Devanagri2_py.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Devanagri2.py",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPveNYE+syVQ6p7oY/NPm/1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Chandigarh-University-students/Devanagri-Text-Classifier/blob/main/Devanagri2_py.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-WIVeDCUZeRN"
      },
      "source": [
        "from google.colab import drive"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uvoC35AcZ8Em",
        "outputId": "a3a88909-908e-4371-b275-0e8a33dc45fe"
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yynFGm6NabHZ"
      },
      "source": [
        "import numpy as np\n",
        "import os"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EAQlFAxbCDS"
      },
      "source": [
        "from PIL import Image\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-WMK4lBbVGX"
      },
      "source": [
        "IMG_WIDTH=32\n",
        "IMG_HEIGHT=32\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87x3cdRdbGV4"
      },
      "source": [
        "def create_dataset_PIL(img_folder):\n",
        "    \n",
        "    img_data_array=[]\n",
        "    class_name=[]\n",
        "    for dir1 in os.listdir(img_folder):\n",
        "        for file in os.listdir(os.path.join(img_folder, dir1)):       \n",
        "            image_path= os.path.join(img_folder, dir1,  file)\n",
        "            image= np.array(Image.open(image_path))\n",
        "            image= np.resize(image,(IMG_HEIGHT,IMG_WIDTH,3))\n",
        "            image = image.astype('float32')\n",
        "            image /= 255  \n",
        "            img_data_array.append(image)\n",
        "            class_name.append(dir1)\n",
        "    return img_data_array , class_name"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "veDX_3h3bGtf"
      },
      "source": [
        "img_train_folder = '/content/drive/MyDrive/Devanagri dataset/DevanagariHandwrittenCharacterDataset/Train'"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vN6DZu0EboDa"
      },
      "source": [
        "PIL_train_img_data, train_class_name=create_dataset_PIL(img_train_folder)\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2yIJulIjcbJ",
        "outputId": "0357b614-a8d3-4979-b858-c5d6b5935772"
      },
      "source": [
        "print(PIL_train_img_data[0])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]\n",
            "  ...\n",
            "  [0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]]\n",
            "\n",
            " [[0.         0.         0.        ]\n",
            "  [0.         0.02745098 0.09019608]\n",
            "  [0.03529412 0.         0.        ]\n",
            "  ...\n",
            "  [0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]]\n",
            "\n",
            " [[0.         0.         0.18039216]\n",
            "  [0.8862745  1.         0.7647059 ]\n",
            "  [0.09019608 0.         0.        ]\n",
            "  ...\n",
            "  [0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.         0.         0.        ]\n",
            "  [0.         0.         0.1764706 ]\n",
            "  [0.9411765  1.         0.99607843]\n",
            "  ...\n",
            "  [1.         0.99607843 0.89411765]\n",
            "  [0.7137255  0.4392157  0.15294118]\n",
            "  [0.02745098 0.         0.        ]]\n",
            "\n",
            " [[0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]\n",
            "  [0.         0.04313726 0.3019608 ]\n",
            "  ...\n",
            "  [0.         0.00392157 0.02352941]\n",
            "  [0.12156863 0.50980395 0.90588236]\n",
            "  [0.84705883 0.         0.        ]]\n",
            "\n",
            " [[0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]\n",
            "  ...\n",
            "  [0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]\n",
            "  [0.         0.         0.        ]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c2BNHUmQNVwi",
        "outputId": "c70a34b1-e6ae-4585-c9aa-615a543d04da"
      },
      "source": [
        "len(PIL_train_img_data)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7518"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qc3cE4HYNZhA",
        "outputId": "ef47291b-3b00-4a18-e99a-157c72ca086c"
      },
      "source": [
        "len(PIL_train_img_data[0])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IUd9Xl0HNydY",
        "outputId": "0cc62a85-b8ab-4ec2-93ac-df0809a75c95"
      },
      "source": [
        "len(PIL_train_img_data[0][0])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJm-GmHMN19A",
        "outputId": "7919ab99-3545-43c7-e076-e9f559b24e7e"
      },
      "source": [
        "len(PIL_train_img_data[0][0][0])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-ivD_5CN9Kg"
      },
      "source": [
        "import tensorflow as tf\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Qay8zwVVP8fx",
        "outputId": "0a12de4d-ee97-4030-ee93-18e8c411718e"
      },
      "source": [
        "for i in range(9):\n",
        "  plt.subplot(331+i)\n",
        "  plt.imshow(PIL_train_img_data[i+1000])"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "XknZwaZoQqup",
        "outputId": "be33dd8a-09af-42ff-ec53-c6d033dcc681"
      },
      "source": [
        "dir1=os.listdir(img_train_folder)[0]\n",
        "img_folder=img_train_folder\n",
        "print(dir1)\n",
        "file=os.listdir(os.path.join(img_folder, dir1))[0]\n",
        "image_path= os.path.join(img_folder, dir1,  file)\n",
        "image= np.array(Image.open(image_path))\n",
        "print(image.shape)\n",
        "image = image.astype('float32')\n",
        "image /= 255  \n",
        "print(image)\n",
        "plt.subplot(111)\n",
        "plt.imshow(image)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "digit_5\n",
            "(32, 32)\n",
            "[[0.         0.         0.         ... 0.         0.         0.        ]\n",
            " [0.         0.         0.         ... 0.         0.         0.        ]\n",
            " [0.         0.         0.         ... 0.         0.         0.        ]\n",
            " ...\n",
            " [0.         0.         0.         ... 0.09803922 0.         0.        ]\n",
            " [0.         0.         0.         ... 0.         0.         0.        ]\n",
            " [0.         0.         0.         ... 0.         0.         0.        ]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6f16ad9890>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATuUlEQVR4nO3de5CddX3H8fd3N5sLSSCEQNhcgEBQCKAB1wAKeAGEeiloLWO0DuMwhnaMt8qMNLUFp44FFCnYqo2SITrIRZHCOGjBVEFojUkwhEC4pokm5Goi2ZBks5dv/zhPZpb0+T57cq67+/u8ZjJ79vc9zzk/HvZznnOe33l+P3N3RGT4a2l2B0SkMRR2kUQo7CKJUNhFEqGwiyRCYRdJxIhqNjazS4FbgVbge+5+Q9H9R9ooH83Yap5SRArs4zX2e5fl1azScXYzawVeAC4GNgDLgLnu/my0zeE20c+2Cyt6PhEZ2FJfwi7fkRv2at7GzwFecve17r4fuBu4rIrHE5E6qibsU4E/9Pt9Q9YmIoNQVZ/Zy2Fm84B5AKM5rN5PJyKBao7sG4Hp/X6flrW9jrsvdPcOd+9oY1QVTyci1agm7MuAk81shpmNBD4CPFibbolIrVX8Nt7de8xsPvCflIbeFrn7MzXrmYjUVFWf2d39IeChGvVFROpI36ATSYTCLpIIhV0kEQq7SCIUdpFE1P0bdDIEWO51EwCMOHZyWPPx8RWMfes35G/T1VV+v6SmdGQXSYTCLpIIhV0kEQq7SCIUdpFEDNuz8S2HxdfOtxwzKaz1/XFnXOvsrKpPg1XrxCPD2vM3HxvWrjj1ybC27DNvyW1v+fXvyu+Y1JSO7CKJUNhFEqGwiyRCYRdJhMIukgiFXSQRQ3rorWX8+LC2+eNnhLXT/ipctIbnFp0e1ibdsSy33Xt6wm2Ggp43TA9rPz7338PaqSPjY8XZZ56X2z758fiiGypcnUjKoyO7SCIUdpFEKOwiiVDYRRKhsIskQmEXSURVQ29mtg7oBHqBHnfvqEWn/t/zjMpfEHL9Z+LhtYevvimsTWiJ/7PPHz0rrHnf8BwaaunpC2v7C44Ho6wtrO06uTe3/djW1nCboT6EOdjVYpz9Xe6+vQaPIyJ1pLfxIomoNuwOPGxmK8xsXi06JCL1Ue3b+PPcfaOZHQM8YmbPuftj/e+QvQjMAxhNPHuMiNRXVUd2d9+Y/dwK3A/MybnPQnfvcPeONvJPtIlI/VUcdjMba2bjD9wG3gOsrlXHRKS2qnkbPxm430pLB40AfujuP69Jrw7S+9ZTc9u/9olF4TbTRowLa1dvODesTflp/rJFAD19+cNJQ13r9l1h7el98RVxc0ZtDmtjp+VPzmkj4j85Db3VV8Vhd/e1wJtr2BcRqSMNvYkkQmEXSYTCLpIIhV0kEQq7SCKGxISTL38y/zXpfYftC7fZ2bsnrK385uywNmH9b8rv2DDRPSVe6+1tY9YWbFmwnp4NzysEhzId2UUSobCLJEJhF0mEwi6SCIVdJBFD4mz8r951W1CJL3b56ra3h7VJj8RnmHuG6xJEFi+7tOPUMWHtxLZ4nrkiu7bl/79p18UuTaMju0giFHaRRCjsIolQ2EUSobCLJEJhF0nEkBh6O65gPrnI/UvOCWszty+rpjvDzu7j4lrREk9F2rbn/2kN1yW0hgId2UUSobCLJEJhF0mEwi6SCIVdJBEKu0giBhx6M7NFwPuBre5+etY2EbgHOAFYB1zh7jvr1818r/btDWtTH42XakpymSGLX9f3H1HZcNievv1hbcLzQcH7KnouqV45R/Y7gEsParsWWOLuJwNLst9FZBAbMOzZeus7Dmq+DFic3V4MXF7jfolIjVX6mX2yu2/Kbm+mtKKriAxiVZ+gc3cHwg99ZjbPzJab2fJuuqp9OhGpUKVh32Jm7QDZz63RHd19obt3uHtHG6MqfDoRqValYX8QuDK7fSXwQG26IyL1Us7Q213AO4FJZrYBuA64AbjXzK4C1gNX1LOTkVd64iGj0VvjYbkUtYyNl2o66qSDz7+WZ7d3h7VxrwS14Tqh5xAwYNjdfW5QurDGfRGROtI36EQSobCLJEJhF0mEwi6SCIVdJBFDYsLJyJNd08PaiK2vhrXhfM1by/jxue2///QZ4TaPvvlrBY84Nqys7R4d1kZv7Mxtj69FlHrTkV0kEQq7SCIUdpFEKOwiiVDYRRKhsIskYkgPvRVqGeKvYy2tYWnE5KPD2oufnZHb/ouP3hRuM6n10NfSA9jnBevAdQ/nAc6haYgnQkTKpbCLJEJhF0mEwi6SCIVdJBFD+mz8h8dtDmv/8Pl4KvtTb4iXLerdEk6UG7IR8W604MIUgL1vOSGsvXJ+/JhnXPBiWHtyxi257W0Wz+zbVTCX3CiLz7jv6osvhKFPyzwNNjqyiyRCYRdJhMIukgiFXSQRCrtIIhR2kUSUs/zTIuD9wFZ3Pz1rux74JLAtu9sCd3+oXp3c0LM7t33aiPgCjuc/9K2w9tUL4vnYvr/q7Lgjlt88s31bfgG4aPKasHblEf8R1o5pjed+6/Z4Jrd/2Xlabvsdd14SbvOxuUvC2oJJz4e1zr4xYc16NNvcYFPOkf0O4NKc9lvcfXb2r25BF5HaGDDs7v4YUNnKfyIyaFTzmX2+ma0ys0VmdmTNeiQidVFp2L8NnATMBjYBN0d3NLN5ZrbczJZ301Xh04lItSoKu7tvcfded+8DvgvMKbjvQnfvcPeONuLvZ4tIfVUUdjNr7/frB4HVtemOiNRLOUNvdwHvBCaZ2QbgOuCdZjYbcGAdcHUd+8iH/+6a3PZ3XPObcJuvHLMirF139LNx7cK4Vmuv9sWvtV/eNius3fXAO8LaiXdtz20/fls8hLbhQ5WdcmlFV7YNJQOG3d3n5jTfXoe+iEgd6Rt0IolQ2EUSobCLJEJhF0mEwi6SiCEx4eQRP1ya2776iePCbWbPfVtYm3LRH8LaO46OJ3Pc0zsyt/3xrSeF22xcE098OeVRD2uH/2Z9WDt+y2/DWm9f/tVmrROOCLdpIe5Hkf0eL1GFV/aYUj86soskQmEXSYTCLpIIhV0kEQq7SCIUdpFEDImht2gYp2fd78NNpt24May13BavUfbfY6Yecj/GvrYp3GTm/riPBMNkAD3xVpUZFc8lMH30KxU95Mtd8bCi79lX0WNK/ejILpIIhV0kEQq7SCIUdpFEKOwiiRgaZ+MrUXCmu++11+LtimpD2eHxUlkdh62t6CGX7Tg+rPmeVyt6TKkfHdlFEqGwiyRCYRdJhMIukgiFXSQRCrtIIspZ/mk68H1gMqXlnha6+61mNhG4BziB0hJQV7j7zvp1dRAzq2y7Bs7T5qPy588DOKplT8GW8QU0u7riC4rG9+4op1vSQOUc2XuAL7j7LOAc4FNmNgu4Flji7icDS7LfRWSQGjDs7r7J3Z/MbncCa4CpwGXA4uxui4HL69VJEaneIX1mN7MTgDOBpcBkdz9wIfdmSm/zRWSQKjvsZjYOuA/4nLvv6l9zd4f8ycfNbJ6ZLTez5d10VdVZEalcWWE3szZKQb/T3X+SNW8xs/as3g5szdvW3Re6e4e7d7QVnOwRkfoaMOxmZpTWY1/j7t/oV3oQuDK7fSXwQO27JyK1Us5Vb28HPg48bWYrs7YFwA3AvWZ2FbAeuKI+XayDgqGyEcfGpx52nZN/lderJ8bLII3aEQ+vjdkZX5m3b0L8mK3748ccv25vbntnezxMNqm1O6wVDb31eoVDjtIUA4bd3R8Hov+rF9a2OyJSL/oGnUgiFHaRRCjsIolQ2EUSobCLJGL4TjhZYMRx08Ja58J4l/zstFtz28e1xMNaRbo9Hnprs3jorcimnt257XsKLrCbNiKejLLI+6Y+E9YevuSC3PbxK+JluXq3bQ9rvn9/3JEGXj04lOnILpIIhV0kEQq7SCIUdpFEKOwiiVDYRRKR5NBb97Sjwtr3Tvm3sDauZWxue6/3hdv8fO9hYa01f74PAE4f+cewNrElnjxyUuuY3PZKh/KKfGnSc2Htr/91RW77PZ2nhNvc+tS7w9rYx/P3PcCxT/wprPHi+tzmvj0Fk2wO06E8HdlFEqGwiyRCYRdJhMIukgiFXSQR5g0883i4TfSzrfkzWRXNM9e5OD57/tgZ9+e2F52Nf9fqvwhrbTdODGstvfFj7p4Szwu378j81+83fiw+c373jP8Ka4NF0T5+ois+Zl2z5i9z27sfOjrcpv2RLWHNN2wKa337CqZK74sveqqlpb6EXb4jdxo5HdlFEqGwiyRCYRdJhMIukgiFXSQRCrtIIgYcejOz6cD3KS3J7MBCd7/VzK4HPglsy+66wN0fKnqswTL0VrT8U+spM8Pay9fnX2Sy5Nxvhdu0t8ZDeR3LPxrWjr0ufh32Z14Ka5EXbj89rK29aFFYK5onb9YP5oc1D667OevcF8Jtvjj1Z2HtTSPjC3kqucinaChvyd54aPO2DReFtZd/NSOsHf/TXWHNnl2b2154sU6gaOitnKveeoAvuPuTZjYeWGFmj2S1W9z964fcIxFpuHLWetsEbMpud5rZGmBqvTsmIrV1SJ/ZzewE4ExgadY038xWmdkiMzuyxn0TkRoqO+xmNg64D/icu+8Cvg2cBMymdOS/OdhunpktN7Pl3RR8nVBE6qqssJtZG6Wg3+nuPwFw9y3u3uvufcB3gTl527r7QnfvcPeOtoK1vkWkvgYMu5kZcDuwxt2/0a+9vd/dPgisrn33RKRWyhl6Ow/4NfA0cGC8YgEwl9JbeAfWAVdnJ/NCg2borUKtE47Ibf/jn88Kt7nsmviKsi8etSas/Wh3PE/ePy2eG9aOv39bbvvMH6wLt7ltyrKwtmRvPKx18/s/FNZ6n88fTmo9PF5qqu+keFmuTefn73uA8ZduDmtff8OPctvfUvAms9L5+iodzrtm9Ydz29svj/8+IlUNvbn740DexoVj6iIyuOgbdCKJUNhFEqGwiyRCYRdJhMIukogkJ5ysuZaCoZo5p4WlS773eFj724n5Q1cAe/r2h7UFW96W2/6lYx4Nt5nUGi+tdPGaD4S1EZfHyy71dXaGtYoUXKnYMib/akQAZh6X27z5/Hiyz96Ld4a1m07/SVi7eMzesNZqh35cvWTK7EPeRhNOiojCLpIKhV0kEQq7SCIUdpFEKOwiidDQW70VDMu9OvetYe07X7k1rM0eVdt5AYomlTzzm58Oa1Nv/J/4QRv4d1WRoqG8gv1rJ+YP5QE8d3U8nHfHB74Tb9c1Jbf9vlOPCbeJaOhNRBR2kVQo7CKJUNhFEqGwiyRCYRdJRDnLP0k1+uJhrSPvWxnWPjHh82Ft/vz4yqurjognX4z8b8++sNb+RHwl16AfXitS0Pe+ffH+4Nl4rbo3LoivHvznRfEkoS2d0T5eF/ejAjqyiyRCYRdJhMIukgiFXSQRCrtIIspZ/mk08BgwitLZ+x+7+3VmNgO4GzgKWAF83N3jydFI9EIYkQaq9kKYLuDd7v5mSmu7XWpm5wA3Are4+0xgJ3BVrTosIrU3YNi9ZHf2a1v2z4F3Az/O2hcDl9elhyJSE+Wuz95qZiuBrcAjwMvAn9y9J7vLBmBqfbooIrVQVtjdvdfdZwPTgDnAKeU+gZnNM7PlZra8m64Kuyki1Tqks/Hu/ifgl8C5wAQzO/B122nAxmCbhe7e4e4dbdR2hhURKd+AYTezo81sQnZ7DHAxsIZS6A+sIn8l8EC9Oiki1SvnQph2YLGZtVJ6cbjX3X9qZs8Cd5vZV4DfAbfXsZ8iUqUBw+7uq4Azc9rXUvr8LiJDgL5BJ5IIhV0kEQq7SCIUdpFEKOwiiWjo8k9mtg1Yn/06CdjesCePqR+vp3683lDrx/HufnReoaFhf90Tmy13946mPLn6oX4k2A+9jRdJhMIukohmhn1hE5+7P/Xj9dSP1xs2/WjaZ3YRaSy9jRdJRFPCbmaXmtnzZvaSmV3bjD5k/VhnZk+b2UozW97A511kZlvNbHW/tolm9oiZvZj9PLJJ/bjezDZm+2Slmb23Af2Ybma/NLNnzewZM/ts1t7QfVLQj4buEzMbbWa/NbOnsn58OWufYWZLs9zcY2YjD+mB3b2h/4BWStNanQiMBJ4CZjW6H1lf1gGTmvC8FwBnAav7td0EXJvdvha4sUn9uB64psH7ox04K7s9HngBmNXofVLQj4buE8CAcdntNmApcA5wL/CRrP07wN8cyuM248g+B3jJ3dd6aerpu4HLmtCPpnH3x4AdBzVfRmniTmjQBJ5BPxrO3Te5+5PZ7U5Kk6NMpcH7pKAfDeUlNZ/ktRlhnwr8od/vzZys0oGHzWyFmc1rUh8OmOzum7Lbm4HJTezLfDNblb3Nr/vHif7M7ARK8ycspYn75KB+QIP3ST0meU39BN157n4W8GfAp8zsgmZ3CEqv7JReiJrh28BJlNYI2ATc3KgnNrNxwH3A59x9V/9aI/dJTj8avk+8ikleI80I+0Zger/fw8kq683dN2Y/twL309yZd7aYWTtA9nNrMzrh7luyP7Q+4Ls0aJ+YWRulgN3p7gcWoG/4PsnrR7P2SfbchzzJa6QZYV8GnJydWRwJfAR4sNGdMLOxZjb+wG3gPcDq4q3q6kFKE3dCEyfwPBCuzAdpwD4xM6M0h+Ead/9Gv1JD90nUj0bvk7pN8tqoM4wHnW18L6UznS8Df9+kPpxIaSTgKeCZRvYDuIvS28FuSp+9rqK0Zt4S4EXgF8DEJvXjB8DTwCpKYWtvQD/Oo/QWfRWwMvv33kbvk4J+NHSfAG+iNInrKkovLP/Y72/2t8BLwI+AUYfyuPoGnUgiUj9BJ5IMhV0kEQq7SCIUdpFEKOwiiVDYRRKhsIskQmEXScT/AQC9S6eYlsnUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oITtzANwUDc6"
      },
      "source": [
        "model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(32,32)), \n",
        "                                    tf.keras.layers.Dense(128, activation='relu'), \n",
        "                                    tf.keras.layers.Dense(46, activation=tf.nn.softmax)])"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8wzTxhJXNgz",
        "outputId": "5a9ad1be-3960-4dc2-b6b9-838bb078edab"
      },
      "source": [
        ""
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7518"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y25GekfLXWfD",
        "outputId": "d058bba1-bba9-47b2-dde6-b760af950764"
      },
      "source": [
        "len(os.listdir(img_train_folder))"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "46"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpKOWjr7XgeS"
      },
      "source": [
        "model.compile(optimizer = 'adam',\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BILxJIJvX7Ti",
        "outputId": "d8194161-ea88-4356-8c3f-66786ac0f32b"
      },
      "source": [
        "model.fit(imageArr, label , epochs=5)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1/1 [==============================] - 0s 318ms/step - loss: 3.6514 - accuracy: 0.0000e+00\n",
            "Epoch 2/5\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 2.7935 - accuracy: 0.0000e+00\n",
            "Epoch 3/5\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 2.1026 - accuracy: 1.0000\n",
            "Epoch 4/5\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.5458 - accuracy: 1.0000\n",
            "Epoch 5/5\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.0994 - accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6f175af7d0>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "btb0xsCHYkJ0",
        "outputId": "b75d8d17-cb03-4298-cd18-0b536a5de957"
      },
      "source": [
        "train_class_name[0]"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'digit_5'"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KzkV9I1SYoLr",
        "outputId": "f8cdfe1d-6e95-47dd-dd6c-37dfaf819d3b"
      },
      "source": [
        "print(type(image))\n",
        "print(type(train_class_name))"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "<class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8iqSnMMqYuRS",
        "outputId": "c9541129-d533-4813-dc18-7955318a293a"
      },
      "source": [
        "label=np.array([5])\n",
        "imageArr=np.array([image])\n",
        "print(type(label))\n",
        "print(label.shape)\n",
        "print(imageArr.shape)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "(1,)\n",
            "(1, 32, 32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k18zT1bGZaUb",
        "outputId": "3160edfb-c72a-4052-b15b-e3f64b5a55eb"
      },
      "source": [
        "type(train_class_name[0])"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "tn4aCPZAbSok",
        "outputId": "a566bf84-e563-450a-e187-8e57094df5f4"
      },
      "source": [
        "file2=os.listdir(os.path.join(img_folder, dir1))[2]\n",
        "image_path2= os.path.join(img_folder, dir1,  file2)\n",
        "image2= np.array(Image.open(image_path2))\n",
        "image2 = image2.astype('float32')\n",
        "image2 /= 255  \n",
        "print(image2)\n",
        "plt.subplot(111)\n",
        "plt.imshow(image2)\n",
        "\n",
        "prediction=model.predict(np.array([image2]))\n",
        "print(np.argmax(prediction[0]))"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qv6Hr0FckRX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}