nlp_tweaking_the_model.ipynb

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      "name": "l09c05_nlp_tweaking_the_model.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QrxSyyyhygUR"
      },
      "source": [
        "# Tweaking the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xiWacy71Cu54"
      },
      "source": [
        "In this colab, you'll investigate how various tweaks to data processing and the model itself can impact results. At the end, you'll once again be able to visualize how the network sees the related sentiment of each word in the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hY-fjvwfy2P9"
      },
      "source": [
        "## Import TensorFlow and related functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drsUfVVXyxJl"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIf1N46jy6Ed"
      },
      "source": [
        "## Get the 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": "m83g42sJzGO0"
      },
      "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 19:03:07--  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/ha0ro937gcuc7l7deffksulhg5h7mbp1/8e68o1mlp4l9cjca6meagmf63jllit0s/1624627950000/11118900490791463723/*/13ySLC_ue6Umt9RJYSeM2t-V0kCv-4C-P [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2021-06-25 19:03:08--  https://doc-08-ak-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/8e68o1mlp4l9cjca6meagmf63jllit0s/1624627950000/11118900490791463723/*/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   551KB/s    in 0.2s    \n",
            "\n",
            "2021-06-25 19:03:09 (551 KB/s) - ‘/tmp/sentiment.csv’ saved [127831/127831]\n",
            "\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "y4e6GG2CzJUq"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "dataset = pd.read_csv('/tmp/sentiment.csv')\n",
        "\n",
        "sentences = dataset['text'].tolist()\n",
        "labels = dataset['sentiment'].tolist()\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[0:training_size]\n",
        "testing_sentences = sentences[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": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drDkTFMuzW6N"
      },
      "source": [
        "## Tokenize the dataset (with tweaks!)\n",
        "\n",
        "Now, we'll tokenize the dataset, but we can make some changes to this from before. Previously, we used: \n",
        "```\n",
        "vocab_size = 1000\n",
        "embedding_dim = 16\n",
        "max_length = 100\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "```\n",
        "\n",
        "How might changing the `vocab_size`, `embedding_dim` or `max_length` affect how the model performs?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjPUJFhQzuee"
      },
      "source": [
        "vocab_size = 500\n",
        "embedding_dim = 16\n",
        "max_length = 50\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<OOV>\"\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",
        "training_sequences = tokenizer.texts_to_sequences(training_sentences)\n",
        "training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n",
        "testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FwFjO1kg0UUK"
      },
      "source": [
        "## Train a Sentiment Model (with tweaks!)\n",
        "\n",
        "We'll use a slightly different model here, using `GlobalAveragePooling1D` instead of `Flatten()`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ectP92fl0dFO"
      },
      "source": [
        "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",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nembedding (Embedding)        (None, 50, 16)            8000      \n_________________________________________________________________\nglobal_average_pooling1d (Gl (None, 16)                0         \n_________________________________________________________________\ndense (Dense)                (None, 6)                 102       \n_________________________________________________________________\ndense_1 (Dense)              (None, 1)                 7         \n=================================================================\nTotal params: 8,109\nTrainable params: 8,109\nNon-trainable params: 0\n_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TQIaGjs073w"
      },
      "source": [
        "num_epochs = 30\n",
        "history = model.fit(training_padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "50/50 [==============================] - 1s 6ms/step - loss: 0.6923 - accuracy: 0.5223 - val_loss: 0.6955 - val_accuracy: 0.4110\n",
            "Epoch 2/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.6900 - accuracy: 0.5223 - val_loss: 0.6971 - val_accuracy: 0.4135\n",
            "Epoch 3/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.6865 - accuracy: 0.5336 - val_loss: 0.6944 - val_accuracy: 0.4361\n",
            "Epoch 4/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.6817 - accuracy: 0.6070 - val_loss: 0.6955 - val_accuracy: 0.4361\n",
            "Epoch 5/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.6731 - accuracy: 0.5662 - val_loss: 0.6787 - val_accuracy: 0.5789\n",
            "Epoch 6/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.6610 - accuracy: 0.6503 - val_loss: 0.6690 - val_accuracy: 0.5940\n",
            "Epoch 7/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.6429 - accuracy: 0.7043 - val_loss: 0.6676 - val_accuracy: 0.5388\n",
            "Epoch 8/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.6216 - accuracy: 0.7068 - val_loss: 0.6478 - val_accuracy: 0.5865\n",
            "Epoch 9/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.5937 - accuracy: 0.7539 - val_loss: 0.6165 - val_accuracy: 0.7193\n",
            "Epoch 10/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.5620 - accuracy: 0.7960 - val_loss: 0.5941 - val_accuracy: 0.7444\n",
            "Epoch 11/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.5293 - accuracy: 0.8186 - val_loss: 0.5825 - val_accuracy: 0.7043\n",
            "Epoch 12/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.4981 - accuracy: 0.8192 - val_loss: 0.5698 - val_accuracy: 0.7043\n",
            "Epoch 13/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.4670 - accuracy: 0.8368 - val_loss: 0.5396 - val_accuracy: 0.7569\n",
            "Epoch 14/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.4380 - accuracy: 0.8462 - val_loss: 0.5253 - val_accuracy: 0.7594\n",
            "Epoch 15/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.4141 - accuracy: 0.8525 - val_loss: 0.5288 - val_accuracy: 0.7218\n",
            "Epoch 16/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3922 - accuracy: 0.8531 - val_loss: 0.5121 - val_accuracy: 0.7343\n",
            "Epoch 17/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3710 - accuracy: 0.8581 - val_loss: 0.5051 - val_accuracy: 0.7469\n",
            "Epoch 18/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3532 - accuracy: 0.8682 - val_loss: 0.4861 - val_accuracy: 0.7794\n",
            "Epoch 19/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3396 - accuracy: 0.8732 - val_loss: 0.5126 - val_accuracy: 0.7243\n",
            "Epoch 20/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3232 - accuracy: 0.8832 - val_loss: 0.4786 - val_accuracy: 0.7845\n",
            "Epoch 21/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.3120 - accuracy: 0.8814 - val_loss: 0.4943 - val_accuracy: 0.7469\n",
            "Epoch 22/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.3003 - accuracy: 0.8851 - val_loss: 0.4922 - val_accuracy: 0.7569\n",
            "Epoch 23/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2913 - accuracy: 0.8864 - val_loss: 0.4796 - val_accuracy: 0.7694\n",
            "Epoch 24/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2816 - accuracy: 0.8914 - val_loss: 0.4797 - val_accuracy: 0.7769\n",
            "Epoch 25/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2725 - accuracy: 0.8945 - val_loss: 0.4875 - val_accuracy: 0.7569\n",
            "Epoch 26/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2640 - accuracy: 0.8983 - val_loss: 0.4936 - val_accuracy: 0.7544\n",
            "Epoch 27/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2589 - accuracy: 0.8964 - val_loss: 0.4850 - val_accuracy: 0.7669\n",
            "Epoch 28/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2509 - accuracy: 0.9027 - val_loss: 0.5240 - val_accuracy: 0.7268\n",
            "Epoch 29/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2475 - accuracy: 0.9014 - val_loss: 0.4998 - val_accuracy: 0.7494\n",
            "Epoch 30/30\n",
            "50/50 [==============================] - 0s 3ms/step - loss: 0.2393 - accuracy: 0.9102 - val_loss: 0.5060 - val_accuracy: 0.7419\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "alAlYort7gWV"
      },
      "source": [
        "## Visualize the training graph\n",
        "\n",
        "You can use the code below to visualize the training and validation accuracy while you try out different tweaks to the hyperparameters and model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9l5vBeU71vH"
      },
      "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": 7,
      "outputs": [
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\n"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZzXE-pT8K57"
      },
      "source": [
        "## Get files for visualizing the network\n",
        "\n",
        "The code below will download two files for visualizing how your network \"sees\" the sentiment related to each word. Head to http://projector.tensorflow.org/ and load these files, then click the checkbox to \"sphereize\" the data.\n",
        "\n",
        "Note: You may run into errors with the projection if your `vocab_size` earlier was larger than the actual number of words in the vocabulary, in which case you'll need to decrease this variable and re-train in order to visualize."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Ex4o7Lc8Njl"
      },
      "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": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(500, 16)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUL1zk5p8WIV"
      },
      "source": [
        "import io\n",
        "\n",
        "# Create the reverse word index\n",
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\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(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": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqyV8QYnD46U"
      },
      "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": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUXAlNNk59gG"
      },
      "source": [
        "## Predicting Sentiment in New Reviews\n",
        "\n",
        "Below, we've again included some example new reviews you can test your results on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JbFTTcaK6Dan"
      },
      "source": [
        "# Use the model to predict a review   \n",
        "fake_reviews = ['I love this phone', 'I hate spaghetti', \n",
        "                'Everything was cold',\n",
        "                'Everything was hot exactly as I wanted', \n",
        "                'Everything was green', \n",
        "                'the host seated us immediately',\n",
        "                'they gave us free chocolate cake', \n",
        "                'not sure about the wilted flowers on the table',\n",
        "                'only works when I stand on tippy toes', \n",
        "                'does not work when I stand on my head']\n",
        "\n",
        "print(fake_reviews) \n",
        "\n",
        "# Create the sequences\n",
        "padding_type='post'\n",
        "sample_sequences = tokenizer.texts_to_sequences(fake_reviews)\n",
        "fakes_padded = pad_sequences(sample_sequences, padding=padding_type, maxlen=max_length)           \n",
        "\n",
        "print('\\nHOT OFF THE PRESS! HERE ARE SOME NEWLY MINTED, ABSOLUTELY GENUINE REVIEWS!\\n')              \n",
        "\n",
        "classes = model.predict(fakes_padded)\n",
        "\n",
        "# The closer the class is to 1, the more positive the review is deemed to be\n",
        "for x in range(len(fake_reviews)):\n",
        "  print(fake_reviews[x])\n",
        "  print(classes[x])\n",
        "  print('\\n')\n",
        "\n",
        "# Try adding reviews of your own\n",
        "# Add some negative words (such as \"not\") to the good reviews and see what happens\n",
        "# For example:\n",
        "# they gave us free chocolate cake and did not charge us"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['I love this phone', 'I hate spaghetti', 'Everything was cold', 'Everything was hot exactly as I wanted', 'Everything was green', 'the host seated us immediately', 'they gave us free chocolate cake', 'not sure about the wilted flowers on the table', 'only works when I stand on tippy toes', 'does not work when I stand on my head']\n\nHOT OFF THE PRESS! HERE ARE SOME NEWLY MINTED, ABSOLUTELY GENUINE REVIEWS!\n\nI love this phone\n[0.93226093]\n\n\nI hate spaghetti\n[0.12672257]\n\n\nEverything was cold\n[0.6067419]\n\n\nEverything was hot exactly as I wanted\n[0.42245477]\n\n\nEverything was green\n[0.6067419]\n\n\nthe host seated us immediately\n[0.6039371]\n\n\nthey gave us free chocolate cake\n[0.89061666]\n\n\nnot sure about the wilted flowers on the table\n[0.06959295]\n\n\nonly works when I stand on tippy toes\n[0.90122473]\n\n\ndoes not work when I stand on my head\n[0.02811927]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}