Training a CNN to identify types of cell images.ipynb

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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ratnakandala/CV/blob/main/Training%20a%20CNN%20to%20identify%20types%20of%20cell%20images.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "56XtEKFkgIaz",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e14c70c-c7ac-4339-a936-37be999bbd63"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.65.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.13)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2022.12.7)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.27.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.65.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.26.15)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n",
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n",
            "{\"username\":\"swimzebra\",\"key\":\"b5feb024964305a9f5de76a1cba38d4b\"}"
          ]
        }
      ],
      "source": [
        "!pip install tqdm\n",
        "!pip install kaggle\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import zipfile\n",
        "from google.colab import drive\n",
        "import os\n",
        "drive.mount('/content/drive')\n",
        "!mkdir ~/.kaggle\n",
        "!cp /content/drive/MyDrive/kaggle.json ~/.kaggle/kaggle.json\n",
        "!cat ~/.kaggle/kaggle.json\n",
        "from kaggle.api.kaggle_api_extended import KaggleApi\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "!kaggle datasets download -d swimzebra/tl-ml-cell-images -p /content/dataset/"
      ],
      "metadata": {
        "id": "JB9yGFJZgU89",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4967879-f1ad-4a64-8dfd-06ec324a4d8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tl-ml-cell-images.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zip_file_path = '/content/dataset/tl-ml-cell-images.zip'\n",
        "extract_dir = '/content/dataset/extracted_dataset'\n",
        "\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(extract_dir)"
      ],
      "metadata": {
        "id": "dIcCsbVHgqQt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reading the labels\n",
        "data = pd.read_csv('/content/dataset/extracted_dataset/label.csv')\n",
        "label_data = np.array(data['label']) - 1"
      ],
      "metadata": {
        "id": "YDbJv3l6g4aU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "##Resizing, normalizing images and creating a images_array\n",
        "import matplotlib.image as mpimg\n",
        "from skimage import io, transform\n",
        "from skimage import exposure\n",
        "N = len(label_data) #total number of images in the dataset\n",
        "new_size = (80, 80)  # Specify the new size (width, height)\n",
        "shape_image_data = (N, new_size[0], new_size[1], 1)\n",
        "resized_images = np.empty(shape_image_data)\n",
        "\n",
        "iterate_array = range(1,N+1)\n",
        "\n",
        "for i, image_ind in tqdm(enumerate(iterate_array)):\n",
        "    image_path = f'/content/dataset/extracted_dataset/cells/cells/{image_ind}.png'\n",
        "    raw_image_0 = io.imread(image_path)\n",
        "\n",
        "    raw_image = np.array(raw_image_0).astype(float)\n",
        "    resized_image = transform.resize(raw_image, new_size)\n",
        "\n",
        "    normalized_image =  exposure.rescale_intensity(resized_image, in_range='image', out_range=(0, 1))\n",
        "\n",
        "    resized_images[i,:,:,0] = normalized_image"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-391c4d7b741b>:13: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for i, image_ind in tqdm(enumerate(iterate_array)):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "from skimage import exposure\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "\n",
        "# plot a random image from the dataset\n",
        "random_number = random.randint(0, len(label_data)-1)\n",
        "image = Image.open(f'/content/dataset/extracted_dataset/cells/cells/{random_number+1}.png')\n",
        "image_array = np.array(image)\n",
        "\n",
        "# Normalize the image\n",
        "normalized_image = resized_images[random_number,:,:,0]\n",
        "\n",
        "# Create a figure with two subplots\n",
        "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot the original image\n",
        "axes[0].imshow(image_array, cmap='gray')\n",
        "axes[0].set_title('Original Image')\n",
        "\n",
        "# Plot the normalized image\n",
        "axes[1].imshow(normalized_image, cmap='gray')\n",
        "axes[1].set_title('Normalized Image')\n",
        "\n",
        "# Remove the axis labels\n",
        "for ax in axes:\n",
        "    ax.axis('off')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "xhbhEMAmhS2i",
        "colab": {
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          "height": 410
        },
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      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, Flatten\n",
        "from keras.utils import to_categorical\n",
        "#2-layer CNN"
      ],
      "metadata": {
        "id": "fr4SwXBJi5t3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def batch_split(selected_types, resized_images, label_data, percentage,  encoding):\n",
        "    from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "   #select only images in selected_types for training/testing\n",
        "    training_indices = []\n",
        "    for image_type in selected_types:\n",
        "        indices = np.where(label_data == image_type)[0] #obtain the images of type image_type\n",
        "        selected_indices = np.random.choice(indices, int(percentage*len(indices)/100)) #select n_per_type randomly chosen images\n",
        "        training_indices = np.append(training_indices, selected_indices)\n",
        "    training_indices = np.array(training_indices).astype(int)\n",
        "    x_train = resized_images[training_indices,:,:,:] #one-hot encode target column\n",
        "\n",
        "\n",
        "    testing_indices = [i for i in range(len(label_data)) \\\n",
        "                    if i not in training_indices and label_data[i] in selected_types]\n",
        "    x_test = resized_images[testing_indices, :, :, :]\n",
        "\n",
        "    if encoding:\n",
        "        y_train = to_categorical(label_data[training_indices], num_classes = 6)\n",
        "        y_test = to_categorical(label_data[testing_indices], num_classes = 6)\n",
        "    else:\n",
        "        y_train = label_data[training_indices]\n",
        "        y_test = label_data[testing_indices]\n",
        "\n",
        "    return x_train, y_train, x_test, y_test"
      ],
      "metadata": {
        "id": "hhzqOf9Fodpa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "percentage = 70 #no. of images of each type\n",
        "selected_types = list(range(6))\n",
        "encoding = 'true'\n",
        "x_train, y_train, x_test, y_test = batch_split(selected_types, resized_images, label_data, percentage, encoding)\n",
        "\n",
        "#create model\n",
        "model = Sequential()    #add model layers\n",
        "model.add(Conv2D(32, kernel_size=3, activation='relu', input_shape=(80,80,1), batch_size=64))\n",
        "model.add(Conv2D(32, kernel_size=3, activation='relu'))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(6, activation='softmax'))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'],run_eagerly=True)"
      ],
      "metadata": {
        "id": "c8ngE31YqfGj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(selected_types)\n",
        "x_train_tensor = tf.convert_to_tensor(x_train, dtype=tf.float32)\n",
        "y_train_tensor = tf.convert_to_tensor(y_train, dtype=tf.float32)\n",
        "\n",
        "x_test_tensor = tf.convert_to_tensor(x_test, dtype=tf.float32)\n",
        "y_test_tensor = tf.convert_to_tensor(y_test, dtype=tf.float32)\n",
        "history = model.fit(x_train_tensor, y_train_tensor, validation_data=(x_test_tensor, y_test_tensor), epochs=3)"
      ],
      "metadata": {
        "id": "Itvof6utjABo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88a27a82-c1a2-4987-e28b-da29ebb7cd2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1, 2, 3, 4, 5]\n",
            "Epoch 1/3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function _BaseOptimizer._update_step_xla at 0x7fc3e51f5fc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:6 out of the last 6 calls to <function _BaseOptimizer._update_step_xla at 0x7fc3e51f5fc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1388/1388 [==============================] - 64s 38ms/step - loss: 0.6289 - accuracy: 0.7583 - val_loss: 0.5996 - val_accuracy: 0.7786\n",
            "Epoch 2/3\n",
            "1388/1388 [==============================] - 56s 40ms/step - loss: 0.3903 - accuracy: 0.8559 - val_loss: 0.5413 - val_accuracy: 0.8105\n",
            "Epoch 3/3\n",
            "1388/1388 [==============================] - 48s 35ms/step - loss: 0.2508 - accuracy: 0.9098 - val_loss: 0.5569 - val_accuracy: 0.8137\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Access the training history\n",
        "training_loss = history.history['loss']\n",
        "validation_loss = history.history['val_loss']\n",
        "\n",
        "# Plot the training and validation loss\n",
        "epochs = range(1, len(training_loss) + 1)\n",
        "\n",
        "plt.plot(epochs, training_loss, 'b-', label='Training Loss')\n",
        "plt.plot(epochs, validation_loss, 'r-', label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(loc = 0)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TswGqU320oUI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "be6091d7-755f-4fb1-ff73-7239c642070d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(['Train', 'Validation'], loc = 0)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "FgtIuuOGK22d",
        "outputId": "f04029e8-780b-4b3e-dd18-4c7076247fb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate F1 score\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "# True labels and predicted labels\n",
        "true_labels = y_test\n",
        "predicted_labels = model.predict(x_test)\n",
        "\n",
        "# Calculate the F1 score\n",
        "f1 = f1_score(true_labels, predicted_labels)\n",
        "\n",
        "print(f\"F1 score: {f1}\")\n",
        "\n"
      ],
      "metadata": {
        "id": "2rOOUG4dL9wF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "edcswqviPLfe"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}