IMDB_In_Keras.ipynb

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   "source": [
    "# Analyzing IMDB Data in Keras"
   ]
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   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import keras\n",
    "from keras.datasets import imdb\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(42)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## 1. Loading the data\n",
    "This dataset comes preloaded with Keras, so one simple command will get us training and testing data. There is a parameter for how many words we want to look at. We've set it at 1000, but feel free to experiment."
   ]
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   "source": [
    "# Loading the data (it's preloaded in Keras)\n",
    "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=1000)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## 2. Examining the data\n",
    "Notice that the data has been already pre-processed, where all the words have numbers, and the reviews come in as a vector with the words that the review contains. For example, if the word 'the' is the first one in our dictionary, and a review contains the word 'the', then there is a 1 in the corresponding vector.\n",
    "\n",
    "The output comes as a vector of 1's and 0's, where 1 is a positive sentiment for the review, and 0 is negative."
   ]
  },
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    "print(x_train[0])\n",
    "print(y_train[0])"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. One-hot encoding the output\n",
    "Here, we'll turn the input vectors into (0,1)-vectors. For example, if the pre-processed vector contains the number 14, then in the processed vector, the 14th entry will be 1."
   ]
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   "source": [
    "# One-hot encoding the output into vector mode, each of length 1000\n",
    "tokenizer = Tokenizer(num_words=1000)\n",
    "x_train = tokenizer.sequences_to_matrix(x_train, mode='binary')\n",
    "x_test = tokenizer.sequences_to_matrix(x_test, mode='binary')\n",
    "print(x_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we'll also one-hot encode the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# One-hot encoding the output\n",
    "num_classes = 2\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Building the  model architecture\n",
    "Build a model here using sequential. Feel free to experiment with different layers and sizes! Also, experiment adding dropout to reduce overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "# TODO: Build the model architecture\n",
    "\n",
    "# TODO: Compile the model using a loss function and an optimizer.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Training the model\n",
    "Run the model here. Experiment with different batch_size, and number of epochs!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
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   },
   "outputs": [],
   "source": [
    "# TODO: Run the model. Feel free to experiment with different batch sizes and number of epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Evaluating the model\n",
    "This will give you the accuracy of the model, as evaluated on the testing set. Can you get something over 85%?"
   ]
  },
  {
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   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"Accuracy: \", score[1])"
   ]
  }
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