Lecture_13.ipynb

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    "colab": {
      "name": "Lecture12.ipynb",
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      "collapsed_sections": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "TPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTDs3wZZ1K1L",
        "colab_type": "text"
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      "source": [
        "### 多标签分类\n",
        "\n",
        "Project\n",
        "\n",
        "https://www.cs.ccu.edu.tw/~wtchu/projects/MoviePoster/index.html\n",
        "\n",
        "Paper\n",
        "\n",
        "https://www.cs.ccu.edu.tw/~wtchu/papers/2017MUSA-chu.pdf"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmgvJnqf1PIy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "65ca79ad-ffba-4536-c2ae-d1c8b4395786"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing import image\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQ54BuZW233y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EunQH5GZ239Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "da09ecce-92af-45fc-e01b-8aab5f54799a"
      },
      "source": [
        "!git clone https://github.com/Jian-Yin-Shine/Movies-Poster_Dataset.git"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Movies-Poster_Dataset'...\n",
            "remote: Enumerating objects: 7873, done.\u001b[K\n",
            "remote: Total 7873 (delta 0), reused 0 (delta 0), pack-reused 7873\u001b[K\n",
            "Receiving objects: 100% (7873/7873), 246.59 MiB | 36.66 MiB/s, done.\n",
            "Resolving deltas: 100% (1/1), done.\n",
            "Checking out files: 100% (7871/7871), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUbFtmae24AV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a1dca4a8-9852-44ba-8324-4075d2ced44f"
      },
      "source": [
        "data = pd.read_csv('Movies-Poster_Dataset/train.csv')\n",
        "data.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7254, 27)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjdDtwuo24DC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "fc91b2ea-84dc-46c8-b066-21ec2d253ecc"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Action</th>\n",
              "      <th>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Biography</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Documentary</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Family</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>History</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Music</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>N/A</th>\n",
              "      <th>News</th>\n",
              "      <th>Reality-TV</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>Short</th>\n",
              "      <th>Sport</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>War</th>\n",
              "      <th>Western</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0086425</td>\n",
              "      <td>['Comedy', 'Drama']</td>\n",
              "      <td>0</td>\n",
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              "      <td>['Drama', 'Romance', 'Music']</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0086465</td>\n",
              "      <td>['Comedy']</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0086567</td>\n",
              "      <td>['Sci-Fi', 'Thriller']</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0086034</td>\n",
              "      <td>['Action', 'Adventure', 'Thriller']</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Id                                Genre  ...  War  Western\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']  ...    0        0\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wbhthCY24GN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import tqdm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0c3xSDuz24JI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c3f47e43-9b49-4cc2-ce2a-6fc392f631e5"
      },
      "source": [
        "img_width = 224\n",
        "img_height = 224\n",
        "\n",
        "x = []\n",
        "\n",
        "for i in tqdm.trange(data.shape[0]):\n",
        "  path = os.path.join('Movies-Poster_Dataset', 'Images', data['Id'][i]+'.jpg')\n",
        "  img = image.load_img(path, target_size=(img_width, img_height, 3))\n",
        "  img = image.img_to_array(img)\n",
        "  img = img / 255.0\n",
        "  x.append(img)\n",
        "\n",
        "x = np.array(x)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 7254/7254 [00:26<00:00, 274.35it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BEvVOeUE50s5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4a1e2a71-e323-422c-8a0f-e5f79ad94236"
      },
      "source": [
        "x.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7254, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqvgWXu5_icH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "058a27dd-b74a-4429-d82a-39a2244fc3da"
      },
      "source": [
        "plt.imshow(x[1])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f66f58a5160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "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": "-KHmpRKIDwVe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9e85f6da-260c-4899-fdd5-b9c7212ae3d4"
      },
      "source": [
        "data['Genre'][1]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"['Drama', 'Romance', 'Music']\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2dcy2SmD9d-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8af6432d-fbab-45e1-a523-3d9e02271485"
      },
      "source": [
        "y = data.drop(['Id', 'Genre'], axis = 1)\n",
        "y = y.to_numpy()\n",
        "y.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7254, 25)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WIsWbXAPEXiI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state = 0, test_size = 0.15)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eJhyh6PNDkr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8306af38-e3a2-42f7-f68c-f8b50312d292"
      },
      "source": [
        "x_train[0].shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "upWTaGjMEnJs",
        "colab_type": "text"
      },
      "source": [
        "### 建立模型&训练模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E3lVllBXElYb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(16, (3,3), activation='relu', input_shape = x_train[0].shape))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool2D(2,2))\n",
        "# model.add(Dropout(0.3))\n",
        "\n",
        "model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool2D(2,2))\n",
        "# model.add(Dropout(0.3))\n",
        "\n",
        "model.add(Conv2D(64, (3,3), activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool2D(2,2))\n",
        "# model.add(Dropout(0.4))\n",
        "\n",
        "model.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool2D(2,2))\n",
        "# model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "# model.add(Dropout(0.5))\n",
        "\n",
        "\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "# model.add(Dropout(0.5))\n",
        "\n",
        "\n",
        "model.add(Dense(25, activation='sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6NPa4_yFuwt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        },
        "outputId": "7eb09a5b-4f72-4281-c488-53d405d15b27"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 222, 222, 16)      448       \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 222, 222, 16)      64        \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 111, 111, 16)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 109, 109, 32)      4640      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 109, 109, 32)      128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 54, 54, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 52, 52, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 52, 52, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 26, 26, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 24, 24, 128)       73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 24, 24, 128)       512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 12, 12, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 18432)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               2359424   \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 25)                3225      \n",
            "=================================================================\n",
            "Total params: 2,478,585\n",
            "Trainable params: 2,477,593\n",
            "Non-trainable params: 992\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "70dtJyI8Fwf-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJq12OK8GAh6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "a2114bb0-a02c-41f9-e068-801928cebfa0"
      },
      "source": [
        "history = model.fit(x_train, y_train, epochs=8, validation_data=(x_test, y_test))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/8\n",
            "193/193 [==============================] - 384s 2s/step - loss: 0.4795 - accuracy: 0.2624 - val_loss: 0.2620 - val_accuracy: 0.1919\n",
            "Epoch 2/8\n",
            "193/193 [==============================] - 388s 2s/step - loss: 0.2338 - accuracy: 0.3392 - val_loss: 0.2406 - val_accuracy: 0.2837\n",
            "Epoch 3/8\n",
            "193/193 [==============================] - 391s 2s/step - loss: 0.2242 - accuracy: 0.3471 - val_loss: 0.2456 - val_accuracy: 0.2544\n",
            "Epoch 4/8\n",
            "193/193 [==============================] - 386s 2s/step - loss: 0.2157 - accuracy: 0.3700 - val_loss: 0.2371 - val_accuracy: 0.2764\n",
            "Epoch 5/8\n",
            "193/193 [==============================] - 387s 2s/step - loss: 0.1992 - accuracy: 0.3964 - val_loss: 0.2429 - val_accuracy: 0.2948\n",
            "Epoch 6/8\n",
            "193/193 [==============================] - 386s 2s/step - loss: 0.1708 - accuracy: 0.4435 - val_loss: 0.2653 - val_accuracy: 0.2773\n",
            "Epoch 7/8\n",
            "193/193 [==============================] - 388s 2s/step - loss: 0.1388 - accuracy: 0.4897 - val_loss: 0.2816 - val_accuracy: 0.2681\n",
            "Epoch 8/8\n",
            "193/193 [==============================] - 390s 2s/step - loss: 0.0937 - accuracy: 0.5265 - val_loss: 0.3019 - val_accuracy: 0.2893\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pngn9LEWGIQ6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_learningCurve(history, epoch):\n",
        "  # Plot training & validation accuracy values\n",
        "  epoch_range = range(1, epoch+1)\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()\n",
        "\n",
        "  # Plot training & validation loss values\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()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuPtH8rziUxc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "13f0355b-bd03-4fa6-bdf7-7da0652a61db"
      },
      "source": [
        "plot_learningCurve(history, 8)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "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": "xu47DyvLiZRM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}