Week2_imdb_reviews_dataset.ipynb

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      "name": "Week2_imdb_reviews_dataset.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Mohamed-derbeli/Natural-Language-Processing-in-TensorFlow/blob/main/Week2_imdb_reviews_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zX4Kg8DUTKWO"
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gqi4N0TcSLf8"
      },
      "source": [
        "**Note:** This notebook can run using TensorFlow 2.5.0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1zuX6UDSLf9"
      },
      "source": [
        "#!pip install tensorflow==2.5.0"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-AhVYeBWgQ3",
        "outputId": "6673d78b-b25c-411f-c4aa-683c7755bae6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "# !pip install -q tensorflow-datasets"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_IoM4VFxWpMR",
        "outputId": "3c491a68-ff16-47dd-cb1e-10919d66010d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "imdb, info = tfds.load(\"imdb_reviews\", with_info=True, as_supervised=True)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset imdb_reviews/plain_text/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"
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          "text": [
            "\n",
            "\n"
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          "text": [
            "Shuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteJ7QFZ5/imdb_reviews-train.tfrecord\n"
          ]
        },
        {
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          "text": [
            "Shuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteJ7QFZ5/imdb_reviews-test.tfrecord\n"
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          "text": [
            "Shuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteJ7QFZ5/imdb_reviews-unsupervised.tfrecord\n"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Dataset is using deprecated text encoder API which will be removed soon. Please use the plain_text version of the dataset and migrate to `tensorflow_text`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHQ2Ko0zl7M4"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "train_data, test_data = imdb['train'], imdb['test']\n",
        "\n",
        "training_sentences = []\n",
        "training_labels = []\n",
        "\n",
        "testing_sentences = []\n",
        "testing_labels = []\n",
        "\n",
        "# str(s.tonumpy()) is needed in Python3 instead of just s.numpy()\n",
        "for s,l in train_data:\n",
        "  training_sentences.append(s.numpy().decode('utf8'))\n",
        "  training_labels.append(l.numpy())\n",
        "  \n",
        "for s,l in test_data:\n",
        "  testing_sentences.append(s.numpy().decode('utf8'))\n",
        "  testing_labels.append(l.numpy())\n",
        "  \n",
        "training_labels_final = np.array(training_labels)\n",
        "testing_labels_final = np.array(testing_labels)\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7n15yyMdmoH1"
      },
      "source": [
        "vocab_size = 10000\n",
        "embedding_dim = 16\n",
        "max_length = 120\n",
        "trunc_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(training_sentences)\n",
        "word_index = tokenizer.word_index\n",
        "sequences = tokenizer.texts_to_sequences(training_sentences)\n",
        "padded = pad_sequences(sequences,maxlen=max_length, truncating=trunc_type)\n",
        "\n",
        "testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n",
        "testing_padded = pad_sequences(testing_sequences,maxlen=max_length)\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9axf0uIXVMhO",
        "outputId": "85ebbcd4-b1bd-4f6b-bf60-d1c49ffbb94b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# The following code is needed for the visualization;  Hello : 1 -->  1 : Hello  \n",
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
        "\n",
        "def decode_review(text):\n",
        "    return ' '.join([reverse_word_index.get(i, '?') for i in text])\n",
        "\n",
        "print(decode_review(padded[3]))\n",
        "print(training_sentences[3])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? this is the kind of film for a snowy sunday afternoon when the rest of the world can go ahead with its own business as you <OOV> into a big arm chair and <OOV> for a couple of hours wonderful performances from cher and nicolas cage as always gently row the plot along there are no <OOV> to cross no dangerous waters just a warm and witty <OOV> through new york life at its best a family film in every sense and one that deserves the praise it received\n",
            "This is the kind of film for a snowy Sunday afternoon when the rest of the world can go ahead with its own business as you descend into a big arm-chair and mellow for a couple of hours. Wonderful performances from Cher and Nicolas Cage (as always) gently row the plot along. There are no rapids to cross, no dangerous waters, just a warm and witty paddle through New York life at its best. A family film in every sense and one that deserves the praise it received.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NEpdhb8AxID",
        "outputId": "25f18c09-4473-4b59-8324-36281debeeb9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(6, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        " ])   # GlobalAveragePooling1D() can be used instead of Flatten()  \n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 120, 16)           160000    \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 1920)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 6)                 11526     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 171,533\n",
            "Trainable params: 171,533\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5LLrXC-uNX6",
        "outputId": "d828ffb7-baf4-435c-ea3f-87b5c7489b96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "num_epochs = 10\n",
        "model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "782/782 [==============================] - 10s 8ms/step - loss: 0.5103 - accuracy: 0.7227 - val_loss: 0.3586 - val_accuracy: 0.8423\n",
            "Epoch 2/10\n",
            "782/782 [==============================] - 5s 7ms/step - loss: 0.2458 - accuracy: 0.9051 - val_loss: 0.3584 - val_accuracy: 0.8451\n",
            "Epoch 3/10\n",
            "782/782 [==============================] - 6s 8ms/step - loss: 0.0977 - accuracy: 0.9753 - val_loss: 0.4291 - val_accuracy: 0.8336\n",
            "Epoch 4/10\n",
            "782/782 [==============================] - 6s 8ms/step - loss: 0.0268 - accuracy: 0.9964 - val_loss: 0.5266 - val_accuracy: 0.8269\n",
            "Epoch 5/10\n",
            "782/782 [==============================] - 6s 8ms/step - loss: 0.0090 - accuracy: 0.9991 - val_loss: 0.5709 - val_accuracy: 0.8285\n",
            "Epoch 6/10\n",
            "782/782 [==============================] - 5s 7ms/step - loss: 0.0028 - accuracy: 0.9998 - val_loss: 0.6269 - val_accuracy: 0.8278\n",
            "Epoch 7/10\n",
            "782/782 [==============================] - 6s 8ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.6789 - val_accuracy: 0.8280\n",
            "Epoch 8/10\n",
            "782/782 [==============================] - 6s 8ms/step - loss: 5.2099e-04 - accuracy: 1.0000 - val_loss: 0.7064 - val_accuracy: 0.8298\n",
            "Epoch 9/10\n",
            "782/782 [==============================] - 6s 8ms/step - loss: 2.9007e-04 - accuracy: 1.0000 - val_loss: 0.7432 - val_accuracy: 0.8311\n",
            "Epoch 10/10\n",
            "782/782 [==============================] - 6s 8ms/step - loss: 1.7604e-04 - accuracy: 1.0000 - val_loss: 0.7786 - val_accuracy: 0.8311\n"
          ]
        },
        {
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          },
          "metadata": {},
          "execution_count": 11
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAmjJqEyCOF_",
        "outputId": "ba84b15b-ac52-4744-dd24-2ac621bb0150",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "e = model.layers[0]\n",
        "weights = e.get_weights()[0]\n",
        "print(weights.shape) # shape: (vocab_size, embedding_dim)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10000, 16)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmB0Uxk0ycP6"
      },
      "source": [
        "import io\n",
        "\n",
        "out_v = io.open('vecs.tsv', 'w', encoding='utf-8')\n",
        "out_m = io.open('meta.tsv', 'w', encoding='utf-8')\n",
        "for word_num in range(1, vocab_size):\n",
        "  word = reverse_word_index[word_num]\n",
        "  embeddings = weights[word_num]\n",
        "  out_m.write(word + \"\\n\")\n",
        "  out_v.write('\\t'.join([str(x) for x in embeddings]) + \"\\n\")\n",
        "out_v.close()\n",
        "out_m.close()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDeqpOCVydtq",
        "outputId": "e209ccdf-f816-4835-a9ce-46d7431083cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "try:\n",
        "  from google.colab import files\n",
        "except ImportError:\n",
        "  pass\n",
        "else:\n",
        "  files.download('vecs.tsv')\n",
        "  files.download('meta.tsv')"
      ],
      "execution_count": 14,
      "outputs": [
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          "data": {
            "application/javascript": [
              "download(\"download_ff22101d-8a0a-424a-860c-52c42f8acbe0\", \"meta.tsv\", 76186)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRxoxc2apscY",
        "outputId": "d4469e17-a862-4849-fbbb-eb23897460ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sentence = \"I really think this is amazing. honest.\"\n",
        "sequence = tokenizer.texts_to_sequences([sentence])\n",
        "print(sequence)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[11, 64, 102, 12, 7, 478, 1200]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDHphRogSLgB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}