Tensorflow - Multi-Label Image Classification Using CNN.ipynb

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        "colab_type": "text",
        "id": "view-in-github"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sahug/ds-tensorflow-colab/blob/master/Tensorflow%20-%20Multi-Label%20Image%20Classification%20Using%20CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYoo7yXOdrt2"
      },
      "source": [
        "**Tensorflow - Multi-Label Image Classification Using CNN**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uPEWXz9Yehs_"
      },
      "source": [
        "Movie poster link - https://www.cs.ccu.edu.tw/~wtchu/projects/MoviePoster/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "v3ZS4-uHgEkx"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import optimizers, activations\n",
        "from keras.preprocessing import image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "GWFkWNUjguXB"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.8.0\n"
          ]
        }
      ],
      "source": [
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "guZt_P8ygyCQ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "pxg3O4tsg5my"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "FTxhn97GhCtt"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "fatal: destination path 'Movies-Poster_Dataset' already exists and is not an empty directory.\n"
          ]
        }
      ],
      "source": [
        "# Clonning data from repository\n",
        "!git clone https://github.com/laxmimerit/Movies-Poster_Dataset.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "r6VylDAChXJ9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(7254, 27)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = pd.read_csv(\"Movies-Poster_Dataset/train.csv\")\n",
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "OlIsEM0ehoFD"
      },
      "outputs": [
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              "          Id                                Genre  Action  Adventure  \\\n",
              "0  tt0086425                  ['Comedy', 'Drama']       0          0   \n",
              "1  tt0085549        ['Drama', 'Romance', 'Music']       0          0   \n",
              "2  tt0086465                           ['Comedy']       0          0   \n",
              "3  tt0086567               ['Sci-Fi', 'Thriller']       0          0   \n",
              "4  tt0086034  ['Action', 'Adventure', 'Thriller']       1          1   \n",
              "\n",
              "   Animation  Biography  Comedy  Crime  Documentary  Drama  ...  N/A  News  \\\n",
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              "   Reality-TV  Romance  Sci-Fi  Short  Sport  Thriller  War  Western  \n",
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            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head() #ID Correspondes to Image Name in the Images Directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "LVNsHh4uiTup"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 7254/7254 [00:39<00:00, 185.78it/s]\n"
          ]
        }
      ],
      "source": [
        "# For Processing we need the data/image in same size. Converting all images to same size\n",
        "img_width = 200\n",
        "img_height = 200\n",
        "\n",
        "x = []\n",
        "\n",
        "for i in tqdm(range(data.shape[0])):\n",
        "  path = \"Movies-Poster_Dataset/Images/\" + data[\"Id\"][i] + \".jpg\"\n",
        "  img = image.load_img(path, target_size=(img_width, img_height, 3)) #Reshapping image. 3 is for color.\n",
        "  img = image.img_to_array(img) #Converting image to array\n",
        "  img = img/255.0 #Bringing down variance to common difference\n",
        "  x.append(img)  \n",
        "\n",
        "x = np.array(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "VZOV2yj3ozCI"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(<matplotlib.image.AxesImage at 0x1b92378f2b0>,\n",
              " \"['Drama', 'Romance', 'Music']\")"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(x[1]), data[\"Genre\"][1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "7IVMZaKho-bI"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(7254, 25)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Prepare Train and Test Data\n",
        "y = data.drop([\"Id\", \"Genre\"], axis = 1)\n",
        "y = y.to_numpy()\n",
        "y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Ca2Of5-QpT3i"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtPkQXntp248"
      },
      "source": [
        "**Build CNN**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "psmwz4RWpxWx"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"conv2d\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 200, 200, 3)]     0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 198, 198, 16)      448       \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 198, 198, 16)     64        \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 99, 99, 16)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 99, 99, 16)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 97, 97, 32)        4640      \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 97, 97, 32)       128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 48, 48, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 48, 48, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 46, 46, 64)        18496     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 46, 46, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 23, 23, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 23, 23, 64)        0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 21, 21, 128)       73856     \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 21, 21, 128)      512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 10, 10, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 10, 10, 128)       0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 12800)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               1638528   \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " batch_normalization_5 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 25)                3225      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,757,689\n",
            "Trainable params: 1,756,697\n",
            "Non-trainable params: 992\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "inputs = keras.Input(shape=(x_train[0].shape))\n",
        "\n",
        "x = keras.layers.Conv2D(16, (3, 3), activation=activations.relu)(inputs)\n",
        "x = keras.layers.BatchNormalization()(x)\n",
        "x = keras.layers.MaxPool2D(2, 2)(x)\n",
        "x = keras.layers.Dropout(0.3)(x)\n",
        "\n",
        "x = keras.layers.Conv2D(32, (3, 3), activation=activations.relu)(x)\n",
        "x = keras.layers.BatchNormalization()(x)\n",
        "x = keras.layers.MaxPool2D(2, 2)(x)\n",
        "x = keras.layers.Dropout(0.3)(x)\n",
        "\n",
        "x = keras.layers.Conv2D(64, (3, 3), activation=activations.relu)(x)\n",
        "x = keras.layers.BatchNormalization()(x)\n",
        "x = keras.layers.MaxPool2D(2, 2)(x)\n",
        "x = keras.layers.Dropout(0.4)(x)\n",
        "\n",
        "x = keras.layers.Conv2D(128, (3, 3), activation=activations.relu)(x)\n",
        "x = keras.layers.BatchNormalization()(x)\n",
        "x = keras.layers.MaxPool2D(2, 2)(x)\n",
        "x = keras.layers.Dropout(0.5)(x)\n",
        "\n",
        "x = keras.layers.Flatten()(x)\n",
        "x = keras.layers.Dense(128, activation=activations.relu)(x)\n",
        "x = keras.layers.BatchNormalization()(x)\n",
        "x = keras.layers.Dropout(0.5)(x)\n",
        "\n",
        "x = keras.layers.Dense(128, activation=activations.relu)(x)\n",
        "x = keras.layers.BatchNormalization()(x)\n",
        "x = keras.layers.Dropout(0.5)(x)\n",
        "\n",
        "outputs = keras.layers.Dense(25, activation=activations.sigmoid)(x)\n",
        "\n",
        "model = keras.Model(inputs=inputs, outputs=[outputs], name=\"conv2d\")\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Xgco2osXsAes"
      },
      "outputs": [],
      "source": [
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
        "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "    metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "-RugtqmgsQCf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "193/193 [==============================] - 389s 2s/step - loss: 0.7075 - accuracy: 0.1036 - val_loss: 1.2765 - val_accuracy: 0.0634\n",
            "Epoch 2/5\n",
            "193/193 [==============================] - 290s 2s/step - loss: 0.3135 - accuracy: 0.2522 - val_loss: 0.4599 - val_accuracy: 0.1901\n",
            "Epoch 3/5\n",
            "193/193 [==============================] - 288s 1s/step - loss: 0.2611 - accuracy: 0.3011 - val_loss: 0.8970 - val_accuracy: 0.0670\n",
            "Epoch 4/5\n",
            "193/193 [==============================] - 318s 2s/step - loss: 0.2508 - accuracy: 0.3114 - val_loss: 3.5476 - val_accuracy: 0.0376\n",
            "Epoch 5/5\n",
            "193/193 [==============================] - 318s 2s/step - loss: 0.2455 - accuracy: 0.3194 - val_loss: 0.4269 - val_accuracy: 0.1497\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "QMpQXbx5utFj"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'loss': [0.7075154185295105,\n",
              "  0.31348538398742676,\n",
              "  0.26107656955718994,\n",
              "  0.25080493092536926,\n",
              "  0.2455192357301712],\n",
              " 'accuracy': [0.10364963859319687,\n",
              "  0.25223034620285034,\n",
              "  0.30105432868003845,\n",
              "  0.3114355206489563,\n",
              "  0.3193836212158203],\n",
              " 'val_loss': [1.276466965675354,\n",
              "  0.45991334319114685,\n",
              "  0.8969794511795044,\n",
              "  3.5476367473602295,\n",
              "  0.42692849040031433],\n",
              " 'val_accuracy': [0.06336088478565216,\n",
              "  0.1900826394557953,\n",
              "  0.06703397631645203,\n",
              "  0.03764921799302101,\n",
              "  0.14967860281467438]}"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "history.history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "oI-jdm8Zuwah"
      },
      "outputs": [],
      "source": [
        "epoch_range = range(1, 6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "HzFm8eiBu0eH"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot training and validation accuracy values\n",
        "# sparse_categorical_accuracy vs val_sparse_categorical_accuracy\n",
        "plt.plot(epoch_range, history.history[\"accuracy\"])\n",
        "plt.plot(epoch_range, history.history[\"val_accuracy\"])\n",
        "plt.title(\"Model Accuracy\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"Train\", \"Val\"], loc=\"upper left\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "G7OtXi9UvCKb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot training and validation loss values\n",
        "# Loss vs Val_Loss\n",
        "plt.plot(epoch_range, history.history[\"loss\"])\n",
        "plt.plot(epoch_range, history.history[\"val_loss\"])\n",
        "plt.title(\"Model Loss\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"Train\", \"Val\"], loc=\"upper left\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mC2Ey0MDvqmc"
      },
      "source": [
        "**Making Predictions**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qtc0f3zJv1of"
      },
      "outputs": [],
      "source": [
        "img = image.load_img(\"Movies-Poster_Dataset/endgame.jpg\", target_size=(img_width, img_height, 3))\n",
        "plt.imshow(img)\n",
        "img = image.img_to_array(img)\n",
        "img = img/255.0\n",
        "img = img.reshape(1, img_width, img_height, 3)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hMf8xKcew8Qo"
      },
      "outputs": [],
      "source": [
        "classes = data.columns[2:]\n",
        "classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cTCh9F3Ww9wW"
      },
      "outputs": [],
      "source": [
        "y_prob = model.predict(img)\n",
        "y_prob, y_prob[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D4ltjQ1hxNRS"
      },
      "outputs": [],
      "source": [
        "#Sorting and getting top 3 predictions\n",
        "top_3_predictions = np.argsort(y_prob[0])[:-4:-1]\n",
        "top_3_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DZ0E3whCx79n"
      },
      "outputs": [],
      "source": [
        "for i in range(3):\n",
        "  print(classes[top_3_predictions[i]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pSfEL0slx2Ov"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "include_colab_link": true,
      "name": "Tensorflow - Multi-Label Image Classification Using CNN.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "0d7fd96aeadd740978789f2880dd43822d8681a39e327029c33ff72656207c4b"
    },
    "kernelspec": {
      "display_name": "Python 3.10.4 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}