9-Text_Classification_NLP.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NETwY22qbJWZ"
      },
      "source": [
        "**Dataset link**\n",
        "[IMDB Dataset](http://ai.stanford.edu/~amaas/data/sentiment/)\n",
        "\n",
        "It has 50000 reviews which are classified as positive and negative"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "es8izT6RVdyG"
      },
      "source": [
        "**Importing datasets**"
      ]
    },
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      "source": [
        "# To load in PC install this first\n",
        "# !pip install -q tensorflow-datasets\n",
        "\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "imdb, info = tfds.load(\"imdb_reviews\", with_info=True, as_supervised=True)\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "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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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
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          ],
          "name": "stdout"
        },
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        {
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            "Shuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incomplete90FSNC/imdb_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
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            "Shuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incomplete90FSNC/imdb_reviews-test.tfrecord\n"
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          "name": "stdout"
        },
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            "Shuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incomplete90FSNC/imdb_reviews-unsupervised.tfrecord\n"
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          "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"
          ],
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        },
        {
          "output_type": "stream",
          "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"
          ],
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkZ5v4MLVVjm"
      },
      "source": [
        "**Splitting Training and Testing Data and converting it to numpy array**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQvUbIp4U8M0"
      },
      "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",
        "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": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UcK3RoN4VmhX"
      },
      "source": [
        "**Preprocess the data and encoding it to tokens**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vyhbeGlGVbsX"
      },
      "source": [
        "vocab_size = 10000  # vocabulary size for 10000 unique words\n",
        "embedding_dim = 16 # dimensions of embedding\n",
        "max_length = 120   # maxium allowed words per sentence\n",
        "trunc_type='post'  # longer than 120 words will be truncated and 0 will be placed there\n",
        "oov_tok = \"<OOV>\"  # out of volcabulary\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": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wb3dqccqWL9h"
      },
      "source": [
        "**Convert the word_index from (key,value) to (value,key)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PFww5lBLVxyo",
        "outputId": "582db30a-8734-4afb-9767-41c2abbada40"
      },
      "source": [
        "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": 5,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xMsmac1uWmRP"
      },
      "source": [
        "**Defining the model**\n",
        "*   vocab_size = 10000\n",
        "*   embedding_dim = 16\n",
        "*   input_length = 120\n",
        "\n",
        "***Instead if flatten() we can also use GlobalAveragePooling1D()***\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjkSX0H9Wg_9"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "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",
        "])\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVIgLGTgW8xQ"
      },
      "source": [
        "**Compile the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0cBjr8bW_Sn"
      },
      "source": [
        "model.compile(\n",
        "    loss='binary_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy'])\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hi9dh3h-XAAP"
      },
      "source": [
        "**Summary of the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Po9xNCeXXM_",
        "outputId": "5053fc2b-b645-443f-80f4-944f497757ee"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 120, 16)           160000    \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 1920)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 6)                 11526     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 171,533\n",
            "Trainable params: 171,533\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vR6FrZMrXq3z"
      },
      "source": [
        "**Training the model**\n",
        "\n",
        "*   Total epochs = 10\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1f6lUkvSXabM",
        "outputId": "7b4b67a1-357c-481e-c0a1-33afd5a2d36c"
      },
      "source": [
        "num_epochs = 10\n",
        "\n",
        "model.fit(padded, \n",
        "          training_labels_final, \n",
        "          epochs=num_epochs, \n",
        "          validation_data=(testing_padded, testing_labels_final))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "782/782 [==============================] - 7s 5ms/step - loss: 0.5036 - accuracy: 0.7309 - val_loss: 0.3414 - val_accuracy: 0.8513\n",
            "Epoch 2/10\n",
            "782/782 [==============================] - 3s 4ms/step - loss: 0.2447 - accuracy: 0.9034 - val_loss: 0.3600 - val_accuracy: 0.8436\n",
            "Epoch 3/10\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.1063 - accuracy: 0.9711 - val_loss: 0.4394 - val_accuracy: 0.8299\n",
            "Epoch 4/10\n",
            "782/782 [==============================] - 3s 4ms/step - loss: 0.0288 - accuracy: 0.9958 - val_loss: 0.5289 - val_accuracy: 0.8267\n",
            "Epoch 5/10\n",
            "782/782 [==============================] - 3s 4ms/step - loss: 0.0075 - accuracy: 0.9994 - val_loss: 0.6064 - val_accuracy: 0.8240\n",
            "Epoch 6/10\n",
            "782/782 [==============================] - 3s 4ms/step - loss: 0.0023 - accuracy: 0.9999 - val_loss: 0.6562 - val_accuracy: 0.8261\n",
            "Epoch 7/10\n",
            "782/782 [==============================] - 3s 4ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.8294\n",
            "Epoch 8/10\n",
            "782/782 [==============================] - 3s 4ms/step - loss: 5.1423e-04 - accuracy: 1.0000 - val_loss: 0.7320 - val_accuracy: 0.8306\n",
            "Epoch 9/10\n",
            "782/782 [==============================] - 3s 4ms/step - loss: 2.9597e-04 - accuracy: 1.0000 - val_loss: 0.7701 - val_accuracy: 0.8294\n",
            "Epoch 10/10\n",
            "782/782 [==============================] - 3s 4ms/step - loss: 1.9883e-04 - accuracy: 1.0000 - val_loss: 0.8108 - val_accuracy: 0.8287\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f5b1121f690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3jHaB3JX0Ex",
        "outputId": "bfe1b375-e543-486d-87c7-cab110c1d13c"
      },
      "source": [
        "e = model.layers[0]\n",
        "weights = e.get_weights()[0]\n",
        "print(weights.shape) # the shape will be: (vocab_size, embedding_dim)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 16)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "laBwTW5XaXRh"
      },
      "source": [
        "**Now visualize it in the embedding projector**\n",
        "\n",
        "Upload the downloaded files to visualize it:\n",
        "1.   https://projector.tensorflow.org/\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9DPyzeU4X7OI"
      },
      "source": [
        "# now iterate through 16 dimensios to pull out values\n",
        "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": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGpfT2fiX9KE"
      },
      "source": [
        "# It will download 2 files for you\n",
        "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fMEzE3mXX_S1",
        "outputId": "889824ac-52bc-4feb-deb6-666652861506"
      },
      "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",
          "text": [
            "[[11, 64, 102, 12, 7, 478, 1200]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jnujy3OrYaxp"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}