BottleModelV4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "37b2a8cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.preprocessing import image\n",
    "import numpy as np\n",
    "from numpy import asarray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import to_categorical\n",
    "from tqdm import tqdm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a38c19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#add the pics to the array\n",
    "arrayPath = ['1_00.JPG',\n",
    " '1_01.JPG',\n",
    " '1_02.JPG',\n",
    " '1_03.JPG',\n",
    " '1_04.JPG',\n",
    " '1_05.JPG',\n",
    " '1_06.JPG',\n",
    " '1_07.JPG',\n",
    " '1_08.JPG',\n",
    " '1_09.JPG',\n",
    " '1_10.JPG',\n",
    " '1_11.JPG',\n",
    " '1_12.JPG',\n",
    " '1_13.JPG',\n",
    " '1_14.JPG',\n",
    " '1_15.JPG',\n",
    " '1_16.JPG',\n",
    " '1_17.JPG',\n",
    " '1_18.JPG',\n",
    " '1_19.JPG',     \n",
    " '2_00.JPG',\n",
    " '2_01.JPG',\n",
    " '2_02.JPG',\n",
    " '2_03.JPG',\n",
    " '2_04.JPG',\n",
    " '2_05.JPG',\n",
    " '2_06.JPG',\n",
    " '2_07.JPG',\n",
    " '2_08.JPG',\n",
    " '2_09.JPG',\n",
    " '2_10.JPG',\n",
    " '2_11.JPG',\n",
    " '2_12.JPG',\n",
    " '2_13.JPG',\n",
    " '2_14.JPG',\n",
    " '2_15.JPG',\n",
    " '2_16.JPG',\n",
    " '2_17.JPG',\n",
    " '2_18.JPG',\n",
    " '2_19.JPG',  \n",
    " '3_00.JPG',\n",
    " '3_01.JPG',\n",
    " '3_02.JPG',\n",
    " '3_03.JPG',\n",
    " '3_04.JPG',\n",
    " '3_05.JPG',\n",
    " '3_06.JPG',\n",
    " '3_07.JPG',\n",
    " '3_08.JPG',\n",
    " '3_09.JPG',\n",
    " '3_10.JPG',\n",
    " '3_11.JPG',\n",
    " '3_12.JPG',\n",
    " '3_13.JPG',\n",
    " '3_14.JPG',\n",
    " '3_15.JPG',\n",
    " '3_16.JPG',\n",
    " '3_17.JPG',\n",
    " '3_18.JPG',\n",
    " '3_19.JPG',  \n",
    " '4_00.JPG',\n",
    " '4_01.JPG',\n",
    " '4_02.JPG',\n",
    " '4_03.JPG',\n",
    " '4_04.JPG',\n",
    " '4_05.JPG',\n",
    " '4_06.JPG',\n",
    " '4_07.JPG',\n",
    " '4_08.JPG',\n",
    " '4_09.JPG',\n",
    " '4_10.JPG',\n",
    " '4_11.JPG',\n",
    " '4_12.JPG',\n",
    " '4_13.JPG',\n",
    " '4_14.JPG',\n",
    " '4_15.JPG',\n",
    " '4_16.JPG',\n",
    " '4_17.JPG',\n",
    " '4_18.JPG',\n",
    " '4_19.JPG',  \n",
    " '5_00.JPG',\n",
    " '5_01.JPG',\n",
    " '5_02.JPG',\n",
    " '5_03.JPG',\n",
    " '5_04.JPG',\n",
    " '5_05.JPG',\n",
    " '5_06.JPG',\n",
    " '5_07.JPG',\n",
    " '5_08.JPG',\n",
    " '5_09.JPG',\n",
    " '5_10.JPG',\n",
    " '5_11.JPG',\n",
    " '5_12.JPG',\n",
    " '5_13.JPG',\n",
    " '5_14.JPG',\n",
    " '5_15.JPG',\n",
    " '5_16.JPG',\n",
    " '5_17.JPG',\n",
    " '5_18.JPG',\n",
    " '5_19.JPG',  \n",
    " '6_00.JPG',\n",
    " '6_01.JPG',\n",
    " '6_02.JPG',\n",
    " '6_03.JPG',\n",
    " '6_04.JPG',\n",
    " '6_05.JPG',\n",
    " '6_06.JPG',\n",
    " '6_07.JPG',\n",
    " '6_08.JPG',\n",
    " '6_09.JPG',\n",
    " '6_10.JPG',\n",
    " '6_11.JPG',\n",
    " '6_12.JPG',\n",
    " '6_13.JPG',\n",
    " '6_14.JPG',\n",
    " '6_15.JPG',\n",
    " '6_16.JPG',\n",
    " '6_17.JPG',\n",
    " '6_18.JPG',\n",
    " '6_19.JPG']  \n",
    "            \n",
    "# arrayPath = [\n",
    "#  '2_00.JPG',\n",
    "#  '2_01.JPG',\n",
    "#  '2_02.JPG',\n",
    "#  '2_03.JPG',\n",
    "#  '2_04.JPG',\n",
    "#  '2_05.JPG',\n",
    "#  '2_06.JPG',\n",
    "#  '2_07.JPG',\n",
    "#  '2_08.JPG',\n",
    "#  '2_09.JPG',\n",
    "#  '2_10.JPG',\n",
    "#  '2_11.JPG',\n",
    "#  '2_12.JPG',\n",
    "#  '2_13.JPG',\n",
    "#  '2_14.JPG',\n",
    "#  '2_15.JPG',\n",
    "#  '2_16.JPG',\n",
    "#  '2_17.JPG',\n",
    "#  '2_18.JPG',\n",
    "#  '2_19.JPG',  \n",
    "#  '3_00.JPG',\n",
    "#  '3_01.JPG',\n",
    "#  '3_02.JPG',\n",
    "#  '3_03.JPG',\n",
    "#  '3_04.JPG',\n",
    "#  '3_05.JPG',\n",
    "#  '3_06.JPG',\n",
    "#  '3_07.JPG',\n",
    "#  '3_08.JPG',\n",
    "#  '3_09.JPG',\n",
    "#  '3_10.JPG',\n",
    "#  '3_11.JPG',\n",
    "#  '3_12.JPG',\n",
    "#  '3_13.JPG',\n",
    "#  '3_14.JPG',\n",
    "#  '3_15.JPG',\n",
    "#  '3_16.JPG',\n",
    "#  '3_17.JPG',\n",
    "#  '3_18.JPG',\n",
    "#  '3_19.JPG',  \n",
    "#  '4_00.JPG',\n",
    "#  '4_01.JPG',\n",
    "#  '4_02.JPG',\n",
    "#  '4_03.JPG',\n",
    "#  '4_04.JPG',\n",
    "#  '4_05.JPG',\n",
    "#  '4_06.JPG',\n",
    "#  '4_07.JPG',\n",
    "#  '4_08.JPG',\n",
    "#  '4_09.JPG',\n",
    "#  '4_10.JPG',\n",
    "#  '4_11.JPG',\n",
    "#  '4_12.JPG',\n",
    "#  '4_13.JPG',\n",
    "#  '4_14.JPG',\n",
    "#  '4_15.JPG',\n",
    "#  '4_16.JPG',\n",
    "#  '4_17.JPG',\n",
    "#  '4_18.JPG',\n",
    "#  '4_19.JPG',  \n",
    "#  '5_00.JPG',\n",
    "#  '5_01.JPG',\n",
    "#  '5_02.JPG',\n",
    "#  '5_03.JPG',\n",
    "#  '5_04.JPG',\n",
    "#  '5_05.JPG',\n",
    "#  '5_06.JPG',\n",
    "#  '5_07.JPG',\n",
    "#  '5_08.JPG',\n",
    "#  '5_09.JPG',\n",
    "#  '5_10.JPG',\n",
    "#  '5_11.JPG',\n",
    "#  '5_12.JPG',\n",
    "#  '5_13.JPG',\n",
    "#  '5_14.JPG',\n",
    "#  '5_15.JPG',\n",
    "#  '5_16.JPG',\n",
    "#  '5_17.JPG',\n",
    "#  '5_18.JPG',\n",
    "#  '5_19.JPG',  \n",
    "#  '6_00.JPG',\n",
    "#  '6_01.JPG',\n",
    "#  '6_02.JPG',\n",
    "#  '6_03.JPG',\n",
    "#  '6_04.JPG',\n",
    "#  '6_05.JPG',\n",
    "#  '6_06.JPG',\n",
    "#  '6_07.JPG',\n",
    "#  '6_08.JPG',\n",
    "#  '6_09.JPG',\n",
    "#  '6_10.JPG',\n",
    "#  '6_11.JPG',\n",
    "#  '6_12.JPG',\n",
    "#  '6_13.JPG',\n",
    "#  '6_14.JPG',\n",
    "#  '6_15.JPG',\n",
    "#  '6_16.JPG',\n",
    "#  '6_17.JPG',\n",
    "#  '6_18.JPG',\n",
    "#  '6_19.JPG'  ]  \n",
    "        \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "413d086a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1_00.JPG',\n",
       " '1_01.JPG',\n",
       " '1_02.JPG',\n",
       " '1_03.JPG',\n",
       " '1_04.JPG',\n",
       " '1_05.JPG',\n",
       " '1_06.JPG',\n",
       " '1_07.JPG',\n",
       " '1_08.JPG',\n",
       " '1_09.JPG',\n",
       " '1_10.JPG',\n",
       " '1_11.JPG',\n",
       " '1_12.JPG',\n",
       " '1_13.JPG',\n",
       " '1_14.JPG',\n",
       " '1_15.JPG',\n",
       " '1_16.JPG',\n",
       " '1_17.JPG',\n",
       " '1_18.JPG',\n",
       " '1_19.JPG',\n",
       " '2_00.JPG',\n",
       " '2_01.JPG',\n",
       " '2_02.JPG',\n",
       " '2_03.JPG',\n",
       " '2_04.JPG',\n",
       " '2_05.JPG',\n",
       " '2_06.JPG',\n",
       " '2_07.JPG',\n",
       " '2_08.JPG',\n",
       " '2_09.JPG',\n",
       " '2_10.JPG',\n",
       " '2_11.JPG',\n",
       " '2_12.JPG',\n",
       " '2_13.JPG',\n",
       " '2_14.JPG',\n",
       " '2_15.JPG',\n",
       " '2_16.JPG',\n",
       " '2_17.JPG',\n",
       " '2_18.JPG',\n",
       " '2_19.JPG',\n",
       " '3_00.JPG',\n",
       " '3_01.JPG',\n",
       " '3_02.JPG',\n",
       " '3_03.JPG',\n",
       " '3_04.JPG',\n",
       " '3_05.JPG',\n",
       " '3_06.JPG',\n",
       " '3_07.JPG',\n",
       " '3_08.JPG',\n",
       " '3_09.JPG',\n",
       " '3_10.JPG',\n",
       " '3_11.JPG',\n",
       " '3_12.JPG',\n",
       " '3_13.JPG',\n",
       " '3_14.JPG',\n",
       " '3_15.JPG',\n",
       " '3_16.JPG',\n",
       " '3_17.JPG',\n",
       " '3_18.JPG',\n",
       " '3_19.JPG',\n",
       " '4_00.JPG',\n",
       " '4_01.JPG',\n",
       " '4_02.JPG',\n",
       " '4_03.JPG',\n",
       " '4_04.JPG',\n",
       " '4_05.JPG',\n",
       " '4_06.JPG',\n",
       " '4_07.JPG',\n",
       " '4_08.JPG',\n",
       " '4_09.JPG',\n",
       " '4_10.JPG',\n",
       " '4_11.JPG',\n",
       " '4_12.JPG',\n",
       " '4_13.JPG',\n",
       " '4_14.JPG',\n",
       " '4_15.JPG',\n",
       " '4_16.JPG',\n",
       " '4_17.JPG',\n",
       " '4_18.JPG',\n",
       " '4_19.JPG',\n",
       " '5_00.JPG',\n",
       " '5_01.JPG',\n",
       " '5_02.JPG',\n",
       " '5_03.JPG',\n",
       " '5_04.JPG',\n",
       " '5_05.JPG',\n",
       " '5_06.JPG',\n",
       " '5_07.JPG',\n",
       " '5_08.JPG',\n",
       " '5_09.JPG',\n",
       " '5_10.JPG',\n",
       " '5_11.JPG',\n",
       " '5_12.JPG',\n",
       " '5_13.JPG',\n",
       " '5_14.JPG',\n",
       " '5_15.JPG',\n",
       " '5_16.JPG',\n",
       " '5_17.JPG',\n",
       " '5_18.JPG',\n",
       " '5_19.JPG',\n",
       " '6_00.JPG',\n",
       " '6_01.JPG',\n",
       " '6_02.JPG',\n",
       " '6_03.JPG',\n",
       " '6_04.JPG',\n",
       " '6_05.JPG',\n",
       " '6_06.JPG',\n",
       " '6_07.JPG',\n",
       " '6_08.JPG',\n",
       " '6_09.JPG',\n",
       " '6_10.JPG',\n",
       " '6_11.JPG',\n",
       " '6_12.JPG',\n",
       " '6_13.JPG',\n",
       " '6_14.JPG',\n",
       " '6_15.JPG',\n",
       " '6_16.JPG',\n",
       " '6_17.JPG',\n",
       " '6_18.JPG',\n",
       " '6_19.JPG']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrayPath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "829acc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imageNumerics = []\n",
    "# for i in arrayPath:\n",
    "#     img = Image.open(i)\n",
    "#     npdata = asarray(img)\n",
    "#     imageNumerics.append(npdata)\n",
    "    \n",
    "# print(imageNumerics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84ff107d",
   "metadata": {},
   "outputs": [],
   "source": [
    "imageNumerics = []\n",
    "for i in arrayPath:\n",
    "    img = image.load_img(i, target_size=(224,224,1))\n",
    "    img = image.img_to_array(img)\n",
    "    img = img/255\n",
    "    imageNumerics.append(img)\n",
    "    \n",
    "#print(imageNumerics)\n",
    "X =np.array(imageNumerics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf6e5008",
   "metadata": {},
   "outputs": [],
   "source": [
    "imageTargets = [1, 1,1, 1,1, 1,1, 1,1, 1,1, 1,1, 1,1, 1,1, 1,1, 1, 2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6]\n",
    "\n",
    "\n",
    "# imageTargets = [2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6]\n",
    "\n",
    "y=imageTargets\n",
    "y = to_categorical(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b5fec039",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "imageNumerics_tuples = [tuple(x) for x in imageNumerics]\n",
    "df = pd.DataFrame({'imageTargets': imageTargets,'imageNumerics': imageNumerics_tuples})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44bce3d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[[0.00392157, 0.00392157, 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.00784314, 0.08235294, 0.10588235],\n",
       "         [0.07450981, 0.14901961, 0.17254902],\n",
       "         [0.01176471, 0.08627451, 0.10980392]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.01960784, 0.01960784, 0.01176471],\n",
       "         ...,\n",
       "         [0.01568628, 0.09019608, 0.11372549],\n",
       "         [0.07058824, 0.14509805, 0.16862746],\n",
       "         [0.04313726, 0.11764706, 0.14117648]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.05098039, 0.1254902 , 0.14117648],\n",
       "         [0.02745098, 0.10196079, 0.1254902 ],\n",
       "         [0.01960784, 0.09411765, 0.11764706]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0.8039216 , 0.78431374, 0.7607843 ],\n",
       "         [0.85882354, 0.8392157 , 0.8156863 ],\n",
       "         [0.8352941 , 0.8156863 , 0.7921569 ],\n",
       "         ...,\n",
       "         [0.6392157 , 0.61960787, 0.59607846],\n",
       "         [0.69411767, 0.6745098 , 0.6509804 ],\n",
       "         [0.7019608 , 0.68235296, 0.65882355]],\n",
       " \n",
       "        [[0.8117647 , 0.7921569 , 0.76862746],\n",
       "         [0.827451  , 0.80784315, 0.78431374],\n",
       "         [0.78431374, 0.7647059 , 0.7411765 ],\n",
       "         ...,\n",
       "         [0.69803923, 0.6784314 , 0.654902  ],\n",
       "         [0.6745098 , 0.654902  , 0.6313726 ],\n",
       "         [0.68235296, 0.6627451 , 0.6392157 ]],\n",
       " \n",
       "        [[0.8039216 , 0.78431374, 0.7607843 ],\n",
       "         [0.78431374, 0.7647059 , 0.7411765 ],\n",
       "         [0.85490197, 0.8352941 , 0.8117647 ],\n",
       "         ...,\n",
       "         [0.6862745 , 0.6666667 , 0.6431373 ],\n",
       "         [0.65882355, 0.6392157 , 0.6156863 ],\n",
       "         [0.627451  , 0.60784316, 0.58431375]]], dtype=float32),\n",
       " array([[[0.46666667, 0.47058824, 0.4509804 ],\n",
       "         [0.3764706 , 0.3647059 , 0.3372549 ],\n",
       "         [0.2901961 , 0.2509804 , 0.21568628],\n",
       "         ...,\n",
       "         [0.19607843, 0.34117648, 0.3764706 ],\n",
       "         [0.18039216, 0.3254902 , 0.36078432],\n",
       "         [0.18431373, 0.32941177, 0.3647059 ]],\n",
       " \n",
       "        [[0.47058824, 0.4745098 , 0.4509804 ],\n",
       "         [0.43529412, 0.4392157 , 0.41568628],\n",
       "         [0.37254903, 0.34509805, 0.30588236],\n",
       "         ...,\n",
       "         [0.20392157, 0.34509805, 0.39215687],\n",
       "         [0.2       , 0.34509805, 0.38039216],\n",
       "         [0.21568628, 0.36078432, 0.39607844]],\n",
       " \n",
       "        [[0.41960785, 0.42352942, 0.40392157],\n",
       "         [0.42352942, 0.42745098, 0.40784314],\n",
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       "         [0.7921569 , 0.75686276, 0.72156864],\n",
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       "         [0.08235294, 0.05098039, 0.        ]],\n",
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       "         [0.11372549, 0.08235294, 0.03921569],\n",
       "         [0.10980392, 0.07843138, 0.02745098]],\n",
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       "         [0.25490198, 0.2509804 , 0.23529412],\n",
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       "         [0.12156863, 0.09019608, 0.03921569]],\n",
       " \n",
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       "        [[0.87058824, 0.83137256, 0.7921569 ],\n",
       "         [0.8745098 , 0.8352941 , 0.79607844],\n",
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       " \n",
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       "         [0.8392157 , 0.8       , 0.7607843 ]],\n",
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       "         [0.78039217, 0.7411765 , 0.7019608 ]]], dtype=float32),\n",
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       "         [0.10980392, 0.07843138, 0.02745098]],\n",
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       "         [0.25490198, 0.2509804 , 0.23529412],\n",
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       "         [0.12156863, 0.09019608, 0.03921569]],\n",
       " \n",
       "        ...,\n",
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       "         [0.8745098 , 0.8352941 , 0.79607844],\n",
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       "         [0.8509804 , 0.8117647 , 0.77254903]],\n",
       " \n",
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       "         [0.8509804 , 0.8117647 , 0.77254903],\n",
       "         [0.8392157 , 0.8       , 0.7607843 ]],\n",
       " \n",
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       "         [0.78039217, 0.7411765 , 0.7019608 ]]], dtype=float32),\n",
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       "         [0.10980392, 0.09803922, 0.0627451 ]],\n",
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       "         [0.10980392, 0.09803922, 0.0627451 ]],\n",
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       "         [0.12156863, 0.10980392, 0.07450981],\n",
       "         [0.12941177, 0.11764706, 0.08235294]],\n",
       " \n",
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       "         [0.8352941 , 0.79607844, 0.75686276],\n",
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       "         [0.76862746, 0.7294118 , 0.6901961 ]],\n",
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       "         [0.77254903, 0.7411765 , 0.69803923]],\n",
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       "         [0.10980392, 0.09803922, 0.0627451 ]],\n",
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       "         [0.12156863, 0.10980392, 0.07450981],\n",
       "         [0.12941177, 0.11764706, 0.08235294]],\n",
       " \n",
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       "         [0.8352941 , 0.79607844, 0.75686276],\n",
       "         [0.8392157 , 0.80784315, 0.7647059 ],\n",
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       "         [0.7764706 , 0.7372549 , 0.69803923],\n",
       "         [0.76862746, 0.7294118 , 0.6901961 ]],\n",
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       "         [0.77254903, 0.7411765 , 0.69803923]],\n",
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       "         [0.8666667 , 0.827451  , 0.78039217]],\n",
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       "         [0.78431374, 0.7372549 , 0.68235296],\n",
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       "         [0.8745098 , 0.827451  , 0.77254903],\n",
       "         [0.8666667 , 0.827451  , 0.78039217]],\n",
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       "         [0.78431374, 0.7372549 , 0.68235296],\n",
       "         ...,\n",
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       "         [0.8745098 , 0.827451  , 0.78039217],\n",
       "         [0.8745098 , 0.827451  , 0.78039217]]], dtype=float32),\n",
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       "         [0.09411765, 0.08235294, 0.0627451 ]],\n",
       " \n",
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       "         [0.04705882, 0.03529412, 0.01568628]],\n",
       " \n",
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       "         [0.19215687, 0.1882353 , 0.16862746],\n",
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       "         [0.08627451, 0.07450981, 0.05490196],\n",
       "         [0.05882353, 0.04705882, 0.02745098]],\n",
       " \n",
       "        ...,\n",
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       "        [[0.9098039 , 0.88235295, 0.81960785],\n",
       "         [0.9137255 , 0.8784314 , 0.81960785],\n",
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       "         ...,\n",
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       "         [0.8392157 , 0.8039216 , 0.74509805]],\n",
       " \n",
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       "         [0.9098039 , 0.88235295, 0.81960785],\n",
       "         [0.9098039 , 0.88235295, 0.81960785],\n",
       "         ...,\n",
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       "         [0.8392157 , 0.8039216 , 0.74509805],\n",
       "         [0.8392157 , 0.8039216 , 0.74509805]],\n",
       " \n",
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       "         [0.9137255 , 0.8862745 , 0.8235294 ],\n",
       "         [0.91764706, 0.8901961 , 0.827451  ],\n",
       "         ...,\n",
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       "         [0.8392157 , 0.8039216 , 0.74509805]]], dtype=float32),\n",
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       "         [0.03137255, 0.01960784, 0.        ],\n",
       "         [0.09411765, 0.08235294, 0.0627451 ]],\n",
       " \n",
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       "         [0.19215687, 0.1882353 , 0.16862746],\n",
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       "         [0.04705882, 0.03529412, 0.01568628]],\n",
       " \n",
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       "         [0.19215687, 0.1882353 , 0.16862746],\n",
       "         [0.1882353 , 0.18431373, 0.16470589],\n",
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       "         [0.08627451, 0.07450981, 0.05490196],\n",
       "         [0.05882353, 0.04705882, 0.02745098]],\n",
       " \n",
       "        ...,\n",
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       "        [[0.9098039 , 0.88235295, 0.81960785],\n",
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       "         [0.8039216 , 0.42745098, 0.2784314 ]],\n",
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       "         [0.5921569 , 0.5647059 , 0.49411765],\n",
       "         [0.5294118 , 0.5019608 , 0.4392157 ]],\n",
       " \n",
       "        [[0.25882354, 0.25882354, 0.22745098],\n",
       "         [0.27058825, 0.27058825, 0.23921569],\n",
       "         [0.23137255, 0.23529412, 0.20392157],\n",
       "         ...,\n",
       "         [0.52156866, 0.49411765, 0.42352942],\n",
       "         [0.5686275 , 0.54509807, 0.48235294],\n",
       "         [0.5568628 , 0.5294118 , 0.45882353]],\n",
       " \n",
       "        [[0.23529412, 0.23529412, 0.20392157],\n",
       "         [0.22745098, 0.23137255, 0.2       ],\n",
       "         [0.24313726, 0.24705882, 0.21568628],\n",
       "         ...,\n",
       "         [0.5254902 , 0.5019608 , 0.4392157 ],\n",
       "         [0.5137255 , 0.49019608, 0.42745098],\n",
       "         [0.50980395, 0.49019608, 0.41568628]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0.20392157, 0.1882353 , 0.14117648],\n",
       "         [0.90588236, 0.8509804 , 0.8       ],\n",
       "         [0.8784314 , 0.8235294 , 0.77254903],\n",
       "         ...,\n",
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       "         [0.9019608 , 0.8784314 , 0.8235294 ],\n",
       "         [0.91764706, 0.89411765, 0.8392157 ]],\n",
       " \n",
       "        [[0.88235295, 0.827451  , 0.78431374],\n",
       "         [0.8901961 , 0.8352941 , 0.78431374],\n",
       "         [0.8745098 , 0.81960785, 0.76862746],\n",
       "         ...,\n",
       "         [0.9098039 , 0.8784314 , 0.827451  ],\n",
       "         [0.90588236, 0.88235295, 0.827451  ],\n",
       "         [0.8980392 , 0.8745098 , 0.81960785]],\n",
       " \n",
       "        [[0.9098039 , 0.84313726, 0.8039216 ],\n",
       "         [0.88235295, 0.827451  , 0.7764706 ],\n",
       "         [0.8901961 , 0.8352941 , 0.78431374],\n",
       "         ...,\n",
       "         [0.9019608 , 0.87058824, 0.81960785],\n",
       "         [0.9254902 , 0.9019608 , 0.84705883],\n",
       "         [0.91764706, 0.89411765, 0.8392157 ]]], dtype=float32)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imageNumerics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f2218952",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 120 entries, 0 to 119\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   imageTargets   120 non-null    int64 \n",
      " 1   imageNumerics  120 non-null    object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 2.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e0fc4342",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "3/3 [==============================] - 9s 3s/step - loss: 3.7064 - accuracy: 0.1410 - val_loss: 1.9297 - val_accuracy: 0.1905\n",
      "Epoch 2/25\n",
      "3/3 [==============================] - 9s 3s/step - loss: 1.9538 - accuracy: 0.2051 - val_loss: 1.8993 - val_accuracy: 0.1190\n",
      "Epoch 3/25\n",
      "3/3 [==============================] - 7s 2s/step - loss: 1.8995 - accuracy: 0.2692 - val_loss: 1.8886 - val_accuracy: 0.1667\n",
      "Epoch 4/25\n",
      "3/3 [==============================] - 9s 3s/step - loss: 1.8244 - accuracy: 0.1923 - val_loss: 1.7878 - val_accuracy: 0.1905\n",
      "Epoch 5/25\n",
      "3/3 [==============================] - 7s 2s/step - loss: 1.8585 - accuracy: 0.1282 - val_loss: 1.7944 - val_accuracy: 0.1905\n",
      "Epoch 6/25\n",
      "3/3 [==============================] - 9s 3s/step - loss: 1.8176 - accuracy: 0.1795 - val_loss: 1.7713 - val_accuracy: 0.2619\n",
      "Epoch 7/25\n",
      "3/3 [==============================] - 7s 2s/step - loss: 1.7562 - accuracy: 0.1795 - val_loss: 1.7328 - val_accuracy: 0.4286\n",
      "Epoch 8/25\n",
      "3/3 [==============================] - 9s 3s/step - loss: 1.6918 - accuracy: 0.3077 - val_loss: 1.7473 - val_accuracy: 0.3810\n",
      "Epoch 9/25\n",
      "3/3 [==============================] - 8s 3s/step - loss: 1.6180 - accuracy: 0.4103 - val_loss: 1.7246 - val_accuracy: 0.4048\n",
      "Epoch 10/25\n",
      "3/3 [==============================] - 9s 3s/step - loss: 1.7839 - accuracy: 0.2308 - val_loss: 1.6126 - val_accuracy: 0.3333\n",
      "Epoch 11/25\n",
      "3/3 [==============================] - 8s 3s/step - loss: 1.5174 - accuracy: 0.4103 - val_loss: 1.6195 - val_accuracy: 0.3333\n",
      "Epoch 12/25\n",
      "3/3 [==============================] - 9s 3s/step - loss: 1.6002 - accuracy: 0.3846 - val_loss: 1.6382 - val_accuracy: 0.2143\n",
      "Epoch 13/25\n",
      "3/3 [==============================] - 13s 5s/step - loss: 1.6362 - accuracy: 0.3205 - val_loss: 1.4782 - val_accuracy: 0.5000\n",
      "Epoch 14/25\n",
      "3/3 [==============================] - 11s 3s/step - loss: 1.3926 - accuracy: 0.4872 - val_loss: 1.4397 - val_accuracy: 0.3333\n",
      "Epoch 15/25\n",
      "3/3 [==============================] - 9s 3s/step - loss: 1.3573 - accuracy: 0.4744 - val_loss: 1.7860 - val_accuracy: 0.1905\n",
      "Epoch 16/25\n",
      "3/3 [==============================] - 8s 3s/step - loss: 1.5767 - accuracy: 0.3462 - val_loss: 1.5718 - val_accuracy: 0.3095\n",
      "Epoch 17/25\n",
      "3/3 [==============================] - 8s 2s/step - loss: 1.3921 - accuracy: 0.5000 - val_loss: 1.4957 - val_accuracy: 0.2857\n",
      "Epoch 18/25\n",
      "3/3 [==============================] - 17s 7s/step - loss: 1.5576 - accuracy: 0.4231 - val_loss: 1.4615 - val_accuracy: 0.4762\n",
      "Epoch 19/25\n",
      "3/3 [==============================] - 11s 3s/step - loss: 1.2463 - accuracy: 0.5256 - val_loss: 1.3809 - val_accuracy: 0.3571\n",
      "Epoch 20/25\n",
      "3/3 [==============================] - 11s 3s/step - loss: 1.1424 - accuracy: 0.5641 - val_loss: 1.3365 - val_accuracy: 0.4286\n",
      "Epoch 21/25\n",
      "3/3 [==============================] - 9s 3s/step - loss: 1.6076 - accuracy: 0.3462 - val_loss: 1.6121 - val_accuracy: 0.5238\n",
      "Epoch 22/25\n",
      "3/3 [==============================] - 8s 3s/step - loss: 1.3234 - accuracy: 0.5897 - val_loss: 1.4754 - val_accuracy: 0.4524\n",
      "Epoch 23/25\n",
      "3/3 [==============================] - 8s 2s/step - loss: 1.0843 - accuracy: 0.5897 - val_loss: 1.3133 - val_accuracy: 0.4286\n",
      "Epoch 24/25\n",
      "3/3 [==============================] - 10s 4s/step - loss: 1.4993 - accuracy: 0.4103 - val_loss: 1.3663 - val_accuracy: 0.4048\n",
      "Epoch 25/25\n",
      "3/3 [==============================] - 9s 3s/step - loss: 1.1699 - accuracy: 0.5897 - val_loss: 1.2983 - val_accuracy: 0.5000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x22646b8f250>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Creating validation set\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2)\n",
    "\n",
    "# # Define the model structure\n",
    "# model = Sequential()\n",
    "# model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=(28,28,1)))\n",
    "# model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "# model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "# model.add(Dropout(0.25))\n",
    "# model.add(Flatten())\n",
    "# model.add(Dense(128, activation='relu'))\n",
    "# model.add(Dropout(0.5))\n",
    "# model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Creating validation set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.35)\n",
    "\n",
    "# Define the model structure\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(224,224,3)))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(7, activation='softmax')) # modified output shape\n",
    "model.compile(loss='categorical_crossentropy', optimizer='SGD', metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train, epochs=25, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a22ebf8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 2s 500ms/step\n",
      "Accuracy on training data: 0.7564102411270142% \n",
      " Error on training data: 0.24358975887298584\n",
      "2/2 [==============================] - 1s 197ms/step\n",
      "Accuracy on test data: 0.5% \n",
      " Error on test data: 0.5\n"
     ]
    }
   ],
   "source": [
    "pred_train= model.predict(X_train)\n",
    "scores = model.evaluate(X_train, y_train, verbose=0)\n",
    "print('Accuracy on training data: {}% \\n Error on training data: {}'.format(scores[1], 1 - scores[1]))   \n",
    " \n",
    "pred_test= model.predict(X_test)\n",
    "scores2 = model.evaluate(X_test, y_test, verbose=0)\n",
    "print('Accuracy on test data: {}% \\n Error on test data: {}'.format(scores2[1], 1 - scores2[1]))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f910ed5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sGB caused less over fitting her"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "345d6f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "img = Image.open('6_02.jpg').convert('L')  # Load the image and convert it to grayscale\n",
    "img = img.resize((28, 28))  # Resize the image to (28, 28)\n",
    "img_array = np.array(img)  # Convert the image to a numpy array\n",
    "img_array = img_array.reshape(1, 28, 28, 1)  # Reshape the array to match the input shape of the model\n",
    "# Pass the preprocessed image through the trained model\n",
    "prediction = model.predict(img_array)\n",
    "# Get the predicted class index\n",
    "predicted_class = np.argmax(prediction, axis=-1)\n",
    "\n",
    "print(\"Predicted class:\", predicted_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "201ce2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image.open('5_08.jpg').convert('L')  # Load the image and convert it to grayscale\n",
    "img = img.resize((28, 28))  # Resize the image to (28, 28)\n",
    "img_array = np.array(img)  # Convert the image to a numpy array\n",
    "img_array = img_array.reshape(1, 28, 28, 1)  # Reshape the array to match the input shape of the model\n",
    "# Pass the preprocessed image through the trained model\n",
    "prediction = model.predict(img_array)\n",
    "# Get the predicted class index\n",
    "predicted_class = np.argmax(prediction, axis=-1)\n",
    "\n",
    "print(\"Predicted class:\", predicted_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b469832",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image.open('6_08.jpg').convert('L')  # Load the image and convert it to grayscale\n",
    "img = img.resize((28, 28))  # Resize the image to (28, 28)\n",
    "img_array = np.array(img)  # Convert the image to a numpy array\n",
    "img_array = img_array.reshape(1, 28, 28, 1)  # Reshape the array to match the input shape of the model\n",
    "# Pass the preprocessed image through the trained model\n",
    "prediction = model.predict(img_array)\n",
    "# Get the predicted class index\n",
    "predicted_class = np.argmax(prediction, axis=-1)\n",
    "\n",
    "print(\"Predicted class:\", predicted_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07a0da4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image.open('d6-8.jpg').convert('L')  # Load the image and convert it to grayscale\n",
    "img = img.resize((28, 28))  # Resize the image to (28, 28)\n",
    "img_array = np.array(img)  # Convert the image to a numpy array\n",
    "img_array = img_array.reshape(1, 28, 28, 1)  # Reshape the array to match the input shape of the model\n",
    "# Pass the preprocessed image through the trained model\n",
    "prediction = model.predict(img_array)\n",
    "# Get the predicted class index\n",
    "\n",
    "predicted_class = np.argmax(prediction, axis=-1)\n",
    "\n",
    "print(\"Predicted class:\", predicted_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cce8bef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"x_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7fb9b17",
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_model = keras.models.load_model(\"x_model\") \n",
    "img = Image.open('4_08.jpg').convert('L')  # Load the image and convert it to grayscale\n",
    "img = img.resize((28, 28))  # Resize the image to (28, 28)\n",
    "img_array = np.array(img)  # Convert the image to a numpy array\n",
    "img_array = img_array.reshape(1, 28, 28, 1)\n",
    "# Reconstruct the set of losses and metric \n",
    "# in the already compiled final model, and test it:\n",
    "prediction = reconstructed_model.predict(img_array)\n",
    "# Get the predicted class index\n",
    "predicted_class = np.argmax(prediction, axis=-1)\n",
    "\n",
    "print(\"Predicted class:\", predicted_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ad2ed53",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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