nlp_multiple_models_for_predicting_sentiment.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "l10c02_nlp_multiple_models_for_predicting_sentiment.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3.8.5 64-bit"
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      "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hAclqSm3OOml"
      },
      "source": [
        "# Using LSTMs, CNNs, GRUs with a larger dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fentd-GnIj-j"
      },
      "source": [
        "In this colab, you use different kinds of layers to see how they affect the model.\n",
        "\n",
        "You will use the glue/sst2 dataset, which is available through tensorflow_datasets. \n",
        "\n",
        "The General Language Understanding Evaluation (GLUE) benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.\n",
        "\n",
        "These resources include the Stanford Sentiment Treebank (SST) dataset that consists of sentences from movie reviews and human annotations of their sentiment. This colab uses version 2 of the SST dataset.\n",
        "\n",
        "The splits are:\n",
        "\n",
        "*   train\t67,349\n",
        "*   validation\t872\n",
        "\n",
        "\n",
        "and the column headings are:\n",
        "\n",
        "*   sentence\n",
        "*   label\n",
        "\n",
        "\n",
        "For more information about the dataset, see [https://www.tensorflow.org/datasets/catalog/glue#gluesst2](https://www.tensorflow.org/datasets/catalog/glue#gluesst2)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L62G7LTwNzoD"
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2P6xdtIJMyc"
      },
      "source": [
        "# Get the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCOtiRJZbxCH"
      },
      "source": [
        "# Get the dataset.\n",
        "# It has 70000 items, so might take a while to download\n",
        "dataset, info = tfds.load('glue/sst2', with_info=True)\n",
        "print(info.features)\n",
        "print(info.features[\"label\"].num_classes)\n",
        "print(info.features[\"label\"].names)\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset 7.09 MiB (download: 7.09 MiB, generated: Unknown size, total: 7.09 MiB) to /home/ritesh/tensorflow_datasets/glue/sst2/1.0.0...\u001b[0m\n",
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            "                                                                                            \u001b[A\n",
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            "                                                                                             \u001b[A\n",
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            "                                                                                         \u001b[A\n",
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            "\u001b[1mDataset glue downloaded and prepared to /home/ritesh/tensorflow_datasets/glue/sst2/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
            "FeaturesDict({\n",
            "    'idx': tf.int32,\n",
            "    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
            "    'sentence': Text(shape=(), dtype=tf.string),\n",
            "})\n",
            "2\n",
            "['negative', 'positive']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBMPhIdStAe2"
      },
      "source": [
        "# Get the training and validation datasets\n",
        "dataset_train, dataset_validation = dataset['train'], dataset['validation']\n",
        "dataset_train"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PrefetchDataset shapes: {idx: (), label: (), sentence: ()}, types: {idx: tf.int32, label: tf.int64, sentence: tf.string}>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jbZ-faiNWu1U"
      },
      "source": [
        "# Print some of the entries\n",
        "for example in dataset_train.take(2):  \n",
        "  review, label = example[\"sentence\"], example[\"label\"]\n",
        "  print(\"Review:\", review)\n",
        "  print(\"Label: %d \\n\" % label.numpy())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Review: tf.Tensor(b'for the uninitiated plays better on video with the sound ', shape=(), dtype=string)\nLabel: 0 \n\nReview: tf.Tensor(b'like a giant commercial for universal studios , where much of the action takes place ', shape=(), dtype=string)\nLabel: 0 \n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_fVZItTeZSbL"
      },
      "source": [
        "# Get the sentences and the labels\n",
        "# for both the training and the validation sets\n",
        "training_reviews = []\n",
        "training_labels = []\n",
        " \n",
        "validation_reviews = []\n",
        "validation_labels = []\n",
        "\n",
        "# The dataset has 67,000 training entries, but that's a lot to process here!\n",
        "\n",
        "# If you want to take the entire dataset: WARNING: takes longer!!\n",
        "# for item in dataset_train.take(-1):\n",
        "\n",
        "# Take 10,000 reviews\n",
        "for item in dataset_train.take(10000):\n",
        "  review, label = item[\"sentence\"], item[\"label\"]\n",
        "  training_reviews.append(str(review.numpy()))\n",
        "  training_labels.append(label.numpy())\n",
        "\n",
        "print (\"\\nNumber of training reviews is: \", len(training_reviews))\n",
        "\n",
        "# print some of the reviews and labels\n",
        "for i in range(0, 2):\n",
        "  print (training_reviews[i])\n",
        "  print (training_labels[i])\n",
        "\n",
        "# Get the validation data\n",
        "# there's only about 800 items, so take them all\n",
        "for item in dataset_validation.take(-1):  \n",
        "  review, label = item[\"sentence\"], item[\"label\"]\n",
        "  validation_reviews.append(str(review.numpy()))\n",
        "  validation_labels.append(label.numpy())\n",
        "\n",
        "print (\"\\nNumber of validation reviews is: \", len(validation_reviews))\n",
        "\n",
        "# Print some of the validation reviews and labels\n",
        "for i in range(0, 2):\n",
        "  print (validation_reviews[i])\n",
        "  print (validation_labels[i])\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\nNumber of training reviews is:  10000\nb'for the uninitiated plays better on video with the sound '\n0\nb'like a giant commercial for universal studios , where much of the action takes place '\n0\n\nNumber of validation reviews is:  872\nb'a valueless kiddie paean to pro basketball underwritten by the nba . '\n0\nb\"featuring a dangerously seductive performance from the great daniel auteuil , `` sade '' covers the same period as kaufmann 's `` quills '' with more unsettlingly realistic results . \"\n1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BY4ZoptJO55o"
      },
      "source": [
        "# Tokenize the words and sequence the sentences\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TWLvXA1Oa_W"
      },
      "source": [
        "# There's a total of 21224 words in the reviews\n",
        "# but many of them are irrelevant like with, it, of, on.\n",
        "# If we take a subset of the training data, then the vocab\n",
        "# will be smaller.\n",
        "\n",
        "# A reasonable review might have about 50 words or so,\n",
        "# so we can set max_length to 50 (but feel free to change it as you like)\n",
        "\n",
        "vocab_size = 4000\n",
        "embedding_dim = 16\n",
        "max_length = 50\n",
        "trunc_type='post'\n",
        "pad_type='post'\n",
        "oov_tok = \"<OOV>\"\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_reviews)\n",
        "word_index = tokenizer.word_index\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JV-Ff5N0ryWv"
      },
      "source": [
        "# Pad the sequences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-3scEznH2Va"
      },
      "source": [
        "# Pad the sequences so that they are all the same length\n",
        "training_sequences = tokenizer.texts_to_sequences(training_reviews)\n",
        "training_padded = pad_sequences(training_sequences,maxlen=max_length, \n",
        "                                truncating=trunc_type, padding=pad_type)\n",
        "\n",
        "validation_sequences = tokenizer.texts_to_sequences(validation_reviews)\n",
        "validation_padded = pad_sequences(validation_sequences,maxlen=max_length)\n",
        "\n",
        "training_labels_final = np.array(training_labels)\n",
        "validation_labels_final = np.array(validation_labels)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PahZm7YEQ8EI"
      },
      "source": [
        "# Create the model using an Embedding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_nyQeI0RCCv"
      },
      "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(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nembedding (Embedding)        (None, 50, 16)            64000     \n_________________________________________________________________\nglobal_average_pooling1d (Gl (None, 16)                0         \n_________________________________________________________________\ndense (Dense)                (None, 1)                 17        \n=================================================================\nTotal params: 64,017\nTrainable params: 64,017\nNon-trainable params: 0\n_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3WRXrx8BRO2L"
      },
      "source": [
        "# Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBKyVYvxRQ_9"
      },
      "source": [
        "num_epochs = 20\n",
        "history = model.fit(training_padded, training_labels_final, epochs=num_epochs, \n",
        "                    validation_data=(validation_padded, validation_labels_final))\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.6831 - accuracy: 0.5592 - val_loss: 0.6897 - val_accuracy: 0.5126\n",
            "Epoch 2/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.6729 - accuracy: 0.5735 - val_loss: 0.6750 - val_accuracy: 0.5711\n",
            "Epoch 3/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.6548 - accuracy: 0.6136 - val_loss: 0.6496 - val_accuracy: 0.6342\n",
            "Epoch 4/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.6275 - accuracy: 0.6685 - val_loss: 0.6197 - val_accuracy: 0.6835\n",
            "Epoch 5/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.5949 - accuracy: 0.7046 - val_loss: 0.5877 - val_accuracy: 0.7133\n",
            "Epoch 6/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.5614 - accuracy: 0.7365 - val_loss: 0.5599 - val_accuracy: 0.7431\n",
            "Epoch 7/20\n",
            "313/313 [==============================] - 0s 2ms/step - loss: 0.5293 - accuracy: 0.7666 - val_loss: 0.5378 - val_accuracy: 0.7305\n",
            "Epoch 8/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.5000 - accuracy: 0.7878 - val_loss: 0.5184 - val_accuracy: 0.7546\n",
            "Epoch 9/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4734 - accuracy: 0.8063 - val_loss: 0.5048 - val_accuracy: 0.7546\n",
            "Epoch 10/20\n",
            "313/313 [==============================] - 0s 2ms/step - loss: 0.4498 - accuracy: 0.8168 - val_loss: 0.4957 - val_accuracy: 0.7557\n",
            "Epoch 11/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4292 - accuracy: 0.8256 - val_loss: 0.4899 - val_accuracy: 0.7626\n",
            "Epoch 12/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4100 - accuracy: 0.8363 - val_loss: 0.4888 - val_accuracy: 0.7534\n",
            "Epoch 13/20\n",
            "313/313 [==============================] - 0s 2ms/step - loss: 0.3934 - accuracy: 0.8409 - val_loss: 0.4845 - val_accuracy: 0.7649\n",
            "Epoch 14/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3781 - accuracy: 0.8471 - val_loss: 0.4838 - val_accuracy: 0.7706\n",
            "Epoch 15/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3636 - accuracy: 0.8523 - val_loss: 0.4901 - val_accuracy: 0.7603\n",
            "Epoch 16/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3514 - accuracy: 0.8528 - val_loss: 0.4918 - val_accuracy: 0.7729\n",
            "Epoch 17/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3389 - accuracy: 0.8596 - val_loss: 0.4948 - val_accuracy: 0.7775\n",
            "Epoch 18/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3289 - accuracy: 0.8638 - val_loss: 0.5032 - val_accuracy: 0.7706\n",
            "Epoch 19/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3192 - accuracy: 0.8659 - val_loss: 0.5095 - val_accuracy: 0.7787\n",
            "Epoch 20/20\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3096 - accuracy: 0.8700 - val_loss: 0.5172 - val_accuracy: 0.7764\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HhLPbUl2AZ0y"
      },
      "source": [
        "# Plot the accurracy and loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jzBM1PpJAYfD"
      },
      "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": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEbcMCVEKToB"
      },
      "source": [
        "# Write a function to predict the sentiment of reviews"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0nKY9M4xzWE"
      },
      "source": [
        "# Write some new reviews \n",
        "\n",
        "review1 = \"\"\"I loved this movie\"\"\"\n",
        "\n",
        "review2 = \"\"\"that was the worst movie I've ever seen\"\"\"\n",
        "\n",
        "review3 = \"\"\"too much violence even for a Bond film\"\"\"\n",
        "\n",
        "review4 = \"\"\"a captivating recounting of a cherished myth\"\"\"\n",
        "\n",
        "new_reviews = [review1, review2, review3, review4]\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qg-maex27KPW"
      },
      "source": [
        "# Define a function to prepare the new reviews for use with a model\n",
        "# and then use the model to predict the sentiment of the new reviews           \n",
        "\n",
        "def predict_review(model, reviews):\n",
        "  # Create the sequences\n",
        "  padding_type='post'\n",
        "  sample_sequences = tokenizer.texts_to_sequences(reviews)\n",
        "  reviews_padded = pad_sequences(sample_sequences, padding=padding_type, \n",
        "                                 maxlen=max_length) \n",
        "  classes = model.predict(reviews_padded)\n",
        "  for x in range(len(reviews_padded)):\n",
        "    print(reviews[x])\n",
        "    print(classes[x])\n",
        "    print('\\n')\n",
        "\n",
        "predict_review(model, new_reviews)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I loved this movie\n[0.36522615]\n\n\nthat was the worst movie I've ever seen\n[0.10685083]\n\n\ntoo much violence even for a Bond film\n[0.02953798]\n\n\na captivating recounting of a cherished myth\n[0.5721201]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycJKbMq3K4iy"
      },
      "source": [
        "# Define a function to train and show the results of models with different layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PevUcINXK3gn"
      },
      "source": [
        "def fit_model_and_show_results (model, reviews):\n",
        "  model.summary()\n",
        "  history = model.fit(training_padded, training_labels_final, epochs=num_epochs, \n",
        "                      validation_data=(validation_padded, validation_labels_final))\n",
        "  plot_graphs(history, \"accuracy\")\n",
        "  plot_graphs(history, \"loss\")\n",
        "  predict_review(model, reviews)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8jW-OLfTrDM"
      },
      "source": [
        "# Use a CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "merAu9T3TtmQ"
      },
      "source": [
        "num_epochs = 30\n",
        "\n",
        "model_cnn = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Conv1D(16, 5, activation='relu'),\n",
        "    tf.keras.layers.GlobalMaxPooling1D(),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Default learning rate for the Adam optimizer is 0.001\n",
        "# Let's slow down the learning rate by 10.\n",
        "learning_rate = 0.0001\n",
        "model_cnn.compile(loss='binary_crossentropy',\n",
        "                  optimizer=tf.keras.optimizers.Adam(learning_rate), \n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "fit_model_and_show_results(model_cnn, new_reviews)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 50, 16)            64000     \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 46, 16)            1296      \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d (Global (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 65,313\n",
            "Trainable params: 65,313\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.6901 - accuracy: 0.5382 - val_loss: 0.6930 - val_accuracy: 0.5080\n",
            "Epoch 2/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.6829 - accuracy: 0.5590 - val_loss: 0.6922 - val_accuracy: 0.5092\n",
            "Epoch 3/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.6776 - accuracy: 0.5590 - val_loss: 0.6898 - val_accuracy: 0.5092\n",
            "Epoch 4/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.6708 - accuracy: 0.5619 - val_loss: 0.6854 - val_accuracy: 0.5252\n",
            "Epoch 5/30\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.6605 - accuracy: 0.5860 - val_loss: 0.6773 - val_accuracy: 0.5963\n",
            "Epoch 6/30\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.6445 - accuracy: 0.6348 - val_loss: 0.6638 - val_accuracy: 0.6812\n",
            "Epoch 7/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.6207 - accuracy: 0.6956 - val_loss: 0.6441 - val_accuracy: 0.7271\n",
            "Epoch 8/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.5893 - accuracy: 0.7531 - val_loss: 0.6194 - val_accuracy: 0.7443\n",
            "Epoch 9/30\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.5525 - accuracy: 0.7903 - val_loss: 0.5924 - val_accuracy: 0.7534\n",
            "Epoch 10/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.5143 - accuracy: 0.8149 - val_loss: 0.5660 - val_accuracy: 0.7672\n",
            "Epoch 11/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4774 - accuracy: 0.8272 - val_loss: 0.5425 - val_accuracy: 0.7729\n",
            "Epoch 12/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4436 - accuracy: 0.8348 - val_loss: 0.5229 - val_accuracy: 0.7695\n",
            "Epoch 13/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4140 - accuracy: 0.8439 - val_loss: 0.5077 - val_accuracy: 0.7775\n",
            "Epoch 14/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3881 - accuracy: 0.8513 - val_loss: 0.4958 - val_accuracy: 0.7775\n",
            "Epoch 15/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3655 - accuracy: 0.8551 - val_loss: 0.4883 - val_accuracy: 0.7787\n",
            "Epoch 16/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3459 - accuracy: 0.8619 - val_loss: 0.4829 - val_accuracy: 0.7867\n",
            "Epoch 17/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3285 - accuracy: 0.8669 - val_loss: 0.4803 - val_accuracy: 0.7844\n",
            "Epoch 18/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3130 - accuracy: 0.8742 - val_loss: 0.4792 - val_accuracy: 0.7810\n",
            "Epoch 19/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.2992 - accuracy: 0.8787 - val_loss: 0.4799 - val_accuracy: 0.7775\n",
            "Epoch 20/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.2868 - accuracy: 0.8824 - val_loss: 0.4821 - val_accuracy: 0.7775\n",
            "Epoch 21/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.2754 - accuracy: 0.8861 - val_loss: 0.4854 - val_accuracy: 0.7741\n",
            "Epoch 22/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.2651 - accuracy: 0.8889 - val_loss: 0.4898 - val_accuracy: 0.7775\n",
            "Epoch 23/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.2555 - accuracy: 0.8911 - val_loss: 0.4954 - val_accuracy: 0.7729\n",
            "Epoch 24/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.2467 - accuracy: 0.8963 - val_loss: 0.5015 - val_accuracy: 0.7706\n",
            "Epoch 25/30\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2385 - accuracy: 0.8984 - val_loss: 0.5089 - val_accuracy: 0.7695\n",
            "Epoch 26/30\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2309 - accuracy: 0.9013 - val_loss: 0.5165 - val_accuracy: 0.7661\n",
            "Epoch 27/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.2239 - accuracy: 0.9045 - val_loss: 0.5252 - val_accuracy: 0.7638\n",
            "Epoch 28/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.2173 - accuracy: 0.9073 - val_loss: 0.5339 - val_accuracy: 0.7626\n",
            "Epoch 29/30\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2110 - accuracy: 0.9102 - val_loss: 0.5432 - val_accuracy: 0.7603\n",
            "Epoch 30/30\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.2052 - accuracy: 0.9120 - val_loss: 0.5526 - val_accuracy: 0.7546\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I loved this movie\n[0.79763854]\n\n\nthat was the worst movie I've ever seen\n[0.0202463]\n\n\ntoo much violence even for a Bond film\n[0.00981453]\n\n\na captivating recounting of a cherished myth\n[0.773011]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXnoq0zITmSM"
      },
      "source": [
        "# Use a GRU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jP6KAzZTpQ6"
      },
      "source": [
        "num_epochs = 30\n",
        "\n",
        "model_gru = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.GRU(32)),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "learning_rate = 0.00003 # slower than the default learning rate\n",
        "model_gru.compile(loss='binary_crossentropy',\n",
        "                  optimizer=tf.keras.optimizers.Adam(learning_rate),\n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "fit_model_and_show_results(model_gru, new_reviews)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, 50, 16)            64000     \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 64)                9600      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 73,665\n",
            "Trainable params: 73,665\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/30\n",
            "313/313 [==============================] - 10s 24ms/step - loss: 0.6901 - accuracy: 0.5579 - val_loss: 0.6930 - val_accuracy: 0.5092\n",
            "Epoch 2/30\n",
            "313/313 [==============================] - 8s 24ms/step - loss: 0.6872 - accuracy: 0.5590 - val_loss: 0.6934 - val_accuracy: 0.5092\n",
            "Epoch 3/30\n",
            "313/313 [==============================] - 7s 22ms/step - loss: 0.6857 - accuracy: 0.5590 - val_loss: 0.6939 - val_accuracy: 0.5092\n",
            "Epoch 4/30\n",
            "313/313 [==============================] - 7s 23ms/step - loss: 0.6846 - accuracy: 0.5590 - val_loss: 0.6943 - val_accuracy: 0.5092\n",
            "Epoch 5/30\n",
            "313/313 [==============================] - 7s 23ms/step - loss: 0.6835 - accuracy: 0.5590 - val_loss: 0.6946 - val_accuracy: 0.5092\n",
            "Epoch 6/30\n",
            "313/313 [==============================] - 7s 22ms/step - loss: 0.6818 - accuracy: 0.5590 - val_loss: 0.6948 - val_accuracy: 0.5092\n",
            "Epoch 7/30\n",
            "313/313 [==============================] - 7s 22ms/step - loss: 0.6793 - accuracy: 0.5590 - val_loss: 0.6953 - val_accuracy: 0.5092\n",
            "Epoch 8/30\n",
            "313/313 [==============================] - 7s 23ms/step - loss: 0.6751 - accuracy: 0.5590 - val_loss: 0.6956 - val_accuracy: 0.5092\n",
            "Epoch 9/30\n",
            "313/313 [==============================] - 7s 22ms/step - loss: 0.6676 - accuracy: 0.5612 - val_loss: 0.6975 - val_accuracy: 0.5092\n",
            "Epoch 10/30\n",
            "313/313 [==============================] - 7s 23ms/step - loss: 0.6526 - accuracy: 0.5831 - val_loss: 0.7006 - val_accuracy: 0.5092\n",
            "Epoch 11/30\n",
            "313/313 [==============================] - 7s 23ms/step - loss: 0.6202 - accuracy: 0.6329 - val_loss: 0.6436 - val_accuracy: 0.5172\n",
            "Epoch 12/30\n",
            "313/313 [==============================] - 7s 23ms/step - loss: 0.5858 - accuracy: 0.6795 - val_loss: 0.6703 - val_accuracy: 0.5677\n",
            "Epoch 13/30\n",
            "313/313 [==============================] - 7s 23ms/step - loss: 0.5650 - accuracy: 0.7070 - val_loss: 0.6644 - val_accuracy: 0.5849\n",
            "Epoch 14/30\n",
            "313/313 [==============================] - 7s 23ms/step - loss: 0.5454 - accuracy: 0.7196 - val_loss: 0.6358 - val_accuracy: 0.6812\n",
            "Epoch 15/30\n",
            "313/313 [==============================] - 7s 23ms/step - loss: 0.5294 - accuracy: 0.7432 - val_loss: 0.6410 - val_accuracy: 0.6904\n",
            "Epoch 16/30\n",
            "313/313 [==============================] - 7s 23ms/step - loss: 0.5115 - accuracy: 0.7509 - val_loss: 0.6463 - val_accuracy: 0.7064\n",
            "Epoch 17/30\n",
            "313/313 [==============================] - 7s 24ms/step - loss: 0.4953 - accuracy: 0.7718 - val_loss: 0.6482 - val_accuracy: 0.7110\n",
            "Epoch 18/30\n",
            "313/313 [==============================] - 7s 23ms/step - loss: 0.4776 - accuracy: 0.7775 - val_loss: 0.6214 - val_accuracy: 0.7328\n",
            "Epoch 19/30\n",
            "313/313 [==============================] - 7s 24ms/step - loss: 0.4606 - accuracy: 0.7929 - val_loss: 0.6225 - val_accuracy: 0.7374\n",
            "Epoch 20/30\n",
            "313/313 [==============================] - 7s 24ms/step - loss: 0.4455 - accuracy: 0.8010 - val_loss: 0.5992 - val_accuracy: 0.7431\n",
            "Epoch 21/30\n",
            "313/313 [==============================] - 10s 32ms/step - loss: 0.4337 - accuracy: 0.8100 - val_loss: 0.6075 - val_accuracy: 0.7443\n",
            "Epoch 22/30\n",
            "313/313 [==============================] - 10s 33ms/step - loss: 0.4226 - accuracy: 0.8168 - val_loss: 0.6178 - val_accuracy: 0.7466\n",
            "Epoch 23/30\n",
            "313/313 [==============================] - 7s 22ms/step - loss: 0.4115 - accuracy: 0.8256 - val_loss: 0.5975 - val_accuracy: 0.7500\n",
            "Epoch 24/30\n",
            "313/313 [==============================] - 8s 26ms/step - loss: 0.4035 - accuracy: 0.8273 - val_loss: 0.5992 - val_accuracy: 0.7523\n",
            "Epoch 25/30\n",
            "313/313 [==============================] - 11s 34ms/step - loss: 0.3959 - accuracy: 0.8303 - val_loss: 0.6042 - val_accuracy: 0.7489\n",
            "Epoch 26/30\n",
            "313/313 [==============================] - 7s 22ms/step - loss: 0.3863 - accuracy: 0.8370 - val_loss: 0.6124 - val_accuracy: 0.7523\n",
            "Epoch 27/30\n",
            "313/313 [==============================] - 7s 21ms/step - loss: 0.3787 - accuracy: 0.8409 - val_loss: 0.6201 - val_accuracy: 0.7592\n",
            "Epoch 28/30\n",
            "313/313 [==============================] - 7s 21ms/step - loss: 0.3719 - accuracy: 0.8422 - val_loss: 0.6389 - val_accuracy: 0.7477\n",
            "Epoch 29/30\n",
            "313/313 [==============================] - 7s 21ms/step - loss: 0.3655 - accuracy: 0.8446 - val_loss: 0.6251 - val_accuracy: 0.7638\n",
            "Epoch 30/30\n",
            "313/313 [==============================] - 7s 21ms/step - loss: 0.3590 - accuracy: 0.8493 - val_loss: 0.6362 - val_accuracy: 0.7638\n"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I loved this movie\n[0.31130624]\n\n\nthat was the worst movie I've ever seen\n[0.17443436]\n\n\ntoo much violence even for a Bond film\n[0.10199329]\n\n\na captivating recounting of a cherished myth\n[0.4573918]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U13JBiJUG1oq"
      },
      "source": [
        "# Add a bidirectional LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scTUsFPAG4zP"
      },
      "source": [
        "num_epochs = 30\n",
        "\n",
        "model_bidi_lstm = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim)), \n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "learning_rate = 0.00003\n",
        "model_bidi_lstm.compile(loss='binary_crossentropy',\n",
        "                        optimizer=tf.keras.optimizers.Adam(learning_rate),\n",
        "                        metrics=['accuracy'])\n",
        "fit_model_and_show_results(model_bidi_lstm, new_reviews)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_3 (Embedding)      (None, 50, 16)            64000     \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, 32)                4224      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 68,257\n",
            "Trainable params: 68,257\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/30\n",
            "313/313 [==============================] - 9s 21ms/step - loss: 0.6914 - accuracy: 0.5531 - val_loss: 0.6932 - val_accuracy: 0.5092\n",
            "Epoch 2/30\n",
            "313/313 [==============================] - 6s 21ms/step - loss: 0.6881 - accuracy: 0.5590 - val_loss: 0.6938 - val_accuracy: 0.5092\n",
            "Epoch 3/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6859 - accuracy: 0.5590 - val_loss: 0.6949 - val_accuracy: 0.5092\n",
            "Epoch 4/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6844 - accuracy: 0.5590 - val_loss: 0.6961 - val_accuracy: 0.5092\n",
            "Epoch 5/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6830 - accuracy: 0.5590 - val_loss: 0.6976 - val_accuracy: 0.5092\n",
            "Epoch 6/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6814 - accuracy: 0.5590 - val_loss: 0.6994 - val_accuracy: 0.5092\n",
            "Epoch 7/30\n",
            "313/313 [==============================] - 6s 21ms/step - loss: 0.6794 - accuracy: 0.5590 - val_loss: 0.7021 - val_accuracy: 0.5092\n",
            "Epoch 8/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6765 - accuracy: 0.5590 - val_loss: 0.7054 - val_accuracy: 0.5092\n",
            "Epoch 9/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6729 - accuracy: 0.5591 - val_loss: 0.7108 - val_accuracy: 0.5092\n",
            "Epoch 10/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6684 - accuracy: 0.5614 - val_loss: 0.7162 - val_accuracy: 0.5092\n",
            "Epoch 11/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6631 - accuracy: 0.5726 - val_loss: 0.7201 - val_accuracy: 0.5092\n",
            "Epoch 12/30\n",
            "313/313 [==============================] - 6s 21ms/step - loss: 0.6571 - accuracy: 0.5993 - val_loss: 0.7249 - val_accuracy: 0.5092\n",
            "Epoch 13/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6499 - accuracy: 0.6217 - val_loss: 0.7351 - val_accuracy: 0.5092\n",
            "Epoch 14/30\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6414 - accuracy: 0.6387 - val_loss: 0.7294 - val_accuracy: 0.5092\n",
            "Epoch 15/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6309 - accuracy: 0.6600 - val_loss: 0.7322 - val_accuracy: 0.5092\n",
            "Epoch 16/30\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6188 - accuracy: 0.6707 - val_loss: 0.7290 - val_accuracy: 0.5092\n",
            "Epoch 17/30\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6040 - accuracy: 0.6849 - val_loss: 0.7229 - val_accuracy: 0.5092\n",
            "Epoch 18/30\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.5876 - accuracy: 0.6988 - val_loss: 0.7089 - val_accuracy: 0.5092\n",
            "Epoch 19/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5704 - accuracy: 0.7143 - val_loss: 0.6767 - val_accuracy: 0.5080\n",
            "Epoch 20/30\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.5541 - accuracy: 0.7305 - val_loss: 0.6532 - val_accuracy: 0.5034\n",
            "Epoch 21/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5408 - accuracy: 0.7460 - val_loss: 0.6381 - val_accuracy: 0.5069\n",
            "Epoch 22/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5277 - accuracy: 0.7552 - val_loss: 0.6229 - val_accuracy: 0.5206\n",
            "Epoch 23/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5188 - accuracy: 0.7631 - val_loss: 0.6119 - val_accuracy: 0.5333\n",
            "Epoch 24/30\n",
            "313/313 [==============================] - 7s 21ms/step - loss: 0.5091 - accuracy: 0.7721 - val_loss: 0.6116 - val_accuracy: 0.5470\n",
            "Epoch 25/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5020 - accuracy: 0.7761 - val_loss: 0.5960 - val_accuracy: 0.5837\n",
            "Epoch 26/30\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4932 - accuracy: 0.7876 - val_loss: 0.5985 - val_accuracy: 0.5929\n",
            "Epoch 27/30\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4851 - accuracy: 0.7901 - val_loss: 0.5969 - val_accuracy: 0.6044\n",
            "Epoch 28/30\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4783 - accuracy: 0.7953 - val_loss: 0.6020 - val_accuracy: 0.6055\n",
            "Epoch 29/30\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4723 - accuracy: 0.8004 - val_loss: 0.5884 - val_accuracy: 0.6376\n",
            "Epoch 30/30\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4657 - accuracy: 0.8032 - val_loss: 0.5940 - val_accuracy: 0.6319\n"
          ]
        },
        {
          "output_type": "display_data",
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I loved this movie\n[0.49755082]\n\n\nthat was the worst movie I've ever seen\n[0.36574292]\n\n\ntoo much violence even for a Bond film\n[0.4155818]\n\n\na captivating recounting of a cherished myth\n[0.61124516]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsxKPbCnPJTj"
      },
      "source": [
        "# Use multiple bidirectional LSTMs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3N6Zul47PMED"
      },
      "source": [
        "num_epochs = 30\n",
        "\n",
        "model_multiple_bidi_lstm = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim, \n",
        "                                                       return_sequences=True)),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim)),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "learning_rate = 0.0003\n",
        "model_multiple_bidi_lstm.compile(loss='binary_crossentropy',\n",
        "                                 optimizer=tf.keras.optimizers.Adam(learning_rate),\n",
        "                                 metrics=['accuracy'])\n",
        "fit_model_and_show_results(model_multiple_bidi_lstm, new_reviews)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_4 (Embedding)      (None, 50, 16)            64000     \n",
            "_________________________________________________________________\n",
            "bidirectional_2 (Bidirection (None, 50, 32)            4224      \n",
            "_________________________________________________________________\n",
            "bidirectional_3 (Bidirection (None, 32)                6272      \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 74,529\n",
            "Trainable params: 74,529\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/30\n",
            "313/313 [==============================] - 17s 40ms/step - loss: 0.6827 - accuracy: 0.5601 - val_loss: 0.7082 - val_accuracy: 0.5092\n",
            "Epoch 2/30\n",
            "313/313 [==============================] - 11s 35ms/step - loss: 0.5063 - accuracy: 0.7500 - val_loss: 0.6648 - val_accuracy: 0.5470\n",
            "Epoch 3/30\n",
            "313/313 [==============================] - 11s 35ms/step - loss: 0.3584 - accuracy: 0.8384 - val_loss: 0.6472 - val_accuracy: 0.6479\n",
            "Epoch 4/30\n",
            "313/313 [==============================] - 11s 35ms/step - loss: 0.3015 - accuracy: 0.8650 - val_loss: 0.6500 - val_accuracy: 0.6284\n",
            "Epoch 5/30\n",
            "313/313 [==============================] - 12s 37ms/step - loss: 0.2700 - accuracy: 0.8803 - val_loss: 0.6467 - val_accuracy: 0.6261\n",
            "Epoch 6/30\n",
            "313/313 [==============================] - 12s 37ms/step - loss: 0.2483 - accuracy: 0.8882 - val_loss: 0.6808 - val_accuracy: 0.5287\n",
            "Epoch 7/30\n",
            "313/313 [==============================] - 11s 36ms/step - loss: 0.2323 - accuracy: 0.8966 - val_loss: 0.6852 - val_accuracy: 0.5275\n",
            "Epoch 8/30\n",
            "313/313 [==============================] - 11s 36ms/step - loss: 0.2179 - accuracy: 0.9025 - val_loss: 0.6839 - val_accuracy: 0.5264\n",
            "Epoch 9/30\n",
            "313/313 [==============================] - 11s 36ms/step - loss: 0.2127 - accuracy: 0.9050 - val_loss: 0.6963 - val_accuracy: 0.5310\n",
            "Epoch 10/30\n",
            "313/313 [==============================] - 11s 36ms/step - loss: 0.2021 - accuracy: 0.9113 - val_loss: 0.6604 - val_accuracy: 0.6032\n",
            "Epoch 11/30\n",
            "313/313 [==============================] - 12s 37ms/step - loss: 0.1969 - accuracy: 0.9147 - val_loss: 0.6812 - val_accuracy: 0.5482\n",
            "Epoch 12/30\n",
            "313/313 [==============================] - 11s 37ms/step - loss: 0.1916 - accuracy: 0.9146 - val_loss: 0.9253 - val_accuracy: 0.5195\n",
            "Epoch 13/30\n",
            "313/313 [==============================] - 11s 37ms/step - loss: 0.1860 - accuracy: 0.9179 - val_loss: 0.8974 - val_accuracy: 0.5344\n",
            "Epoch 14/30\n",
            "313/313 [==============================] - 12s 37ms/step - loss: 0.1835 - accuracy: 0.9187 - val_loss: 0.7886 - val_accuracy: 0.5218\n",
            "Epoch 15/30\n",
            "313/313 [==============================] - 12s 37ms/step - loss: 0.1795 - accuracy: 0.9185 - val_loss: 0.7590 - val_accuracy: 0.5310\n",
            "Epoch 16/30\n",
            "313/313 [==============================] - 12s 37ms/step - loss: 0.1790 - accuracy: 0.9207 - val_loss: 0.7586 - val_accuracy: 0.5229\n",
            "Epoch 17/30\n",
            "313/313 [==============================] - 12s 39ms/step - loss: 0.1763 - accuracy: 0.9191 - val_loss: 0.7889 - val_accuracy: 0.5206\n",
            "Epoch 18/30\n",
            "313/313 [==============================] - 12s 37ms/step - loss: 0.1704 - accuracy: 0.9235 - val_loss: 0.7679 - val_accuracy: 0.5482\n",
            "Epoch 19/30\n",
            "313/313 [==============================] - 12s 38ms/step - loss: 0.1651 - accuracy: 0.9264 - val_loss: 0.7630 - val_accuracy: 0.5218\n",
            "Epoch 20/30\n",
            "313/313 [==============================] - 15s 48ms/step - loss: 0.1633 - accuracy: 0.9230 - val_loss: 0.8269 - val_accuracy: 0.5264\n",
            "Epoch 21/30\n",
            "313/313 [==============================] - 12s 38ms/step - loss: 0.1626 - accuracy: 0.9235 - val_loss: 0.7243 - val_accuracy: 0.5883\n",
            "Epoch 22/30\n",
            "313/313 [==============================] - 12s 39ms/step - loss: 0.1616 - accuracy: 0.9231 - val_loss: 0.7388 - val_accuracy: 0.5665\n",
            "Epoch 23/30\n",
            "313/313 [==============================] - 12s 38ms/step - loss: 0.1588 - accuracy: 0.9262 - val_loss: 0.7702 - val_accuracy: 0.4851\n",
            "Epoch 24/30\n",
            "313/313 [==============================] - 12s 38ms/step - loss: 0.1577 - accuracy: 0.9252 - val_loss: 0.7471 - val_accuracy: 0.5608\n",
            "Epoch 25/30\n",
            "313/313 [==============================] - 14s 46ms/step - loss: 0.1569 - accuracy: 0.9269 - val_loss: 0.7376 - val_accuracy: 0.5768\n",
            "Epoch 26/30\n",
            "313/313 [==============================] - 12s 38ms/step - loss: 0.1503 - accuracy: 0.9290 - val_loss: 0.8507 - val_accuracy: 0.5275\n",
            "Epoch 27/30\n",
            "313/313 [==============================] - 12s 38ms/step - loss: 0.1502 - accuracy: 0.9283 - val_loss: 0.7765 - val_accuracy: 0.5768\n",
            "Epoch 28/30\n",
            "313/313 [==============================] - 15s 47ms/step - loss: 0.1496 - accuracy: 0.9315 - val_loss: 0.7488 - val_accuracy: 0.5780\n",
            "Epoch 29/30\n",
            "313/313 [==============================] - 15s 48ms/step - loss: 0.1465 - accuracy: 0.9299 - val_loss: 0.7971 - val_accuracy: 0.5906\n",
            "Epoch 30/30\n",
            "313/313 [==============================] - 15s 49ms/step - loss: 0.1496 - accuracy: 0.9296 - val_loss: 0.7790 - val_accuracy: 0.5183\n"
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\n"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7ff83d21a550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7ff83d21a550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "I loved this movie\n",
            "[0.3280544]\n",
            "\n",
            "\n",
            "that was the worst movie I've ever seen\n",
            "[0.20839119]\n",
            "\n",
            "\n",
            "too much violence even for a Bond film\n",
            "[0.01077247]\n",
            "\n",
            "\n",
            "a captivating recounting of a cherished myth\n",
            "[0.9946215]\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdN2tdW4YYJ1"
      },
      "source": [
        "# Try some more reviews"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e45UQxQl_QAI"
      },
      "source": [
        "# Write some new reviews \n",
        "\n",
        "review1 = \"\"\"I loved this movie\"\"\"\n",
        "\n",
        "review2 = \"\"\"that was the worst movie I've ever seen\"\"\"\n",
        "\n",
        "review3 = \"\"\"too much violence even for a Bond film\"\"\"\n",
        "\n",
        "review4 = \"\"\"a captivating recounting of a cherished myth\"\"\"\n",
        "\n",
        "review5 = \"\"\"I saw this movie yesterday and I was feeling low to start with,\n",
        " but it was such a wonderful movie that it lifted my spirits and brightened \n",
        " my day, you can\\'t go wrong with a movie with Whoopi Goldberg in it.\"\"\"\n",
        "\n",
        "review6 = \"\"\"I don\\'t understand why it received an oscar recommendation\n",
        " for best movie, it was long and boring\"\"\"\n",
        "\n",
        "review7 = \"\"\"the scenery was magnificent, the CGI of the dogs was so realistic I\n",
        " thought they were played by real dogs even though they talked!\"\"\"\n",
        "\n",
        "review8 = \"\"\"The ending was so sad and yet so uplifting at the same time. \n",
        " I'm looking for an excuse to see it again\"\"\"\n",
        "\n",
        "review9 = \"\"\"I had expected so much more from a movie made by the director \n",
        " who made my most favorite movie ever, I was very disappointed in the tedious \n",
        " story\"\"\"\n",
        "\n",
        "review10 = \"I wish I could watch this movie every day for the rest of my life\"\n",
        "\n",
        "more_reviews = [review1, review2, review3, review4, review5, review6, review7, \n",
        "               review8, review9, review10]\n"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQ4YZHOjYXQ2"
      },
      "source": [
        "print(\"============================\\n\",\"Embeddings only:\\n\", \"============================\")\n",
        "predict_review(model, more_reviews)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================\n Embeddings only:\n ============================\nI loved this movie\n[0.36522615]\n\n\nthat was the worst movie I've ever seen\n[0.10685083]\n\n\ntoo much violence even for a Bond film\n[0.02953798]\n\n\na captivating recounting of a cherished myth\n[0.5721201]\n\n\nI saw this movie yesterday and I was feeling low to start with,\n but it was such a wonderful movie that it lifted my spirits and brightened \n my day, you can't go wrong with a movie with Whoopi Goldberg in it.\n[0.25225288]\n\n\nI don't understand why it received an oscar recommendation\n for best movie, it was long and boring\n[0.44361305]\n\n\nthe scenery was magnificent, the CGI of the dogs was so realistic I\n thought they were played by real dogs even though they talked!\n[0.25323802]\n\n\nThe ending was so sad and yet so uplifting at the same time. \n I'm looking for an excuse to see it again\n[0.49294737]\n\n\nI had expected so much more from a movie made by the director \n who made my most favorite movie ever, I was very disappointed in the tedious \n story\n[0.01069573]\n\n\nI wish I could watch this movie every day for the rest of my life\n[0.2931418]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NI04noTAHK5t"
      },
      "source": [
        "print(\"============================\\n\",\"With CNN\\n\", \"============================\")\n",
        "predict_review(model_cnn, more_reviews)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================\n With CNN\n ============================\nI loved this movie\n[0.79763854]\n\n\nthat was the worst movie I've ever seen\n[0.0202463]\n\n\ntoo much violence even for a Bond film\n[0.00981453]\n\n\na captivating recounting of a cherished myth\n[0.773011]\n\n\nI saw this movie yesterday and I was feeling low to start with,\n but it was such a wonderful movie that it lifted my spirits and brightened \n my day, you can't go wrong with a movie with Whoopi Goldberg in it.\n[0.83789814]\n\n\nI don't understand why it received an oscar recommendation\n for best movie, it was long and boring\n[0.91515404]\n\n\nthe scenery was magnificent, the CGI of the dogs was so realistic I\n thought they were played by real dogs even though they talked!\n[0.39931244]\n\n\nThe ending was so sad and yet so uplifting at the same time. \n I'm looking for an excuse to see it again\n[0.46312398]\n\n\nI had expected so much more from a movie made by the director \n who made my most favorite movie ever, I was very disappointed in the tedious \n story\n[0.03130296]\n\n\nI wish I could watch this movie every day for the rest of my life\n[0.20812741]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGJ32sRUHNu6"
      },
      "source": [
        "print(\"===========================\\n\",\"With bidirectional GRU\\n\", \"============================\")\n",
        "predict_review(model_gru, more_reviews)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===========================\n With bidirectional GRU\n ============================\nI loved this movie\n[0.31130624]\n\n\nthat was the worst movie I've ever seen\n[0.17443436]\n\n\ntoo much violence even for a Bond film\n[0.10199329]\n\n\na captivating recounting of a cherished myth\n[0.4573918]\n\n\nI saw this movie yesterday and I was feeling low to start with,\n but it was such a wonderful movie that it lifted my spirits and brightened \n my day, you can't go wrong with a movie with Whoopi Goldberg in it.\n[0.13058805]\n\n\nI don't understand why it received an oscar recommendation\n for best movie, it was long and boring\n[0.19078735]\n\n\nthe scenery was magnificent, the CGI of the dogs was so realistic I\n thought they were played by real dogs even though they talked!\n[0.1319355]\n\n\nThe ending was so sad and yet so uplifting at the same time. \n I'm looking for an excuse to see it again\n[0.19602671]\n\n\nI had expected so much more from a movie made by the director \n who made my most favorite movie ever, I was very disappointed in the tedious \n story\n[0.06222531]\n\n\nI wish I could watch this movie every day for the rest of my life\n[0.23514214]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFw9Q0iQHP2P"
      },
      "source": [
        "print(\"===========================\\n\", \"With a single bidirectional LSTM:\\n\", \"===========================\")\n",
        "predict_review(model_bidi_lstm, more_reviews)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===========================\n With a single bidirectional LSTM:\n ===========================\nI loved this movie\n[0.49755082]\n\n\nthat was the worst movie I've ever seen\n[0.36574292]\n\n\ntoo much violence even for a Bond film\n[0.4155818]\n\n\na captivating recounting of a cherished myth\n[0.61124516]\n\n\nI saw this movie yesterday and I was feeling low to start with,\n but it was such a wonderful movie that it lifted my spirits and brightened \n my day, you can't go wrong with a movie with Whoopi Goldberg in it.\n[0.29149258]\n\n\nI don't understand why it received an oscar recommendation\n for best movie, it was long and boring\n[0.25366277]\n\n\nthe scenery was magnificent, the CGI of the dogs was so realistic I\n thought they were played by real dogs even though they talked!\n[0.246694]\n\n\nThe ending was so sad and yet so uplifting at the same time. \n I'm looking for an excuse to see it again\n[0.36962685]\n\n\nI had expected so much more from a movie made by the director \n who made my most favorite movie ever, I was very disappointed in the tedious \n story\n[0.19607124]\n\n\nI wish I could watch this movie every day for the rest of my life\n[0.47829577]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zaw7fEVQHSWK"
      },
      "source": [
        "print(\"===========================\\n\", \"With multiple bidirectional LSTM:\\n\", \"==========================\")\n",
        "predict_review(model_multiple_bidi_lstm, more_reviews)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===========================\n With multiple bidirectional LSTM:\n ==========================\nI loved this movie\n[0.3280544]\n\n\nthat was the worst movie I've ever seen\n[0.20839119]\n\n\ntoo much violence even for a Bond film\n[0.01077247]\n\n\na captivating recounting of a cherished myth\n[0.9946215]\n\n\nI saw this movie yesterday and I was feeling low to start with,\n but it was such a wonderful movie that it lifted my spirits and brightened \n my day, you can't go wrong with a movie with Whoopi Goldberg in it.\n[0.8407966]\n\n\nI don't understand why it received an oscar recommendation\n for best movie, it was long and boring\n[0.99388784]\n\n\nthe scenery was magnificent, the CGI of the dogs was so realistic I\n thought they were played by real dogs even though they talked!\n[0.9954164]\n\n\nThe ending was so sad and yet so uplifting at the same time. \n I'm looking for an excuse to see it again\n[0.99373007]\n\n\nI had expected so much more from a movie made by the director \n who made my most favorite movie ever, I was very disappointed in the tedious \n story\n[0.01279277]\n\n\nI wish I could watch this movie every day for the rest of my life\n[0.01481488]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}