E09_LSTM_sentimentClassification.ipynb

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        "\n",
        "# Deep Learning Course\n",
        "\n",
        " <img src=\"http://usblogs.pwc.com/emerging-technology/wp-content/uploads/2017/07/deep-learning-900x280.png\" >\n",
        "\n",
        "\n",
        "# Deep Learning Excercise 09\n",
        "## Long short-term memory (LSTM) - Recurrent Neural Netwok \n",
        "\n",
        "\n",
        "Developed by [Sergio Castelblanco](http://www.linkedin.com/in/sergio-castelblanco/) - [Jhon Gonzalez](http://linkedin.com/in/jhon-franklin-gonzalez-gamboa-b6123230/)\n",
        "\n",
        "Andes University\n",
        "\n",
        "version 3.2, July 15 2018"
      ]
    },
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      "source": [
        "# Exercise 09\n",
        "\n",
        "## Sequence Classification using LSTM\n",
        "\n",
        "Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time and the task is to predict a category for the sequence.\n",
        "\n",
        "What makes this problem difficult is that the sequences can vary in length, be comprised of a very large vocabulary of input symbols and may require the model to learn the long-term context or dependencies between symbols in the input sequence."
      ]
    },
    {
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        "id": "CHr2kp3S-sDd",
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      "cell_type": "markdown",
      "source": [
        "[The Large Movie Review Dataset](http://ai.stanford.edu/~amaas/data/sentiment/) (often referred to as the IMDB dataset) contains 25,000 highly-polar movie reviews (good or bad) for training and the same amount again for testing. The problem is to determine whether a given movie review has a positive or negative sentiment."
      ]
    },
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            "userId": "104485759475975710875"
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      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from keras.datasets import imdb\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers.embeddings import Embedding\n",
        "from keras.preprocessing import sequence\n",
        "from keras.callbacks import History\n",
        "np.random.seed(7)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
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      "source": [
        "!pip3 install livelossplot"
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            "Collecting livelossplot\n",
            "  Downloading https://files.pythonhosted.org/packages/0c/01/3e34559dbdb98580d5e8f62bc3853975c2a2079834bfba2c1bdffe680804/livelossplot-0.2.0.tar.gz\n",
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          ],
          "name": "stdout"
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      "cell_type": "code",
      "source": [
        "# load the dataset but only keep the top n words, zero the rest\n",
        "top_words = 5000\n",
        "index_from = 3\n",
        "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words, index_from=index_from)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/text-datasets/imdb.npz\n",
            "17465344/17464789 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "source": [
        "y_train[0]"
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              "1"
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      },
      "cell_type": "code",
      "source": [
        "np.min([np.min(x) for x in X_train]), np.max([np.max(x) for x in X_train])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 4999)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
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      "source": [
        "The words have been replaced by integers that indicate the ordered frequency of each word in the dataset. The sentences in each review are therefore comprised of a sequence of integers."
      ]
    },
    {
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      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
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        "executionInfo": {
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      },
      "cell_type": "code",
      "source": [
        "print(X_train[0])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 2, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WCOv6dEh-sD6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Next, we need to truncate and pad the input sequences so that they are all the same length for modeling. The model will learn the zero values carry no information so indeed the sequences are not the same length in terms of content, but same length vectors is required to perform the computation in Keras."
      ]
    },
    {
      "metadata": {
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        "colab_type": "code",
        "colab": {
          "autoexec": {
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            "wait_interval": 0
          }
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      },
      "cell_type": "code",
      "source": [
        "# truncate and pad input sequences\n",
        "max_review_length = 500\n",
        "X_train_pad = sequence.pad_sequences(X_train, maxlen=max_review_length)\n",
        "X_test_pad = sequence.pad_sequences(X_test, maxlen=max_review_length)"
      ],
      "execution_count": 0,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "X_train_pad.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25000, 500)"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 9
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      "source": [
        "### Word Embedding\n",
        "\n",
        "We will map each movie review into a real vector domain, a popular technique when working with text called word embedding. This is a technique where words are encoded as real-valued vectors in a high dimensional space, where the similarity between words in terms of meaning translates to closeness in the vector space.\n",
        "\n",
        "Keras provides a convenient way to convert positive integer representations of words into a word embedding by an Embedding layer.\n",
        "\n",
        "We will map each word onto a 32 length real valued vector. We will also limit the total number of words that we are interested in modeling to the 5000 most frequent words, and zero out the rest. Finally, the sequence length (number of words) in each review varies, so we will constrain each review to be 500 words, truncating long reviews and pad the shorter reviews with zero values.\n",
        "\n",
        "Now that we have defined our problem and how the data will be prepared and modeled, we are ready to develop an LSTM model to classify the sentiment of movie reviews."
      ]
    },
    {
      "metadata": {
        "id": "LU_jy3ku-sET",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Exercise 09.1\n",
        "\n",
        "Train a Deep Neural Network with the following architecture:\n",
        "\n",
        "- Input = pad_sequences (input_length=max_review_length)\n",
        "- Embedding(top_words, 32, input_length)\n",
        "- LSTM(100)\n",
        "- Dense(1, sigmoid)\n",
        "\n",
        "Optimized using adam using as loss binary_crossentropy\n",
        "\n",
        "Hints: \n",
        "- test with two iterations then try more. \n",
        "- learning can be ajusted\n",
        "\n",
        "Evaluate the performance using the testing set (accuracy aprox 87% with 10 epochs)"
      ]
    },
    {
      "metadata": {
        "id": "Z2SU332T-sEd",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "003f1cee-370f-42c9-d3e8-584bef9729c6",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531844514499,
          "user_tz": 300,
          "elapsed": 1049,
          "user": {
            "displayName": "sergi castel",
            "photoUrl": "//lh5.googleusercontent.com/-t7uPTy_PD5c/AAAAAAAAAAI/AAAAAAAABjU/YrQqLvCov5A/s50-c-k-no/photo.jpg",
            "userId": "104485759475975710875"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.optimizers import adam\n",
        "from keras.optimizers import SGD\n",
        "from keras.optimizers import adamax\n",
        "from keras.models import Sequential\n",
        "from keras.layers.recurrent import LSTM\n",
        "from keras.layers.core import Dense, Dropout\n",
        "from keras.layers.embeddings import Embedding\n",
        "from keras.preprocessing import sequence\n",
        "#from livelossplot import PlotLossesKeras\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Embedding(top_words, 32, input_length=max_review_length))\n",
        "model.add(LSTM(100))\n",
        "#model.add(Dropout(0.5))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 500, 32)           160000    \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 100)               53200     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 213,301\n",
            "Trainable params: 213,301\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uCo8D-Co-sE7",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "209ee831-1d8a-44b9-cb36-474193443a6f",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531845152930,
          "user_tz": 300,
          "elapsed": 9,
          "user": {
            "displayName": "sergi castel",
            "photoUrl": "//lh5.googleusercontent.com/-t7uPTy_PD5c/AAAAAAAAAAI/AAAAAAAABjU/YrQqLvCov5A/s50-c-k-no/photo.jpg",
            "userId": "104485759475975710875"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from livelossplot import PlotLossesKeras\n",
        "%matplotlib inline\n",
        "model.fit(X_train_pad,y_train, validation_data=[X_test_pad, y_test], \n",
        "          batch_size=128, epochs=2, verbose=1,\n",
        "          callbacks=[PlotLossesKeras()])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CC9zDBgeaSHrFhvHxqv18tmY//5y3gyWbs5mSkkiH8Ja+DlFEREREROrY0RPnmTk/g217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\n/w883Hioy5mnsAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff87dddcc88>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff8d5d7ce48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "ykhld4zF-sFG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We carry out the build of the neural net with the architecture propose, this model with 2 Epoch has an `accuracy of 0.85`"
      ]
    },
    {
      "metadata": {
        "id": "WlSakBfm-sFK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Later, we run the same deep neural network with 20 epoch"
      ]
    },
    {
      "metadata": {
        "id": "EXl1iQ20-sFQ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "2bb429e9-b2bd-4c08-9560-be13ca8d4b71",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531850591500,
          "user_tz": 300,
          "elapsed": 230,
          "user": {
            "displayName": "sergi castel",
            "photoUrl": "//lh5.googleusercontent.com/-t7uPTy_PD5c/AAAAAAAAAAI/AAAAAAAABjU/YrQqLvCov5A/s50-c-k-no/photo.jpg",
            "userId": "104485759475975710875"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "model.fit(X_train_pad,y_train, validation_data=[X_test_pad, y_test], \n",
        "          batch_size=128, epochs=20, verbose=1,\n",
        "          callbacks=[PlotLossesKeras()])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SjtKaVl76OJ+qhg7iIoP41p15ZKVEXFg/3HY4DIOqho4Ld7iKKlpobO2+sN7P18ykxHDy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\n6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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff879c253c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff87dd9bd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "A5rpuKkcavmJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can see in the last plot (Accuracy) that the training line has a growth exponential and the validation data has a linear and constant tend, Then, with 20 epoch the accuracy is 0.86. The loss function in both lines has a exponential behavior while the training data decreases the validation  curve grows until 0.6."
      ]
    },
    {
      "metadata": {
        "id": "-AYEmGAQ-sFi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Exercise 09.2\n",
        "\n",
        "Predict the sentiment of the following reviews"
      ]
    },
    {
      "metadata": {
        "id": "csCWveQB-sFk",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "reviews = [\"I was fortunate enough to see this movie on pre-release last night and, though I wasn't expecting to, actually really enjoyed the movie for the most part. The rescues and sea effects were amazing to watch and definitely provided edge of the seat tense moments, probably all the more so knowing that there are guys who do this for a living. The weaker parts of the movie revolve largely around using stereotypical set scenes. I'm not going to spoil the movie but this really follows along the lines of An Officer and a Gentleman and those moments give it a little bit of a cheesy aftertaste.<br /><br />Like I said over all this movie is pretty good and worth checking out as long as you can get past the clichés.\",\n",
        "           '\"The Dresser\" is perhaps the most refined of backstage films. The film is brimming with wit and spirit, for the most part provided by the \"energetic\" character of Norman (Tom Courtenay). Although his character is clearly gay, and certainly has an attraction for the lead performer (Albert Finney) that he assists, the film never dwells on it or makes it more than it is.<br /><br />The gritty style of Peter Yates that worked so well in \"Bullitt\" is again on display, and gives the film a sense of realism and coherence. This is much appreciated in a story that could so easily have become tedious. In the end, \"The Dresser\" will bore many people silly, but it will truly be a delight to those who love British cinema.<br /><br />7.7 out of 10',\n",
        "           \"So real and surreal, all in one. I remember feeling like Tessa. Heck, I remember being Tessa. This was a beautiful vignette of a relationship ending. I especially liked the protesters tangent. It is nice to see symbolism in a movie without being smacked over the head with it. If you get the chance to see this, take it. It is well worth the 30 minutes.\",\n",
        "           \"This is a pale imitation of 'Officer and a Gentleman.' There is NO chemistry between Kutcher and the unknown woman who plays his love interest. The dialog is wooden, the situations hackneyed. It's too long and the climax is anti-climactic(!). I love the USCG, its men and women are fearless and tough. The action scenes are awesome, but this movie doesn't do much for recruiting, I fear. The script is formulaic, but confusing. Kutcher's character is trying to redeem himself for an accident that wasn't his fault? Costner's is raging against the dying of the light, but why? His 'conflict' with his wife is about as deep as a mud puddle. I saw this sneak preview for free and certainly felt I got my money's worth.\",\n",
        "           \"I was at Wrestlemania VI in Toronto as a 10 year old, and the event I saw then was pretty different from what I saw on the Wrestlemania Collection DVD I just watched. I don't understand how the wwE doesn't have the rights to some of the old music, since most of those songs were created by the WWF they shouldn't have to worry about the licensing and royalty fees that prevent shows like SNL from releasing season sets. Its pretty stupid to whine about, but for me hearing Demolition come out to their theme music at a Wrestlemania in person was a memory that I never forgot, and it didn't exist on this DVD. What is the point of them even owning the rights to this huge library of video if they have to edit it so drastically to use it?\",\n",
        "           \"Wow! What a movie if you want to blow your budget on the title and have it look real bad ask the guys that made this movie on how to do that. They could have spent the money on a good rewrite or something else. Or they could have spent it on beer when they made this movie at least it would have come out better.\"\n",
        "          ]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "E2sjwTcP-sFs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Reviews must be preprocessed"
      ]
    },
    {
      "metadata": {
        "id": "w8oXF1wX-sFv",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "66c66fae-3109-42ba-b302-48e964a2138d",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531852241015,
          "user_tz": 300,
          "elapsed": 1477,
          "user": {
            "displayName": "sergi castel",
            "photoUrl": "//lh5.googleusercontent.com/-t7uPTy_PD5c/AAAAAAAAAAI/AAAAAAAABjU/YrQqLvCov5A/s50-c-k-no/photo.jpg",
            "userId": "104485759475975710875"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.datasets.imdb import get_word_index\n",
        "\n",
        "vocab = get_word_index()\n",
        "\n",
        "vocab = {k:(v+index_from) for k,v in vocab.items()}\n",
        "vocab[\"<PAD>\"] = 0\n",
        "vocab[\"<START>\"] = 1\n",
        "vocab[\"<UNK>\"] = 2"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/text-datasets/imdb_word_index.json\n",
            "1646592/1641221 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "l2s35-Zu-sF2",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "c15bcc19-f580-4815-943d-1ca236f8b894",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531852244086,
          "user_tz": 300,
          "elapsed": 831,
          "user": {
            "displayName": "sergi castel",
            "photoUrl": "//lh5.googleusercontent.com/-t7uPTy_PD5c/AAAAAAAAAAI/AAAAAAAABjU/YrQqLvCov5A/s50-c-k-no/photo.jpg",
            "userId": "104485759475975710875"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "{k:vocab[k] for k in list(vocab.keys())[:10]}"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'fawn': 34704,\n",
              " 'hanging': 2348,\n",
              " 'nunnery': 52010,\n",
              " 'sonja': 16819,\n",
              " 'spiders': 16118,\n",
              " 'trawling': 52011,\n",
              " 'tsukino': 52009,\n",
              " 'vani': 63954,\n",
              " 'woods': 1411,\n",
              " 'woody': 2292}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "DNucnrJo-sF_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " Lets see how X_train is encoded"
      ]
    },
    {
      "metadata": {
        "id": "JSfuo7fu-sGA",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "outputId": "605cc75e-26c9-4a1a-91f3-1eaf1740c0db",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531852246876,
          "user_tz": 300,
          "elapsed": 861,
          "user": {
            "displayName": "sergi castel",
            "photoUrl": "//lh5.googleusercontent.com/-t7uPTy_PD5c/AAAAAAAAAAI/AAAAAAAABjU/YrQqLvCov5A/s50-c-k-no/photo.jpg",
            "userId": "104485759475975710875"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "X_train_pad[0]"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    1,   14,   22,   16,\n",
              "         43,  530,  973, 1622, 1385,   65,  458, 4468,   66, 3941,    4,\n",
              "        173,   36,  256,    5,   25,  100,   43,  838,  112,   50,  670,\n",
              "          2,    9,   35,  480,  284,    5,  150,    4,  172,  112,  167,\n",
              "          2,  336,  385,   39,    4,  172, 4536, 1111,   17,  546,   38,\n",
              "         13,  447,    4,  192,   50,   16,    6,  147, 2025,   19,   14,\n",
              "         22,    4, 1920, 4613,  469,    4,   22,   71,   87,   12,   16,\n",
              "         43,  530,   38,   76,   15,   13, 1247,    4,   22,   17,  515,\n",
              "         17,   12,   16,  626,   18,    2,    5,   62,  386,   12,    8,\n",
              "        316,    8,  106,    5,    4, 2223,    2,   16,  480,   66, 3785,\n",
              "         33,    4,  130,   12,   16,   38,  619,    5,   25,  124,   51,\n",
              "         36,  135,   48,   25, 1415,   33,    6,   22,   12,  215,   28,\n",
              "         77,   52,    5,   14,  407,   16,   82,    2,    8,    4,  107,\n",
              "        117,    2,   15,  256,    4,    2,    7, 3766,    5,  723,   36,\n",
              "         71,   43,  530,  476,   26,  400,  317,   46,    7,    4,    2,\n",
              "       1029,   13,  104,   88,    4,  381,   15,  297,   98,   32, 2071,\n",
              "         56,   26,  141,    6,  194,    2,   18,    4,  226,   22,   21,\n",
              "        134,  476,   26,  480,    5,  144,   30,    2,   18,   51,   36,\n",
              "         28,  224,   92,   25,  104,    4,  226,   65,   16,   38, 1334,\n",
              "         88,   12,   16,  283,    5,   16, 4472,  113,  103,   32,   15,\n",
              "         16,    2,   19,  178,   32], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "v3IhYMc3-sGL",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "ef8a90cc-b879-4a3c-ac62-d9ddbb5a1212",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531852249076,
          "user_tz": 300,
          "elapsed": 821,
          "user": {
            "displayName": "sergi castel",
            "photoUrl": "//lh5.googleusercontent.com/-t7uPTy_PD5c/AAAAAAAAAAI/AAAAAAAABjU/YrQqLvCov5A/s50-c-k-no/photo.jpg",
            "userId": "104485759475975710875"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "print(X_train[0])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 2, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1_Q1l_mM-sGR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Lets get the text"
      ]
    },
    {
      "metadata": {
        "id": "XGHRNGMz-sGT",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "48516ba7-67cc-4cdd-f16c-d029b9bd2f35",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531852249803,
          "user_tz": 300,
          "elapsed": 655,
          "user": {
            "displayName": "sergi castel",
            "photoUrl": "//lh5.googleusercontent.com/-t7uPTy_PD5c/AAAAAAAAAAI/AAAAAAAABjU/YrQqLvCov5A/s50-c-k-no/photo.jpg",
            "userId": "104485759475975710875"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "id_to_word = {value:key for key,value in vocab.items()}\n",
        "print(' '.join(id_to_word[id] for id in X_train[0] ))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<START> this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert <UNK> is an amazing actor and now the same being director <UNK> father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for <UNK> and would recommend it to everyone to watch and the fly <UNK> was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also <UNK> to the two little <UNK> that played the <UNK> of norman and paul they were just brilliant children are often left out of the <UNK> list i think because the stars that play them all grown up are such a big <UNK> for the whole film but these children are amazing and should be <UNK> for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was <UNK> with us all\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9S_4jsMu-sGc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Compared with the original review in lowercase:\n",
        "\n",
        "\"this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert redford's is an amazing actor and now the same being director norman's father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for retail and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also congratulations to the two little boy's that played the part's of norman and paul they were just brilliant children are often left out of the praising list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\""
      ]
    },
    {
      "metadata": {
        "id": "Efgl0qjO-sGe",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "max_url_len = 500\n",
        "reviews = [x[:max_url_len] for x in reviews]\n",
        "# Convert characters to int and pad\n",
        "reviews = [[vocab[x1] for x1 in x if x1 in vocab.keys()] for x in reviews]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Uzn5qPUm-sGi",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "X_pad = sequence.pad_sequences(reviews, maxlen=max_url_len)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qYk8uiVS-sGv",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "9f260d4f-b35d-4f68-9fde-8e6ab28ff1b6",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531852254244,
          "user_tz": 300,
          "elapsed": 843,
          "user": {
            "displayName": "sergi castel",
            "photoUrl": "//lh5.googleusercontent.com/-t7uPTy_PD5c/AAAAAAAAAAI/AAAAAAAABjU/YrQqLvCov5A/s50-c-k-no/photo.jpg",
            "userId": "104485759475975710875"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "X_pad"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   0,    0,    0, ...,  590, 1604, 1209],\n",
              "       [   0,    0,    0, ..., 1209, 1479,  963],\n",
              "       [   0,    0,    0, ...,  830,  963,  590],\n",
              "       [   0,    0,    0, ..., 1992,    6,  590],\n",
              "       [   0,    0,    0, ..., 2023,  963,   13],\n",
              "       [   0,    0,    0, ...,  830,  963, 1479]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "KbynL7Wc-sG3",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8af4f110-fcea-4dac-cc81-3e7f5fb3832c",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531852465456,
          "user_tz": 300,
          "elapsed": 950,
          "user": {
            "displayName": "sergi castel",
            "photoUrl": "//lh5.googleusercontent.com/-t7uPTy_PD5c/AAAAAAAAAAI/AAAAAAAABjU/YrQqLvCov5A/s50-c-k-no/photo.jpg",
            "userId": "104485759475975710875"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "X_pad[X_pad>5000]=2\n",
        "X_pad.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6, 500)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "quQurCVihcwD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "with the last trained model, we predict the sentiment of the reviews."
      ]
    },
    {
      "metadata": {
        "id": "kXMOiY1g-sG9",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "a93d77ca-43c5-4c33-bbfa-33b8c7c751f2",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531852256084,
          "user_tz": 300,
          "elapsed": 830,
          "user": {
            "displayName": "sergi castel",
            "photoUrl": "//lh5.googleusercontent.com/-t7uPTy_PD5c/AAAAAAAAAAI/AAAAAAAABjU/YrQqLvCov5A/s50-c-k-no/photo.jpg",
            "userId": "104485759475975710875"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "model.predict(X_pad)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.46029680e-03],\n",
              "       [1.12645479e-03],\n",
              "       [6.85931563e-01],\n",
              "       [4.32731496e-04],\n",
              "       [4.26584244e-04],\n",
              "       [1.02138634e-04]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "QRjbqZj3izGZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The final result is the probability associated with each  binary class. we can interprety that the probabilities less to 0.5 are negative sentiment and the probabilities greater than 0.5 are positive sentiment, in this case only se second probability is a greater postive sentiment than the others."
      ]
    },
    {
      "metadata": {
        "id": "S-ltHfj_jSjZ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}