notebooks/11-3.convolutional_sentiment_classifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "TensorFlow 2.3 on Python 3.6 (CUDA 10.1)",
      "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": "3.6.9"
    },
    "colab": {
      "name": "11-3.convolutional_sentiment_classifier.ipynb",
      "provenance": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "auGi4mKeedKV"
      },
      "source": [
        "# 합성곱 신경망 감성 분류기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdydZ8qLedKZ"
      },
      "source": [
        "이 노트북에서는 *합성곱 신경망* 을 만들어 감성에 따라 IMDB 영화 리뷰를 분류합니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yaRCfJDGedKZ"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rickiepark/dl-illustrated/blob/master/notebooks/11-3.convolutional_sentiment_classifier.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPCt_xn8edKa"
      },
      "source": [
        "#### 라이브러리를 적재합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9J5OaqoedKa"
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras.datasets import imdb\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Embedding\n",
        "from tensorflow.keras.layers import SpatialDropout1D, Conv1D, GlobalMaxPooling1D # new! \n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "import os\n",
        "from sklearn.metrics import roc_auc_score \n",
        "import matplotlib.pyplot as plt \n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BaeNAlqcedKa"
      },
      "source": [
        "#### 하이퍼파라미터를 설정합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MK_04yixedKb"
      },
      "source": [
        "# 출력 디렉토리\n",
        "output_dir = 'model_output/conv'\n",
        "\n",
        "# 훈련\n",
        "epochs = 4\n",
        "batch_size = 128\n",
        "\n",
        "# 벡터 공간 임베딩\n",
        "n_dim = 64\n",
        "n_unique_words = 5000 \n",
        "max_review_length = 400\n",
        "pad_type = trunc_type = 'pre'\n",
        "drop_embed = 0.2 # new!\n",
        "\n",
        "# 합성곱 층 구조\n",
        "n_conv = 256 # 필터 또는 커널\n",
        "k_conv = 3 # 커널 크기\n",
        "\n",
        "# 밀집 층 구조\n",
        "n_dense = 256\n",
        "dropout = 0.2"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBZnWN9gedKb"
      },
      "source": [
        "#### 데이터를 적재합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yg9LmFJ8edKb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8f3b62c-38cb-43d5-f658-7ab37d740bd3"
      },
      "source": [
        "(x_train, y_train), (x_valid, y_valid) = imdb.load_data(num_words=n_unique_words) # n_words_to_skip 삭제"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17464789/17464789 [==============================] - 2s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dk3Gka77edKb"
      },
      "source": [
        "#### 데이터 전처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbJE8l5eedKb"
      },
      "source": [
        "x_train = pad_sequences(x_train, maxlen=max_review_length, padding=pad_type, truncating=trunc_type, value=0)\n",
        "x_valid = pad_sequences(x_valid, maxlen=max_review_length, padding=pad_type, truncating=trunc_type, value=0)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "wu4BFVGledKc"
      },
      "source": [
        "#### 신경망 만들기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "URo-oVAaedKc"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(n_unique_words, n_dim, input_length=max_review_length)) \n",
        "model.add(SpatialDropout1D(drop_embed))\n",
        "model.add(Conv1D(n_conv, k_conv, activation='relu'))\n",
        "# model.add(Conv1D(n_conv, k_conv, activation='relu'))\n",
        "model.add(GlobalMaxPooling1D())\n",
        "model.add(Dense(n_dense, activation='relu'))\n",
        "model.add(Dropout(dropout))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojXnH4rWedKc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94f9861e-4333-479c-8a88-841d8d27c2c8"
      },
      "source": [
        "model.summary() "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 400, 64)           320000    \n",
            "                                                                 \n",
            " spatial_dropout1d (SpatialD  (None, 400, 64)          0         \n",
            " ropout1D)                                                       \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 398, 256)          49408     \n",
            "                                                                 \n",
            " global_max_pooling1d (Globa  (None, 256)              0         \n",
            " lMaxPooling1D)                                                  \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               65792     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 257       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 435,457\n",
            "Trainable params: 435,457\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIkDFiPvedKd"
      },
      "source": [
        "#### 모델 설정"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRr-3qHpedKd"
      },
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0hsJJDFedKd"
      },
      "source": [
        "modelcheckpoint = ModelCheckpoint(filepath=output_dir+\"/weights.{epoch:02d}.hdf5\")\n",
        "if not os.path.exists(output_dir):\n",
        "    os.makedirs(output_dir)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2SH1LfBKedKd"
      },
      "source": [
        "#### 훈련!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJaA6icoedKd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33390274-e6fb-42c2-b292-f8b985bc6315"
      },
      "source": [
        "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_valid, y_valid), callbacks=[modelcheckpoint])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "196/196 [==============================] - 14s 19ms/step - loss: 0.4874 - accuracy: 0.7458 - val_loss: 0.2939 - val_accuracy: 0.8754\n",
            "Epoch 2/4\n",
            "196/196 [==============================] - 3s 17ms/step - loss: 0.2406 - accuracy: 0.9043 - val_loss: 0.2569 - val_accuracy: 0.8920\n",
            "Epoch 3/4\n",
            "196/196 [==============================] - 4s 20ms/step - loss: 0.1649 - accuracy: 0.9382 - val_loss: 0.2686 - val_accuracy: 0.8928\n",
            "Epoch 4/4\n",
            "196/196 [==============================] - 4s 20ms/step - loss: 0.1058 - accuracy: 0.9646 - val_loss: 0.2996 - val_accuracy: 0.8888\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f330ed0b100>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "tMmBgaAAedKd"
      },
      "source": [
        "#### 평가"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DUIAj7AedKe"
      },
      "source": [
        "model.load_weights(output_dir+\"/weights.03.hdf5\") "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWdoH1XvedKe",
        "outputId": "d33cdf61-5877-4a93-c0c7-eaee680e3be8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_hat = model.predict(x_valid)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 2s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "WRUXrp89edKe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "ec4ddd78-9463-4f90-9de1-c4df74c608b1"
      },
      "source": [
        "plt.hist(y_hat)\n",
        "_ = plt.axvline(x=0.5, color='orange')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQM27NWTedKe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "831eda35-c1a5-4122-be13-0ad69a00ad80"
      },
      "source": [
        "\"{:0.2f}\".format(roc_auc_score(y_valid, y_hat)*100.0)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'96.01'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    }
  ]
}