nlp_subwords.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "l09c06_nlp_subwords.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CH5gnvxl-N3U"
      },
      "source": [
        "# What's in a (sub)word?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykxAKKa1Dl0s"
      },
      "source": [
        "In this colab, we'll work with subwords, or words made up of the pieces of larger words, and see how that impacts our network and related embeddings."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQCr_NAT-g5w"
      },
      "source": [
        "## Import TensorFlow and related functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8Wa_ZlX-mPH"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRHk-4Te-yLJ"
      },
      "source": [
        "## Get the original dataset\n",
        "\n",
        "We'll once again use the dataset containing Amazon and Yelp reviews. This dataset was originally extracted from [here](https://www.kaggle.com/marklvl/sentiment-labelled-sentences-data-set)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJAxrOLi-02C"
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    'https://drive.google.com/uc?id=13ySLC_ue6Umt9RJYSeM2t-V0kCv-4C-P' \\\n",
        "    -O /tmp/sentiment.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-06-25 20:02:22--  https://drive.google.com/uc?id=13ySLC_ue6Umt9RJYSeM2t-V0kCv-4C-P\n",
            "Resolving drive.google.com (drive.google.com)... 172.217.160.206, 2404:6800:4009:80b::200e\n",
            "Connecting to drive.google.com (drive.google.com)|172.217.160.206|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-08-ak-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/0f6e0b9i3bjcuj3kke8ltq2bi8b4vh8u/1624631475000/11118900490791463723/*/13ySLC_ue6Umt9RJYSeM2t-V0kCv-4C-P [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2021-06-25 20:02:24--  https://doc-08-ak-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/0f6e0b9i3bjcuj3kke8ltq2bi8b4vh8u/1624631475000/11118900490791463723/*/13ySLC_ue6Umt9RJYSeM2t-V0kCv-4C-P\n",
            "Resolving doc-08-ak-docs.googleusercontent.com (doc-08-ak-docs.googleusercontent.com)... 142.250.192.97, 2404:6800:4009:82a::2001\n",
            "Connecting to doc-08-ak-docs.googleusercontent.com (doc-08-ak-docs.googleusercontent.com)|142.250.192.97|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 127831 (125K) [text/csv]\n",
            "Saving to: ‘/tmp/sentiment.csv’\n",
            "\n",
            "/tmp/sentiment.csv  100%[===================>] 124.83K   578KB/s    in 0.2s    \n",
            "\n",
            "2021-06-25 20:02:25 (578 KB/s) - ‘/tmp/sentiment.csv’ saved [127831/127831]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dr-EDUKP_HBl"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "dataset = pd.read_csv('/tmp/sentiment.csv')\n",
        "\n",
        "# Just extract out sentences and labels first - we will create subwords here\n",
        "sentences = dataset['text'].tolist()\n",
        "labels = dataset['sentiment'].tolist()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8zut9Wng_R3B"
      },
      "source": [
        "## Create a subwords dataset\n",
        "\n",
        "We can use the existing Amazon and Yelp reviews dataset with `tensorflow_datasets`'s `SubwordTextEncoder` functionality. `SubwordTextEncoder.build_from_corpus()` will create a tokenizer for us. You could also use this functionality to get subwords from a much larger corpus of text as well, but we'll just use our existing dataset here.\n",
        "\n",
        "The Amazon and Yelp dataset we are using isn't super large, so we'll create a subword `vocab_size` of only the 1,000 most common words, as well as cutting off each subword to be at most 5 characters.\n",
        "\n",
        "Check out the related documentation [here](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/text/SubwordTextEncoder#build_from_corpus)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aElsgxia_43g"
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "\n",
        "vocab_size = 1000\n",
        "tokenizer = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(sentences, vocab_size, max_subword_length=5)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XNZWGKqBDc3"
      },
      "source": [
        "# Check that the tokenizer works appropriately\n",
        "num = 5\n",
        "print(sentences[num])\n",
        "encoded = tokenizer.encode(sentences[num])\n",
        "print(encoded)\n",
        "# Separately print out each subword, decoded\n",
        "for i in encoded:\n",
        "  print(tokenizer.decode([i]))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I have to jiggle the plug to get it to line up right to get decent volume.\n[4, 31, 6, 849, 162, 450, 12, 1, 600, 438, 775, 6, 175, 14, 6, 55, 213, 159, 474, 775, 6, 175, 614, 380, 295, 148, 72, 789]\nI \nhave \nto \nj\nig\ngl\ne \nthe \npl\nug\n \nto \nget \nit \nto \nli\nne \nup \nright\n \nto \nget \ndec\nent \nvo\nlu\nme\n.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gYnbqctXGKcC"
      },
      "source": [
        "## Replace sentence data with encoded subwords\n",
        "\n",
        "Now, we'll re-create the dataset to be used for training by actually encoding each of the individual sentences. This is equivalent to `text_to_sequences` with the `Tokenizer` we used in earlier exercises."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAmql34aGfeV"
      },
      "source": [
        "for i, sentence in enumerate(sentences):\n",
        "  sentences[i] = tokenizer.encode(sentence)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNnee_csG5Iz"
      },
      "source": [
        "# Check the sentences are appropriately replaced\n",
        "print(sentences[1])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[625, 677, 626, 274, 380, 633, 148, 844, 789]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zpIigjecHVkF"
      },
      "source": [
        "## Final pre-processing\n",
        "\n",
        "Before training, we still need to pad the sequences, as well as split into training and test sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INIFSAcEHool"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "max_length = 50\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "\n",
        "# Pad all sentences\n",
        "sentences_padded = pad_sequences(sentences, maxlen=max_length, \n",
        "                                 padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "# Separate out the sentences and labels into training and test sets\n",
        "training_size = int(len(sentences) * 0.8)\n",
        "\n",
        "training_sentences = sentences_padded[0:training_size]\n",
        "testing_sentences = sentences_padded[training_size:]\n",
        "training_labels = labels[0:training_size]\n",
        "testing_labels = labels[training_size:]\n",
        "\n",
        "# Make labels into numpy arrays for use with the network later\n",
        "training_labels_final = np.array(training_labels)\n",
        "testing_labels_final = np.array(testing_labels)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QC9A-sTpPPiL"
      },
      "source": [
        "## Train a Sentiment Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDKcL64IPcfy"
      },
      "source": [
        "embedding_dim = 16\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.GlobalAveragePooling1D(),\n",
        "    tf.keras.layers.Dense(6, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nembedding (Embedding)        (None, 50, 16)            16000     \n_________________________________________________________________\nglobal_average_pooling1d (Gl (None, 16)                0         \n_________________________________________________________________\ndense (Dense)                (None, 6)                 102       \n_________________________________________________________________\ndense_1 (Dense)              (None, 1)                 7         \n=================================================================\nTotal params: 16,109\nTrainable params: 16,109\nNon-trainable params: 0\n_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VqkMNtIeP3oz"
      },
      "source": [
        "num_epochs = 30\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "history = model.fit(training_sentences, training_labels_final, epochs=num_epochs, \n",
        "                    validation_data=(testing_sentences, testing_labels_final))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "50/50 [==============================] - 1s 6ms/step - loss: 0.6926 - accuracy: 0.5047 - val_loss: 0.6947 - val_accuracy: 0.4261\n",
            "Epoch 2/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.6891 - accuracy: 0.5417 - val_loss: 0.6951 - val_accuracy: 0.4461\n",
            "Epoch 3/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.6840 - accuracy: 0.5763 - val_loss: 0.6917 - val_accuracy: 0.4887\n",
            "Epoch 4/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.6763 - accuracy: 0.6014 - val_loss: 0.6851 - val_accuracy: 0.5138\n",
            "Epoch 5/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.6639 - accuracy: 0.6761 - val_loss: 0.6781 - val_accuracy: 0.5539\n",
            "Epoch 6/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.6446 - accuracy: 0.7175 - val_loss: 0.6566 - val_accuracy: 0.6566\n",
            "Epoch 7/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.6192 - accuracy: 0.7439 - val_loss: 0.6332 - val_accuracy: 0.6992\n",
            "Epoch 8/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.5887 - accuracy: 0.7954 - val_loss: 0.6140 - val_accuracy: 0.7168\n",
            "Epoch 9/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.5553 - accuracy: 0.8067 - val_loss: 0.5883 - val_accuracy: 0.7719\n",
            "Epoch 10/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.5213 - accuracy: 0.8236 - val_loss: 0.5672 - val_accuracy: 0.7845\n",
            "Epoch 11/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.4874 - accuracy: 0.8487 - val_loss: 0.5575 - val_accuracy: 0.7469\n",
            "Epoch 12/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.4564 - accuracy: 0.8506 - val_loss: 0.5351 - val_accuracy: 0.7794\n",
            "Epoch 13/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.4268 - accuracy: 0.8644 - val_loss: 0.5334 - val_accuracy: 0.7444\n",
            "Epoch 14/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.4003 - accuracy: 0.8682 - val_loss: 0.5216 - val_accuracy: 0.7569\n",
            "Epoch 15/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3771 - accuracy: 0.8770 - val_loss: 0.5202 - val_accuracy: 0.7494\n",
            "Epoch 16/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.3567 - accuracy: 0.8820 - val_loss: 0.5086 - val_accuracy: 0.7569\n",
            "Epoch 17/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.3358 - accuracy: 0.8845 - val_loss: 0.5112 - val_accuracy: 0.7569\n",
            "Epoch 18/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.3189 - accuracy: 0.8958 - val_loss: 0.5075 - val_accuracy: 0.7594\n",
            "Epoch 19/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.3036 - accuracy: 0.8952 - val_loss: 0.5045 - val_accuracy: 0.7469\n",
            "Epoch 20/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2892 - accuracy: 0.9033 - val_loss: 0.5017 - val_accuracy: 0.7569\n",
            "Epoch 21/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2771 - accuracy: 0.9046 - val_loss: 0.5039 - val_accuracy: 0.7544\n",
            "Epoch 22/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2647 - accuracy: 0.9071 - val_loss: 0.4967 - val_accuracy: 0.7619\n",
            "Epoch 23/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2539 - accuracy: 0.9109 - val_loss: 0.5113 - val_accuracy: 0.7494\n",
            "Epoch 24/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2444 - accuracy: 0.9146 - val_loss: 0.5574 - val_accuracy: 0.7293\n",
            "Epoch 25/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2379 - accuracy: 0.9184 - val_loss: 0.5185 - val_accuracy: 0.7469\n",
            "Epoch 26/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2255 - accuracy: 0.9247 - val_loss: 0.5467 - val_accuracy: 0.7469\n",
            "Epoch 27/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2184 - accuracy: 0.9291 - val_loss: 0.5399 - val_accuracy: 0.7494\n",
            "Epoch 28/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2088 - accuracy: 0.9309 - val_loss: 0.5612 - val_accuracy: 0.7419\n",
            "Epoch 29/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2044 - accuracy: 0.9303 - val_loss: 0.5505 - val_accuracy: 0.7469\n",
            "Epoch 30/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.1957 - accuracy: 0.9353 - val_loss: 0.5465 - val_accuracy: 0.7519\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sj18M42kQkCi"
      },
      "source": [
        "## Visualize the Training Graph\n",
        "\n",
        "We can visualize the training graph below again. Does there appear to be a difference in how validation accuracy and loss is trending compared to with full words?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uy8KIMPIQlvH"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_m7QzouQQ1Rs"
      },
      "source": [
        "## Get files for visualizing the network\n",
        "\n",
        "Once again, you can visualize the sentiment related to all of the subwords using the below code and by heading to http://projector.tensorflow.org/ to upload and view the data.\n",
        "\n",
        "Note that the below code does have a few small changes to handle the different way text is encoded in our dataset compared to before with the built in `Tokenizer`.\n",
        "\n",
        "You may get an error like \"Number of tensors (999) do not match the number of lines in metadata (992).\" As long as you load the vectors first without error and wait a few seconds after this pops up, you will be able to click outside the file load menu and still view the visualization."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dezs4wE5RMQu"
      },
      "source": [
        "# First get the weights of the embedding layer\n",
        "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": [
            "(1000, 16)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXKqy9Z1RSmt"
      },
      "source": [
        "import io\n",
        "\n",
        "# Write out the embedding vectors and metadata\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(0, vocab_size - 1):\n",
        "  word = tokenizer.decode([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": "v04wBMybRoGx"
      },
      "source": [
        "# Download the files\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": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}