bhwcr_cmaterdb_keras.ipynb

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  "metadata": {
    "colab": {
      "name": "bhwcr-cmaterdb-keras.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.2"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/raihan-tanvir/bengali-hwcr/blob/master/bhwcr_cmaterdb_keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VkxGIZc8C9T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "42fb7a15-e324-460d-b8d3-2cb2b5035bf2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LhLUDd6cYOpe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import keras\n",
        "import os\n",
        "from PIL import Image\n",
        "from keras.models import Sequential,load_model\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras import backend as K\n",
        "from keras.models import model_from_json\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsSaJUWd0yHc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "3b62aabd-cca4-4f35-f4bd-83021a5e17dc"
      },
      "source": [
        "!wget https://www.dropbox.com/s/55bhfr3ycvsewsi/CMATERdb%203.1.2.rar\n",
        "!unrar x CMATERdb\\ 3.1.2.rar -idq"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-09-19 08:45:35--  https://www.dropbox.com/s/55bhfr3ycvsewsi/CMATERdb%203.1.2.rar\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.82.1, 2620:100:6032:1::a27d:5201\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.82.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/55bhfr3ycvsewsi/CMATERdb%203.1.2.rar [following]\n",
            "--2020-09-19 08:45:35--  https://www.dropbox.com/s/raw/55bhfr3ycvsewsi/CMATERdb%203.1.2.rar\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://ucdd22d2909f77e6f6f8b8473eda.dl.dropboxusercontent.com/cd/0/inline/A_pGAWR3FXIgUhZgCAfAjixVO4-b1I2sb0qzPCWpZm79oWBj5xS0Oiahudm6O4HtVwGHE0DKVxp1q5i0OTtcYkvwWwe7MCIjp6Sq0xLwslY1rg/file# [following]\n",
            "--2020-09-19 08:45:35--  https://ucdd22d2909f77e6f6f8b8473eda.dl.dropboxusercontent.com/cd/0/inline/A_pGAWR3FXIgUhZgCAfAjixVO4-b1I2sb0qzPCWpZm79oWBj5xS0Oiahudm6O4HtVwGHE0DKVxp1q5i0OTtcYkvwWwe7MCIjp6Sq0xLwslY1rg/file\n",
            "Resolving ucdd22d2909f77e6f6f8b8473eda.dl.dropboxusercontent.com (ucdd22d2909f77e6f6f8b8473eda.dl.dropboxusercontent.com)... 162.125.82.15, 2620:100:6032:15::a27d:520f\n",
            "Connecting to ucdd22d2909f77e6f6f8b8473eda.dl.dropboxusercontent.com (ucdd22d2909f77e6f6f8b8473eda.dl.dropboxusercontent.com)|162.125.82.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /cd/0/inline2/A_pBaOUnGLEttYA7Z68H9V0JSDUe6FzvhEowHVW2-fpexgfNhRh75TI6v44XBziiQG4xnbICqlsXhUzbsXll2oykgrRL2ufnp5cw-lZX6osxypjgBaIE5ompd_ahDLEukNUI4D8JTnBMYyx1LXdUekQ2veJ1cMeFY9o-j04RrmLqv4aSvCQnlRk2APvKjpJaZjVs24N8C00pfY9_iFPhTlrwmvhC28qO6B8ToL5HhrzGXaBRLx5MZjv8xKNFriC2pl-aKPp7wKA1bl4fEp4DfGNRyzD_S3CqScSfGVdCUYQfhHrlQJ-G90XNCZBOzUG3Gsxj09wH_-vqt4O5-LOl7y2F/file [following]\n",
            "--2020-09-19 08:45:36--  https://ucdd22d2909f77e6f6f8b8473eda.dl.dropboxusercontent.com/cd/0/inline2/A_pBaOUnGLEttYA7Z68H9V0JSDUe6FzvhEowHVW2-fpexgfNhRh75TI6v44XBziiQG4xnbICqlsXhUzbsXll2oykgrRL2ufnp5cw-lZX6osxypjgBaIE5ompd_ahDLEukNUI4D8JTnBMYyx1LXdUekQ2veJ1cMeFY9o-j04RrmLqv4aSvCQnlRk2APvKjpJaZjVs24N8C00pfY9_iFPhTlrwmvhC28qO6B8ToL5HhrzGXaBRLx5MZjv8xKNFriC2pl-aKPp7wKA1bl4fEp4DfGNRyzD_S3CqScSfGVdCUYQfhHrlQJ-G90XNCZBOzUG3Gsxj09wH_-vqt4O5-LOl7y2F/file\n",
            "Reusing existing connection to ucdd22d2909f77e6f6f8b8473eda.dl.dropboxusercontent.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 8720925 (8.3M) [application/rar]\n",
            "Saving to: ‘CMATERdb 3.1.2.rar’\n",
            "\n",
            "CMATERdb 3.1.2.rar  100%[===================>]   8.32M  19.3MB/s    in 0.4s    \n",
            "\n",
            "2020-09-19 08:45:37 (19.3 MB/s) - ‘CMATERdb 3.1.2.rar’ saved [8720925/8720925]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuEAIhzGYOps",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d5112ded-ffa5-410f-a1f1-d9511b4119a3"
      },
      "source": [
        "train_path = os.getcwd()+'/BasicFinalDatabase/Train'\n",
        "folders = []\n",
        "[folders.append('/'+i) for i in sorted(os.listdir(train_path))]\n",
        "label_map = ['অ','আ','ই ','ঈ','উ','ঊ','ঋ','এ','ঐ','ও','ঔ',\n",
        "             'ক','খ','গ','ঘ','ঙ',\n",
        "             'চ','ছ','জ','ঝ','ঞ', \n",
        "             'ট','ঠ','ড','ঢ','ণ',\n",
        "             'ত','থ','দ','ধ','ন',\n",
        "             'প','ফ','ব','ভ','ম',\n",
        "             'য','র','ল',\n",
        "             'শ','ষ','স','হ',\n",
        "             'য়','ড়','ঢ়','ৎ','ং','ঃ','ঁ']\n",
        "Temp = []\n",
        "for folder in folders:\n",
        "    images = os.listdir((train_path+folder))\n",
        "    temp = []\n",
        "    for image in images:\n",
        "      try:\n",
        "        img = Image.open(train_path+folder+'/'+image).convert('L')\n",
        "        img = img.resize((32,32))\n",
        "        arr = np.array(img)[..., np.newaxis]\n",
        "        arr = arr / 255.0\n",
        "        Temp.append(arr)\n",
        "      except:\n",
        "        continue\n",
        "X_orig = np.stack(Temp)\n",
        "print(X_orig.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(12000, 32, 32, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09lL4rUrYOp2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8fa65542-9bbc-4ef1-9f64-352e6e385b52"
      },
      "source": [
        "Y_orig = []\n",
        "[Y_orig.extend([bc]*240) for bc in range(len(label_map))]\n",
        "Y_orig = np.stack(Y_orig)\n",
        "Y_orig = keras.utils.to_categorical(Y_orig, len(np.unique(Y_orig)))\n",
        "print(Y_orig.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(12000, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rYDP9nmYOp9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0236fc0b-0a3e-4f83-8e13-f75ef26e0a0c"
      },
      "source": [
        "indices = np.arange(X_orig.shape[0])\n",
        "np.random.shuffle(indices)\n",
        "X_orig = X_orig[indices]\n",
        "Y_orig = Y_orig[indices]\n",
        "\n",
        "print(X_orig.shape,Y_orig.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(12000, 32, 32, 1) (12000, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tgCraJsF6AAa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "121fbb78-6890-430d-d690-1784c28fc86c"
      },
      "source": [
        "test_path = os.getcwd()+'/BasicFinalDatabase/Test'\n",
        "folders = []\n",
        "[folders.append('/'+i) for i in sorted(os.listdir(test_path))]\n",
        "\n",
        "Temp2 = []\n",
        "for folder in folders:\n",
        "    images = os.listdir((test_path+folder))\n",
        "    for image in images:\n",
        "      try:\n",
        "        img = Image.open(test_path+folder+'/'+image).convert('L')\n",
        "        img = img.resize((32,32))\n",
        "        arr = np.array(img)[..., np.newaxis]\n",
        "        arr = arr / 255.0\n",
        "        Temp2.append(arr)\n",
        "      except:\n",
        "        continue\n",
        "        \n",
        "X_test = np.stack(Temp2)\n",
        "print(X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3000, 32, 32, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_2I8AptoYOqO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e64183ba-be50-41a3-ca4e-4548d4dda101"
      },
      "source": [
        "Y_test = []\n",
        "[Y_test.extend([bc]*60) for bc in range(len(label_map))]\n",
        "Y_test = np.stack(Y_test)\n",
        "Y_test = keras.utils.to_categorical(Y_test, len(np.unique(Y_test)))\n",
        "print(Y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3000, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6grHqRUxYOqV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "73defb80-3b26-4607-e76a-7cac08434cf5"
      },
      "source": [
        "indices = np.arange(X_test.shape[0])\n",
        "np.random.shuffle(indices)\n",
        "X_test = X_test[indices]\n",
        "Y_test = Y_test[indices]\n",
        "\n",
        "print(X_test.shape,Y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3000, 32, 32, 1) (3000, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36Tcq5-GYOqg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8a794b78-2a09-4d12-e4ef-d43489382f6d"
      },
      "source": [
        "X_train,X_dev,Y_train,Y_dev = train_test_split(X_orig, Y_orig, test_size=0.2, shuffle=False)\n",
        "X_train.shape,Y_train.shape,X_dev.shape,Y_dev.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((9600, 32, 32, 1), (9600, 50), (2400, 32, 32, 1), (2400, 50))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCIqsQiEYOqs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "outputId": "7abb9e67-222d-460d-80dd-0ad274ea73a1"
      },
      "source": [
        "bs=64\n",
        "ep=25\n",
        "drop_prob=0.2\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(3, 3),\n",
        "                 activation='relu',\n",
        "                 input_shape=(32,32,1)))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(drop_prob))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(50, activation='softmax'))\n",
        "\n",
        "opt = keras.optimizers.Adam()\n",
        "\n",
        "model.compile(loss=keras.losses.categorical_crossentropy,\n",
        "              optimizer=opt,\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X_train, Y_train,\n",
        "          batch_size=bs,\n",
        "          epochs=ep,\n",
        "          verbose=1,\n",
        "          validation_data=(X_dev, Y_dev))\n",
        "print(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "150/150 [==============================] - 11s 76ms/step - loss: 2.9023 - accuracy: 0.2526 - val_loss: 1.5675 - val_accuracy: 0.5754\n",
            "Epoch 2/25\n",
            "150/150 [==============================] - 12s 81ms/step - loss: 1.2088 - accuracy: 0.6573 - val_loss: 0.9251 - val_accuracy: 0.7446\n",
            "Epoch 3/25\n",
            "150/150 [==============================] - 12s 79ms/step - loss: 0.7835 - accuracy: 0.7782 - val_loss: 0.7623 - val_accuracy: 0.7879\n",
            "Epoch 4/25\n",
            "150/150 [==============================] - 12s 82ms/step - loss: 0.6025 - accuracy: 0.8248 - val_loss: 0.6829 - val_accuracy: 0.7971\n",
            "Epoch 5/25\n",
            "150/150 [==============================] - 11s 76ms/step - loss: 0.4836 - accuracy: 0.8557 - val_loss: 0.6200 - val_accuracy: 0.8213\n",
            "Epoch 6/25\n",
            "150/150 [==============================] - 12s 79ms/step - loss: 0.3818 - accuracy: 0.8843 - val_loss: 0.5723 - val_accuracy: 0.8400\n",
            "Epoch 7/25\n",
            "150/150 [==============================] - 12s 78ms/step - loss: 0.3078 - accuracy: 0.9095 - val_loss: 0.5491 - val_accuracy: 0.8442\n",
            "Epoch 8/25\n",
            "150/150 [==============================] - 12s 77ms/step - loss: 0.2500 - accuracy: 0.9269 - val_loss: 0.5656 - val_accuracy: 0.8442\n",
            "Epoch 9/25\n",
            "150/150 [==============================] - 12s 80ms/step - loss: 0.2123 - accuracy: 0.9336 - val_loss: 0.5456 - val_accuracy: 0.8475\n",
            "Epoch 10/25\n",
            "150/150 [==============================] - 12s 77ms/step - loss: 0.1681 - accuracy: 0.9472 - val_loss: 0.5393 - val_accuracy: 0.8587\n",
            "Epoch 11/25\n",
            "150/150 [==============================] - 12s 82ms/step - loss: 0.1301 - accuracy: 0.9614 - val_loss: 0.5314 - val_accuracy: 0.8646\n",
            "Epoch 12/25\n",
            "150/150 [==============================] - 12s 80ms/step - loss: 0.1154 - accuracy: 0.9630 - val_loss: 0.5637 - val_accuracy: 0.8575\n",
            "Epoch 13/25\n",
            "150/150 [==============================] - 13s 86ms/step - loss: 0.1009 - accuracy: 0.9695 - val_loss: 0.5754 - val_accuracy: 0.8646\n",
            "Epoch 14/25\n",
            "150/150 [==============================] - 12s 78ms/step - loss: 0.0875 - accuracy: 0.9723 - val_loss: 0.5828 - val_accuracy: 0.8617\n",
            "Epoch 15/25\n",
            "150/150 [==============================] - 12s 82ms/step - loss: 0.0796 - accuracy: 0.9741 - val_loss: 0.5893 - val_accuracy: 0.8550\n",
            "Epoch 16/25\n",
            "150/150 [==============================] - 12s 81ms/step - loss: 0.0692 - accuracy: 0.9776 - val_loss: 0.5733 - val_accuracy: 0.8637\n",
            "Epoch 17/25\n",
            "150/150 [==============================] - 12s 82ms/step - loss: 0.0665 - accuracy: 0.9793 - val_loss: 0.6217 - val_accuracy: 0.8579\n",
            "Epoch 18/25\n",
            "150/150 [==============================] - 12s 78ms/step - loss: 0.0654 - accuracy: 0.9777 - val_loss: 0.6460 - val_accuracy: 0.8550\n",
            "Epoch 19/25\n",
            "150/150 [==============================] - 12s 80ms/step - loss: 0.0451 - accuracy: 0.9857 - val_loss: 0.6347 - val_accuracy: 0.8608\n",
            "Epoch 20/25\n",
            "150/150 [==============================] - 12s 78ms/step - loss: 0.0452 - accuracy: 0.9864 - val_loss: 0.6636 - val_accuracy: 0.8662\n",
            "Epoch 21/25\n",
            "150/150 [==============================] - 12s 78ms/step - loss: 0.0469 - accuracy: 0.9848 - val_loss: 0.6419 - val_accuracy: 0.8633\n",
            "Epoch 22/25\n",
            "150/150 [==============================] - 12s 81ms/step - loss: 0.0402 - accuracy: 0.9874 - val_loss: 0.6621 - val_accuracy: 0.8625\n",
            "Epoch 23/25\n",
            "150/150 [==============================] - 12s 80ms/step - loss: 0.0405 - accuracy: 0.9871 - val_loss: 0.6575 - val_accuracy: 0.8667\n",
            "Epoch 24/25\n",
            "150/150 [==============================] - 12s 81ms/step - loss: 0.0499 - accuracy: 0.9827 - val_loss: 0.6689 - val_accuracy: 0.8650\n",
            "Epoch 25/25\n",
            "150/150 [==============================] - 12s 79ms/step - loss: 0.0384 - accuracy: 0.9879 - val_loss: 0.6681 - val_accuracy: 0.8571\n",
            "<tensorflow.python.keras.callbacks.History object at 0x7f5161a2c7b8>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8EAMpvf_-e9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from datetime import datetime\n",
        "now = str(datetime.now())\n",
        "\n",
        "project_dir = '/content/drive/Shared drives/4.2/SoftcomProject/bhwcr-cmaterdb-keras/'\n",
        "\n",
        "model_json = model.to_json()\n",
        "with open(\"/content/drive/Shared drives/4.2/SoftcomProject/bhwcr-cmaterdb-keras/model_25_64.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "\n",
        "model.save_weights(project_dir+\"/model_\"+str(ep)+\"_\"+str(bs)+\"_\"+now+\".h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtloKQWIYOq4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "41de275e-0f08-40eb-9a07-94e27971395b"
      },
      "source": [
        "score = model.evaluate(X_test, Y_test, verbose=1)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "94/94 [==============================] - 1s 11ms/step - loss: 0.7168 - accuracy: 0.8607\n",
            "Test loss: 0.7168163061141968\n",
            "Test accuracy: 0.8606666922569275\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmVTNbI-EEMS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "outputId": "b1c342a9-0350-4840-83a6-671c2ac61d79"
      },
      "source": [
        "print(history.history.keys())\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ugAUFC18Atuh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "71eaa818-9393-4a63-bf55-2cab32e3e459"
      },
      "source": [
        "model_dir=\"/content/drive/Shared drives/4.2/SoftcomProject/bhwcr-cmaterdb-keras/model_25_64_2020-09-19 08:57:06.588633.h5\"\n",
        "model_json=\"/content/drive/Shared drives/4.2/SoftcomProject/bhwcr-cmaterdb-keras/model_25_64.json\"\n",
        "\n",
        "json_file = open(model_json, 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "\n",
        "loaded_model.load_weights(model_dir)\n",
        "print(\"model loaded\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "model loaded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGMDK04SAx3p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "21d67dd3-066e-4d29-a1e8-b7c5f28b5042"
      },
      "source": [
        "loaded_model.compile(loss=keras.losses.categorical_crossentropy,\n",
        "              optimizer=keras.optimizers.Adagrad(),\n",
        "              metrics=['accuracy'])\n",
        "score = loaded_model.evaluate(X_test, Y_test, verbose=1)\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score[1]*100))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "94/94 [==============================] - 1s 13ms/step - loss: 0.7168 - accuracy: 0.8607\n",
            "accuracy: 86.07%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MoyPD-9_ou8u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''\n",
        "label_map = np.array(['অ','আ','ই ','ঈ','উ','ঊ','ঋ','এ','ঐ','ও','ঔ',\n",
        "             'ক','খ','গ','ঘ','ঙ',\n",
        "             'চ','ছ','জ','ঝ','ঞ', \n",
        "             'ট','ঠ','ড','ঢ','ণ',\n",
        "             'ত','থ','দ','ধ','ন',\n",
        "             'প','ফ','ব','ভ','ম',\n",
        "             'য','র','ল',\n",
        "             'শ','ষ','স','হ',\n",
        "             'য়','ড়','ঢ়','ৎ','ং','ঃ','ঁ'])\n",
        "\n",
        "preds = model.predict(X_test) \n",
        "preds_single = Y_test[np.argmax(Y_test, axis = -1)]\n",
        "actual_single = Y_test[np.argmax(Y_test, axis = -1)]\n",
        "\n",
        "n_to_show = 10\n",
        "indices = np.random.choice(range(len(X_test)), n_to_show)\n",
        "\n",
        "fig = plt.figure(figsize=(15, 3))\n",
        "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
        "\n",
        "for i, idx in enumerate(indices):\n",
        "    img = X_test[idx]\n",
        "    ax = fig.add_subplot(1, n_to_show, i+1)\n",
        "    ax.axis('off')\n",
        "    ax.text(0.5, -0.35, 'pred = ' + str(preds_single[idx]), fontsize=10\n",
        "       , ha='center', transform=ax.transAxes)\n",
        "    ax.text(0.5, -0.7, 'act = ' + str(actual_single[idx]), fontsize=10\n",
        "        , ha='center', transform=ax.transAxes)\n",
        "    ax.imshow(img.fromarray(data, 'RGB'))\n",
        "  '''"
      ],
      "execution_count": 41,
      "outputs": []
    }
  ]
}