nlp_lstms_with_reviews_subwords_dataset.ipynb

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    "colab": {
      "name": "l10c01_nlp_lstms_with_reviews_subwords_dataset.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hAclqSm3OOml"
      },
      "source": [
        "# Using LSTMs with the subwords dataset\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTVx8__oGR9J"
      },
      "source": [
        "In this colab, you'll compare the results of using a model with an Embedding layer and then adding bidirectional LSTM layers.\n",
        "\n",
        "You'll work with the dataset of subwords for the combined Yelp and Amazon reviews.\n",
        "\n",
        "You'll use your models to predict the sentiment of new reviews."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L62G7LTwNzoD"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLcl0QHvDjTV"
      },
      "source": [
        "# Get the dataset\n",
        "\n",
        "Start by getting the dataset containing Amazon and Yelp reviews, with their related sentiment (1 for positive, 0 for negative). This dataset was originally extracted from [here](https://www.kaggle.com/marklvl/sentiment-labelled-sentences-data-set).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCOtiRJZbxCH"
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    'https://drive.google.com/uc?id=13ySLC_ue6Umt9RJYSeM2t-V0kCv-4C-P' -O /tmp/sentiment.csv"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-06-27 19:58:10--  https://drive.google.com/uc?id=13ySLC_ue6Umt9RJYSeM2t-V0kCv-4C-P\n",
            "Resolving drive.google.com (drive.google.com)... 172.217.27.206, 2404:6800:4009:800::200e\n",
            "Connecting to drive.google.com (drive.google.com)|172.217.27.206|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-08-ak-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/m2qd5jqpip5v5co2dmc2l4ocj58ptmom/1624804050000/11118900490791463723/*/13ySLC_ue6Umt9RJYSeM2t-V0kCv-4C-P [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2021-06-27 19:58:11--  https://doc-08-ak-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/m2qd5jqpip5v5co2dmc2l4ocj58ptmom/1624804050000/11118900490791463723/*/13ySLC_ue6Umt9RJYSeM2t-V0kCv-4C-P\n",
            "Resolving doc-08-ak-docs.googleusercontent.com (doc-08-ak-docs.googleusercontent.com)... 142.250.192.65, 2404:6800:4009:829::2001\n",
            "Connecting to doc-08-ak-docs.googleusercontent.com (doc-08-ak-docs.googleusercontent.com)|142.250.192.65|: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   372KB/s    in 0.3s    \n",
            "\n",
            "2021-06-27 19:58:12 (372 KB/s) - ‘/tmp/sentiment.csv’ saved [127831/127831]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XuqER_KMD-xS"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "dataset = pd.read_csv('/tmp/sentiment.csv')\n",
        "\n",
        "# Extract out sentences and labels\n",
        "sentences = dataset['text'].tolist()\n",
        "labels = dataset['sentiment'].tolist()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tbsx1T2CXPNO"
      },
      "source": [
        "# Print some example sentences and labels\n",
        "for x in range(2):\n",
        "  print(sentences[x])\n",
        "  print(labels[x])\n",
        "  print(\"\\n\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "So there is no way for me to plug it in here in the US unless I go by a converter.\n0\n\n\nGood case Excellent value.\n1\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33AthPiALFZK"
      },
      "source": [
        "# Create a subwords dataset\n",
        "\n",
        "We will use the Amazon and Yelp reviews dataset with tensorflow_datasets's SubwordTextEncoder functionality. \n",
        "\n",
        "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",
        "We'll create a subword vocab_size of only the 1,000 most common subwords, as well as cutting off each subword to be at most 5 characters.\n",
        "\n",
        "Check out the related documentation for the the subword text encoder [here](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/text/SubwordTextEncoder#build_from_corpus)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NaicNCcLYyf"
      },
      "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)\n",
        "\n",
        "# How big is the vocab size?\n",
        "print(\"Vocab size is \", tokenizer.vocab_size)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocab size is  999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvRVoeIVLevh"
      },
      "source": [
        "# Check that the tokenizer works appropriately\n",
        "num = 5\n",
        "print(sentences[num])\n",
        "encoded = tokenizer.encode(sentences[num])\n",
        "print(encoded)"
      ],
      "execution_count": 8,
      "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]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_vacTCifklV"
      },
      "source": [
        "# Separately print out each subword, decoded\n",
        "for i in encoded:\n",
        "  print(tokenizer.decode([i]))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I \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": "cT528cptLupl"
      },
      "source": [
        "## Replace sentence data with encoded subwords\n",
        "\n",
        "Now, we'll create the sequences 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": "lkseMhxjL09F"
      },
      "source": [
        "for i, sentence in enumerate(sentences):\n",
        "  sentences[i] = tokenizer.encode(sentence)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y21yRuzmL43U"
      },
      "source": [
        "# Check the sentences are appropriately replaced\n",
        "print(sentences[5])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[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]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HrcPHESMBMs"
      },
      "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": "50-hTsogLSL-"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "max_length = 50\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "\n",
        "# Pad all sequences\n",
        "sequences_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_sequences = sequences_padded[0:training_size]\n",
        "testing_sequences = sequences_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": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PahZm7YEQ8EI"
      },
      "source": [
        "# Create the model using an Embedding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_nyQeI0RCCv"
      },
      "source": [
        "embedding_dim = 16\n",
        "\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": 13,
      "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": "markdown",
      "metadata": {
        "id": "3WRXrx8BRO2L"
      },
      "source": [
        "# Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBKyVYvxRQ_9"
      },
      "source": [
        "num_epochs = 30\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "history = model.fit(training_sequences, training_labels_final, epochs=num_epochs, validation_data=(testing_sequences, testing_labels_final))\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "50/50 [==============================] - 1s 4ms/step - loss: 0.6914 - accuracy: 0.5217 - val_loss: 0.6980 - val_accuracy: 0.4110\n",
            "Epoch 2/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.6861 - accuracy: 0.5223 - val_loss: 0.7008 - val_accuracy: 0.4110\n",
            "Epoch 3/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.6791 - accuracy: 0.5549 - val_loss: 0.6931 - val_accuracy: 0.4787\n",
            "Epoch 4/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.6673 - accuracy: 0.5907 - val_loss: 0.6875 - val_accuracy: 0.4862\n",
            "Epoch 5/30\n",
            "50/50 [==============================] - 0s 1ms/step - loss: 0.6520 - accuracy: 0.6177 - val_loss: 0.6712 - val_accuracy: 0.5439\n",
            "Epoch 6/30\n",
            "50/50 [==============================] - 0s 1ms/step - loss: 0.6299 - accuracy: 0.7188 - val_loss: 0.6673 - val_accuracy: 0.5263\n",
            "Epoch 7/30\n",
            "50/50 [==============================] - 0s 1ms/step - loss: 0.6005 - accuracy: 0.7596 - val_loss: 0.6432 - val_accuracy: 0.6090\n",
            "Epoch 8/30\n",
            "50/50 [==============================] - 0s 1ms/step - loss: 0.5652 - accuracy: 0.7753 - val_loss: 0.6196 - val_accuracy: 0.6441\n",
            "Epoch 9/30\n",
            "50/50 [==============================] - 0s 1ms/step - loss: 0.5273 - accuracy: 0.8293 - val_loss: 0.6020 - val_accuracy: 0.6541\n",
            "Epoch 10/30\n",
            "50/50 [==============================] - 0s 1ms/step - loss: 0.4891 - accuracy: 0.8286 - val_loss: 0.5602 - val_accuracy: 0.7419\n",
            "Epoch 11/30\n",
            "50/50 [==============================] - 0s 1ms/step - loss: 0.4510 - accuracy: 0.8525 - val_loss: 0.5329 - val_accuracy: 0.7694\n",
            "Epoch 12/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.4175 - accuracy: 0.8669 - val_loss: 0.5280 - val_accuracy: 0.7419\n",
            "Epoch 13/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3880 - accuracy: 0.8701 - val_loss: 0.5137 - val_accuracy: 0.7419\n",
            "Epoch 14/30\n",
            "50/50 [==============================] - 0s 1ms/step - loss: 0.3602 - accuracy: 0.8757 - val_loss: 0.4954 - val_accuracy: 0.7644\n",
            "Epoch 15/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3404 - accuracy: 0.8820 - val_loss: 0.5117 - val_accuracy: 0.7444\n",
            "Epoch 16/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3184 - accuracy: 0.8883 - val_loss: 0.5100 - val_accuracy: 0.7494\n",
            "Epoch 17/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3012 - accuracy: 0.8964 - val_loss: 0.4983 - val_accuracy: 0.7619\n",
            "Epoch 18/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2849 - accuracy: 0.9033 - val_loss: 0.5229 - val_accuracy: 0.7569\n",
            "Epoch 19/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2730 - accuracy: 0.9096 - val_loss: 0.4995 - val_accuracy: 0.7619\n",
            "Epoch 20/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2585 - accuracy: 0.9127 - val_loss: 0.5214 - val_accuracy: 0.7594\n",
            "Epoch 21/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2486 - accuracy: 0.9146 - val_loss: 0.5108 - val_accuracy: 0.7619\n",
            "Epoch 22/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2377 - accuracy: 0.9247 - val_loss: 0.5129 - val_accuracy: 0.7569\n",
            "Epoch 23/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2275 - accuracy: 0.9266 - val_loss: 0.5355 - val_accuracy: 0.7619\n",
            "Epoch 24/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.2199 - accuracy: 0.9297 - val_loss: 0.5193 - val_accuracy: 0.7644\n",
            "Epoch 25/30\n",
            "50/50 [==============================] - 0s 1ms/step - loss: 0.2115 - accuracy: 0.9309 - val_loss: 0.5413 - val_accuracy: 0.7569\n",
            "Epoch 26/30\n",
            "50/50 [==============================] - 0s 1ms/step - loss: 0.2012 - accuracy: 0.9335 - val_loss: 0.5580 - val_accuracy: 0.7594\n",
            "Epoch 27/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.1946 - accuracy: 0.9410 - val_loss: 0.5769 - val_accuracy: 0.7494\n",
            "Epoch 28/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.1880 - accuracy: 0.9429 - val_loss: 0.5581 - val_accuracy: 0.7594\n",
            "Epoch 29/30\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.1807 - accuracy: 0.9485 - val_loss: 0.5769 - val_accuracy: 0.7594\n",
            "Epoch 30/30\n",
            "50/50 [==============================] - 0s 1ms/step - loss: 0.1748 - accuracy: 0.9517 - val_loss: 0.5695 - val_accuracy: 0.7619\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HhLPbUl2AZ0y"
      },
      "source": [
        "# Plot the accuracy 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": 15,
      "outputs": [
        {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fwr5inBiWffb"
      },
      "source": [
        "# Define a function to predict the sentiment of reviews\n",
        "\n",
        "We'll be creating models with some differences and will use each model to predict the sentiment of some new reviews.\n",
        "\n",
        "To save time, create a function that will take in a model and some new reviews, and print out the sentiment of each reviews.\n",
        "\n",
        "The higher the sentiment value is to 1, the more positive the review is."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPNOYiiaha2y"
      },
      "source": [
        "# Define a function to take a series of reviews\n",
        "# and predict whether each one is a positive or negative review\n",
        "\n",
        "# max_length = 100 # previously defined\n",
        "\n",
        "def predict_review(model, new_sentences, maxlen=max_length, show_padded_sequence=True ):\n",
        "  # Keep the original sentences so that we can keep using them later\n",
        "  # Create an array to hold the encoded sequences\n",
        "  new_sequences = []\n",
        "\n",
        "  # Convert the new reviews to sequences\n",
        "  for i, frvw in enumerate(new_sentences):\n",
        "    new_sequences.append(tokenizer.encode(frvw))\n",
        "\n",
        "  trunc_type='post' \n",
        "  padding_type='post'\n",
        "\n",
        "  # Pad all sequences for the new reviews\n",
        "  new_reviews_padded = pad_sequences(new_sequences, maxlen=max_length, \n",
        "                                 padding=padding_type, truncating=trunc_type)             \n",
        "\n",
        "  classes = model.predict(new_reviews_padded)\n",
        "\n",
        "  # The closer the class is to 1, the more positive the review is\n",
        "  for x in range(len(new_sentences)):\n",
        "    \n",
        "    # We can see the padded sequence if desired\n",
        "    # Print the sequence\n",
        "    if (show_padded_sequence):\n",
        "      print(new_reviews_padded[x])\n",
        "    # Print the review as text\n",
        "    print(new_sentences[x])\n",
        "    # Print its predicted class\n",
        "    print(classes[x])\n",
        "    print(\"\\n\")"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qg-maex27KPW"
      },
      "source": [
        "# Use the model to predict some reviews   \n",
        "fake_reviews = [\"I love this phone\", \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",
        "                \"we couldn't hear each other talk because of the shouting in the kitchen\"\n",
        "              ]\n",
        "\n",
        "predict_review(model, fake_reviews)\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  4 281  16  25   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nI love this phone\n[0.87686205]\n\n\n[812 227 864 100 775   9 525 843   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nEverything was cold\n[0.0998618]\n\n\n[812 227 864 100 775   9 109   8 333 731  24  61   4 171  59  77   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nEverything was hot exactly as I wanted\n[0.292274]\n\n\n[812 227 864 100 775   9 157 359 853   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nEverything was green\n[0.08235297]\n\n\n[  1 109 228 540 237 635 241 423 340  89 298   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nthe host seated us immediately\n[0.44034928]\n\n\n[154 242  47 635 341  12 569 547 147 319 775 125  85   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nthey gave us free chocolate cake\n[0.6666034]\n\n\n[158 190 853 782   8 607 775 210 232 146 775 470  67 305 101  15   1 328\n 296  26  19   1 661 641 195   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nwe couldn't hear each other talk because of the shouting in the kitchen\n[0.025765]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycJKbMq3K4iy"
      },
      "source": [
        "# Define a function to train and show the results of models with different layers\n",
        "\n",
        "In the rest of this colab, we will define models, and then see the results. \n",
        "\n",
        "Define a function that will take the model, compile it, train it, graph the accuracy and loss, and then predict some results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PevUcINXK3gn"
      },
      "source": [
        "def fit_model_now (model, sentences) :\n",
        "  model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "  model.summary()\n",
        "  history = model.fit(training_sequences, training_labels_final, epochs=num_epochs, \n",
        "                      validation_data=(testing_sequences, testing_labels_final))\n",
        "  return history\n",
        "\n",
        "def plot_results (history):\n",
        "  plot_graphs(history, \"accuracy\")\n",
        "  plot_graphs(history, \"loss\")\n",
        "\n",
        "def fit_model_and_show_results (model, sentences):\n",
        "  history = fit_model_now(model, sentences)\n",
        "  plot_results(history)\n",
        "  predict_review(model, sentences)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U13JBiJUG1oq"
      },
      "source": [
        "# Add a bidirectional LSTM\n",
        "\n",
        "Create a new model that uses a bidirectional LSTM.\n",
        "\n",
        "Then use the function we have already defined to compile the model, train it, graph the accuracy and loss, then predict some results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scTUsFPAG4zP"
      },
      "source": [
        "# Define the model\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(6, activation='relu'), \n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Compile and train the model and then show the predictions for our extra sentences\n",
        "fit_model_and_show_results(model_bidi_lstm, fake_reviews)\n"
      ],
      "execution_count": 19,
      "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)            16000     \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 32)                4224      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 6)                 198       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 20,429\n",
            "Trainable params: 20,429\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/30\n",
            "50/50 [==============================] - 4s 29ms/step - loss: 0.6891 - accuracy: 0.5217 - val_loss: 0.6961 - val_accuracy: 0.4311\n",
            "Epoch 2/30\n",
            "50/50 [==============================] - 1s 18ms/step - loss: 0.6462 - accuracy: 0.6660 - val_loss: 0.7078 - val_accuracy: 0.5113\n",
            "Epoch 3/30\n",
            "50/50 [==============================] - 1s 17ms/step - loss: 0.5251 - accuracy: 0.7696 - val_loss: 0.5651 - val_accuracy: 0.7093\n",
            "Epoch 4/30\n",
            "50/50 [==============================] - 1s 17ms/step - loss: 0.3931 - accuracy: 0.8412 - val_loss: 0.5212 - val_accuracy: 0.7368\n",
            "Epoch 5/30\n",
            "50/50 [==============================] - 1s 19ms/step - loss: 0.2988 - accuracy: 0.8782 - val_loss: 0.5540 - val_accuracy: 0.7519\n",
            "Epoch 6/30\n",
            "50/50 [==============================] - 1s 20ms/step - loss: 0.2435 - accuracy: 0.9127 - val_loss: 0.5385 - val_accuracy: 0.7594\n",
            "Epoch 7/30\n",
            "50/50 [==============================] - 1s 17ms/step - loss: 0.2105 - accuracy: 0.9335 - val_loss: 0.5854 - val_accuracy: 0.7644\n",
            "Epoch 8/30\n",
            "50/50 [==============================] - 1s 19ms/step - loss: 0.1750 - accuracy: 0.9473 - val_loss: 0.6330 - val_accuracy: 0.7669\n",
            "Epoch 9/30\n",
            "50/50 [==============================] - 1s 17ms/step - loss: 0.1467 - accuracy: 0.9567 - val_loss: 0.6929 - val_accuracy: 0.7594\n",
            "Epoch 10/30\n",
            "50/50 [==============================] - 1s 18ms/step - loss: 0.1266 - accuracy: 0.9655 - val_loss: 0.7165 - val_accuracy: 0.7594\n",
            "Epoch 11/30\n",
            "50/50 [==============================] - 1s 19ms/step - loss: 0.1224 - accuracy: 0.9655 - val_loss: 0.7934 - val_accuracy: 0.7569\n",
            "Epoch 12/30\n",
            "50/50 [==============================] - 1s 19ms/step - loss: 0.1038 - accuracy: 0.9780 - val_loss: 0.8237 - val_accuracy: 0.7469\n",
            "Epoch 13/30\n",
            "50/50 [==============================] - 1s 21ms/step - loss: 0.0868 - accuracy: 0.9831 - val_loss: 0.8908 - val_accuracy: 0.7444\n",
            "Epoch 14/30\n",
            "50/50 [==============================] - 1s 20ms/step - loss: 0.0924 - accuracy: 0.9774 - val_loss: 0.8142 - val_accuracy: 0.7594\n",
            "Epoch 15/30\n",
            "50/50 [==============================] - 1s 19ms/step - loss: 0.0911 - accuracy: 0.9755 - val_loss: 0.9456 - val_accuracy: 0.7544\n",
            "Epoch 16/30\n",
            "50/50 [==============================] - 1s 23ms/step - loss: 0.0840 - accuracy: 0.9787 - val_loss: 0.8660 - val_accuracy: 0.7569\n",
            "Epoch 17/30\n",
            "50/50 [==============================] - 1s 22ms/step - loss: 0.0703 - accuracy: 0.9824 - val_loss: 1.0595 - val_accuracy: 0.7444\n",
            "Epoch 18/30\n",
            "50/50 [==============================] - 3s 67ms/step - loss: 0.0573 - accuracy: 0.9893 - val_loss: 1.0106 - val_accuracy: 0.7519\n",
            "Epoch 19/30\n",
            "50/50 [==============================] - 2s 30ms/step - loss: 0.0460 - accuracy: 0.9925 - val_loss: 1.0487 - val_accuracy: 0.7494\n",
            "Epoch 20/30\n",
            "50/50 [==============================] - 1s 19ms/step - loss: 0.0412 - accuracy: 0.9925 - val_loss: 1.1166 - val_accuracy: 0.7544\n",
            "Epoch 21/30\n",
            "50/50 [==============================] - 1s 18ms/step - loss: 0.0384 - accuracy: 0.9931 - val_loss: 1.1084 - val_accuracy: 0.7519\n",
            "Epoch 22/30\n",
            "50/50 [==============================] - 1s 20ms/step - loss: 0.0356 - accuracy: 0.9931 - val_loss: 1.1653 - val_accuracy: 0.7594\n",
            "Epoch 23/30\n",
            "50/50 [==============================] - 1s 21ms/step - loss: 0.0338 - accuracy: 0.9931 - val_loss: 1.1954 - val_accuracy: 0.7569\n",
            "Epoch 24/30\n",
            "50/50 [==============================] - 1s 20ms/step - loss: 0.0324 - accuracy: 0.9937 - val_loss: 1.2339 - val_accuracy: 0.7569\n",
            "Epoch 25/30\n",
            "50/50 [==============================] - 1s 19ms/step - loss: 0.0304 - accuracy: 0.9937 - val_loss: 1.1973 - val_accuracy: 0.7519\n",
            "Epoch 26/30\n",
            "50/50 [==============================] - 1s 20ms/step - loss: 0.0289 - accuracy: 0.9944 - val_loss: 1.2960 - val_accuracy: 0.7444\n",
            "Epoch 27/30\n",
            "50/50 [==============================] - 1s 22ms/step - loss: 0.0262 - accuracy: 0.9950 - val_loss: 1.2991 - val_accuracy: 0.7544\n",
            "Epoch 28/30\n",
            "50/50 [==============================] - 1s 20ms/step - loss: 0.0241 - accuracy: 0.9950 - val_loss: 1.3231 - val_accuracy: 0.7544\n",
            "Epoch 29/30\n",
            "50/50 [==============================] - 1s 20ms/step - loss: 0.0511 - accuracy: 0.9843 - val_loss: 0.9581 - val_accuracy: 0.7619\n",
            "Epoch 30/30\n",
            "50/50 [==============================] - 1s 20ms/step - loss: 0.1060 - accuracy: 0.9636 - val_loss: 1.0966 - val_accuracy: 0.7619\n"
          ]
        },
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          "output_type": "display_data",
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3FWDg7REw+4mKGxz3bIYpN8DrQyD7Fxj5NNy2ADqfq4nAX5X2KCpvDZbifG+pYBC0ObXWQtJkoJSTNnxnR5YmX370sdb94db50GM0/PBPmxR2bzx8PH8PzHoQXkyxk8oNnGAXXO97k00oyn8l9LCfe972o4+lvQ37d9ZaW0Epn1UTichbwHlAljHmqKWPxK5R+TwwEjgIjDPGLPVVPEr5pRWpdsK3iqaBDo+Gi1+FjmfBF/fAqwNhxD+hcB/88KRdb7jXlTDkAdt1VAWGso3IZbv6FufD/OcgaSAknVarIfmyzeAd4EXgvQqOnwN09P70A17xPipVPxTut5OW9byi8kVlul8KiX1to/K0O+y+dkNg+GOH58lXgaNZN0Bsu0GnEYf3L3nHlgoue6vWQ/JZMjDGzBGRpGOcciHwnrELFy8QkUYi0twYU065Sak66Jfpdm2A8qqIytOoFVw7DX7+wI4g7jDMt/Ep3wmLsl1+y/YoKs6Hec95SwWn13pITvYmaglsLbOd4d13VDIQkZuBmwFat9b5VFQdseITiGkNrapRIA4KhlOu9V1MqvYkJMP2ZYe3l7wL+3fApW84Ek5ANCAbYyYZY1KMMSlxcdpfWtUB+7Ng42xIvsyuG6Dqn4RkO76kIBeKC2Des9DmdGg70JFwnCwZZAKtymwnevcpVfetmmoXoO9RxSoiVfc072kfd6yCnau8pYLXHQvHyT9JpgHXiNUfyNX2AlVvrEy1fxnGd3E6EuWU0ob/jMXeUsFptr3AIb7sWvoxMBiIFZEM4K+AC8AY8yowA9utNB3btfQ6X8WilF/ZtcGuNnbWY05HopwU2Qwi4mDeM7aq6JJJjg4S9GVvojGVHDfA7b56f6X81opUQGx7gaq/ROzgsw3fQutTHS0VQIA0ICtVZ5TOUJp0uq4roA4PPht8v+NTh+hEdUodD4/bdvOsrsyldkqJ0++p+ZhU4Ol7s53FtO0gpyPRkoFS1bZ8MjzZDrYsrP5rV6ba6Yu7XFDzcanAE93CTifiBxMKajJQqjqK8+F/j0DBXpg85vcTx1XGXWIXrTnpbGjQyEcBKnV8NBkoVR2LXrczTZ7/gh0n8OEoOLi7aq/97Xu7cpWOLVB+SJOBUlVVsM92A2w/zE4JccXHsHcLTB4LJYWVv35FKoTHQMfhvo9VqWrSZKBUVf30kl1DYNhDdrvNALjoFdjyI3x+G3g8Fb+26ACs/RK6XgghYbUTr1LVoL2JlKqKAznw04v2y7xF78P7ky+DvZvh20ehcdLhRHGkdV9B8QG7UI1SfkiTgVJVMe9ZO930kAeOPnb6vXbCsblP24Rw8tVHn7MiFaIT7eAipfyQVhMpVZncTNtw3PNKiOt09HEROPcZaD8UvvyDXcqyrAM5dpRp8qU6Q6nyW/o/U9VtWxfBtLtsXf/xmvOk7Tk0+L6Kzwl2wah3ILYTpF4LO9ccPrb6M/CUVH0RG6UcoMlA1V2/zYX3LoKl78LHY+wYgeratQGWvg8p10OjShZWCo+Bsangami7nO7zTsK7IhXiu0HCUUuBK+U3NBmoumnDbPuF3KgVjHwatiyAT2+0A7+qY/Y/bO+fQX+s2vkxiTYh5O+Bj0fDztWQsQh6jKr+PShVizQZqLpn/Sz4aDQ0bQ/jpkPfm2DEP+GXL2HGBDtZXFXsWAmrpkD/8RAZX/X3b94TRr1tX//2SLuvu85QqvybJgNVt/wyHSZfCfGd4dovICLW7u9/q50cbsk78MP/Ve1a3/3dVv2cemf14zjpbDjnSTttRZvTbAlFKT+mXUtV3bH6M1sV1LwXXPXp0fP/DPsr5O2E75+wC4ukHGM9pS0LYf1X9jUNGh9fPH1vsq9tpm0Fyv9pMlB1w4pU+OwWaNUPrkyF8OijzxGBC16Agzkw/V67ylSX844+zxg7iCwiHvrdcmJx6QI2KkBoNZEKfD9/AFNvttUxY6eUnwhKlXYBbdEbPr0BNv909DkbZ8PmeTBoIoRG+CxspfyJJgMV2NLegv/eDu0G2xJBWGTlrwmNgCv/Y3v+fDwastYePlZaKmjUGk4Z56uolfI7mgxU4Fr4Gnx5D3Q8G8ZMhtCGVX9tRFO4aiqEhMMHl0Juht2/9gvY9jMM/jOEhPombqX8kCYDFXgK99v+/1/9CTqfB6M/AFd49a/TuI1taC7Mg/cvsdNGfPe4HUWsE8qpekYbkFXg2LsVFr0GS96Dwlzbd//iV207wPFKSIYrPoIPLoFXToP9O+Dy949vfWOlAphPSwYiMkJE1olIuojcX87x1iIyW0R+FpEVIjLSl/GoAGSM7eaZei083xN+ehk6DIUb/geXvXliiaBU24FwySTYv9N2S+1y/olfU6kA47OSgYgEAy8BZwEZwGIRmWaMKTODFw8CqcaYV0SkKzADSPJVTCqAuIthzX9hwcuQucQ7+OsO6HOTbwZwdbvYjj1o1NovFidXqrb5spqoL5BujNkIICKTgQuBssnAAKX9AGOAbT6MRwWCg7thyduw6A3I2wZNO9i5hXqOqVpPoRPRRtcaUPWXL5NBS2Brme0MoN8R5zwCzBKRO4EI4MzyLiQiNwM3A7RuXcnMkSpwrf4cPrsVSvJtV9Hzn4MOZ+kaAErVAqd/y8YA7xhjEoGRwPsiclRMxphJxpgUY0xKXFxcrQepasFvc2DqTbZBd/xPcM1/7fw+mgiUqhW+LBlkAmUrdxO9+8q6ARgBYIz5SUTCgVggy4dxKX+zYyVMHgtN2tvpn493LiCl1HHz5Z9di4GOItJWREKBK4BpR5yzBRgGICJdgHAg24cxKX+zdwt8cBmERsJVUzQRKOUQnyUDY0wJcAcwE1iL7TW0WkQeFZELvKdNAG4SkeXAx8A4Y6o62bwKeAd328FeJflw9VQ7PYRSyhE+HXRmjJmB7S5adt/DZZ6vAU7zZQzKTxUdtAvQ7N0C13wO8V2cjkipek1HIKva5y6BKddDxmK4/D3t0qmUH9BkoGqXMXYtgfVf2fEDXS+o/DVKKZ/Tfnuqdn3/T1j6Lgz8o10JTCnlFzQZqNqT9jb88E/odRUMfdDpaJRSZWgyULXjl+m2eqjjcDuyWOf/UcqvaDJQvrf5R9tg3KK3XXKyJmYaVUrVKG1AVr6Rt8PONbTqU8hYZEcXX5mqawor5aeqlAxE5G7gbSAPeAPoDdxvjJnlw9hqh8cNezZB1hrI+sU+5qyH7pfAwAlORxdYDuyCtf+FVVNh0zzAQLNkGPYw9L4GImKdjlApVYGqlgyuN8Y8LyJnA42Bq4H3gcBJBh4P5G61i59nr7WPWWvtF39JweHzGrWGoBD44UnofTVExjsXcyDI32vbA1Z9Chu/B+OGph3hjPtsQo3r5HSESqkqqGoyKG3tGwm8751WIqBaAM2Kycjn4w/viGphR722HWQf47rYL66wSMhJh5f6wE8vwlmPOhe0P8v5Fb75K6R/A+4im0RPuwu6XWJnHg2s/x5K1XtVTQZLRGQW0Bb4s4hEAR7fhVXzfijoyLfuG+nZuz/nDx9GWGSTik+O7WC/1Ba/Caf9ARoe49z6aNN8mHyl/cLvcyN0vxRanqIJQKkAVtXeRDcA9wN9jDEHARdwnc+i8oEuXZPZ3Xksf1zUkJGvrWDhxl3HfsHACVC0Hxa+VjsBBooV/4H3L7LVZzd/DyOegMQUTQRKBbiqJoMBwDpjzF4RuQq7dnGu78Kqec2iw3lp7Mm8fV0fCks8jJ60gIn/Wc7uA0UVvKArdD4PFr4CBftqN1h/ZAzMeRqm3giJfeGGWdA4yemolFI1pKrJ4BXgoIj0xE47vQF4z2dR+dCQTvF8c88ZjB/cns9+zmTYv77nP2lbKXfm7IEToCAX0t6s/UD9ibsYpt0J3z0GPUbb6aZ13QGl6pSqJoMS7zoDFwIvGmNeAqJ8F5ZvNQgN5r4RnZl+10DaxUUyccoKrpi0gPSs/b8/seXJ0H4Y/PiinXK5PirYBx+Ogp/fh0F/gotfg5Awp6NSStWwqiaDPBH5M7ZL6XTvOsUBP4y0U0IU/7llAE9cksza7fs45/k5PDNrHQXF7sMnDZoIB3Ps5Gr1TW4GvDUCNs2FC16EoQ9o24BSdVRVk8FooBA73mAHdj3jp3wWVS0KChLG9G3NtxMGc25yc174Lp0Rz81h6ZY99oQ2A6DN6TD/BSgpdDbY2rR9Bbxxph2bMfY/cPLVTkeklPKhKiUDbwL4EIgRkfOAAmNMQLYZVCQuKoznrujNBzf0o9htuOPDpeQXeUsIgyZA3jZY9pGzQdaWX7+Bt88BCYbrv4b2Q52OSCnlY1VKBiJyObAIGAVcDiwUkct8GZhTTu8Yy78u78m23ALemLvR7mw3xPajn/eMbUytq4yBRa/b5SibtIMb/wfNujkdlVKqFlS1mugB7BiDa40x1wB9gYd8F5az+rdryohuCbz8/QZ27iuw9eSDJtr1eldOcTo839ifBR+PgRl/hA5nwnVfQXRzp6NSStWSqiaDIGNMVpntXdV4bUD688jOuD2GJ79eZ3ecNAKadbelA4/72C8ONGumwcv9YcN3cPYTMGaynZZDKVVvVPUL/WsRmSki40RkHDAdmFHZi0RkhIisE5F0Ebm/gnMuF5E1IrJaRPymUr5N0wiuOz2JT5dmsCJjry0dDJxgJ7ZbO83p8GpG/l6YegukXg0xreCWOTDgNgiq03leKVUOKXewVXknilwKnObdnGuM+ayS84OB9cBZQAawGBhjjFlT5pyOQCow1BizR0TijyiBHCUlJcWkpaVVKeYTlVdQzJCnv6dtbASptwxAjAde6gch4XDr3MDuZrnxe/j8dsjbDoP+aKvBdNEZpeosEVlijEmp6HiV/wQ0xnxqjLnX+3PMRODVF0g3xmw0xhQBk7GD1sq6CXjJGLPH+x7HTAS1LSrcxYThnVi8aQ8zVu6AoGBbOti5EtbPdDq841OcD1/dD+9dCK5wuOEbGPIXTQRK1XPHTAYikici+8r5yRORyibsaQlsLbOd4d1X1knASSIyX0QWiMiICuK4WUTSRCQtOzu7snuqUZentKJzQhT/mLHWDkZLvsxO1zznKdv7JpBkLoHXBtn5lvreArfMhcRTnI5KKeUHjpkMjDFRxpjocn6ijDHRNfD+IUBHYDAwBnhdRBqVE8ckY0yKMSYlLi6uBt626oKDhIfP60rm3nzenPeb/Qv69HsgM81WtQQCdzF8/0944ywoOgBXfw4jn4TQhk5HppTyE75sKcwEWpXZTvTuKysDmGaMKTbG/IZtY+jow5iOy6kdYjmrazNenp1OVl4B9BoLUc1h7r+cDq1yWb/YkcTfP2FLNeN/hPZDnI5KKeVnfJkMFgMdRaStiIQCVwBHdsP5HFsqQERisdVGG30Y03H7y8guFLk9/GvmejtR26l32Tl7Nv/kdGjl87jtFBqvDbJTSox6Fy6ZBA0aOR2ZUsoP+SwZGGNKgDuAmcBaINW7XOajInKB97SZwC4RWQPMBiYaYypZdcYZbWMjGHdqEqlLtrIqMxdOuRYaxsLcp50O7Wi7N8I758I3D9kBZLctgG4XOR2VUsqPVblrqb+oza6lR8rNt11NO8ZHMvnm/si8Z+Hbv8HJ19i1kp2e498Yu/bCrIcgyAXn/B/0vCKwu8AqpWpEjXUtVRDTwMU9Z53Ewt92M3P1Thhwh60u+vlDeLEvrP78xHsY7dpg5wf6ba5t7K2q3Ax4/2KYPgFa9YPbfoReYzQRKKWqREsG1VTi9jDyhbkUFHv45t5BhIUEw/bldiWw7cuh00gY+TTEHNmLthI7VtmpLlZ/BsZj90kwJHS3X+6t+kFiH9uttewXvDGwfDJ8dR94imH4Y5BygyYBpdTvVFYy0GRwHOb+ms3Vby7iz+d05pYz2tud7hLbf/+7v0NQCJz5V/ulXNnUDlsX2V5J67+G0CjocwP0vtrW+29dCBmLIGMJFHtLCZEJ0KqPTQ7Ne8KCV2HddGg9AC562c42qpRSR9Bk4CM3vLOYhb/t5vuJg4mNLLMM5J5N8OU9dtK3xL5wwQsQ3+X3LzYGNs6Guc/YHkkNmkD/26DvjeW3O7hLIGu1TRxbF9kEsWeTPRYcBsMesq8PCvbV7SqlApwmAx/ZkL2fs5+dw+V9WvGPi5N/f9AYWJEKX98PhXl2kNrACRAcav+Kn/sv2PYzRLWAU++0PZNCI6oXQN5O2LYUYk+Cpu1r7saUUnWSJgMf+tsXq3n3x018/YdBnNQs6ugTDuTAzL/Aik+gaUeQIMhZB43b2gTR8wpdXF4pVSu0N5EP3TW0IxGhIfxr1rryT4iItQO9rvrUNuiGhMKlb8IdabY0oIlAKeUnQpwOIJA1jgjlxoHtePZ/61m+dS89WzUq/8QOZ8IdZ9ZqbEopVR1aMjhBNwxsS5OIUJ6uqHSglFIBQJPBCYoMC+G2we2Z+2sOP23wy5k0lFKqUpoMasBV/duQEB3O07PWEWgN8kopBZoMakS4K5i7hnVkyeY9zF7nV4u1KaVUlWgyqCGjUhJp07QhT81cj8ejpQOlVGDRZFBDXMFB3HPmSazdvo/pK7c7HY5SSlWLJoMadH7PFnRqFsWz36ynxO1xOhyllKoyTQY1KDhImDD8JDbmHGDq0iNX+FRKKf+lyaCGndW1GT1bNeK5/62nsMTtdDhKKVUlmgxqmIgwcXgntuUW8NHCLU6Ho5RSVaLJwAdO69CUAe2a8tLsdA4UljgdjlJKVUqTgQ+ICH88uxM5+4t458dNToejlFKV0mTgI6e0acyZXeJ59YcN5B4sdjocpZQ6Jk0GPjRheCfyCkp4bc4Gp0NRSqlj8mkyEJERIrJORNJF5P5jnHepiBgRqXDhhUDUpXk05/dswdvzN5GdV+h0OEopVSGfJQMRCQZeAs4BugJjRKRrOedFAXcDC30Vi5PuObMjRW4PL81OdzoUpZSqkC9LBn2BdGPMRmNMETAZuLCc8x4D/g8o8GEsjmkXF8moUxL5aOEWMvYcdDocpZQqly+TQUtga5ntDO++Q0TkZKCVMWb6sS4kIjeLSJqIpGVnZ9d8pD5217COADzx1S86xbVSyi851oAsIkHAM8CEys41xkwyxqQYY1Li4uJ8H1wNa9GoAXcO7cD0FduZsiTD6XCUUuoovkwGmUCrMtuJ3n2looDuwPcisgnoD0yra43IpW4b0oH+7Zrw8H9XsyF7v9PhKKXU7/gyGSwGOopIWxEJBa4AppUeNMbkGmNijTFJxpgkYAFwgTEmzYcxOSY4SHhudG/CXUHc+dHPOm+RUsqv+CwZGGNKgDuAmcBaINUYs1pEHhWRC3z1vv4sISacpy7ryZrt+3hixi9Oh6OUUoeE+PLixpgZwIwj9j1cwbmDfRmLvzizazPGnZrEOz9u4vQOsZzZtZnTISmllI5AdsKfR3amW4toJk5Zzo7cOtmjVikVYDQZOCAsJJh/j+lNYYmHuyf/jFvXTFZKOUyTgUPaxUXy6IXdWfjbbl78TkcnK6WcpcnAQZee3JKLerXg+W/Xs+i33U6Ho5SqxzQZOEhEePziZFo1acgfJv/M3oNFToeklKqnNBk4LDIshH+P6U32/kL+NGWFTlehlHKEJgM/0COxEfeN6MysNTv5YMFmp8NRStVDmgz8xPWntWVwpzgem76Wtdv3OR2OUqqe0WTgJ4KChKdH9SSmgYs7PlrKrv26GI5SqvZoMvAjsZFhPH9FLzL25HP+v+exMiPX6ZCUUvWEJgM/c2r7WKbceioAl736I1OX6pTXSinf02Tgh5ITY5h25+n0atWIe1OX8+gXayhxe5wOSylVh2ky8FOxkWF8cGM/xp2axFvzf+PqNxdpO4JSymc0GfgxV3AQj1zQjX+N6smSLXu44MX5rMrUdgSlVM3TZBAALj0lkSm3DsBjDJe9+iP/XZZZ+YuUUqoaNBkEiB6JjfjiztPpkdiIuycv4/EvtR1BKVVzNBkEkNjIMD70tiO8Me83rn1b2xGUUjVDk0GAKW1HeOqyHizetIdBT87mia/WkpWni+QopY6fT5e9VL4zKqUVvVo14oXv0nl9zkbemb+J0X1acfOgdiQ2buh0eEqpACOBNktmSkqKSUtLczoMv/JbzgFe/X4DU3/OwBi4qHdLxg9uT/u4SKdDU0r5CRFZYoxJqfC4JoO6Y9vefCbN2cjkxVsoLPEwsntzxg9uT/eWMU6HppRyWGXJwKdtBiIyQkTWiUi6iNxfzvF7RWSNiKwQkW9FpI0v46nrWjRqwCMXdGPefUMZf0Z75qzP5rx/z+O6txeRtklXUlNKVcxnJQMRCQbWA2cBGcBiYIwxZk2Zc4YAC40xB0VkPDDYGDP6WNfVkkHV5eYX8/5Pm3hr/iZ2HyhiYMdYJgzvRK9WjZwOTSlVy5wsGfQF0o0xG40xRcBk4MKyJxhjZhtjDno3FwCJPoyn3olp4OKOoR2Zd98Q/jKyM6u37eOil+Zz47uLWb1NRzIrpQ7zZTJoCWwts53h3VeRG4CvyjsgIjeLSJqIpGVnZ9dgiPVDw9AQbh7Unjl/GsIfh5/Eot92c+4L87j9w6WkZ+U5HZ5Syg/4xTgDEbkKSAGeKu+4MWaSMSbFGJMSFxdXu8HVIZFhIdwxtCNz7xvKXUM78P26LIY/O4d7PlnGppwDToenlHKQL5NBJtCqzHaid9/viMiZwAPABcYYHU5bC2IauLh3eCfm3jeUmwa246tV2xn2zA/c/+kKMvfmOx2eUsoBvmxADsE2IA/DJoHFwJXGmNVlzukNTAFGGGN+rcp1tQG55mXlFfDy7A18tHALBsPFvVtydf8kkhO1S6pSdYWj4wxEZCTwHBAMvGWM+buIPAqkGWOmicj/gGRgu/clW4wxFxzrmpoMfGfb3nxe/j6dT5dkkl/spmdiDGP7t+H8Hi1oEBrsdHhKqROgg85UteXmF/PZ0gw+WLiF9Kz9RIeHcNkprRjbv7WOalYqQGkyUMfNGMOCjbv5YOFmZq7aQYnHcFqHplzVrw1ndm2GK9gv+h8opaqgsmSgE9WpCokIA9o3ZUD7pmTlFZC6eCsfLdzC+A+XEh8Vxug+rRjeNYFuLaIJChKnw1VKnQAtGahqcXsM3/2SxQcLNjPn12yMgbioMIZ0imNIp3hO7xhLVLjL6TCVUkfQaiLlMzn7C/lhXTbfrctizvps8gpKcAULfZKaMLRzPIM7xdM+LgIRLTUo5TRNBqpWlLg9LNm8h9nrspn9SxbrdtqRza2bNGRo53g6JUQRHe4iukEIUeEuosNDiG7gIio8hLAQ7amklK9pMlCOyNhzkO+9iWH+hhwKiiterzksJIjoBjZBNI0Mo1/bJpzeIZberRsTGqKN1ErVBE0GynGFJW52HyhiX34J+wqKySsoPvR8X34xeQWlz0vI2JvPyoy9eAw0DA2mf7umnN4hloEdY+kQH6lVTkodJ+1NpBwXFhJM85gGNK/igObc/GJ+2rCLeenZzPs1h+9+yQKgWXQYp3kTw2kdYomPCvdh1Er5l+/XZdG1RbTP/t9ryUD5va27DzI/PYe56Tn8mJ7DnoPFAHSMj6Rfuyb0a9uUfu2aaHJQdZLHY/j3d+k89+16xvRtzT8uTj6u62g1kapTPB7D6m37mJuezYKNu1myaTcHitwAtIuLoF/bpvT3JoiEmKolh4JiN7sOFLHnQBHFbg+u4CBCgoWQoCBcwUJIcBCuIPsYEiy4goIIDQkiWMdWKB/LKyjm3tTlfLNmJxf3bskTlyQT7jq+DheaDFSdVuL2sGrbPhZu3MXC33az+Lfd5BWWANCmaUP6tW1CtxYx7C8sYbf3C3/XgSJ2l/nJL3ZX+31FoGlEKM2iw70/YcRH2ecJMYefN40I1QF56rikZ+3n5vfT2LzrIA+e24VxpyadUJuZJgNVr7g9hrXb97HAmxwW/bab3HxbrdQwNJgmEaGHfxrax8YRoTT1PrqChWK3ocRtKPF4KHYb3N7HEreHEo+h2G3IL3aTnVfAzn2F7NxnH3cdKOTIX6eQIKFtbATJLWPo3jKG5MQYujaPJiJMm+tUxWau3sGE1OWEhQTx0tiT6d+u6QlfU5OBqtc8HkPO/kKiG7iOu3hdVcVuD9l5h5NDVl4B23MLWL8jj5WZuWTl2eU6RKBd2QTRMoZuLWOI1ARR77k9huf+t55/f5dOz8QYXrnqFFo0alAj19beRKpeCwoS4qNrp2HZFRxEi0YNKvzlzdpXwMrMXFZm5rIqM5efNu7i82XbAJsg2sZG0LV5NN1axNC1RTRdm0cTFxVWK7Er5+XmF/OHyT8ze102o05J5LGLuvv8D5iyNBkoVUvio8MZFh3OsC7NDu3Lyitgdea+Q0li2da9fLli+6HjcVFhdG0eTdcW0XTzJoikphHaDlHHrNuRxy3vp5G5N5/HLurOVf1a1/qYGk0GSjkoPiqc+M7hDOkcf2hf7sFi1mzfZ3+22cf5czZS4rFVug1Dg+nYLIqO8ZH2p1kkHeOjaNmogSaJADR9xXYmTllORFgIH9/Un5SkJo7EoclAKT8T09B1aOrwUoUlbtKz9rNm2z5Wb9vHr1l5zFmfzZQlGYfOCXcF0SHeJoYO3kTRLi6CRg1DiWng0vUn/EiJ28PsddlMXrSFb3/J4uTWjXjlqlNoVktVmuXRBmSlAljuwWLSs/P4ded+fs3az/qdeaRn7Wd7bsFR50aGhRDTwEVMAxeNGtofux1Ko4YuIsJCaOAKpmFoMA1cwYSXPvduH3p0BWsJ5Dht3nWA1LSt/Cctg6y8QuKiwhjTtzV3DOng83m4tAFZqTospqGLU9o04ZQ2v69ayCsoJj1rP1t2H2TvwWL2HiwmN7+YvflF5Hqfr9+5n9z8YnIPFlPkrngiwfKEhQQdThqhhxNIg9AQGriCDj0PCwkiLMQO0nMF28fQ0sfSY97t8gb3hQSLHfjnfR4aXOY6IUGEBInfz1dVUOxm1pqdfLJ4C/PTdxEkMKRTPKP7tGJI53i/KbFpMlCqDooKd9G7dWN6t25c6bnG2HETBwrd5Be5yS92c7CohPziw9tlHw8WuSkoLj3PPhZ4H/flF5O17/CxwmI3RW4PRSUePD6ohBCB0ODShBN8KPGUJhhXsBxKHiFB9rkrxCad0uelry9bEgp3HS4FNQy1Ca90uzSJhbmCvcmp/IS0fmcekxdtZerPGew9WExi4wZMOOskRqW0qvLo+NqkyUCpek5EaBgaQsNQ334dlLg9hxJDUYmHwpLfb5ccGtxnKPZ47MA/t4dij3fAn9tQ5PZQXOY1RW7vdUoOP9pruikssa8pdns4UFhCsfe5/TGHHkvPLSypXumolAiHSjilCSIoCLbuzic0OIjh3ZpxRZ/WnNq+qV9Xr/n00xeREcDzQDDwhjHmn0ccDwPeA04BdgGjjTGbfBmTUsoZtvoniIahTkdSPo/HUFDiLe2UKf3kF7k5WKb0UzbxFHoTSem+Qu++ohIP1w5I4pKTE2kS4ac3fASfJQMRCQZeAs4CMoDFIjLNGLOmzGk3AHuMMR1E5Arg/4DRvopJKaUqEhRUOyUkf+XLlou+QLoxZqMxpgiYDFx4xDkXAu96n08Bhom/twYppVQd5Mtk0BLYWmY7w7uv3HOMMSVALnDiMzIppZSqFv/o01QJEblZRNJEJC07O9vpcJRSqs7xZTLIBFqV2U707iv3HBEJAWKwDcm/Y4yZZIxJMcakxMXF+ShcpZSqv3yZDBYDHUWkrYiEAlcA0444Zxpwrff5ZcB3JtCGRCulVB3gs2ZzY0yJiNwBzMR2LX3LGLNaRB4F0owx04A3gfdFJB3YjU0YSimlaplP+1AZY2YAM47Y93CZ5wXAKF/GoJRSqnIB0YCslFLKtwJu1lIRyQY2H+fLY4GcGgzHH9S1e6pr9wN1757q2v1A3bun8u6njTGmwh44AZcMToSIpB1rCtdAVNfuqa7dD9S9e6pr9wN1756O5360mkgppZQmA6WUUvUvGUxyOgAfqGv3VNfuB+rePdW1+4G6d0/Vvp961WaglFKqfPWtZKCUUqocmgyUUkrVn2QgIiNEZJ2IpIvI/U7HUxNEZJOIrBSRZSKS5nQ81SUib4lIloisKrOviYh8IyK/eh8rX8TXj1RwT4+ISKb3c1omIiOdjLE6RKSViMwWkTUislpE7vbuD8jP6Rj3E8ifUbiILBKR5d57+pt3f1sRWej9zvvEO0dcxdepD20G3lXX1lNm1TVgzBGrrgUcEdkEpBhjAnKwjIgMAvYD7xljunv3PQnsNsb805u0Gxtj7nMyzuqo4J4eAfYbY552MrbjISLNgebGmKUiEgUsAS4CxhGAn9Mx7udyAvczEiDCGLNfRFzAPOBu4F5gqjFmsoi8Ciw3xrxS0XXqS8mgKquuqVpmjJmDnaCwrLKr372L/UUNGBXcU8Ayxmw3xiz1Ps8D1mIXpQrIz+kY9xOwjLXfu+ny/hhgKHYFSajCZ1RfkkFVVl0LRAaYJSJLRORmp4OpIc2MMdu9z3cAzZwMpgbdISIrvNVIAVGlciQRSQJ6AwupA5/TEfcDAfwZiUiwiCwDsoBvgA3AXu8KklCF77z6kgzqqtONMScD5wC3e6so6gzv2hZ1oR7zFaA90AvYDvzL0WiOg4hEAp8CfzDG7Ct7LBA/p3LuJ6A/I2OM2xjTC7uIWF+gc3WvUV+SQVVWXQs4xphM72MW8Bn2P0Gg2+mt1y2t381yOJ4TZozZ6f1l9QCvE2Cfk7ce+lPgQ2PMVO/ugP2cyrufQP+MShlj9gKzgQFAI+8KklCF77z6kgyqsupaQBGRCG8DGCISAQwHVh37VQGh7Op31wL/dTCWGlH6pel1MQH0OXkbJ98E1hpjnilzKCA/p4ruJ8A/ozgRaeR93gDbUWYtNilc5j2t0s+oXvQmAvB2FXuOw6uu/d3ZiE6MiLTDlgbALlL0UaDdk4h8DAzGTre7E/gr8DmQCrTGTlV+uTEmYBpkK7inwdjqBwNsAm4pU9/u10TkdGAusBLweHf/BVvPHnCf0zHuZwyB+xn1wDYQB2P/wE81xjzq/Y6YDDQBfgauMsYUVnid+pIMlFJKVay+VBMppZQ6Bk0GSimlNBkopZTSZKCUUgpNBkoppdBkoNQhIuIuM2vlspqc3VZEksrOZKqUvwmp/BSl6o1875B+peodLRkoVQnvuhFPeteOWCQiHbz7k0TkO+/kZt+KSGvv/mYi8pl3fvnlInKq91LBIvK6d875Wd7RoojIXd759VeIyGSHblPVc5oMlDqswRHVRKPLHMs1xiQDL2JHsgP8G3jXGNMD+BB4wbv/BeAHY0xP4GRgtXd/R+AlY0w3YC9wqXf//UBv73Vu9c2tKXVsOgJZKS8R2W+MiSxn/yZgqDFmo3eSsx3GmKYikoNdKKXYu3+7MSZWRLKBxLJD/73TJX9jjOno3b4PcBljHheRr7EL4nwOfF5mbnqlao2WDJSqGlPB8+ooOy+Mm8NtducCL2FLEYvLzDSpVK3RZKBU1Ywu8/iT9/mP2BlwAcZiJ0AD+BYYD4cWHYmp6KIiEgS0MsbMBu4DYoCjSidK+Zr+BaLUYQ28q0WV+toYU9q9tLGIrMD+dT/Gu+9O4G0RmQhkA9d5998NTBKRG7AlgPHYBVPKEwx84E0YArzgnZNeqVqlbQZKVcLbZpBijMlxOhalfEWriZRSSmnJQCmllJYMlFJKoclAKaUUmgyUUkqhyUAppRSaDJRSSgH/D0nLnjrjAnE4AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  4 281  16  25   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nI love this phone\n[0.99917847]\n\n\n[812 227 864 100 775   9 525 843   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nEverything was cold\n[0.05145222]\n\n\n[812 227 864 100 775   9 109   8 333 731  24  61   4 171  59  77   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nEverything was hot exactly as I wanted\n[0.89534366]\n\n\n[812 227 864 100 775   9 157 359 853   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nEverything was green\n[0.09924155]\n\n\n[  1 109 228 540 237 635 241 423 340  89 298   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nthe host seated us immediately\n[0.9972669]\n\n\n[154 242  47 635 341  12 569 547 147 319 775 125  85   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nthey gave us free chocolate cake\n[0.9984962]\n\n\n[158 190 853 782   8 607 775 210 232 146 775 470  67 305 101  15   1 328\n 296  26  19   1 661 641 195   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nwe couldn't hear each other talk because of the shouting in the kitchen\n[0.00601384]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsxKPbCnPJTj"
      },
      "source": [
        "# Use multiple bidirectional layers\n",
        "\n",
        "Now let's see if we get any improvements from adding another Bidirectional LSTM layer to the model.\n",
        "\n",
        "Notice that the first Bidirectionl LSTM layer returns a sequence."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3N6Zul47PMED"
      },
      "source": [
        "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(6, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "fit_model_and_show_results(model_multiple_bidi_lstm, fake_reviews)"
      ],
      "execution_count": 20,
      "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)            16000     \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, 50, 32)            4224      \n",
            "_________________________________________________________________\n",
            "bidirectional_2 (Bidirection (None, 32)                6272      \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 6)                 198       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 26,701\n",
            "Trainable params: 26,701\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/30\n",
            "50/50 [==============================] - 7s 56ms/step - loss: 0.6918 - accuracy: 0.5229 - val_loss: 0.7018 - val_accuracy: 0.4110\n",
            "Epoch 2/30\n",
            "50/50 [==============================] - 2s 34ms/step - loss: 0.6590 - accuracy: 0.5851 - val_loss: 0.6748 - val_accuracy: 0.6065\n",
            "Epoch 3/30\n",
            "50/50 [==============================] - 2s 33ms/step - loss: 0.4372 - accuracy: 0.8198 - val_loss: 0.5407 - val_accuracy: 0.7619\n",
            "Epoch 4/30\n",
            "50/50 [==============================] - 2s 34ms/step - loss: 0.3231 - accuracy: 0.8745 - val_loss: 0.5330 - val_accuracy: 0.7644\n",
            "Epoch 5/30\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.2553 - accuracy: 0.9065 - val_loss: 0.5980 - val_accuracy: 0.7469\n",
            "Epoch 6/30\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.2011 - accuracy: 0.9385 - val_loss: 0.6774 - val_accuracy: 0.7393\n",
            "Epoch 7/30\n",
            "50/50 [==============================] - 2s 37ms/step - loss: 0.1646 - accuracy: 0.9517 - val_loss: 0.6518 - val_accuracy: 0.7419\n",
            "Epoch 8/30\n",
            "50/50 [==============================] - 2s 40ms/step - loss: 0.1400 - accuracy: 0.9630 - val_loss: 0.6672 - val_accuracy: 0.7669\n",
            "Epoch 9/30\n",
            "50/50 [==============================] - 2s 38ms/step - loss: 0.1151 - accuracy: 0.9711 - val_loss: 0.9375 - val_accuracy: 0.7168\n",
            "Epoch 10/30\n",
            "50/50 [==============================] - 2s 38ms/step - loss: 0.1159 - accuracy: 0.9724 - val_loss: 0.8239 - val_accuracy: 0.7444\n",
            "Epoch 11/30\n",
            "50/50 [==============================] - 2s 38ms/step - loss: 0.1033 - accuracy: 0.9736 - val_loss: 0.7620 - val_accuracy: 0.7644\n",
            "Epoch 12/30\n",
            "50/50 [==============================] - 2s 43ms/step - loss: 0.0893 - accuracy: 0.9818 - val_loss: 0.8049 - val_accuracy: 0.7619\n",
            "Epoch 13/30\n",
            "50/50 [==============================] - 2s 39ms/step - loss: 0.0768 - accuracy: 0.9849 - val_loss: 0.9370 - val_accuracy: 0.7393\n",
            "Epoch 14/30\n",
            "50/50 [==============================] - 2s 42ms/step - loss: 0.0847 - accuracy: 0.9818 - val_loss: 0.9169 - val_accuracy: 0.7469\n",
            "Epoch 15/30\n",
            "50/50 [==============================] - 2s 46ms/step - loss: 0.0778 - accuracy: 0.9818 - val_loss: 0.9269 - val_accuracy: 0.7519\n",
            "Epoch 16/30\n",
            "50/50 [==============================] - 2s 42ms/step - loss: 0.0797 - accuracy: 0.9812 - val_loss: 0.9855 - val_accuracy: 0.7419\n",
            "Epoch 17/30\n",
            "50/50 [==============================] - 2s 40ms/step - loss: 0.0664 - accuracy: 0.9862 - val_loss: 0.9063 - val_accuracy: 0.7569\n",
            "Epoch 18/30\n",
            "50/50 [==============================] - 2s 47ms/step - loss: 0.0552 - accuracy: 0.9893 - val_loss: 0.9768 - val_accuracy: 0.7419\n",
            "Epoch 19/30\n",
            "50/50 [==============================] - 2s 45ms/step - loss: 0.0523 - accuracy: 0.9900 - val_loss: 1.0290 - val_accuracy: 0.7469\n",
            "Epoch 20/30\n",
            "50/50 [==============================] - 5s 99ms/step - loss: 0.0515 - accuracy: 0.9900 - val_loss: 1.0829 - val_accuracy: 0.7519\n",
            "Epoch 21/30\n",
            "50/50 [==============================] - 2s 42ms/step - loss: 0.0495 - accuracy: 0.9906 - val_loss: 1.0917 - val_accuracy: 0.7519\n",
            "Epoch 22/30\n",
            "50/50 [==============================] - 5s 97ms/step - loss: 0.0490 - accuracy: 0.9906 - val_loss: 1.1016 - val_accuracy: 0.7519\n",
            "Epoch 23/30\n",
            "50/50 [==============================] - 2s 39ms/step - loss: 0.0488 - accuracy: 0.9906 - val_loss: 1.1119 - val_accuracy: 0.7519\n",
            "Epoch 24/30\n",
            "50/50 [==============================] - 2s 42ms/step - loss: 0.0486 - accuracy: 0.9906 - val_loss: 1.1236 - val_accuracy: 0.7494\n",
            "Epoch 25/30\n",
            "50/50 [==============================] - 5s 95ms/step - loss: 0.0485 - accuracy: 0.9906 - val_loss: 1.1313 - val_accuracy: 0.7544\n",
            "Epoch 26/30\n",
            "50/50 [==============================] - 3s 56ms/step - loss: 0.0482 - accuracy: 0.9906 - val_loss: 1.1524 - val_accuracy: 0.7544\n",
            "Epoch 27/30\n",
            "50/50 [==============================] - 3s 59ms/step - loss: 0.0480 - accuracy: 0.9906 - val_loss: 1.1430 - val_accuracy: 0.7569\n",
            "Epoch 28/30\n",
            "50/50 [==============================] - 3s 60ms/step - loss: 0.0477 - accuracy: 0.9906 - val_loss: 1.1655 - val_accuracy: 0.7544\n",
            "Epoch 29/30\n",
            "50/50 [==============================] - 3s 62ms/step - loss: 0.0472 - accuracy: 0.9906 - val_loss: 1.1677 - val_accuracy: 0.7544\n",
            "Epoch 30/30\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.0466 - accuracy: 0.9906 - val_loss: 1.1782 - val_accuracy: 0.7544\n"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  4 281  16  25   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nI love this phone\n[0.9980887]\n\n\n[812 227 864 100 775   9 525 843   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nEverything was cold\n[0.0183675]\n\n\n[812 227 864 100 775   9 109   8 333 731  24  61   4 171  59  77   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nEverything was hot exactly as I wanted\n[0.9446658]\n\n\n[812 227 864 100 775   9 157 359 853   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nEverything was green\n[0.01726285]\n\n\n[  1 109 228 540 237 635 241 423 340  89 298   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nthe host seated us immediately\n[0.02188393]\n\n\n[154 242  47 635 341  12 569 547 147 319 775 125  85   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nthey gave us free chocolate cake\n[0.9980438]\n\n\n[158 190 853 782   8 607 775 210 232 146 775 470  67 305 101  15   1 328\n 296  26  19   1 661 641 195   0   0   0   0   0   0   0   0   0   0   0\n   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\nwe couldn't hear each other talk because of the shouting in the kitchen\n[0.02332455]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABVYYPwba8Hx"
      },
      "source": [
        "# Compare predictions for all the models\n",
        "\n",
        "It can be hard to see which model gives a better prediction for different reviews when you examine each model separately. So for comparison purposes, here we define some more reviews and print out the predictions that each of the three models gives for each review:\n",
        "\n",
        "*   Embeddings and a Global Average Pooling layer\n",
        "*   Embeddings and a Bidirectional LSTM layer\n",
        "*   Embeddings and two Bidirectional LSTM layers\n",
        "\n",
        "The results are not always what you might expect. The input dataset is fairly small, it has less than 2000 reviews. Some of the reviews are fairly short, and some of the short ones are fairly repetitive which reduces their impact on improving the  model, such as these two reviews:\n",
        "\n",
        "*   Bad Quality.\n",
        "*   Low Quality.\n",
        "\n",
        "Feel free to add more reviews of your own, or change the reviews. The results will depend on the combination of words in the reviews, and how well they match to reviews in the training set. \n",
        "\n",
        "How do the different models handle things like \"wasn't good\" which contains a positive word (good) but is a poor review?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XebrXt0jtOy"
      },
      "source": [
        "my_reviews =[\"lovely\", \"dreadful\", \"stay away\",\n",
        "             \"everything was hot exactly as I wanted\",\n",
        "             \"everything was not exactly as I wanted\",\n",
        "             \"they gave us free chocolate cake\",\n",
        "             \"I've never eaten anything so spicy in my life, my throat burned for hours\",\n",
        "             \"for a phone that is as expensive as this one I expect it to be much easier to use than this thing is\",\n",
        "             \"we left there very full for a low price so I'd say you just can't go wrong at this place\",\n",
        "             \"that place does not have quality meals and it isn't a good place to go for dinner\",\n",
        "             ]"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRWGjkJLkY2y"
      },
      "source": [
        "print(\"===================================\\n\",\"Embeddings only:\\n\", \"===================================\",)\n",
        "predict_review(model, my_reviews, show_padded_sequence=False)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===================================\n Embeddings only:\n ===================================\nlovely\n[0.90298367]\n\n\ndreadful\n[0.23601511]\n\n\nstay away\n[0.55999434]\n\n\neverything was hot exactly as I wanted\n[0.726402]\n\n\neverything was not exactly as I wanted\n[0.4345067]\n\n\nthey gave us free chocolate cake\n[0.6666034]\n\n\nI've never eaten anything so spicy in my life, my throat burned for hours\n[0.03658628]\n\n\nfor a phone that is as expensive as this one I expect it to be much easier to use than this thing is\n[0.38714007]\n\n\nwe left there very full for a low price so I'd say you just can't go wrong at this place\n[0.754407]\n\n\nthat place does not have quality meals and it isn't a good place to go for dinner\n[0.8026236]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2FJR3IVBt30"
      },
      "source": [
        "print(\"===================================\\n\", \"With a single bidirectional LSTM:\\n\", \"===================================\")\n",
        "predict_review(model_bidi_lstm, my_reviews, show_padded_sequence=False)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===================================\n With a single bidirectional LSTM:\n ===================================\nlovely\n[0.9947212]\n\n\ndreadful\n[0.02578875]\n\n\nstay away\n[0.72861207]\n\n\neverything was hot exactly as I wanted\n[0.98556197]\n\n\neverything was not exactly as I wanted\n[0.17025512]\n\n\nthey gave us free chocolate cake\n[0.9984962]\n\n\nI've never eaten anything so spicy in my life, my throat burned for hours\n[0.62480855]\n\n\nfor a phone that is as expensive as this one I expect it to be much easier to use than this thing is\n[0.12915769]\n\n\nwe left there very full for a low price so I'd say you just can't go wrong at this place\n[0.9537308]\n\n\nthat place does not have quality meals and it isn't a good place to go for dinner\n[0.93053997]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81v1r3Y2BwvC"
      },
      "source": [
        "print(\"===================================\\n\",\"With two bidirectional LSTMs:\\n\", \"===================================\")\n",
        "predict_review(model_multiple_bidi_lstm, my_reviews, show_padded_sequence=False)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===================================\n With two bidirectional LSTMs:\n ===================================\nlovely\n[0.9976604]\n\n\ndreadful\n[0.01439178]\n\n\nstay away\n[0.02296168]\n\n\neverything was hot exactly as I wanted\n[0.99883264]\n\n\neverything was not exactly as I wanted\n[0.08400646]\n\n\nthey gave us free chocolate cake\n[0.9980438]\n\n\nI've never eaten anything so spicy in my life, my throat burned for hours\n[0.02493432]\n\n\nfor a phone that is as expensive as this one I expect it to be much easier to use than this thing is\n[0.01879367]\n\n\nwe left there very full for a low price so I'd say you just can't go wrong at this place\n[0.972583]\n\n\nthat place does not have quality meals and it isn't a good place to go for dinner\n[0.98762846]\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}