EMNIST.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of Copy of EMNIST_keras.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_fCCbHn7yxx",
        "colab_type": "code",
        "outputId": "b432c22e-c52a-464c-af97-bbef68f1ddb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsfE9X7K8C1R",
        "colab_type": "code",
        "outputId": "8f73555f-a04b-45d5-a6e4-50debcd8027f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import itertools\n",
        "import cv2\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential,load_model\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from keras.utils import np_utils\n",
        "import sklearn.metrics as metrics"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0R8v8hHa834j",
        "colab_type": "code",
        "outputId": "301dfa52-ee99-4f42-bfd4-80495047e6c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train = pd.read_csv(\"/content/drive/My Drive/emnist-balanced-train.csv\",delimiter = ',')\n",
        "test = pd.read_csv(\"/content/drive/My Drive/emnist-balanced-test.csv\", delimiter = ',')\n",
        "mapp = pd.read_csv(\"/content/drive/My Drive/emnist-balanced-mapping.txt\", delimiter = ' ', \\\n",
        "                   index_col=0, header=None, squeeze=True)\n",
        "print(\"Train: %s, Test: %s, Map: %s\" %(train.shape, test.shape, mapp.shape))\n",
        "\n",
        "HEIGHT = 28\n",
        "WIDTH = 28\n",
        "\n",
        "#Dividing Train data into train_x and train_y\n",
        "train_x = train.iloc[:,1:]\n",
        "train_y = train.iloc[:,0]\n",
        "del train\n",
        "\n",
        "#Dividing Test data into test_x and test_y\n",
        "test_x = test.iloc[:,1:]\n",
        "test_y = test.iloc[:,0]\n",
        "del test"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train: (112799, 785), Test: (18799, 785), Map: (47,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOi0wnpe-Etr",
        "colab_type": "code",
        "outputId": "0f4ea00b-2bce-4a3e-c8d7-81d37df4af2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "def rotate(image):\n",
        "    image = image.reshape([HEIGHT, WIDTH])\n",
        "    image = np.fliplr(image)\n",
        "    image = np.rot90(image)\n",
        "    return image\n",
        "\n",
        "#Images by default are rotated hence bring them back to normal form by rotating 270 degree\n",
        "\n",
        "train_x = np.asarray(train_x)\n",
        "train_x = np.apply_along_axis(rotate, 1, train_x)\n",
        "print (\"train_x:\",train_x.shape)\n",
        "\n",
        "test_x = np.asarray(test_x)\n",
        "test_x = np.apply_along_axis(rotate, 1, test_x)\n",
        "print (\"test_x:\",test_x.shape)\n",
        "\n",
        "#Normalise the data\n",
        "train_x = train_x.astype('float32')\n",
        "train_x /= 255\n",
        "test_x = test_x.astype('float32')\n",
        "test_x /= 255\n",
        "\n",
        "num_classes = train_y.nunique()\n",
        "\n",
        "#Convert the output atrribute into categorical data\n",
        "train_y = np_utils.to_categorical(train_y, num_classes)\n",
        "test_y = np_utils.to_categorical(test_y, num_classes)\n",
        "print(\"train_y: \", train_y.shape)\n",
        "print(\"test_y: \", test_y.shape)\n",
        "\n",
        "train_x = train_x.reshape(-1, HEIGHT, WIDTH, 1)\n",
        "test_x = test_x.reshape(-1, HEIGHT, WIDTH, 1)\n",
        "\n",
        "#Split the train data into train and validation \n",
        "train_x, val_x, train_y, val_y = train_test_split(train_x, train_y, test_size= 0.10, random_state=7)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train_x: (112799, 28, 28)\n",
            "test_x: (18799, 28, 28)\n",
            "train_y:  (112799, 47)\n",
            "test_y:  (18799, 47)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBLdEfQq-9N1",
        "colab_type": "code",
        "outputId": "d19085b1-0eac-470f-d18a-dd8c90fba6f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        }
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "#Adding 1st Convolution block\n",
        "model.add(Conv2D(filters=256, kernel_size=(3,3), padding = 'same', activation='relu',\\\n",
        "                 input_shape=(HEIGHT, WIDTH,1)))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "#Adding 2nd Convolution block\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3) , padding = 'same', activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "#Adding 3rd Convolution block\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), padding = 'same', activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(units=128, activation='relu'))\n",
        "model.add(Dropout(.4))\n",
        "\n",
        "model.add(Dense(units=64, activation='relu'))\n",
        "model.add(Dropout(.4))\n",
        "\n",
        "model.add(Dense(units=num_classes, activation='softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 28, 28, 256)       2560      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 14, 14, 256)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 14, 14, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 14, 14, 128)       295040    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 7, 7, 64)          73792     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 3, 3, 64)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 3, 3, 64)          256       \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 128)               73856     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 47)                3055      \n",
            "=================================================================\n",
            "Total params: 458,351\n",
            "Trainable params: 457,455\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVt8ki9x_BlM",
        "colab_type": "code",
        "outputId": "dff9c286-ec9e-41fa-b3b9-df8727f54e4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "checkpoint = ModelCheckpoint(\"/content/drive/My Drive/model1.h5\", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)\n",
        "\n",
        "history = model.fit(train_x, train_y, epochs=50, batch_size=200, verbose=1, \\\n",
        "                    validation_data=(val_x, val_y), callbacks=[checkpoint])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 101519 samples, validate on 11280 samples\n",
            "Epoch 1/50\n",
            "101519/101519 [==============================] - 12s 121us/step - loss: 0.7926 - acc: 0.7463 - val_loss: 0.4506 - val_acc: 0.8426\n",
            "\n",
            "Epoch 00001: val_acc improved from -inf to 0.84264, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 2/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.5963 - acc: 0.8044 - val_loss: 0.3977 - val_acc: 0.8562\n",
            "\n",
            "Epoch 00002: val_acc improved from 0.84264 to 0.85621, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 3/50\n",
            "101519/101519 [==============================] - 11s 110us/step - loss: 0.5226 - acc: 0.8263 - val_loss: 0.4003 - val_acc: 0.8563\n",
            "\n",
            "Epoch 00003: val_acc improved from 0.85621 to 0.85629, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 4/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.4850 - acc: 0.8365 - val_loss: 0.3556 - val_acc: 0.8660\n",
            "\n",
            "Epoch 00004: val_acc improved from 0.85629 to 0.86605, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 5/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.4515 - acc: 0.8478 - val_loss: 0.3463 - val_acc: 0.8754\n",
            "\n",
            "Epoch 00005: val_acc improved from 0.86605 to 0.87544, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 6/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.4288 - acc: 0.8539 - val_loss: 0.3463 - val_acc: 0.8760\n",
            "\n",
            "Epoch 00006: val_acc improved from 0.87544 to 0.87598, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 7/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.4131 - acc: 0.8575 - val_loss: 0.3321 - val_acc: 0.8793\n",
            "\n",
            "Epoch 00007: val_acc improved from 0.87598 to 0.87926, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 8/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.3950 - acc: 0.8630 - val_loss: 0.3384 - val_acc: 0.8800\n",
            "\n",
            "Epoch 00008: val_acc improved from 0.87926 to 0.87996, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 9/50\n",
            "101519/101519 [==============================] - 11s 110us/step - loss: 0.3847 - acc: 0.8656 - val_loss: 0.3394 - val_acc: 0.8762\n",
            "\n",
            "Epoch 00009: val_acc did not improve from 0.87996\n",
            "Epoch 10/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.3701 - acc: 0.8697 - val_loss: 0.3326 - val_acc: 0.8779\n",
            "\n",
            "Epoch 00010: val_acc did not improve from 0.87996\n",
            "Epoch 11/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.3611 - acc: 0.8715 - val_loss: 0.3347 - val_acc: 0.8792\n",
            "\n",
            "Epoch 00011: val_acc did not improve from 0.87996\n",
            "Epoch 12/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.3529 - acc: 0.8755 - val_loss: 0.3377 - val_acc: 0.8802\n",
            "\n",
            "Epoch 00012: val_acc improved from 0.87996 to 0.88023, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 13/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.3400 - acc: 0.8782 - val_loss: 0.3320 - val_acc: 0.8852\n",
            "\n",
            "Epoch 00013: val_acc improved from 0.88023 to 0.88520, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 14/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.3353 - acc: 0.8816 - val_loss: 0.3315 - val_acc: 0.8818\n",
            "\n",
            "Epoch 00014: val_acc did not improve from 0.88520\n",
            "Epoch 15/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.3256 - acc: 0.8828 - val_loss: 0.3354 - val_acc: 0.8827\n",
            "\n",
            "Epoch 00015: val_acc did not improve from 0.88520\n",
            "Epoch 16/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.3216 - acc: 0.8845 - val_loss: 0.3378 - val_acc: 0.8832\n",
            "\n",
            "Epoch 00016: val_acc did not improve from 0.88520\n",
            "Epoch 17/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.3127 - acc: 0.8867 - val_loss: 0.3414 - val_acc: 0.8835\n",
            "\n",
            "Epoch 00017: val_acc did not improve from 0.88520\n",
            "Epoch 18/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.3038 - acc: 0.8890 - val_loss: 0.3283 - val_acc: 0.8877\n",
            "\n",
            "Epoch 00018: val_acc improved from 0.88520 to 0.88768, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 19/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.3027 - acc: 0.8908 - val_loss: 0.3427 - val_acc: 0.8840\n",
            "\n",
            "Epoch 00019: val_acc did not improve from 0.88768\n",
            "Epoch 20/50\n",
            "101519/101519 [==============================] - 11s 110us/step - loss: 0.2929 - acc: 0.8927 - val_loss: 0.3532 - val_acc: 0.8818\n",
            "\n",
            "Epoch 00020: val_acc did not improve from 0.88768\n",
            "Epoch 21/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2906 - acc: 0.8922 - val_loss: 0.3536 - val_acc: 0.8809\n",
            "\n",
            "Epoch 00021: val_acc did not improve from 0.88768\n",
            "Epoch 22/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2863 - acc: 0.8949 - val_loss: 0.3469 - val_acc: 0.8853\n",
            "\n",
            "Epoch 00022: val_acc did not improve from 0.88768\n",
            "Epoch 23/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2792 - acc: 0.8957 - val_loss: 0.3405 - val_acc: 0.8850\n",
            "\n",
            "Epoch 00023: val_acc did not improve from 0.88768\n",
            "Epoch 24/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2761 - acc: 0.8961 - val_loss: 0.3439 - val_acc: 0.8846\n",
            "\n",
            "Epoch 00024: val_acc did not improve from 0.88768\n",
            "Epoch 25/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2717 - acc: 0.8995 - val_loss: 0.3545 - val_acc: 0.8854\n",
            "\n",
            "Epoch 00025: val_acc did not improve from 0.88768\n",
            "Epoch 26/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2693 - acc: 0.9005 - val_loss: 0.3535 - val_acc: 0.8876\n",
            "\n",
            "Epoch 00026: val_acc did not improve from 0.88768\n",
            "Epoch 27/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2636 - acc: 0.9013 - val_loss: 0.3595 - val_acc: 0.8856\n",
            "\n",
            "Epoch 00027: val_acc did not improve from 0.88768\n",
            "Epoch 28/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2595 - acc: 0.9026 - val_loss: 0.3541 - val_acc: 0.8845\n",
            "\n",
            "Epoch 00028: val_acc did not improve from 0.88768\n",
            "Epoch 29/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2523 - acc: 0.9049 - val_loss: 0.3552 - val_acc: 0.8862\n",
            "\n",
            "Epoch 00029: val_acc did not improve from 0.88768\n",
            "Epoch 30/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2561 - acc: 0.9037 - val_loss: 0.3691 - val_acc: 0.8833\n",
            "\n",
            "Epoch 00030: val_acc did not improve from 0.88768\n",
            "Epoch 31/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2532 - acc: 0.9049 - val_loss: 0.3749 - val_acc: 0.8842\n",
            "\n",
            "Epoch 00031: val_acc did not improve from 0.88768\n",
            "Epoch 32/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2472 - acc: 0.9070 - val_loss: 0.3691 - val_acc: 0.8841\n",
            "\n",
            "Epoch 00032: val_acc did not improve from 0.88768\n",
            "Epoch 33/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2431 - acc: 0.9078 - val_loss: 0.3579 - val_acc: 0.8879\n",
            "\n",
            "Epoch 00033: val_acc improved from 0.88768 to 0.88794, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 34/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2391 - acc: 0.9095 - val_loss: 0.3641 - val_acc: 0.8873\n",
            "\n",
            "Epoch 00034: val_acc did not improve from 0.88794\n",
            "Epoch 35/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2376 - acc: 0.9098 - val_loss: 0.3708 - val_acc: 0.8880\n",
            "\n",
            "Epoch 00035: val_acc improved from 0.88794 to 0.88803, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 36/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2325 - acc: 0.9118 - val_loss: 0.3785 - val_acc: 0.8845\n",
            "\n",
            "Epoch 00036: val_acc did not improve from 0.88803\n",
            "Epoch 37/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2300 - acc: 0.9127 - val_loss: 0.3873 - val_acc: 0.8840\n",
            "\n",
            "Epoch 00037: val_acc did not improve from 0.88803\n",
            "Epoch 38/50\n",
            "101519/101519 [==============================] - 12s 113us/step - loss: 0.2264 - acc: 0.9133 - val_loss: 0.3868 - val_acc: 0.8878\n",
            "\n",
            "Epoch 00038: val_acc did not improve from 0.88803\n",
            "Epoch 39/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2279 - acc: 0.9143 - val_loss: 0.3964 - val_acc: 0.8843\n",
            "\n",
            "Epoch 00039: val_acc did not improve from 0.88803\n",
            "Epoch 40/50\n",
            "101519/101519 [==============================] - 11s 111us/step - loss: 0.2225 - acc: 0.9152 - val_loss: 0.3890 - val_acc: 0.8840\n",
            "\n",
            "Epoch 00040: val_acc did not improve from 0.88803\n",
            "Epoch 41/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2228 - acc: 0.9154 - val_loss: 0.3815 - val_acc: 0.8876\n",
            "\n",
            "Epoch 00041: val_acc did not improve from 0.88803\n",
            "Epoch 42/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2192 - acc: 0.9168 - val_loss: 0.3932 - val_acc: 0.8884\n",
            "\n",
            "Epoch 00042: val_acc improved from 0.88803 to 0.88839, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 43/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2156 - acc: 0.9172 - val_loss: 0.4113 - val_acc: 0.8848\n",
            "\n",
            "Epoch 00043: val_acc did not improve from 0.88839\n",
            "Epoch 44/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2142 - acc: 0.9180 - val_loss: 0.3998 - val_acc: 0.8848\n",
            "\n",
            "Epoch 00044: val_acc did not improve from 0.88839\n",
            "Epoch 45/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2092 - acc: 0.9207 - val_loss: 0.4013 - val_acc: 0.8858\n",
            "\n",
            "Epoch 00045: val_acc did not improve from 0.88839\n",
            "Epoch 46/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2105 - acc: 0.9203 - val_loss: 0.4189 - val_acc: 0.8841\n",
            "\n",
            "Epoch 00046: val_acc did not improve from 0.88839\n",
            "Epoch 47/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2050 - acc: 0.9221 - val_loss: 0.4250 - val_acc: 0.8861\n",
            "\n",
            "Epoch 00047: val_acc did not improve from 0.88839\n",
            "Epoch 48/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2061 - acc: 0.9210 - val_loss: 0.4106 - val_acc: 0.8873\n",
            "\n",
            "Epoch 00048: val_acc did not improve from 0.88839\n",
            "Epoch 49/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2036 - acc: 0.9223 - val_loss: 0.4186 - val_acc: 0.8899\n",
            "\n",
            "Epoch 00049: val_acc improved from 0.88839 to 0.88989, saving model to /content/drive/My Drive/model1.h5\n",
            "Epoch 50/50\n",
            "101519/101519 [==============================] - 11s 112us/step - loss: 0.2009 - acc: 0.9231 - val_loss: 0.4259 - val_acc: 0.8837\n",
            "\n",
            "Epoch 00050: val_acc did not improve from 0.88989\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIkGv-Jc_QMg",
        "colab_type": "code",
        "outputId": "d6500e9a-63f9-4cad-b685-90e517458d2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "#plot accuracy and loss\n",
        "def plotgraph(epochs, acc, val_acc,title,Xtitle):\n",
        "    #Plot training & validation accuracy values\n",
        "    plt.plot(epochs, acc, 'b')\n",
        "    plt.plot(epochs, val_acc, 'r')\n",
        "    plt.title(title)\n",
        "    plt.ylabel(Xtitle)\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['Train', 'Val'], loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "epochs = range(1,len(acc)+1)\n",
        "\n",
        "plotgraph(epochs, acc, val_acc,'Model Accuracy','Accuracy')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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r3JZLO3ZY0kaPtkzJ6adbq82q0HDFOReeUAOHiAwAHgPSgL+q6uhi+9sBE4Am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\n8cDhnHMuLv8PfF6dXa/ENMEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ARo2DP4n1_w",
        "colab_type": "code",
        "outputId": "46f1fb9e-53d7-48af-e5c9-0766fd816250",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "finalPrediction = [\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\",\"I\",\"J\",\"K\",\"L\",\"M\",\"N\",\"O\",\"P\",\"Q\",\"R\",\"S\",\"T\",\"U\",\"V\",\"W\",\"X\",\"Y\",\"Z\",\"a\",\"b\",\"d\",\"e\",\"f\",\"g\",\"h\",\"n\",\"q\",\"r\",\"t\"]\n",
        "\n",
        "y_pred = model.predict(test_x)\n",
        "cm = metrics.confusion_matrix(test_y.argmax(axis=1), y_pred.argmax(axis=1))\n",
        "\n",
        "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    plt.figure(figsize=(15,15))\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, finalPrediction, rotation=45)\n",
        "    plt.yticks(tick_marks, finalPrediction)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n",
        "plot_confusion_matrix(cm, classes=range(47))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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er1mVdalOn8oEMa5EH5PvXSLN6dc7l/z8vKTZAT21USurNaR6b7z5auvz4/LN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\nA2gCP7xzVSA5ItWRjOdU96kL6NzE+xu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sD86eJTmGM7L372fwwP5075JIStckHnt0Xo1y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\nfvE8Pnt56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Jr3D/bmbPNxW6XU6erjD96nQcNGdDwr4YT//97bxpNydg+6nN3D6+z8/HxG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\nc9I7I4UfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1lLKTau_goX",
        "colab_type": "code",
        "outputId": "43881bea-1951-4b32-87a0-9cb6164d2a2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Test Accuracy\n",
        "loaded_model=load_model(\"/content/drive/My Drive/model1.h5\")\n",
        "\n",
        "score = loaded_model.evaluate(test_x, test_y, verbose=1)\n",
        "\n",
        "print(\"Test accuracy:\", score[1])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18799/18799 [==============================] - 2s 98us/step\n",
            "Test accuracy: 0.8862173519963197\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dr76GKmwaBBJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Predicting the output for given Image\n",
        "\n",
        "finalPrediction = [\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\",\"I\",\"J\",\"K\",\"L\",\"M\",\"N\",\"O\",\"P\",\"Q\",\"R\",\"S\",\"T\",\"U\",\"V\",\"W\",\"X\",\"Y\",\"Z\",\"a\",\"b\",\"d\",\"e\",\"f\",\"g\",\"h\",\"n\",\"q\",\"r\",\"t\"]\n",
        "\n",
        "#Reading Input\n",
        "print(\"Enter the Location of image : \")\n",
        "path=input()\n",
        "print(\"Enter no of Letters/Digits : \")\n",
        "n=int(input())\n",
        "\n",
        "gray=cv2.imread(path,0)\n",
        "(thresh,gray1)=cv2.threshold(gray,128,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n",
        "\n",
        "#Resizing input image\n",
        "gray1=cv2.resize((255-gray1),(28*n,28))\n",
        "print(\"Output = \",end='')\n",
        "for i in range(n):\n",
        "  #creating Sub-Images for the given image\n",
        "  sub_image = gray1[0: 28*n, i*28:(i+1)*28]\n",
        "  img=sub_image.flatten()/255.0\n",
        "  image=img.reshape(-1,28,28,1)\n",
        "  \n",
        "  #plot the Image\n",
        "  #plt.figure()\n",
        "  plt.imshow(sub_image)\n",
        "\n",
        "  result=loaded_model.predict(image)\n",
        "  x=np.where(result[0]==np.amax(result[0]))\n",
        "  f=x[0][0]\n",
        "  if(result[0][x]>0.5):\n",
        "  #display the prediction\n",
        "    print(finalPrediction[f],end='')\n",
        "  else:\n",
        "    print('Not found')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}