tensorflow_start.ipynb

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   "source": [
    "import sys\n",
    "print(sys.executable)\n",
    "print(sys.version)\n",
    "print(sys.version_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "conda update python\n",
    "conda update anaconda\n",
    "#conda install python==$type the version you want$`"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "#import sys\n",
    "#!conda install --yes --prefix {sys.prefix} -c conda-forge keras\n",
    "#!{sys.executable} -m pip install tensorflow"
   ]
  },
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   "source": [
    "#conda install -c conda-forge keras, tensorflow \n",
    "#conda install -c hesi_m keras"
   ]
  },
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   "source": [
    "# CPU version\n",
    "#pip install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0-py3-none-any.whl"
   ]
  },
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   "source": [
    "## Install TensorFlow (CPU), Keras, and some other tools to a new anaconda environment in the Anaconda Shell...\n",
    "#Open Anaconda Prompt and...\n",
    "conda -V\n",
    "python -V\n",
    "\n",
    "cd Anaconda3\n",
    "\n",
    "conda create --name tensorflow python=3.5\n",
    "#######After tensorflow environment is created we need to activate the conda environment\n",
    "conda activate tensorflow\n",
    "\n",
    "# If you missed a step or made a mistake, you can always remove the conda environment and start over.\n",
    "######### if start over: \n",
    "conda remove --name tensorflow --all\n",
    "conda clean --packages --tarballs\n",
    "##################################\n",
    "\n",
    "\n",
    "\n",
    "######install the CPU-only version of TensorFlow\n",
    "pip install --ignore-installed --upgrade tensorflow \n",
    "######install the GPU version of TensorFlow\n",
    "pip install --ignore-installed --upgrade tensorflow-gpu\n",
    "\n",
    "###then Install Jupyter Notebook, Spyder, and Scipy to work with tensorflow comfortably\n",
    "conda install jupyter\n",
    "conda install scipy\n",
    "conda install spyder\n",
    "conda install pandas\n",
    "conda install scikit-learn\n",
    "conda install matplotlib \n",
    "\n",
    "#install Keras and Theano \n",
    "conda install --channel https://conda.anaconda.org/conda-forge keras\n",
    "#or\n",
    "conda install -c conda-forge keras"
   ]
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   "source": [
    "## Install TensorFlow (CPU), Keras, and some other tools to a new anaconda environment in the Anaconda Shell...\n",
    "#Open Anaconda Prompt and...       dl2_p35k12tf10 \n",
    "conda -V\n",
    "python -V\n",
    "\n",
    "cd Anaconda3\n",
    "\n",
    "conda create --name deeplearn python=3.5 scipy numpy matplotlib scikit-learn pandas pillow statsmodels ipykernel bcolz gensim nltk h5py\n",
    "conda activate deeplearn #conda deactivate\n",
    "\n",
    "#If you missed a step or made a mistake, you can always remove the conda environment and start over.\n",
    "###################################\n",
    "conda remove --name deeplearn --all\n",
    "###################################\n",
    "\n",
    "#for CPU\n",
    "pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.0.1-cp35-cp35m-win_amd64.whl \n",
    "'''\n",
    "mkl-fft 1.0.0 requires cython, which is not installed.\n",
    "distributed 1.22.0 requires msgpack, which is not installed.\n",
    "mkl-random 1.0.1 requires cython, which is not installed.    \n",
    "'''    \n",
    "#for GPU\n",
    "pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.0.1-cp35-cp35m-win_amd64.whl\n",
    "\n",
    "    \n",
    "    \n",
    "pip install keras==1.2.2\n",
    "pip install keras-tqdm\n",
    "\n",
    "#Give jupyter notebook the ability to run this environment (aka kernel in Jupyter)\n",
    "ipython kernel install --name deeplearn"
   ]
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   "cell_type": "code",
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   "source": [
    "#Use the Kernels menu in jupyter notebook to select the environment you created.\n",
    "# scipy\n",
    "import scipy\n",
    "print('scipy: %s' % scipy.__version__)\n",
    "# numpy\n",
    "import numpy\n",
    "print('numpy: %s' % numpy.__version__)\n",
    "# matplotlib\n",
    "import matplotlib\n",
    "print('matplotlib: %s' % matplotlib.__version__)\n",
    "# pandas\n",
    "import pandas\n",
    "print('pandas: %s' % pandas.__version__)\n",
    "# statsmodels\n",
    "import statsmodels\n",
    "print('statsmodels: %s' % statsmodels.__version__)\n",
    "# scikit-learn\n",
    "import sklearn\n",
    "print('sklearn: %s' % sklearn.__version__)\n",
    "# keras\n",
    "import keras\n",
    "print('keras: %s' % keras.__version__)\n",
    "# tensorflow\n",
    "import tensorflow\n",
    "print('tensorflow: %s' % tensorflow.__version__)\n",
    "# bcolz\n",
    "import bcolz\n",
    "print('bcolz: %s' % bcolz.__version__)\n",
    "# gensim\n",
    "import gensim\n",
    "print('gensim: %s' % gensim.__version__)\n",
    "# nltk\n",
    "import nltk\n",
    "print('nltk: %s' % nltk.__version__)\n",
    "# h5py\n",
    "import h5py\n",
    "print('h5py: %s' % h5py.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
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   "source": [
    "import numpy as np\n",
    "from keras.utils import np_utils\n",
    "import tensorflow as tf\n",
    "#tf.python.control_flow_ops = tf ??????????????????????????????????????????????"
   ]
  },
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   "source": [
    "#from tensorflow.python.ops import control_flow_ops\n",
    "\n",
    "#tensorflow.python.ops.control_flow_ops = tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
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   "source": [
    "# Set random seed\n",
    "np.random.seed(42)"
   ]
  },
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   "source": [
    "# Our data\n",
    "X = np.array([[0,0],[0,1],[1,0],[1,1]]).astype('float32')\n",
    "y = np.array([[0],[1],[1],[0]]).astype('float32') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
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   "source": [
    "# One-hot encoding the output\n",
    "y = np_utils.to_categorical(y)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 27,
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    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 27,
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    "y"
   ]
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   "cell_type": "code",
   "execution_count": 28,
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   "source": [
    "# Initial Setup for Keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
    "from keras.optimizers import SGD\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
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   "source": [
    "# Building the model\n",
    "xor = Sequential()\n",
    "xor.add(Dense(8, input_dim=2))\n",
    "xor.add(Activation(\"sigmoid\"))\n",
    "xor.add(Dense(2))\n",
    "xor.add(Activation(\"sigmoid\"))\n",
    "\n",
    "xor.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics = ['accuracy'])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 30,
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     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_5 (Dense)              (None, 8)                 24        \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 2)                 18        \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 2)                 0         \n",
      "=================================================================\n",
      "Total params: 42\n",
      "Trainable params: 42\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# print the model architecture\n",
    "xor.summary()"
   ]
  },
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   "execution_count": 31,
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     "text": [
      "C:\\Users\\Minkun\\Anaconda3\\lib\\site-packages\\keras\\models.py:981: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  warnings.warn('The `nb_epoch` argument in `fit` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 8ms/step\n",
      "\n",
      "Accuracy:  0.75\n",
      "\n",
      "Predictions:\n",
      "[[0.56487805 0.5484973 ]\n",
      " [0.55052924 0.5791934 ]\n",
      " [0.61709493 0.62842995]\n",
      " [0.6026052  0.6551815 ]]\n"
     ]
    }
   ],
   "source": [
    "# Fitting the model\n",
    "history = xor.fit(X, y, nb_epoch=50, verbose=0)\n",
    "\n",
    "# Scoring the model\n",
    "score = xor.evaluate(X, y)\n",
    "print(\"\\nAccuracy: \", score[-1])\n",
    "\n",
    "# Checking the predictions\n",
    "print(\"\\nPredictions:\")\n",
    "print(xor.predict_proba(X))"
   ]
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   "execution_count": 32,
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     "data": {
      "text/plain": [
       "[0.6916418075561523, 0.75]"
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   "source": [
    "score"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Out of 4 input points, we're correctly classifying only 3 of them? Let's try to change some parameters around to improve. For example, you can increase the number of epochs. Can you reach 100% ?"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "---\n",
    "## Mini Project_01: Student Admissions\n",
    "Build a neural network which analyzes the dataset of student admissions at UCLA \n",
    "\n",
    "we predict student admissions to graduate school at UCLA based on three pieces of data:\n",
    "- GRE Scores (Test)\n",
    "- GPA Scores (Grades)\n",
    "- Class rank (1-4)\n",
    "\n",
    "The dataset originally came from here: http://www.ats.ucla.edu/\n",
    "\n",
    "## Loading the data\n",
    "To load the data and format it nicely, we will use two very useful packages called Pandas and Numpy. You can read on the documentation here:\n",
    "- https://pandas.pydata.org/pandas-docs/stable/\n",
    "- https://docs.scipy.org/"
   ]
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   "execution_count": 33,
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admit</th>\n",
       "      <th>gre</th>\n",
       "      <th>gpa</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>380</td>\n",
       "      <td>3.61</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>660</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>800</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>640</td>\n",
       "      <td>3.19</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>520</td>\n",
       "      <td>2.93</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>760</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>560</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>3.08</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>540</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>3.92</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   admit  gre   gpa  rank\n",
       "0      0  380  3.61     3\n",
       "1      1  660  3.67     3\n",
       "2      1  800  4.00     1\n",
       "3      1  640  3.19     4\n",
       "4      0  520  2.93     4\n",
       "5      1  760  3.00     2\n",
       "6      1  560  2.98     1\n",
       "7      0  400  3.08     2\n",
       "8      1  540  3.39     3\n",
       "9      0  700  3.92     2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing pandas and numpy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Reading the csv file into a pandas DataFrame\n",
    "data = pd.read_csv('student_data.csv')\n",
    "\n",
    "# Printing out the first 10 rows of our data\n",
    "data[:10]\n",
    "# Here we can see that the first column is the label y, which corresponds to acceptance/rejection. Namely, a label of 1 means \n",
    "#the student got accepted, and a label of 0 means the student got rejected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
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    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21d3dda5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First let's make a plot of our data to see how it looks. In order to have a 2D plot, let's ingore the rank.\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Function to help us plot\n",
    "def plot_points(data):\n",
    "    X = np.array(data[[\"gre\",\"gpa\"]])\n",
    "    y = np.array(data[\"admit\"])\n",
    "    admitted = X[np.argwhere(y==1)]\n",
    "    rejected = X[np.argwhere(y==0)]\n",
    "    plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'red', edgecolor = 'k')\n",
    "    plt.scatter([s[0][0] for s in admitted], [s[0][1] for s in admitted], s = 25, color = 'cyan', edgecolor = 'k')\n",
    "    plt.xlabel('Test (GRE)')\n",
    "    plt.ylabel('Grades (GPA)')\n",
    "    \n",
    "# Plotting the points\n",
    "plot_points(data)\n",
    "plt.show()\n",
    "# the data is not as nicely separable as we'd hope..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Minkun\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21d377278d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Roughly, it looks like the students with high scores in the grades and test passed, while the ones with low scores didn't, \n",
    "#but the data is not as nicely separable as we hoped it would. Maybe it would help to take the rank into account? Let's make \n",
    "#4 plots, each one for each rank. So we make this one graph for each of the 4 ranks.\n",
    "\n",
    "# Separating the ranks\n",
    "data_rank1 = data[data[\"rank\"]==1]\n",
    "data_rank2 = data[data[\"rank\"]==2]\n",
    "data_rank3 = data[data[\"rank\"]==3]\n",
    "data_rank4 = data[data[\"rank\"]==4]\n",
    "\n",
    "# Plotting the graphs\n",
    "#plot_points(data_rank1)\n",
    "#plt.title(\"Rank 1\")\n",
    "#plt.show()\n",
    "#plot_points(data_rank2)\n",
    "#plt.title(\"Rank 2\")\n",
    "#plt.show()\n",
    "#plot_points(data_rank3)\n",
    "#plt.title(\"Rank 3\")\n",
    "#plt.show()\n",
    "#plot_points(data_rank4)\n",
    "#plt.title(\"Rank 4\")\n",
    "#plt.show()\n",
    "\n",
    "fig = plt.figure(figsize = (11,8))\n",
    "\n",
    "for i, rank in enumerate([data_rank1,data_rank2,data_rank3,data_rank4]):\n",
    "    ax = fig.add_subplot(2, 2, i+1)\n",
    "    plot_points(rank)\n",
    "    ax.set_title('Rank-%s'%(i+1), fontsize = 14)\n",
    "    \n",
    "fig.tight_layout()\n",
    "fig.show()\n",
    "\n",
    "\n",
    "# This looks more promising, as it seems that the lower the rank, the higher the acceptance rate. Let's use the rank as one of\n",
    "#our inputs. In order to do this, we should one-hot encode it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## TODO: One-hot encoding the rank\n",
    " It seems like the better grades and test the student has, the more likely they are to be accepted. And the rank has something to do with it. So what we'll do is, we'll one-hot encode the rank, and our 6 input variables will be: \n",
    " - Test (GPA)\n",
    " - Grades (GRE)\n",
    " - Rank 1\n",
    " - Rank 2\n",
    " - Rank 3\n",
    " - Rank 4\n",
    "#### The last 4 inputs will be binary variables that have a value of 1 if the student has that rank, or 0 otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admit</th>\n",
       "      <th>gre</th>\n",
       "      <th>gpa</th>\n",
       "      <th>rank_1</th>\n",
       "      <th>rank_2</th>\n",
       "      <th>rank_3</th>\n",
       "      <th>rank_4</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>4.00</td>\n",
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       "      <td>3.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>520</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   admit  gre   gpa  rank_1  rank_2  rank_3  rank_4\n",
       "0      0  380  3.61     0.0     0.0     1.0     0.0\n",
       "1      1  660  3.67     0.0     0.0     1.0     0.0\n",
       "2      1  800  4.00     1.0     0.0     0.0     0.0\n",
       "3      1  640  3.19     0.0     0.0     0.0     1.0\n",
       "4      0  520  2.93     0.0     0.0     0.0     1.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the get_dummies function in numpy in order to one-hot encode the data.\n",
    "\n",
    "# TODO:  Make dummy variables for rank\n",
    "#one_hot_data = pd.concat([data, pd.get_dummies(data['rank'], prefix='rank')], axis=1)\n",
    "one_hot_data = pd.get_dummies(data, columns=['rank'])\n",
    "\n",
    "# TODO: Drop the previous rank column\n",
    "#one_hot_data = one_hot_data.drop('rank', axis=1)\n",
    "\n",
    "# Print the first 5 rows of our data\n",
    "one_hot_data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## TODO: Scaling the data\n",
    "The next step is to scale the data. So, first things first, let's notice that the **test scores** have a range of 800, while the **grades** have a range of 4. This is a huge discrepancy: We notice that the range for grades is 1.0-4.0, whereas the range for test scores is roughly 200-800, which is much larger. This means our data is skewed, and that makes it hard for a neural network to handle and it will affect our training. Normally, the best thing to do is to normalize the scores so **they are between 0 and 1**. Let's fit our two features into a range of 0-1, by dividing the grades by 4.0, and the test score by 800."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admit</th>\n",
       "      <th>gre</th>\n",
       "      <th>gpa</th>\n",
       "      <th>rank_1</th>\n",
       "      <th>rank_2</th>\n",
       "      <th>rank_3</th>\n",
       "      <th>rank_4</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.9175</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.7975</td>\n",
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      ],
      "text/plain": [
       "   admit    gre     gpa  rank_1  rank_2  rank_3  rank_4\n",
       "0      0  0.475  0.9025     0.0     0.0     1.0     0.0\n",
       "1      1  0.825  0.9175     0.0     0.0     1.0     0.0\n",
       "2      1  1.000  1.0000     1.0     0.0     0.0     0.0\n",
       "3      1  0.800  0.7975     0.0     0.0     0.0     1.0\n",
       "4      0  0.650  0.7325     0.0     0.0     0.0     1.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making a copy of our data\n",
    "processed_data = one_hot_data[:]\n",
    "\n",
    "# TODO: Scale the columns\n",
    "processed_data['gre'] = processed_data['gre']/800\n",
    "processed_data['gpa'] = processed_data['gpa']/4\n",
    "\n",
    "# Printing the first 10 rows of our procesed data\n",
    "processed_data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Splitting the data into Training and Testing\n",
    "In order to test our algorithm, we'll split the data into a Training and a Testing set. The size of the testing set will be 10% of the total data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples is 360\n",
      "Number of testing samples is 40\n",
      "     admit    gre     gpa  rank_1  rank_2  rank_3  rank_4\n",
      "373      1  0.775  0.8425     1.0     0.0     0.0     0.0\n",
      "255      0  0.800  0.8375     0.0     0.0     1.0     0.0\n",
      "355      1  0.950  0.7025     1.0     0.0     0.0     0.0\n",
      "87       0  0.750  0.8700     0.0     1.0     0.0     0.0\n",
      "391      1  0.825  0.9700     0.0     1.0     0.0     0.0\n",
      "    admit    gre     gpa  rank_1  rank_2  rank_3  rank_4\n",
      "4       0  0.650  0.7325     0.0     0.0     0.0     1.0\n",
      "12      1  0.950  1.0000     1.0     0.0     0.0     0.0\n",
      "14      1  0.875  1.0000     1.0     0.0     0.0     0.0\n",
      "28      1  0.975  0.8050     0.0     1.0     0.0     0.0\n",
      "34      0  0.450  0.7850     1.0     0.0     0.0     0.0\n"
     ]
    }
   ],
   "source": [
    "sample_90 = np.random.choice(processed_data.index, size=int(len(processed_data)*0.9), replace=False)\n",
    "train_data, test_data = processed_data.iloc[sample_90], processed_data.drop(sample_90) \n",
    "\n",
    "print(\"Number of training samples is\", len(train_data))\n",
    "print(\"Number of testing samples is\", len(test_data))\n",
    "print(train_data[:5])\n",
    "print(test_data[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Splitting the data into features and targets (labels)\n",
    "Now, we split our data input into X, and the labels y , and one-hot encode the output, so it appears as two classes (accepted and not accepted). As a final step before the training, we'll split the data into features (X) and targets (y). Also, in Keras, we need to one-hot encode the output. We'll do this with the \"`to_categorical function`\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.65   0.7325 0.     0.     0.     1.    ]\n",
      " [0.95   1.     1.     0.     0.     0.    ]\n",
      " [0.875  1.     1.     0.     0.     0.    ]\n",
      " [0.975  0.805  0.     1.     0.     0.    ]\n",
      " [0.45   0.785  1.     0.     0.     0.    ]]\n",
      "[[1. 0.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "#X = np.array(processed_data)[:,1:]\n",
    "#y = np_utils.to_categorical(np.array(processed_data[\"admit\"]))\n",
    "\n",
    "# Separate data and one-hot encode the output\n",
    "# Note: We're also turning the data into numpy arrays, in order to train the model in Keras\n",
    "train_X = np.array(train_data.drop('admit', axis=1))\n",
    "train_y = np.array(np_utils.to_categorical(train_data['admit'], 2))\n",
    "\n",
    "test_X = np.array(test_data.drop('admit', axis=1))\n",
    "test_y = np.array(np_utils.to_categorical(test_data['admit'], 2))\n",
    "\n",
    "#train_X = train_data.drop('admit', axis=1)\n",
    "#train_y = train_data['admit']\n",
    "\n",
    "#test_X = test_data.drop('admit', axis=1)\n",
    "#test_y = test_data['admit']\n",
    "\n",
    "print(test_X[:5])\n",
    "print(test_y[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Training the 2-layer Neural Network\n",
    "The following function trains the 2-layer neural network. First, we'll write some helper functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_10 (Dense)             (None, 128)               896       \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 2)                 130       \n",
      "=================================================================\n",
      "Total params: 9,282\n",
      "Trainable params: 9,282\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "'''\n",
    "model = Sequential()\n",
    "model.add(Dense(128, input_dim=6))\n",
    "model.add(Activation('sigmoid'))\n",
    "model.add(Dense(32))\n",
    "model.add(Activation('sigmoid'))\n",
    "model.add(Dense(2))\n",
    "model.add(Activation('sigmoid'))\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()\n",
    "'''\n",
    "\n",
    "# Building the model\n",
    "model = Sequential()\n",
    "model.add(Dense(128, activation='relu', input_shape=(6,)))\n",
    "model.add(Dropout(.2))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(.1))\n",
    "model.add(Dense(2, activation='sigmoid'))  ## why not softmax ??\n",
    "\n",
    "# Compiling the model\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Training and Scoring the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Minkun\\Anaconda3\\lib\\site-packages\\keras\\models.py:981: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  warnings.warn('The `nb_epoch` argument in `fit` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "360/360 [==============================] - 0s 208us/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "\n",
      " Training Accuracy: 0.7138888888888889\n",
      "\n",
      "Predictions:\n",
      "\n",
      "Prob: [[0.40389404 0.49628845]\n",
      " [0.73156244 0.26621738]\n",
      " [0.3882761  0.5221291 ]\n",
      " [0.5978608  0.379129  ]\n",
      " [0.5557443  0.47069323]\n",
      " [0.6562224  0.39767146]\n",
      " [0.6084609  0.35670024]\n",
      " [0.75993717 0.2225512 ]]\n",
      "40/40 [==============================] - 0s 249us/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "\n",
      " Testing Accuracy: 0.725\n",
      "\n",
      "Predictions:\n",
      "\n",
      "Prob: [[0.83520985 0.13344029]\n",
      " [0.3405835  0.65776384]\n",
      " [0.34727305 0.6367347 ]\n",
      " [0.5667216  0.44031984]\n",
      " [0.56384814 0.19796444]\n",
      " [0.6582174  0.26594996]\n",
      " [0.6133998  0.34585392]\n",
      " [0.6034477  0.36104298]]\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "model.fit(train_X, train_y, nb_epoch=200, batch_size=100, verbose=0)\n",
    "\n",
    "# Evaluating the model on the training and testing set\n",
    "score = model.evaluate(train_X, train_y)\n",
    "print(\"\\n Training Accuracy:\", score[1])\n",
    "print(\"\\nPredictions:\")\n",
    "print(\"\\nProb:\", model.predict_proba(train_X)[:8])\n",
    "\n",
    "score = model.evaluate(test_X, test_y)\n",
    "print(\"\\n Testing Accuracy:\", score[1])\n",
    "print(\"\\nPredictions:\")\n",
    "print(\"\\nProb:\", model.predict_proba(test_X)[:8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Challenge: Play with the parameters!\n",
    "You can see that we made several decisions in our training. For instance, the number of layers, the sizes of the layers, the number of epochs, etc.\n",
    "It's your turn to play with parameters! Can you improve the accuracy? The following are other suggestions for these parameters. We'll learn the definitions later in the class:\n",
    "- Activation function: relu and sigmoid\n",
    "- Loss function: categorical_crossentropy, mean_squared_error\n",
    "- Optimizer: rmsprop, adam, ada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# W/O keras\n",
    "## TODO: Backpropagate the error\n",
    "Now it's your turn to shine. Write the error term. Remember that this is given by the equation $$ -(y-\\hat{y}) \\sigma'(x) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Activation (sigmoid) function\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "def sigmoid_prime(x):\n",
    "    return sigmoid(x) * (1-sigmoid(x))\n",
    "def error_formula(y, output):\n",
    "    return - y*np.log(output) - (1 - y) * np.log(1-output)\n",
    "\n",
    "\n",
    "# TODO: Write the error term formula\n",
    "def error_term_formula(y, output):\n",
    "    pass\n",
    "\n",
    "# Neural Network hyperparameters\n",
    "epochs = 1000\n",
    "learnrate = 0.5\n",
    "\n",
    "# Training function\n",
    "def train_nn(features, targets, epochs, learnrate):\n",
    "    \n",
    "    # Use to same seed to make debugging easier\n",
    "    np.random.seed(42)\n",
    "\n",
    "    n_records, n_features = features.shape\n",
    "    last_loss = None\n",
    "\n",
    "    # Initialize weights\n",
    "    weights = np.random.normal(scale=1 / n_features**.5, size=n_features)\n",
    "\n",
    "    for e in range(epochs):\n",
    "        del_w = np.zeros(weights.shape)\n",
    "        for x, y in zip(features.values, targets):\n",
    "            # Loop through all records, x is the input, y is the target\n",
    "\n",
    "            # Activation of the output unit\n",
    "            #   Notice we multiply the inputs and the weights here \n",
    "            #   rather than storing h as a separate variable \n",
    "            output = sigmoid(np.dot(x, weights))\n",
    "\n",
    "            # The error, the target minus the network output\n",
    "            error = error_formula(y, output)\n",
    "\n",
    "            # The error term\n",
    "            #   Notice we calulate f'(h) here instead of defining a separate\n",
    "            #   sigmoid_prime function. This just makes it faster because we\n",
    "            #   can re-use the result of the sigmoid function stored in\n",
    "            #   the output variable\n",
    "            error_term = error_term_formula(y, output)\n",
    "\n",
    "            # The gradient descent step, the error times the gradient times the inputs\n",
    "            del_w += error_term * x\n",
    "\n",
    "        # Update the weights here. The learning rate times the \n",
    "        # change in weights, divided by the number of records to average\n",
    "        weights += learnrate * del_w / n_records\n",
    "\n",
    "        # Printing out the mean square error on the training set\n",
    "        if e % (epochs / 10) == 0:\n",
    "            out = sigmoid(np.dot(features, weights))\n",
    "            loss = np.mean((out - targets) ** 2)\n",
    "            print(\"Epoch:\", e)\n",
    "            if last_loss and last_loss < loss:\n",
    "                print(\"Train loss: \", loss, \"  WARNING - Loss Increasing\")\n",
    "            else:\n",
    "                print(\"Train loss: \", loss)\n",
    "            last_loss = loss\n",
    "            print(\"=========\")\n",
    "    print(\"Finished training!\")\n",
    "    return weights\n",
    "    \n",
    "weights = train_nn(features, targets, epochs, learnrate)\n",
    "\n",
    "\n",
    "\n",
    "# Calculate accuracy on test data\n",
    "tes_out = sigmoid(np.dot(features_test, weights))\n",
    "predictions = tes_out > 0.5\n",
    "accuracy = np.mean(predictions == targets_test)\n",
    "print(\"Prediction accuracy: {:.3f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "## Mini Project_02. Analyzing IMDB Data in Keras\n",
    "we will analyze a dataset from IMDB and use it to predict the sentiment analysis of a review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 1. Loading the data\n",
    "This lab uses a dataset of 25,000 IMDB reviews. Each review, comes with a label. A label of 0 is given to a negative review, and a label of 1 is given to a positive review. The goal of this lab is to create a model that will predict the sentiment of a review, **based on the words** on it. \n",
    "\n",
    "Now, the input already comes preprocessed for us for convenience. Each review is encoded as a sequence of indexes, corresponding to the words in the review. **The words are ordered by frequency**, so the integer 1 corresponds to the most frequent word (\"the\"), the integer 2 to the second most frequent word, etc. By convention, the integer 0 corresponds to unknown words.\n",
    "\n",
    "Then, the sentence is turned into a vector by simply concatenating these integers. For instance, if the sentence is \"To be or not to be.\" and the indices of the words are as follows:\n",
    "- \"to\": 5\n",
    "- \"be\": 8\n",
    "- \"or\": 21\n",
    "- \"not\": 3\n",
    "\n",
    "Then the sentence gets encoded as the vector [5,8,21,3,5,8].\n",
    "\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.\n",
    "- **num_words**: Top most frequent words to consider. This is useful if you don't want to consider very obscure words such as \"Ultracrepidarian\"\n",
    "- **skip_top**: Top words to ignore. This is useful if you don't want to consider the most common words. For example, the word \"the\" would add no information to the review, so we can skip it by setting skip_top to 2 or higher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000,)\n",
      "(25000,)\n"
     ]
    }
   ],
   "source": [
    "#(x_train, y_train), (x_test, y_test) = imdb.load_data(path=\"imdb.npz\",\n",
    "#                                                     num_words=None,\n",
    "#                                                     skip_top=0,\n",
    "#                                                     maxlen=None,\n",
    "#                                                     seed=113,\n",
    "#                                                     start_char=1,\n",
    "#                                                     oov_char=2,\n",
    "#                                                     index_from=3)\n",
    "\n",
    "# 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[list([1, 591, 202, 14, 31, 6, 717, 10, 10, 2, 2, 5, 4, 360, 7, 4, 177, 2, 394, 354, 4, 123, 9, 2, 2, 2, 10, 10, 13, 92, 124, 89, 488, 2, 100, 28, 2, 14, 31, 23, 27, 2, 29, 220, 468, 8, 124, 14, 286, 170, 8, 157, 46, 5, 27, 239, 16, 179, 2, 38, 32, 25, 2, 451, 202, 14, 6, 717])\n",
      " list([1, 14, 22, 2, 6, 176, 7, 2, 88, 12, 2, 23, 2, 5, 109, 943, 4, 114, 9, 55, 606, 5, 111, 7, 4, 139, 193, 273, 23, 4, 172, 270, 11, 2, 2, 4, 2, 2, 109, 2, 21, 4, 22, 2, 8, 6, 2, 2, 10, 10, 4, 105, 987, 35, 841, 2, 19, 861, 2, 5, 2, 2, 45, 55, 221, 15, 670, 2, 526, 14, 2, 4, 405, 5, 2, 7, 27, 85, 108, 131, 4, 2, 2, 2, 405, 9, 2, 133, 5, 50, 13, 104, 51, 66, 166, 14, 22, 157, 9, 4, 530, 239, 34, 2, 2, 45, 407, 31, 7, 41, 2, 105, 21, 59, 299, 12, 38, 950, 5, 2, 15, 45, 629, 488, 2, 127, 6, 52, 292, 17, 4, 2, 185, 132, 2, 2, 2, 488, 2, 47, 6, 392, 173, 4, 2, 2, 270, 2, 4, 2, 7, 4, 65, 55, 73, 11, 346, 14, 20, 9, 6, 976, 2, 7, 2, 861, 2, 5, 2, 30, 2, 2, 56, 4, 841, 5, 990, 692, 8, 4, 2, 398, 229, 10, 10, 13, 2, 670, 2, 14, 9, 31, 7, 27, 111, 108, 15, 2, 19, 2, 2, 875, 551, 14, 22, 9, 2, 21, 45, 2, 5, 45, 252, 8, 2, 6, 565, 921, 2, 39, 4, 529, 48, 25, 181, 8, 67, 35, 2, 22, 49, 238, 60, 135, 2, 14, 9, 290, 4, 58, 10, 10, 472, 45, 55, 878, 8, 169, 11, 374, 2, 25, 203, 28, 8, 818, 12, 125, 4, 2])]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(x_test[:2])\n",
    "y_test[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 14, 22, 16, 43, 530, 973, 2, 2, 65, 458, 2, 66, 2, 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, 2, 2, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2, 19, 14, 22, 4, 2, 2, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 2, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2, 2, 16, 480, 66, 2, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 2, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 2, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 2, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2, 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, 2, 88, 12, 16, 283, 5, 16, 2, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(x_train[0])\n",
    "print(y_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3. One-hot encoding the input / 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " 0. 1. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 1. 0. 1. 1. 0. 0. 0. 1. 0. 0. 1. 0.\n",
      " 1. 0. 1. 1. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 1.\n",
      " 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0.\n",
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      " 1. 0. 0. 0. 0. 0. 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. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
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      " 0. 0. 0. 0. 0. 0. 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. 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. 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. 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. 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. 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. 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. 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. 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. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# One-hot encoding the output into vector mode, each of length 1000\n",
    "'''\n",
    "keras.preprocessing.text.Tokenizer(num_words=None, filters='!\"#$%&()*+,-./:;<=>?@[\\]^_`{|}~ \n",
    "', lower=True, split=' ', char_level=False, oov_token=None)\n",
    "\n",
    "turning each text into either a sequence of integers (each integer being the index of a token in a dictionary) or into a\n",
    "vector where the coefficient for each token could be binary, based on word count, based on tf,.... \n",
    "\n",
    "- num_words: the maximum number of words to keep, based on word frequency. Only the most common num_words words will be kept.\n",
    "- filters: a string where each element is a character that will be filtered from the texts. The default is all punctuation, \n",
    "plus tabs and line breaks, minus the ' character.\n",
    "- lower: boolean. Whether to convert the texts to lowercase.\n",
    "- split: str. Separator for word splitting.\n",
    "- char_level: if True, every character will be treated as a token.\n",
    "- oov_token: if given, it will be added to word_index and used to replace out-of-vocabulary words during text_to_sequence calls\n",
    "\n",
    "By default, all punctuation is removed, turning the texts into space-separated sequences of words (words maybe include the \n",
    "' character). These sequences are then split into lists of tokens. They will then be indexed or vectorized.\n",
    "\n",
    "0 is a reserved index that won't be assigned to any word.\n",
    "'''\n",
    "tokenizer = Tokenizer(num_words=1000)\n",
    "\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])\n",
    "print(x_test[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "And we'll also one-hot encode the **output**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 2)\n",
      "(25000, 2)\n"
     ]
    }
   ],
   "source": [
    "# One-hot encoding the output\n",
    "num_classes = 2\n",
    "\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": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "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": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 512)               512512    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 2)                 1026      \n",
      "=================================================================\n",
      "Total params: 513,538\n",
      "Trainable params: 513,538\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# TODO: Build the model architecture\n",
    "model = Sequential()\n",
    "model.add(Dense(512, activation='relu', input_dim=1000))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='sigmoid')) ## why not 'softmax'??\n",
    "model.summary()\n",
    "\n",
    "# TODO: Compile the model using a loss function and an optimizer.\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      " - 18s - loss: 0.4013 - acc: 0.8319 - val_loss: 0.3461 - val_acc: 0.8565\n",
      "Epoch 2/10\n",
      " - 19s - loss: 0.3342 - acc: 0.8655 - val_loss: 0.3432 - val_acc: 0.8558\n",
      "Epoch 3/10\n",
      " - 18s - loss: 0.3116 - acc: 0.8752 - val_loss: 0.3435 - val_acc: 0.8624\n",
      "Epoch 4/10\n",
      " - 18s - loss: 0.2994 - acc: 0.8849 - val_loss: 0.3379 - val_acc: 0.8637\n",
      "Epoch 5/10\n",
      " - 18s - loss: 0.2894 - acc: 0.8904 - val_loss: 0.3578 - val_acc: 0.8614\n",
      "Epoch 6/10\n",
      " - 18s - loss: 0.2790 - acc: 0.8957 - val_loss: 0.3660 - val_acc: 0.8618\n",
      "Epoch 7/10\n",
      " - 18s - loss: 0.2653 - acc: 0.9040 - val_loss: 0.3836 - val_acc: 0.8612\n",
      "Epoch 8/10\n",
      " - 19s - loss: 0.2536 - acc: 0.9107 - val_loss: 0.3974 - val_acc: 0.8602\n",
      "Epoch 9/10\n",
      " - 18s - loss: 0.2427 - acc: 0.9154 - val_loss: 0.4290 - val_acc: 0.8601\n",
      "Epoch 10/10\n",
      " - 17s - loss: 0.2282 - acc: 0.9239 - val_loss: 0.4396 - val_acc: 0.8606\n"
     ]
    }
   ],
   "source": [
    "# TODO: Run the model. Feel free to experiment with different batch sizes and number of epochs.\n",
    "hist = model.fit(x_train, y_train,\n",
    "          batch_size=32,\n",
    "          epochs=10,\n",
    "          validation_data=(x_test, y_test), \n",
    "          verbose=2)"
   ]
  },
  {
   "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%?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.86064\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"Accuracy: \", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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