worked_model_for_character.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "_rUTmC8HpkOU"
      },
      "outputs": [],
      "source": [
        "# https://colab.research.google.com/drive/1CpV4osCtZdmtnDStA4qrYjIAlWnTjtVK?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "HcT3ANdtXmjk"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "np.random.seed(2)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import itertools\n",
        "\n",
        "from keras.utils.np_utils import to_categorical # convert to one-hot-encoding\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.callbacks import ReduceLROnPlateau"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GSboKOp-VO7n",
        "outputId": "23bada03-aef5-49fa-e5ee-578013cb31b6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "!cp /content/drive/MyDrive/abdulla/Character/anotation_28x28/Test.csv .\n",
        "!cp /content/drive/MyDrive/abdulla/Character/anotation_28x28/Train.csv .\n",
        "\n",
        "!rm -rf sample_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "enMBFPZLVRMM"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "\n",
        "train = pd.read_csv(\"Train.csv\")\n",
        "test = pd.read_csv(\"Test.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "1pmGo6loWRIe",
        "outputId": "ab4b3093-94be-4112-dbc5-139e383fd1a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     6000\n",
              "26    5999\n",
              "20    5999\n",
              "21    5999\n",
              "22    5999\n",
              "23    5999\n",
              "24    5999\n",
              "25    5999\n",
              "27    5999\n",
              "18    5999\n",
              "28    5999\n",
              "29    5999\n",
              "30    5999\n",
              "31    5999\n",
              "32    5999\n",
              "33    5999\n",
              "19    5999\n",
              "17    5999\n",
              "1     5999\n",
              "8     5999\n",
              "2     5999\n",
              "3     5999\n",
              "4     5999\n",
              "5     5999\n",
              "6     5999\n",
              "7     5999\n",
              "9     5999\n",
              "16    5999\n",
              "10    5999\n",
              "11    5999\n",
              "12    5999\n",
              "13    5999\n",
              "14    5999\n",
              "15    5999\n",
              "34    5999\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "Y_train = train[\"label\"]\n",
        "\n",
        "# Drop 'label' column\n",
        "X_train = train.drop(labels = [\"label\"],axis = 1) \n",
        "\n",
        "# free some space\n",
        "del train \n",
        "\n",
        "g = sns.countplot(Y_train)\n",
        "\n",
        "Y_train.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Xj_yM6YWOUM",
        "outputId": "c2fcd6fa-2fa1-4b4c-ebd4-ef36153e7b97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       784\n",
              "unique        1\n",
              "top       False\n",
              "freq        784\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Check the data\n",
        "X_train.isnull().any().describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sliD4w8nWM0P",
        "outputId": "7b7d1a42-4ee9-4410-fbb7-67cd56d9c1af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       784\n",
              "unique        1\n",
              "top       False\n",
              "freq        784\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "test.isnull().any().describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "XUcDWzR8WLM9"
      },
      "outputs": [],
      "source": [
        "# Normalize the data\n",
        "X_train = X_train / 255.0\n",
        "test = test / 255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "CDME31IbWJhk"
      },
      "outputs": [],
      "source": [
        "# Reshape image in 3 dimensions (height = 28px, width = 28px , canal = 1)\n",
        "X_train = X_train.values.reshape(-1,28,28,1)\n",
        "test = test.values.reshape(-1,28,28,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "LQiqgLHoWIoG"
      },
      "outputs": [],
      "source": [
        "from keras.utils import to_categorical\n",
        "\n",
        "# Encode labels to one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])\n",
        "Y_train = to_categorical(Y_train, num_classes = 35)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "VRDDgdpQWD8f"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the train and the validation set for the fitting\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "nGY-GLhYVbYP"
      },
      "outputs": [],
      "source": [
        "from keras.utils import to_categorical\n",
        "\n",
        "y_train = to_categorical(Y_train, num_classes = 35, dtype = 'float32')\n",
        "y_test = to_categorical(Y_val, num_classes = 35, dtype = 'float32')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4M6qt1zIVfLL",
        "outputId": "0ff5291a-0f69-4ffc-83ab-0ae5bd105bbb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(170072, 35, 35)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "b-SXYqjmVgd5"
      },
      "outputs": [],
      "source": [
        "# # # Reshaping the array to include the RGB value. Here RGB value is 1 since it is a b&w image\n",
        "\n",
        "# x_train = X_train.values.reshape(-1,28,28,1)\n",
        "# x_test = X_val.values.reshape(-1,28,28,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "caVVu9RgVh5-"
      },
      "outputs": [],
      "source": [
        "# Converting the arrays to float so that we can get decimal points after division\n",
        "x_train = X_train.astype('float32')\n",
        "x_test = X_val.astype('float32')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "WVRBSoPZV0XP"
      },
      "outputs": [],
      "source": [
        "# Normalizing the RGB codes by dividing it to the maximum RGB value.\n",
        "x_train /= 255\n",
        "x_test /= 255"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_NjSAOXV18y",
        "outputId": "a89f8955-1979-491b-de04-0b37cda3d753"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape: (170072, 28, 28, 1)\n",
            "y_train shape: (170072, 35, 35)\n",
            "Number of images in x_train 170072\n",
            "Number of images in x_test 18897\n"
          ]
        }
      ],
      "source": [
        "#Checking the shapes of x_train and y_train \n",
        "print('x_train shape:', x_train.shape)\n",
        "print('y_train shape:', y_train.shape)\n",
        "print('Number of images in x_train', x_train.shape[0])\n",
        "print('Number of images in x_test', x_test.shape[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "lUGM8CYEV32s"
      },
      "outputs": [],
      "source": [
        "#Initializing the input shape\n",
        "input_shape = (28, 28, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HU0hRCWXUWb-",
        "outputId": "6681b951-95a0-4c46-8bca-915f8bf9345d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "5315/5315 [==============================] - 35s 5ms/step - loss: 0.4619 - accuracy: 0.8517\n",
            "Epoch 2/30\n",
            "5315/5315 [==============================] - 24s 4ms/step - loss: 0.2044 - accuracy: 0.9346\n",
            "Epoch 3/30\n",
            "5315/5315 [==============================] - 25s 5ms/step - loss: 0.1578 - accuracy: 0.9497\n",
            "Epoch 4/30\n",
            "5315/5315 [==============================] - 25s 5ms/step - loss: 0.1356 - accuracy: 0.9578\n",
            "Epoch 5/30\n",
            "5315/5315 [==============================] - 25s 5ms/step - loss: 0.1189 - accuracy: 0.9633\n",
            "Epoch 6/30\n",
            "5315/5315 [==============================] - 25s 5ms/step - loss: 0.1115 - accuracy: 0.9665\n",
            "Epoch 7/30\n",
            "5315/5315 [==============================] - 25s 5ms/step - loss: 0.1090 - accuracy: 0.9686\n",
            "Epoch 8/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1064 - accuracy: 0.9708\n",
            "Epoch 9/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1042 - accuracy: 0.9724\n",
            "Epoch 10/30\n",
            "5315/5315 [==============================] - 25s 5ms/step - loss: 0.1085 - accuracy: 0.9734\n",
            "Epoch 11/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1013 - accuracy: 0.9754\n",
            "Epoch 12/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1059 - accuracy: 0.9760\n",
            "Epoch 13/30\n",
            "5315/5315 [==============================] - 24s 4ms/step - loss: 0.1048 - accuracy: 0.9770\n",
            "Epoch 14/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1105 - accuracy: 0.9773\n",
            "Epoch 15/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1086 - accuracy: 0.9783\n",
            "Epoch 16/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1107 - accuracy: 0.9785\n",
            "Epoch 17/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1130 - accuracy: 0.9791\n",
            "Epoch 18/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1181 - accuracy: 0.9799\n",
            "Epoch 19/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1206 - accuracy: 0.9800\n",
            "Epoch 20/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1219 - accuracy: 0.9813\n",
            "Epoch 21/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1205 - accuracy: 0.9814\n",
            "Epoch 22/30\n",
            "5315/5315 [==============================] - 24s 4ms/step - loss: 0.1270 - accuracy: 0.9818\n",
            "Epoch 23/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1319 - accuracy: 0.9819\n",
            "Epoch 24/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1313 - accuracy: 0.9828\n",
            "Epoch 25/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1327 - accuracy: 0.9832\n",
            "Epoch 26/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1456 - accuracy: 0.9829\n",
            "Epoch 27/30\n",
            "5315/5315 [==============================] - 24s 4ms/step - loss: 0.1409 - accuracy: 0.9842\n",
            "Epoch 28/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1479 - accuracy: 0.9843\n",
            "Epoch 29/30\n",
            "5315/5315 [==============================] - 25s 5ms/step - loss: 0.1428 - accuracy: 0.9847\n",
            "Epoch 30/30\n",
            "5315/5315 [==============================] - 24s 5ms/step - loss: 0.1545 - accuracy: 0.9853\n",
            "591/591 [==============================] - 2s 3ms/step - loss: 0.9125 - accuracy: 0.9543\n",
            "5315/5315 [==============================] - 18s 3ms/step - loss: 0.0560 - accuracy: 0.9925\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.05595627427101135, 0.9924561381340027]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "# load the data\n",
        "X_train, X_val, Y_train, Y_val\n",
        "\n",
        "# (train_img,train_label),(test_img,test_label) = keras.datasets.mnist.load_data()\n",
        "# train_img = train_img.reshape([-1, 28, 28, 1])\n",
        "# test_img = test_img.reshape([-1, 28, 28, 1])\n",
        "# train_img = train_img/255.0\n",
        "# test_img = test_img/255.0\n",
        "# train_label = keras.utils.to_categorical(train_label)\n",
        "# test_label = keras.utils.to_categorical(test_label)\n",
        "\n",
        "# define the model architecture\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Conv2D(32, (5, 5), padding=\"same\", input_shape=[28, 28, 1]),\n",
        "    keras.layers.MaxPool2D((2,2)),\n",
        "    keras.layers.Conv2D(64, (5, 5), padding=\"same\"),\n",
        "    keras.layers.MaxPool2D((2,2)),\n",
        "    keras.layers.Flatten(),\n",
        "    keras.layers.Dense(1024, activation='relu'),\n",
        "    keras.layers.Dropout(0.2),\n",
        "    keras.layers.Dense(35, activation='softmax')\n",
        "])\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# train the model\n",
        "model.fit(x=X_train,y=Y_train, epochs=30)\n",
        "model.evaluate(X_val, Y_val)\n",
        "model.evaluate(X_train, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "lDmeKhy1UzMQ"
      },
      "outputs": [],
      "source": [
        "model.save(\"model.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R8Ncx-PcZE56"
      },
      "outputs": [],
      "source": [
        "!cp -r /content/drive/MyDrive/abdulla/Number/ ."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.10.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}