3_RNN_LSTM_Emotions_情意分析.ipynb

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    "colab": {
      "name": "3 RNN LSTM Emotions 情意分析.ipynb",
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      "collapsed_sections": [],
      "include_colab_link": true
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    "accelerator": "GPU"
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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3yNYyYU-veg"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pU0MI3Jm-vej"
      },
      "source": [
        "### 1. Import深度學習套件各層的函式庫"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGQHjFkg-vek"
      },
      "source": [
        "from tensorflow.keras.preprocessing import sequence\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Embedding\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras.datasets import imdb"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "upzJSb2Y-vek"
      },
      "source": [
        "### 2. 讀入數據\n",
        "\n",
        "一般NLP自然語言處理, 我們會限制最大要使用的字數，如25000。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMRJOWWD-vek",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3690c868-bcef-45d5-cb9a-d46a8e1f1fa2"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<string>:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/datasets/imdb.py:155: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/datasets/imdb.py:156: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1ewtpvu-vel",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90c5acf9-80b9-406a-b4da-43701df71f7b"
      },
      "source": [
        "len(x_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4GlhXmlJ-vem",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e793cbd-7225-4657-f12b-86dbbd682668"
      },
      "source": [
        "len(x_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q2HndB0cAH_d"
      },
      "source": [
        "注意每筆評論的長度當然是不一樣的，列印出訓練資料的長度，即可發現。故需於下一階段限制最大長度及進行補零Padding。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_SFQH8rg-vem",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0360a818-6ee3-4063-92bf-734f3666b64a"
      },
      "source": [
        "len(x_train[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "218"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTxLvQOz-ven",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33d8e22c-9a2b-47d9-cc6d-8ef2439afa7d"
      },
      "source": [
        "len(x_train[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "189"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TwmfAdEm-ven",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d760753-509b-47fd-ffca-3b7e023acace"
      },
      "source": [
        "y_train[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qVETeL4-veo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15c60dbc-9f4f-4526-ffc3-db04549eb1eb"
      },
      "source": [
        "y_train[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kY0bt0Mv-veo"
      },
      "source": [
        "### 3. 資料處理 Padding 補零\n",
        "\n",
        "雖然我們可以做真的 seq2seq, 可是資料長度不一樣對計算上有麻煩, 因此平常還是會固定一定長度, 其餘補 0。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynXfhV0S-veo"
      },
      "source": [
        "x_train = sequence.pad_sequences(x_train, maxlen=100)\n",
        "x_test = sequence.pad_sequences(x_test, maxlen=100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLw1t5YZ-vep"
      },
      "source": [
        "### 4. step 01: 打造一個函數學習機"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3WoAWzg-vep"
      },
      "source": [
        "model = Sequential()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "paCqtY9E-vep"
      },
      "source": [
        "model.add(Embedding(10000, 128))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3ZFi6IO-vep"
      },
      "source": [
        "model.add(LSTM(128))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYMx8FF7-veq"
      },
      "source": [
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XbmruxHeAxT8"
      },
      "source": [
        "#### 組裝"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hc3kJo8u-veq"
      },
      "source": [
        "model.compile(loss='binary_crossentropy',\n",
        "             optimizer='adam',\n",
        "             metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_s0X6DHA6A-"
      },
      "source": [
        "#### model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKwxPjN6-veq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb82f43a-f316-4904-9cab-8276bf5612b5"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 128)         1280000   \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 128)               131584    \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 1,411,713\n",
            "Trainable params: 1,411,713\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O19zWWcl-veq"
      },
      "source": [
        "### 5. step 02: 訓練"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iIL00x1u-veq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efbeea1d-3a67-4a12-e40d-c10582014d3f"
      },
      "source": [
        "model.fit(x_train, y_train, batch_size=32, epochs=10,\n",
        "         validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "782/782 [==============================] - 22s 24ms/step - loss: 0.4183 - accuracy: 0.8095 - val_loss: 0.3533 - val_accuracy: 0.8436\n",
            "Epoch 2/10\n",
            "782/782 [==============================] - 18s 24ms/step - loss: 0.2572 - accuracy: 0.8975 - val_loss: 0.3465 - val_accuracy: 0.8459\n",
            "Epoch 3/10\n",
            "782/782 [==============================] - 19s 24ms/step - loss: 0.1825 - accuracy: 0.9292 - val_loss: 0.4315 - val_accuracy: 0.8412\n",
            "Epoch 4/10\n",
            "782/782 [==============================] - 18s 23ms/step - loss: 0.1317 - accuracy: 0.9512 - val_loss: 0.4378 - val_accuracy: 0.8267\n",
            "Epoch 5/10\n",
            "782/782 [==============================] - 19s 24ms/step - loss: 0.0930 - accuracy: 0.9672 - val_loss: 0.6900 - val_accuracy: 0.8292\n",
            "Epoch 6/10\n",
            "782/782 [==============================] - 18s 24ms/step - loss: 0.0794 - accuracy: 0.9730 - val_loss: 0.5700 - val_accuracy: 0.8333\n",
            "Epoch 7/10\n",
            "782/782 [==============================] - 19s 24ms/step - loss: 0.0505 - accuracy: 0.9833 - val_loss: 0.6798 - val_accuracy: 0.8370\n",
            "Epoch 8/10\n",
            "782/782 [==============================] - 18s 24ms/step - loss: 0.0460 - accuracy: 0.9848 - val_loss: 0.6846 - val_accuracy: 0.8300\n",
            "Epoch 9/10\n",
            "782/782 [==============================] - 18s 23ms/step - loss: 0.0302 - accuracy: 0.9912 - val_loss: 0.7452 - val_accuracy: 0.8279\n",
            "Epoch 10/10\n",
            "782/782 [==============================] - 18s 23ms/step - loss: 0.0195 - accuracy: 0.9941 - val_loss: 0.8609 - val_accuracy: 0.8259\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f8b51a9e490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQ9uuTiji5ya"
      },
      "source": [
        "### 6. step 03: 測試"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xt-JiZyljijE"
      },
      "source": [
        "from tensorflow.keras.datasets.imdb import get_word_index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPemkM0tj02R"
      },
      "source": [
        "word_index = get_word_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1P-xWqckJLQ",
        "outputId": "3464744c-e9a3-404c-acfb-6c3655deba04",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "word_index['this']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5SAz0wikLFt"
      },
      "source": [
        "text = \"this movie is worth seeing\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gyQfVVTekgJd"
      },
      "source": [
        "seq = [word_index[x] for x in text.split()]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiqL9mE_kulv",
        "outputId": "4d81e797-7970-487a-9fb8-2845bf235802",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.predict([seq])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.98741376]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iV3i05AOk-iI"
      },
      "source": [
        "text = \"could of been so much better if properly cast directed and a better script\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qJd1ZRom_QY"
      },
      "source": [
        "seq = [word_index[x] for x in text.split()]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GgMNjvVSnB5J",
        "outputId": "13110bc3-bd96-4c40-ff9f-9c4dfec49c83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.predict([seq])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.9204154]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKNqM34x-ver"
      },
      "source": [
        "### 6. 換個存檔方式\n",
        "\n",
        "這次是把 model 和訓練權重分開存, 使用上更有彈性。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UkHz6PbACrg-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5897b5a-650d-42ab-d9f2-872a92f8ad1f"
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2puuhaa-C0-Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2c97d72-4417-4f62-8a2d-22d40bc78f9b"
      },
      "source": [
        "%cd '/content/drive/My Drive/Colab Notebooks'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Colab Notebooks\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTbM9T7A-ver"
      },
      "source": [
        "model_json = model.to_json()\n",
        "open('imdb_model_architecture.json', 'w').write(model_json)\n",
        "model.save_weights('ismdb_model_weights.h5')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}