IMDB dataset sentiment prediction.ipynb

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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/dmarinere/ds/blob/master/IMDB%20dataset%20sentiment%20prediction.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",
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        "id": "yv1yFyxdGuCX",
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          "height": 34
        },
        "outputId": "6826b181-82ea-43bb-bac7-60dee260d2ab"
      },
      "source": [
        "from keras.preprocessing import sequence\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, Flatten, Dense\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Dab9LeE_wHA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.datasets import imdb\n",
        "top_words = 25000\n",
        "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=top_words)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
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        "outputId": "ce81e2a9-e8fa-4d1e-bee3-259f86f58d41"
      },
      "source": [
        "x_train[0]"
      ],
      "execution_count": 5,
      "outputs": [
        {
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          "metadata": {
            "tags": []
          },
          "execution_count": 5
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      ]
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    {
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        "id": "9bmHVcSf_zIE",
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          "height": 1000
        },
        "outputId": "6ebe3363-d64d-45ce-d0e6-fe4897e8ac76"
      },
      "source": [
        "imdb.get_word_index()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'fawn': 34701,\n",
              " 'tsukino': 52006,\n",
              " 'nunnery': 52007,\n",
              " 'sonja': 16816,\n",
              " 'vani': 63951,\n",
              " 'woods': 1408,\n",
              " 'spiders': 16115,\n",
              " 'hanging': 2345,\n",
              " 'woody': 2289,\n",
              " 'trawling': 52008,\n",
              " \"hold's\": 52009,\n",
              " 'comically': 11307,\n",
              " 'localized': 40830,\n",
              " 'disobeying': 30568,\n",
              " \"'royale\": 52010,\n",
              " \"harpo's\": 40831,\n",
              " 'canet': 52011,\n",
              " 'aileen': 19313,\n",
              " 'acurately': 52012,\n",
              " \"diplomat's\": 52013,\n",
              " 'rickman': 25242,\n",
              " 'arranged': 6746,\n",
              " 'rumbustious': 52014,\n",
              " 'familiarness': 52015,\n",
              " \"spider'\": 52016,\n",
              " 'hahahah': 68804,\n",
              " \"wood'\": 52017,\n",
              " 'transvestism': 40833,\n",
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              " 'bringing': 2338,\n",
              " 'seamier': 40834,\n",
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              " 'bravora': 52018,\n",
              " 'grueling': 16817,\n",
              " 'wooden': 1636,\n",
              " 'wednesday': 16818,\n",
              " \"'prix\": 52019,\n",
              " 'altagracia': 34704,\n",
              " 'circuitry': 52020,\n",
              " 'crotch': 11585,\n",
              " 'busybody': 57766,\n",
              " \"tart'n'tangy\": 52021,\n",
              " 'burgade': 14129,\n",
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              " \"tom's\": 11038,\n",
              " 'snuggles': 52025,\n",
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              " 'complainers': 52027,\n",
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              " 'bronte': 11926,\n",
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              " 'quart': 40841,\n",
              " 'kids': 359,\n",
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              " 'kida': 21906,\n",
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              " \"cervi's\": 52046,\n",
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              " 'yahoo': 10307,\n",
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              " 'gershon': 16821,\n",
              " 'ludicrosity': 40850,\n",
              " 'coprophilia': 52062,\n",
              " 'expressively': 40851,\n",
              " \"india's\": 19500,\n",
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              " 'wana': 52063,\n",
              " 'wang': 5283,\n",
              " 'wand': 30571,\n",
              " 'wane': 25245,\n",
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              " 'tchaikovsky': 21908,\n",
              " 'travel': 2103,\n",
              " \"'victory'\": 52066,\n",
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              " \"chapters'\": 52067,\n",
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              " \"effect'\": 34713,\n",
              " 'snugly': 27637,\n",
              " 'kuei': 40856,\n",
              " 'welcomed': 9092,\n",
              " 'dishonor': 30071,\n",
              " 'concurrence': 52075,\n",
              " 'stoicism': 23382,\n",
              " \"guys'\": 14896,\n",
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              " 'butcher': 6703,\n",
              " \"melfi's\": 40857,\n",
              " 'aargh': 30623,\n",
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              " 'wickedly': 11308,\n",
              " 'fit': 1180,\n",
              " 'labratory': 52078,\n",
              " 'lifeline': 40859,\n",
              " 'screaming': 1927,\n",
              " 'fix': 4287,\n",
              " 'cineliterate': 52079,\n",
              " 'fic': 52080,\n",
              " 'fia': 52081,\n",
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              " 'fmvs': 52082,\n",
              " 'fie': 52083,\n",
              " 'reentered': 52084,\n",
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              " 'fil': 52086,\n",
              " 'zucker': 12606,\n",
              " 'ached': 31931,\n",
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              " 'songwriter': 13885,\n",
              " 'shivam': 34715,\n",
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              " 'effects': 299,\n",
              " 'slauther': 52090,\n",
              " \"'flame'\": 52091,\n",
              " 'sommerset': 52092,\n",
              " 'interwhined': 52093,\n",
              " 'whacking': 27638,\n",
              " 'bartok': 52094,\n",
              " 'barton': 8775,\n",
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              " \"fi'\": 52095,\n",
              " 'ingrid': 6192,\n",
              " 'stribor': 30575,\n",
              " 'approporiately': 52096,\n",
              " 'wobblyhand': 52097,\n",
              " 'tantalisingly': 52098,\n",
              " 'ankylosaurus': 52099,\n",
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              " \"jenkins'\": 52101,\n",
              " 'metafiction': 52102,\n",
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              " \"reeves'\": 23383,\n",
              " \"inamorata's\": 57781,\n",
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              " 'abbots': 40861,\n",
              " 'lanisha': 17649,\n",
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              " 'bakelite': 52118,\n",
              " 'renovated': 27639,\n",
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              " \"edie's\": 25905,\n",
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              " 'anniversaries': 52127,\n",
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              " 'exclaimed': 34721,\n",
              " \"'unfunny'\": 52129,\n",
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              " 'feeling': 544,\n",
              " \"wanda's\": 52131,\n",
              " 'dolan': 33266,\n",
              " '278': 52133,\n",
              " 'peacoat': 52134,\n",
              " 'brawny': 40870,\n",
              " 'mishra': 40871,\n",
              " 'worlders': 40872,\n",
              " 'protags': 52135,\n",
              " 'skullcap': 52136,\n",
              " 'dastagir': 57596,\n",
              " 'affairs': 5622,\n",
              " 'wholesome': 7799,\n",
              " 'hymen': 52137,\n",
              " 'paramedics': 25246,\n",
              " 'unpersons': 52138,\n",
              " 'heavyarms': 52139,\n",
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              " 'coulisses': 52141,\n",
              " 'hymer': 40873,\n",
              " 'kremlin': 52142,\n",
              " 'shipments': 30581,\n",
              " 'pixilated': 52143,\n",
              " \"'00s\": 30582,\n",
              " 'diminishing': 18512,\n",
              " 'cinematic': 1357,\n",
              " 'resonates': 14898,\n",
              " 'simplify': 40874,\n",
              " \"nature'\": 40875,\n",
              " 'temptresses': 40876,\n",
              " 'reverence': 16822,\n",
              " 'resonated': 19502,\n",
              " 'dailey': 34722,\n",
              " '2\\x85': 52144,\n",
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              " 'teck': 30583,\n",
              " \"'e'\": 76125,\n",
              " 'doesn’t': 40881,\n",
              " 'purged': 52149,\n",
              " 'saying': 657,\n",
              " \"martians'\": 41095,\n",
              " 'norliss': 23418,\n",
              " 'dickey': 27642,\n",
              " 'dicker': 52152,\n",
              " \"'sependipity\": 52153,\n",
              " 'padded': 8422,\n",
              " 'ordell': 57792,\n",
              " \"sturges'\": 40882,\n",
              " 'independentcritics': 52154,\n",
              " 'tempted': 5745,\n",
              " \"atkinson's\": 34724,\n",
              " 'hounded': 25247,\n",
              " 'apace': 52155,\n",
              " 'clicked': 15494,\n",
              " \"'humor'\": 30584,\n",
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              " \"zemeckis's\": 34725,\n",
              " 'lube': 21911,\n",
              " 'shocky': 52157,\n",
              " 'plate': 7476,\n",
              " 'plata': 40883,\n",
              " 'sturgess': 40884,\n",
              " \"nerds'\": 40885,\n",
              " 'plato': 20600,\n",
              " 'plath': 34726,\n",
              " 'platt': 40886,\n",
              " 'mcnab': 52159,\n",
              " 'clumsiness': 27643,\n",
              " 'altogether': 3899,\n",
              " 'massacring': 42584,\n",
              " 'bicenntinial': 52160,\n",
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              " 'affords': 17641,\n",
              " 'concert': 3249,\n",
              " \"yara's\": 87955,\n",
              " 'someome': 52386,\n",
              " 'lingering': 8424,\n",
              " \"abraham's\": 41511,\n",
              " 'beesley': 34769,\n",
              " 'cherbourg': 34770,\n",
              " 'kagan': 28624,\n",
              " 'snatch': 9097,\n",
              " \"miyazaki's\": 9260,\n",
              " 'absorbs': 25264,\n",
              " \"koltai's\": 40970,\n",
              " 'tingled': 64027,\n",
              " 'crossroads': 19511,\n",
              " 'rehab': 16121,\n",
              " 'falworth': 52389,\n",
              " 'sequals': 52390,\n",
              " ...}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3wQakmXBMoH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "21dd4ee8-72e1-4251-f8c7-360d37ee79c1"
      },
      "source": [
        "word_dict = imdb.get_word_index()\n",
        "word_dict = { key:(value + 3) for key, value in word_dict.items() }\n",
        "word_dict[''] = 0  # Padding\n",
        "word_dict['>'] = 1 # Start\n",
        "word_dict['?'] = 2 # Unknown word\n",
        "reverse_word_dict = { value:key for key, value in word_dict.items() }\n",
        "print(' '.join(reverse_word_dict[id] for id in x_train[0]))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                                                                                                                                                                                                                                                                                          > this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert redford's is an amazing actor and now the same being director norman's father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for retail and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also congratulations to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the praising list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yeOcPH0kBjun",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "max_review_length = 500\n",
        "x_train = sequence.pad_sequences(x_train, maxlen=max_review_length)\n",
        "x_test = sequence.pad_sequences(x_test, maxlen=max_review_length)\n"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pK-ouDlJB3qm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "681c2058-81bd-420d-cf64-06467b9be4b1"
      },
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, Flatten, Dense\n",
        "\n",
        "embedding_vector_length = 32\n",
        "model = Sequential()\n",
        "model.add(layers.Embedding(top_words, embedding_vector_length, input_length=max_review_length))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(16, activation='relu'))\n",
        "model.add(Dense(16, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])\n",
        "print(model.summary())"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 500, 32)           800000    \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 16000)             0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 16)                256016    \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 1,056,305\n",
            "Trainable params: 1,056,305\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6kfQlfqJqEf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "1a6788ae-656a-4261-f4f1-8a965b5ef508"
      },
      "source": [
        "hist = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=5, batch_size=128)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "196/196 [==============================] - 6s 29ms/step - loss: 0.5058 - accuracy: 0.7158 - val_loss: 0.2870 - val_accuracy: 0.8784\n",
            "Epoch 2/5\n",
            "196/196 [==============================] - 6s 30ms/step - loss: 0.1614 - accuracy: 0.9410 - val_loss: 0.3018 - val_accuracy: 0.8750\n",
            "Epoch 3/5\n",
            "196/196 [==============================] - 6s 30ms/step - loss: 0.0388 - accuracy: 0.9915 - val_loss: 0.3801 - val_accuracy: 0.8680\n",
            "Epoch 4/5\n",
            "196/196 [==============================] - 6s 30ms/step - loss: 0.0082 - accuracy: 0.9988 - val_loss: 0.4188 - val_accuracy: 0.8704\n",
            "Epoch 5/5\n",
            "196/196 [==============================] - 6s 30ms/step - loss: 0.0021 - accuracy: 0.9999 - val_loss: 0.4530 - val_accuracy: 0.8731\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFi2GOCrJvkt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "cc5289a6-b8eb-4dac-de08-05d77f93983b"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set()\n",
        "acc = hist.history['loss']\n",
        "val = hist.history['val_loss']\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.plot(epochs, acc, '-', label='Training accuracy')\n",
        "plt.plot(epochs, val, ':', label='Validation accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(loc='upper left')\n",
        "plt.plot()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}