_cols =  ['p_' + str(i) for i in rng]\n",
    "new_cols.append('label')\n",
    "# ensure the length of the new columns list is equal to the length of df's columns\n",
    "df.columns = new_cols[:num_cols]\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data and Labels \n",
    "* IF needed, Might want to separate labels from feature\n",
    "* This resturns data into Panas Series "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract labels (all rows, and last column)\n",
    "n = df.shape[1] # number of columns \n",
    "labels =df['label']\n",
    "\n",
    "features= df.iloc[:,0:n-1]\n",
    "type(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p_1</th>\n",
       "      <th>p_2</th>\n",
       "      <th>p_3</th>\n",
       "      <th>p_4</th>\n",
       "      <th>p_5</th>\n",
       "      <th>p_6</th>\n",
       "      <th>p_7</th>\n",
       "      <th>p_8</th>\n",
       "      <th>p_9</th>\n",
       "      <th>p_10</th>\n",
       "      <th>...</th>\n",
       "      <th>p_9991</th>\n",
       "      <th>p_9992</th>\n",
       "      <th>p_9993</th>\n",
       "      <th>p_9994</th>\n",
       "      <th>p_9995</th>\n",
       "      <th>p_9996</th>\n",
       "      <th>p_9997</th>\n",
       "      <th>p_9998</th>\n",
       "      <th>p_9999</th>\n",
       "      <th>p_10000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 10000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   p_1  p_2  p_3  p_4  p_5  p_6  p_7  p_8  p_9  p_10  ...  p_9991  p_9992  \\\n",
       "0  255  255  255  255  255  255  255  255  255   255  ...     255     255   \n",
       "1  255  255  255  255  255  255  255  255  255   255  ...       0       0   \n",
       "2  255  255  255  255  255    0    0    0    0     0  ...     255     255   \n",
       "3  255  255  255  255  255  255  255  255  255   255  ...       0       0   \n",
       "4    0    0    0    0    0    0    0  255  255   255  ...     255     255   \n",
       "\n",
       "   p_9993  p_9994  p_9995  p_9996  p_9997  p_9998  p_9999  p_10000  \n",
       "0       0       0       0       0       0       0       0        0  \n",
       "1       0       0       0       0       0     255     255      255  \n",
       "2     255     255     255     255     255     255     255      255  \n",
       "3       0       0       0       0       0       0       0        0  \n",
       "4     255     255     255     255     255     255     255      255  \n",
       "\n",
       "[5 rows x 10000 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show some random symbols (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(8, 8, figsize=(8, 8))\n",
    "fx = features.values # returns numpy array\n",
    "\n",
    "for i, axi in enumerate(ax.flat):\n",
    "    value = randint(0, df.shape[0])\n",
    "    axi.imshow(fx[value].reshape(100,100), cmap='gray')\n",
    "    axi.set(xticks=[], yticks=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Class distribution \n",
    "* An extremly imbalanced problem "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(15,7))\n",
    "\n",
    "plt.hist(labels,bins=39)\n",
    "plt.title('Symbols Distribution')\n",
    "plt.xlabel('Symbol Class')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check and remove least frequent symbols \n",
    "\n",
    "* Some classes rarely appear\n",
    "* and needs to be removed, \n",
    "* first check the numbers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sensor                        392\n",
       "Reducer                       285\n",
       "Arrowhead                     239\n",
       "Valve Ball                    173\n",
       "DB&BBV                        127\n",
       "Valve Check                   126\n",
       "Continuity Label              116\n",
       "DB&BPV                        113\n",
       "Valve Plug                     88\n",
       "Arrowhead + Triangle           83\n",
       "Triangle                       74\n",
       "Valve Butterfly                71\n",
       "ESDV Valve Ball                65\n",
       "Flange Single T-Shape          61\n",
       "Flange Joint                   46\n",
       "Injector Point                 43\n",
       "Spectacle Blind                42\n",
       "Valve                          40\n",
       "DB&BBV + Valve Check           39\n",
       "Valve Globe                    28\n",
       "Valve Angle                    27\n",
       "Valve Slab Gate                24\n",
       "Control Valve Globe            23\n",
       "Flange + Triangle              17\n",
       "Control                        13\n",
       "Exit to Atmosphere             12\n",
       "Rupture Disc                   10\n",
       "ESDV Valve Slab Gate            9\n",
       "Box                             7\n",
       "ESDV Valve Butterfly            7\n",
       "Temporary Strainer              6\n",
       "Control Valve                   5\n",
       "Valve Gate Through Conduit      4\n",
       "Deluge                          4\n",
       "Vessel                          4\n",
       "Line Blindspacer                4\n",
       "Control Valve Angle Choke       2\n",
       "Ultrasonic Flow Meter           1\n",
       "Barred Tee                      1\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check class distribution \n",
    "df['label'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove symbols with less than 7 instances in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sensor                   392\n",
       "Reducer                  285\n",
       "Arrowhead                239\n",
       "Valve Ball               173\n",
       "DB&BBV                   127\n",
       "Valve Check              126\n",
       "Continuity Label         116\n",
       "DB&BPV                   113\n",
       "Valve Plug                88\n",
       "Arrowhead + Triangle      83\n",
       "Triangle                  74\n",
       "Valve Butterfly           71\n",
       "ESDV Valve Ball           65\n",
       "Flange Single T-Shape     61\n",
       "Flange Joint              46\n",
       "Injector Point            43\n",
       "Spectacle Blind           42\n",
       "Valve                     40\n",
       "DB&BBV + Valve Check      39\n",
       "Valve Globe               28\n",
       "Valve Angle               27\n",
       "Valve Slab Gate           24\n",
       "Control Valve Globe       23\n",
       "Flange + Triangle         17\n",
       "Control                   13\n",
       "Exit to Atmosphere        12\n",
       "Rupture Disc              10\n",
       "ESDV Valve Slab Gate       9\n",
       "Box                        7\n",
       "ESDV Valve Butterfly       7\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = df[~df['label'].isin(['Ultrasonic Flow Meter', 'Barred Tee','Temporary Strainer',\n",
    "                     'Control Valve Angle Choke','Line Blindspacer',\n",
    "                     'Vessel','Valve Gate Through Conduit','Deluge','Control Valve'])]\n",
    "\n",
    "df['label'].value_counts()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Networks (CNNs)\n",
    "\n",
    "* Prepare the dataset\n",
    "* check the shapes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2400, 10000)\n",
      "(2400,)\n"
     ]
    }
   ],
   "source": [
    "# labels and features (separate data frames)\n",
    "# retrieve numpy array (to meet keras requirements )\n",
    "dataset = df.values\n",
    "# split into input (X) and output (y) variables\n",
    "X = dataset[:, :-1]\n",
    "y = dataset[:,-1]\n",
    "\n",
    "print(X.shape)\n",
    "print(y.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 30 Unique Symbol in the dataset\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {len(df.label.unique())} Unique Symbol in the dataset') # check number of labels \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare the data for the model\n",
    "* Check the shapes of the input\n",
    "* Split the data into train/ test subsets \n",
    "* Reshape the data \n",
    "* Prepare targets (labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train images shape: (1920, 10000)\n",
      "Train labels shape: (1920,)\n",
      "Test images shape: (480, 10000)\n",
      "Test labels shape: (480,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# simple functions to check the shapes of all compoents of the dataset (train, test, labels)\n",
    "def data_summary(X_train, y_train, X_test, y_test):\n",
    "    \"\"\"Summarize current state of dataset\"\"\"\n",
    "    print('Train images shape:', X_train.shape)\n",
    "    print('Train labels shape:', y_train.shape)\n",
    "    print('Test images shape:', X_test.shape)\n",
    "    print('Test labels shape:', y_test.shape)\n",
    "    \n",
    "# split the data and check the shapes of results sets  \n",
    "# train_test_split(...) returns numpy arrays \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "data_summary(X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare the data for the model\n",
    "* Reshape the data\n",
    "* Prepare targets (labels)\n",
    "* Check shape of the arrays before and after "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train  (1920, 10000)\n",
      "y_train  (1920,)\n",
      "X-test  (480, 10000)\n",
      "y_test  (480,)\n",
      "___ AFTER RESHAPE________\n",
      "X_train  (1920, 100, 100, 1)\n",
      "y_train  (1920,)\n",
      "X-test  (480, 100, 100, 1)\n",
      "y_test  (480,)\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.utils import np_utils\n",
    "\n",
    "\n",
    "print('X_train ',X_train.shape)\n",
    "print('y_train ',y_train.shape)\n",
    "print('X-test ',X_test.shape)\n",
    "print('y_test ',y_test.shape)\n",
    "\n",
    "X_train = X_train.reshape(X_train.shape[0], 100,100,1)\n",
    "X_test = X_test.reshape(X_test.shape[0], 100,100,1)\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "\n",
    "print('___ AFTER RESHAPE________')\n",
    "print('X_train ',X_train.shape)\n",
    "print('y_train ',y_train.shape)\n",
    "print('X-test ',X_test.shape)\n",
    "print('y_test ',y_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare the data for the model\n",
    "\n",
    "* Prepare targets (labels)\n",
    "* String labels have to be encoded first "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train  (1920,)\n",
      "y_test  (480,)\n"
     ]
    }
   ],
   "source": [
    "# Encode target variables \n",
    "# prepare target\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "y_train_cat = y_train\n",
    "y_test_cat = y_test\n",
    "\n",
    "# prepare target\n",
    "def prepare_targets(y_train, y_test):\n",
    "    le = LabelEncoder()\n",
    "    le.fit(y_train)\n",
    "    y_train_enc = le.transform(y_train)\n",
    "    y_test_enc = le.transform(y_test)\n",
    "    return y_train_enc, y_test_enc\n",
    "\n",
    "y_train, y_test = prepare_targets(y_train,y_test)\n",
    "\n",
    "print('y_train ',y_train.shape)\n",
    "print('y_test ',y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Encode Labes Data\n",
    "* Notice how the shape of the arrays changes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train  (1920, 30)\n",
      "y_test  (480, 30)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)\n",
    "num_classes = y_test.shape[1]\n",
    "print('y_train ',y_train.shape)\n",
    "print('y_test ',y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle the training dataset (5 times!)\n",
    "\n",
    "for _ in range(5): \n",
    "    indexes = np.random.permutation(len(X_train))\n",
    "\n",
    "X_train = X_train[indexes]\n",
    "y_train = y_train[indexes]\n",
    "\n",
    "# Keep 10% of the training data for validation \n",
    "# cross-validation sets\n",
    "val_perc = 0.10\n",
    "val_count = int(val_perc * len(X_train))\n",
    "\n",
    "# Validation set (val_data)\n",
    "val_data = X_train[:val_count,:]\n",
    "val_labels_cat = y_train[:val_count,:]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Model \n",
    "* Train, validate and testing sets  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_16 (Conv2D)           (None, 100, 100, 32)      320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 50, 50, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 50, 50, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 50, 50, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 25, 25, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 25, 25, 128)       73856     \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 25, 25, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 18432)             0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 128)               2359424   \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 30)                1950      \n",
      "=================================================================\n",
      "Total params: 2,646,814\n",
      "Trainable params: 2,646,814\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def build_modelU():\n",
    "    model = Sequential()\n",
    "    # add Convolutional layers\n",
    "    model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu', padding='same',\n",
    "                     input_shape=(100, 100, 1)))\n",
    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "    \n",
    "    model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'))\n",
    "    model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'))\n",
    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "    \n",
    "    model.add(Conv2D(filters=128, kernel_size=(3,3), activation='relu', padding='same'))\n",
    "    model.add(Conv2D(filters=128, kernel_size=(3,3), activation='relu', padding='same'))\n",
    "    model.add(MaxPooling2D(pool_size=(2,2)))    \n",
    "    model.add(Flatten())\n",
    "    # Densely connected layers\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dropout(0.1))\n",
    "    # output layer\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    # compile with adam optimizer & categorical_crossentropy loss function\n",
    "    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "model = build_modelU()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1920 samples, validate on 192 samples\n",
      "Epoch 1/25\n",
      "1920/1920 [==============================] - 1s 716us/step - loss: 6.7777 - accuracy: 0.0927 - val_loss: 2.8985 - val_accuracy: 0.3542\n",
      "Epoch 2/25\n",
      "1920/1920 [==============================] - 1s 655us/step - loss: 2.5294 - accuracy: 0.3005 - val_loss: 1.9306 - val_accuracy: 0.4427\n",
      "Epoch 3/25\n",
      "1920/1920 [==============================] - 1s 661us/step - loss: 1.9349 - accuracy: 0.4453 - val_loss: 1.4530 - val_accuracy: 0.6719\n",
      "Epoch 4/25\n",
      "1920/1920 [==============================] - 1s 660us/step - loss: 1.4966 - accuracy: 0.5760 - val_loss: 0.9578 - val_accuracy: 0.7917\n",
      "Epoch 5/25\n",
      "1920/1920 [==============================] - 1s 656us/step - loss: 1.2186 - accuracy: 0.6562 - val_loss: 0.6551 - val_accuracy: 0.8385\n",
      "Epoch 6/25\n",
      "1920/1920 [==============================] - 1s 657us/step - loss: 0.9849 - accuracy: 0.7172 - val_loss: 0.5208 - val_accuracy: 0.8646\n",
      "Epoch 7/25\n",
      "1920/1920 [==============================] - 1s 660us/step - loss: 0.8779 - accuracy: 0.7516 - val_loss: 0.4891 - val_accuracy: 0.8750\n",
      "Epoch 8/25\n",
      "1920/1920 [==============================] - 1s 657us/step - loss: 0.7715 - accuracy: 0.7734 - val_loss: 0.3573 - val_accuracy: 0.8906\n",
      "Epoch 9/25\n",
      "1920/1920 [==============================] - 1s 660us/step - loss: 0.7359 - accuracy: 0.7833 - val_loss: 0.3225 - val_accuracy: 0.9062\n",
      "Epoch 10/25\n",
      "1920/1920 [==============================] - 1s 659us/step - loss: 0.6296 - accuracy: 0.8172 - val_loss: 0.2952 - val_accuracy: 0.9167\n",
      "Epoch 11/25\n",
      "1920/1920 [==============================] - 1s 656us/step - loss: 0.5792 - accuracy: 0.8234 - val_loss: 0.2970 - val_accuracy: 0.9219\n",
      "Epoch 12/25\n",
      "1920/1920 [==============================] - 1s 655us/step - loss: 0.5127 - accuracy: 0.8438 - val_loss: 0.2111 - val_accuracy: 0.9479\n",
      "Epoch 13/25\n",
      "1920/1920 [==============================] - 1s 656us/step - loss: 0.4931 - accuracy: 0.8438 - val_loss: 0.1964 - val_accuracy: 0.9427\n",
      "Epoch 14/25\n",
      "1920/1920 [==============================] - 1s 657us/step - loss: 0.4785 - accuracy: 0.8464 - val_loss: 0.1819 - val_accuracy: 0.9531\n",
      "Epoch 15/25\n",
      "1920/1920 [==============================] - 1s 655us/step - loss: 0.4763 - accuracy: 0.8479 - val_loss: 0.2361 - val_accuracy: 0.9427\n",
      "Epoch 16/25\n",
      "1920/1920 [==============================] - 1s 659us/step - loss: 0.4273 - accuracy: 0.8651 - val_loss: 0.1988 - val_accuracy: 0.9635\n",
      "Epoch 17/25\n",
      "1920/1920 [==============================] - 1s 658us/step - loss: 0.4115 - accuracy: 0.8703 - val_loss: 0.1328 - val_accuracy: 0.9896\n",
      "Epoch 18/25\n",
      "1920/1920 [==============================] - 1s 657us/step - loss: 0.3369 - accuracy: 0.8906 - val_loss: 0.1418 - val_accuracy: 0.9740\n",
      "Epoch 19/25\n",
      "1920/1920 [==============================] - 1s 656us/step - loss: 0.3631 - accuracy: 0.8813 - val_loss: 0.0974 - val_accuracy: 0.9844\n",
      "Epoch 20/25\n",
      "1920/1920 [==============================] - 1s 661us/step - loss: 0.3280 - accuracy: 0.8938 - val_loss: 0.1086 - val_accuracy: 0.9844\n",
      "Epoch 21/25\n",
      "1920/1920 [==============================] - 1s 660us/step - loss: 0.3173 - accuracy: 0.9031 - val_loss: 0.0653 - val_accuracy: 0.9844\n",
      "Epoch 22/25\n",
      "1920/1920 [==============================] - 1s 660us/step - loss: 0.3069 - accuracy: 0.9078 - val_loss: 0.0611 - val_accuracy: 0.9896\n",
      "Epoch 23/25\n",
      "1920/1920 [==============================] - 1s 655us/step - loss: 0.3101 - accuracy: 0.9052 - val_loss: 0.0614 - val_accuracy: 0.9948\n",
      "Epoch 24/25\n",
      "1920/1920 [==============================] - 1s 657us/step - loss: 0.2725 - accuracy: 0.9073 - val_loss: 0.0493 - val_accuracy: 0.9948\n",
      "Epoch 25/25\n",
      "1920/1920 [==============================] - 1s 660us/step - loss: 0.2646 - accuracy: 0.9141 - val_loss: 0.0551 - val_accuracy: 0.9948\n"
     ]
    }
   ],
   "source": [
    "nEpochs = 25 \n",
    "results = model.fit(X_train, y_train, \n",
    "                    epochs=nEpochs, batch_size=64,\n",
    "                    validation_data=(val_data, val_labels_cat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_accuracy', 'loss', 'accuracy'])\n"
     ]
    }
   ],
   "source": [
    "history_dict = results.history\n",
    "print(history_dict.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "480/480 [==============================] - 0s 265us/step\n",
      "Test loss: 0.2366 accuracy: 0.9375\n"
     ]
    }
   ],
   "source": [
    "# accuracy, test \n",
    "test_loss, test_accuracy = \\\n",
    "  model.evaluate(X_test, y_test, batch_size=64)\n",
    "print('Test loss: %.4f accuracy: %.4f' % (test_loss, test_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9375\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "preds = np.argmax(predictions, axis=1)\n",
    "actuals = np.argmax(y_test,axis=1)\n",
    "print('Accuracy: ',sum(preds==actuals)/X_test.shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.9375\n",
      "Incorrectly Identified Symbols:  30\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, y_test, verbose=0)\n",
    "print('Test accuracy:', score[1])\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "preds = np.argmax(predictions, axis=1)\n",
    "actuals = np.argmax(y_test,axis=1)\n",
    "#print(confusion_matrix(preds,actuals))\n",
    "\n",
    "# correctly identified symbols \n",
    "print('Incorrectly Identified Symbols: ',(np.argmax(predictions, axis=1) != \\\n",
    "                        np.argmax(y_test,axis=1)).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.03238845623078911\n",
      "Train accuracy: 0.9958333373069763\n",
      "Incorrectly Identified Symbols:  8\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_train, y_train, verbose=0)\n",
    "print('Train loss:', score[0])\n",
    "print('Train accuracy:', score[1])\n",
    "\n",
    "predictions = model.predict(X_train)\n",
    "preds = np.argmax(predictions, axis=1)\n",
    "actuals = np.argmax(y_train,axis=1)\n",
    "#print(confusion_matrix(preds,actuals))\n",
    "\n",
    "# correctly identified symbols \n",
    "print('Incorrectly Identified Symbols: ',(np.argmax(predictions, axis=1) != \\\n",
    "                        np.argmax(y_train,axis=1)).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss and accuracy curves for training and validation \n",
    "sns.set_style('whitegrid')\n",
    "fig, ax = plt.subplots(1,2, figsize=(14, 7))\n",
    "ax[0].plot(results.history['loss'], color='b', label=\"Training loss\")\n",
    "ax[0].plot(results.history['val_loss'], color='r', label=\"validation loss\",axes =ax[0])\n",
    "legend = ax[0].legend(loc='best', shadow=True)\n",
    "\n",
    "ax[1].plot(results.history['accuracy'], color='b', label=\"Training accuracy\")\n",
    "ax[1].plot(results.history['val_accuracy'], color='r',label=\"Validation accuracy\")\n",
    "legend = ax[1].legend(loc='best', shadow=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show some Predictions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "X_test__ = X_test.reshape(X_test.shape[0], 100, 100)\n",
    "\n",
    "fig, axis = plt.subplots(4, 4, figsize=(8, 12))\n",
    "for i, ax in enumerate(axis.flat):\n",
    "    ax.imshow(X_test__[i], cmap='binary')\n",
    "    ax.set(title = f\"Actual Symbol {y_test[i].argmax()}\\nPredicted is {y_pred[i].argmax()}\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}