src/Cataract/cataract_cnn.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "igDkSG_CxTFu"
      },
      "source": [
        "# Importing packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VDNOxvhzxYv8"
      },
      "outputs": [],
      "source": [
        "# Packages for data preparation\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.optimizers import Adam\n",
        "from tensorflow.keras.optimizers.experimental import RMSprop\n",
        "\n",
        "# Packages for modeling\n",
        "import tensorflow as tf\n",
        "\n",
        "# Standard packages\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vu_DcH2L2QO3"
      },
      "source": [
        "# Load the Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FLTBl1td8wwl",
        "outputId": "8c117e82-4772-4c78-f379-1f8349e5a5fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFkXI5XQ4xmg",
        "outputId": "5d1c0ae0-4b1d-4edf-aaba-43b45e8ce036"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/Cataract/.DS_Store\n",
            "/content/drive/MyDrive/Cataract/test/.DS_Store\n",
            "/content/drive/MyDrive/Cataract/test/cataract/image_254.png\n",
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            "/content/drive/MyDrive/Cataract/train/cataract/image_51.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_244.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_231.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_29.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_47.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_48.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_32.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_27.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_43.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_4.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_70.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_88.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_80.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_7.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_66.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_8.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_63.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_58.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_68.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_6.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_74.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_84.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_79.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_57.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_59.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_82.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_56.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_78.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_65.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_61.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_55.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_73.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_60.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_71.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_85.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_86.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_64.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_75.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_72.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_9.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_87.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_83.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_69.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_76.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_89.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_67.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_90.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_62.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_81.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_77.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_95.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_99.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_97.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_94.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_93.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_96.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_92.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_98.png\n",
            "/content/drive/MyDrive/Cataract/train/cataract/image_91.png\n"
          ]
        }
      ],
      "source": [
        "for dirname, _, filenames in os.walk('/content/drive/MyDrive/Cataract'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HPY7PTuHnB99"
      },
      "outputs": [],
      "source": [
        "train_dir = '/content/drive/MyDrive/Cataract/train'\n",
        "test_dir = '/content/drive/MyDrive/Cataract/test'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BaWBVdztglti",
        "outputId": "27e60df4-ca6d-4321-8293-1325ccdcadd2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 491 files belonging to 2 classes.\n",
            "Found 121 files belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "img_height, img_width = 32, 32\n",
        "batch_size = 16\n",
        "\n",
        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    train_dir,\n",
        "    image_size = (img_height, img_width),\n",
        "    batch_size = batch_size\n",
        ")\n",
        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    test_dir,\n",
        "    image_size = (img_height, img_width),\n",
        "    batch_size = batch_size\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23N7jkTmQ_Ep"
      },
      "source": [
        "# Adam Optimizer + Cross Entropy Loss\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WO43pL2yAHWW"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential(\n",
        "    [\n",
        "    #  tf.keras.layers.Rescaling(1./255),\n",
        "     tf.keras.layers.Conv2D(32, 3, activation=\"relu\"),\n",
        "     tf.keras.layers.MaxPooling2D(),\n",
        "     tf.keras.layers.Conv2D(32, 3, activation=\"relu\"),\n",
        "     tf.keras.layers.MaxPooling2D(),\n",
        "     tf.keras.layers.Conv2D(32, 3, activation=\"relu\"),\n",
        "     tf.keras.layers.MaxPooling2D(),\n",
        "     tf.keras.layers.Flatten(),\n",
        "     tf.keras.layers.Dense(128, activation=\"relu\"),\n",
        "     tf.keras.layers.Dense(2)\n",
        "    ]\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BgWPP7WyALHu"
      },
      "outputs": [],
      "source": [
        "model.compile(\n",
        "    optimizer=\"adam\",\n",
        "    loss=tf.keras.losses.SparseCategoricalCrossentropy(\n",
        "    from_logits=True,\n",
        "    ignore_class=None,\n",
        "    name='sparse_categorical_crossentropy'\n",
        "),\n",
        "    metrics=['accuracy']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpRdkoaXANUm",
        "outputId": "6983d781-1652-4841-efb8-c5e7c2d0850b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "31/31 [==============================] - 90s 2s/step - loss: 2.7996 - accuracy: 0.6008 - val_loss: 0.7031 - val_accuracy: 0.6777\n",
            "Epoch 2/10\n",
            "31/31 [==============================] - 16s 398ms/step - loss: 0.5232 - accuracy: 0.7821 - val_loss: 0.4616 - val_accuracy: 0.7851\n",
            "Epoch 3/10\n",
            "31/31 [==============================] - 16s 397ms/step - loss: 0.3171 - accuracy: 0.8676 - val_loss: 0.3136 - val_accuracy: 0.8926\n",
            "Epoch 4/10\n",
            "31/31 [==============================] - 16s 405ms/step - loss: 0.2084 - accuracy: 0.9369 - val_loss: 0.2342 - val_accuracy: 0.9174\n",
            "Epoch 5/10\n",
            "31/31 [==============================] - 21s 566ms/step - loss: 0.1401 - accuracy: 0.9613 - val_loss: 0.2457 - val_accuracy: 0.9008\n",
            "Epoch 6/10\n",
            "31/31 [==============================] - 16s 366ms/step - loss: 0.1007 - accuracy: 0.9674 - val_loss: 0.2364 - val_accuracy: 0.9174\n",
            "Epoch 7/10\n",
            "31/31 [==============================] - 16s 414ms/step - loss: 0.0950 - accuracy: 0.9695 - val_loss: 0.2452 - val_accuracy: 0.8926\n",
            "Epoch 8/10\n",
            "31/31 [==============================] - 17s 416ms/step - loss: 0.0634 - accuracy: 0.9837 - val_loss: 0.2394 - val_accuracy: 0.9256\n",
            "Epoch 9/10\n",
            "31/31 [==============================] - 15s 394ms/step - loss: 0.0363 - accuracy: 0.9959 - val_loss: 0.2594 - val_accuracy: 0.9256\n",
            "Epoch 10/10\n",
            "31/31 [==============================] - 15s 392ms/step - loss: 0.0326 - accuracy: 0.9939 - val_loss: 0.2992 - val_accuracy: 0.9008\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7c705caa6a10>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(\n",
        "    train_ds,\n",
        "    validation_data = test_ds,\n",
        "    epochs = 10\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PofSuTGaGA_k",
        "outputId": "a5ca8070-af39-4a36-ffbe-b5f4c436b377"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 30, 30, 32)        896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 15, 15, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 13, 13, 32)        9248      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 6, 6, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 4, 4, 32)          9248      \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 2, 2, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               16512     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 2)                 258       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 36,162\n",
            "Trainable params: 36,162\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JQ2uT745TlH6"
      },
      "source": [
        "# Adam + Binary cross"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTrpAgJ5rSHg",
        "outputId": "fa83f8d4-b373-413f-8b4e-258c33ccc8e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 491 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "image_size = (55, 94, 3)\n",
        "target_size = (55, 94)\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1/255)\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size = target_size,\n",
        "    class_mode = 'binary'\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KWie1OsHjcRW"
      },
      "outputs": [],
      "source": [
        "model = Sequential([\n",
        "    Conv2D(16, (3,3), activation='relu',input_shape=image_size),\n",
        "    MaxPooling2D(2, 2),\n",
        "    Conv2D(32, (3,3), activation='relu'),\n",
        "    MaxPooling2D(2,2),\n",
        "    Flatten(),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "-vxIvvlOjQDp"
      },
      "outputs": [],
      "source": [
        "model.compile(\n",
        "    optimizer=RMSprop(learning_rate=0.001),\n",
        "    loss = 'binary_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XuOfTYmNjXsq",
        "outputId": "4823bdcc-2bee-47b9-90e2-9c0af6f48e4f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-17-bd96f6f1d896>:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  model.fit_generator(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "16/16 [==============================] - 16s 939ms/step - loss: 0.1775 - accuracy: 0.9430\n",
            "Epoch 2/15\n",
            "16/16 [==============================] - 16s 939ms/step - loss: 0.1293 - accuracy: 0.9470\n",
            "Epoch 3/15\n",
            "16/16 [==============================] - 15s 970ms/step - loss: 0.1220 - accuracy: 0.9470\n",
            "Epoch 4/15\n",
            "16/16 [==============================] - 15s 944ms/step - loss: 0.1446 - accuracy: 0.9491\n",
            "Epoch 5/15\n",
            "16/16 [==============================] - 15s 955ms/step - loss: 0.0837 - accuracy: 0.9633\n",
            "Epoch 6/15\n",
            "16/16 [==============================] - 16s 1s/step - loss: 0.0790 - accuracy: 0.9695\n",
            "Epoch 7/15\n",
            "16/16 [==============================] - 15s 983ms/step - loss: 0.0466 - accuracy: 0.9857\n",
            "Epoch 8/15\n",
            "16/16 [==============================] - 16s 995ms/step - loss: 0.0984 - accuracy: 0.9593\n",
            "Epoch 9/15\n",
            "16/16 [==============================] - 15s 963ms/step - loss: 0.1218 - accuracy: 0.9511\n",
            "Epoch 10/15\n",
            "16/16 [==============================] - 15s 891ms/step - loss: 0.0406 - accuracy: 0.9919\n",
            "Epoch 11/15\n",
            "16/16 [==============================] - 15s 928ms/step - loss: 0.0711 - accuracy: 0.9715\n",
            "Epoch 12/15\n",
            "16/16 [==============================] - 15s 910ms/step - loss: 0.0625 - accuracy: 0.9857\n",
            "Epoch 13/15\n",
            "16/16 [==============================] - 15s 947ms/step - loss: 0.0381 - accuracy: 0.9878\n",
            "Epoch 14/15\n",
            "16/16 [==============================] - 15s 941ms/step - loss: 0.0298 - accuracy: 0.9919\n",
            "Epoch 15/15\n",
            "16/16 [==============================] - 16s 1s/step - loss: 0.0737 - accuracy: 0.9715\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7c6ff71848b0>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit_generator(\n",
        "    train_generator,\n",
        "    epochs=15\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ef-tzh1lHRK1",
        "outputId": "3e8af11c-c9f1-401e-8ee7-7e10168faf90"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_3 (Conv2D)           (None, 53, 92, 16)        448       \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 26, 46, 16)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 24, 44, 32)        4640      \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 12, 22, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 8448)              0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               1081472   \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,086,689\n",
            "Trainable params: 1,086,689\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "Vu_DcH2L2QO3",
        "23N7jkTmQ_Ep",
        "JQ2uT745TlH6"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}