DeepLearning.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning Playlist\n",
    "### Discussion\n",
    "\n",
    "Deep learning terms and topics\n",
    "\n",
    "- Understand what they mean\n",
    "- How they fit in to the overall deep learning framework\n",
    "- How certain topics are implemented in code\n",
    "  - Using Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning (ML)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine Learning is the practice of using algorithms to analyze data, learn from that data, and then make a determination or prediction about new data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning vs. Traditional Programming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: Analyzing the sentament of a popular media outlet and classifying that sentiment as positive or negative\n",
    "\n",
    "- Traditional Programming approach\n",
    " - The algorithm may first look for particular words associated with a negative or positive sentiment.\n",
    " - With conditional statements, the algorithm would classify articles as positive or negative based on the words that it knows are positive or negative.\n",
    " \n",
    "- Machine Learning approach\n",
    " - The algorithm would analyze large amounts of given media data and *learn* the features that classify what a negative article looks like versus a positive article.\n",
    " - With what it's learned, the algorithm could then classify new articles as positive or negative."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Learning (DL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This video will cover the most basic idea about what deep learning is and how it’s used. In later videos, we’ll be cover more detailed concepts, terms, and tools within the field of deep learning.\n",
    "\n",
    "**Definition:**\n",
    "\n",
    "- Deep learning is a subfield of machine learning that uses algorithms inspired by the structure and function of the brain’s neural networks. \n",
    "- As a subfield of ML, DL also algorithms to analyze data, learn from that data, and then make a determination or prediction about new data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Supervised Learning** occurs when your deep learning model learns and makes inferences from data that has already been *labeled*.\n",
    "\n",
    "**Unsupervised Learning** occurs when the model learns and makes inferences from *unlabeled* data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Labeled vs. Unlabeled example**\n",
    "\n",
    "- Classify images of cats and dogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Artificial Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Artificial neural networks are deep learning models that are based on the structure of the brain's neural networks. \n",
    "\n",
    "- Artificial neural networks are used interchangeably with the terms\n",
    "  - neural net\n",
    "  - net\n",
    "  - model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Artificial Neural Networks (ANNs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Artificial neural networks are computing systems that are inspired by the brain’s neural networks.\n",
    "\n",
    "- These networks are based on a collection of connected units called **artificial neurons**, or **neurons**. \n",
    "- Each connection between neurons can transmit a signal from one neuron to another. \n",
    "- The receiving neuron processes the signal and signals downstream neurons connected to it.\n",
    "- Neurons are organized in layers.\n",
    " - Input layer\n",
    " - Hidden layers\n",
    " - Output layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keras Sequential Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Sequential model is a linear stack of layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "\n",
    "model = Sequential([\n",
    "    Dense(32, input_shape=(10,), activation='relu'),\n",
    "    Dense(2, activation='softmax'),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Layers in an ANN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Artificial neural network are typically organized in layers. \n",
    "Different types of layers include\n",
    "\n",
    "- Dense (or fully connected) Layers\n",
    "- Convolutional layers\n",
    "- Pooling Layers\n",
    "- Recurrent Layers\n",
    "- Normalization Layers\n",
    "- Many others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "\n",
    "model = Sequential([\n",
    "    Dense(5, input_shape=(3,), activation='relu'),\n",
    "    Dense(2, activation='softmax'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0b8ba3ac50>"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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S0pJmzZrRrFmzFPsIIYiIiCAyMpLY2FjUajWPHz/m5s2bBAYGIkkSdnZ2mJiY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\npaygoKCQi1CUsoKCgkIu4v8Ax2lCszVy0xEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b92d936d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import ndimage\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "img = np.expand_dims(ndimage.imread('NN.PNG'),0)\n",
    "plt.imshow(img[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Activation Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "In an artificial neural network, the activation function of a neuron defines the output of that neuron given a set of inputs.\n",
    "\n",
    "- Biologically inspired by activity in our brains, where different neurons fire, or are *activated*, by different stimuli. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "\n",
    "model = Sequential([\n",
    "    Dense(5, input_shape=(3,), activation='relu')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(5, input_shape=(3,)))\n",
    "model.add(Activation('relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from random import randint\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_labels =  []\n",
    "train_samples = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example data: \n",
    "- An experiemental drug was tested on individuals from ages 13 to 100. \n",
    "- The trial had 2100 participants. Half were under 65 years old, half were over 65 years old.\n",
    "- 95% of patientes 65 or older experienced side effects.\n",
    "- 95% of patients under 65 experienced no side effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(50):\n",
    "    # The 5% of younger individuals who did experience side effects\n",
    "    random_younger = randint(13,64)\n",
    "    train_samples.append(random_younger)\n",
    "    train_labels.append(1)\n",
    "    \n",
    "    # The 5% of older individuals who did not experience side effects\n",
    "    random_older = randint(65,100)\n",
    "    train_samples.append(random_older)\n",
    "    train_labels.append(0)\n",
    "\n",
    "for i in range(1000):\n",
    "    # The 95% of younger individuals who did not experience side effects\n",
    "    random_younger = randint(13,64)\n",
    "    train_samples.append(random_younger)\n",
    "    train_labels.append(0)\n",
    "    \n",
    "    # The 95% of older individuals who did experience side effects\n",
    "    random_older = randint(65,100)\n",
    "    train_samples.append(random_older)\n",
    "    train_labels.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_labels = np.array(train_labels)\n",
    "train_samples = np.array(train_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/epoch/anaconda2/envs/fastai34/lib/python3.4/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_train_samples = scaler.fit_transform((train_samples).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0b8b576588>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S0pJmzZrRrFmzFPsIIYiIiCAyMpLY2FjUajWPHz/m5s2bBAYGIkkSdnZ2mJiY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\npaygoKCQi1CUsoKCgkIu4v8Ax2lCszVy0xEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b97af07f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import ndimage\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "img = np.expand_dims(ndimage.imread('NN.PNG'),0)\n",
    "plt.imshow(img[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Solving an optimization problem\n",
    " - Optimizing weights \n",
    "  - with Stochastic Gradient Descent (SGD)\n",
    " - Objective: Minimize the loss function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers.core import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras.metrics import categorical_crossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(16, input_shape=(1,), activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(2, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.compile(Adam(lr=.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.loss = 'sparse_categorical_crossentropy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sparse_categorical_crossentropy'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "0s - loss: 0.6400 - acc: 0.5576\n",
      "Epoch 2/20\n",
      "0s - loss: 0.6061 - acc: 0.6310\n",
      "Epoch 3/20\n",
      "0s - loss: 0.5748 - acc: 0.7010\n",
      "Epoch 4/20\n",
      "0s - loss: 0.5401 - acc: 0.7633\n",
      "Epoch 5/20\n",
      "0s - loss: 0.5050 - acc: 0.7990\n",
      "Epoch 6/20\n",
      "0s - loss: 0.4702 - acc: 0.8300\n",
      "Epoch 7/20\n",
      "0s - loss: 0.4366 - acc: 0.8495\n",
      "Epoch 8/20\n",
      "0s - loss: 0.4066 - acc: 0.8767\n",
      "Epoch 9/20\n",
      "0s - loss: 0.3808 - acc: 0.8814\n",
      "Epoch 10/20\n",
      "0s - loss: 0.3596 - acc: 0.8962\n",
      "Epoch 11/20\n",
      "0s - loss: 0.3420 - acc: 0.9043\n",
      "Epoch 12/20\n",
      "0s - loss: 0.3282 - acc: 0.9090\n",
      "Epoch 13/20\n",
      "0s - loss: 0.3170 - acc: 0.9129\n",
      "Epoch 14/20\n",
      "0s - loss: 0.3081 - acc: 0.9210\n",
      "Epoch 15/20\n",
      "0s - loss: 0.3014 - acc: 0.9190\n",
      "Epoch 16/20\n",
      "0s - loss: 0.2959 - acc: 0.9205\n",
      "Epoch 17/20\n",
      "0s - loss: 0.2916 - acc: 0.9238\n",
      "Epoch 18/20\n",
      "0s - loss: 0.2879 - acc: 0.9267\n",
      "Epoch 19/20\n",
      "0s - loss: 0.2848 - acc: 0.9252\n",
      "Epoch 20/20\n",
      "0s - loss: 0.2824 - acc: 0.9286\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f0b82249898>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(scaled_train_samples, train_labels, batch_size=10, epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.27586207],\n",
       "       [ 0.87356322],\n",
       "       [ 0.26436782],\n",
       "       ..., \n",
       "       [ 0.96551724],\n",
       "       [ 0.2183908 ],\n",
       "       [ 0.75862069]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_train_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 1, 0, 1])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1680 samples, validate on 420 samples\n",
      "Epoch 1/20\n",
      "0s - loss: 0.6994 - acc: 0.4970 - val_loss: 0.6960 - val_acc: 0.5000\n",
      "Epoch 2/20\n",
      "0s - loss: 0.6906 - acc: 0.5774 - val_loss: 0.6815 - val_acc: 0.6952\n",
      "Epoch 3/20\n",
      "0s - loss: 0.6754 - acc: 0.7179 - val_loss: 0.6613 - val_acc: 0.7857\n",
      "Epoch 4/20\n",
      "0s - loss: 0.6548 - acc: 0.7720 - val_loss: 0.6341 - val_acc: 0.8333\n",
      "Epoch 5/20\n",
      "0s - loss: 0.6296 - acc: 0.7958 - val_loss: 0.6001 - val_acc: 0.8571\n",
      "Epoch 6/20\n",
      "0s - loss: 0.5951 - acc: 0.8161 - val_loss: 0.5516 - val_acc: 0.8714\n",
      "Epoch 7/20\n",
      "0s - loss: 0.5545 - acc: 0.8250 - val_loss: 0.5004 - val_acc: 0.8881\n",
      "Epoch 8/20\n",
      "0s - loss: 0.5091 - acc: 0.8446 - val_loss: 0.4400 - val_acc: 0.9119\n",
      "Epoch 9/20\n",
      "0s - loss: 0.4637 - acc: 0.8726 - val_loss: 0.3886 - val_acc: 0.9310\n",
      "Epoch 10/20\n",
      "0s - loss: 0.4283 - acc: 0.8798 - val_loss: 0.3454 - val_acc: 0.9381\n",
      "Epoch 11/20\n",
      "0s - loss: 0.3997 - acc: 0.8863 - val_loss: 0.3096 - val_acc: 0.9524\n",
      "Epoch 12/20\n",
      "0s - loss: 0.3776 - acc: 0.8917 - val_loss: 0.2805 - val_acc: 0.9524\n",
      "Epoch 13/20\n",
      "0s - loss: 0.3602 - acc: 0.8988 - val_loss: 0.2572 - val_acc: 0.9643\n",
      "Epoch 14/20\n",
      "0s - loss: 0.3467 - acc: 0.9083 - val_loss: 0.2369 - val_acc: 0.9643\n",
      "Epoch 15/20\n",
      "0s - loss: 0.3364 - acc: 0.9113 - val_loss: 0.2214 - val_acc: 0.9643\n",
      "Epoch 16/20\n",
      "0s - loss: 0.3288 - acc: 0.9119 - val_loss: 0.2083 - val_acc: 0.9714\n",
      "Epoch 17/20\n",
      "0s - loss: 0.3227 - acc: 0.9155 - val_loss: 0.1971 - val_acc: 0.9643\n",
      "Epoch 18/20\n",
      "0s - loss: 0.3182 - acc: 0.9137 - val_loss: 0.1889 - val_acc: 0.9786\n",
      "Epoch 19/20\n",
      "0s - loss: 0.3144 - acc: 0.9167 - val_loss: 0.1809 - val_acc: 0.9714\n",
      "Epoch 20/20\n",
      "0s - loss: 0.3115 - acc: 0.9161 - val_loss: 0.1749 - val_acc: 0.9714\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f0b7ec2ee10>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(scaled_train_samples, train_labels, validation_split = 0.20, batch_size=10, \n",
    "          epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valid_set = [(sample, label), (sample, label), ... , (sample, label)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.fit(scaled_train_samples, train_labels, validation_data = valid_set, batch_size=10, \n",
    "          epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers.core import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(16, input_shape=(1,), activation='relu'),\n",
    "    Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)),\n",
    "    Dense(2, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(Adam(lr=0.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.optimizer.lr = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.optimizer.lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.fit(scaled_train_samples, train_labels, validation_data = valid_set, batch_size=10, \n",
    "          epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers.core import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras.metrics import categorical_crossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(16, input_shape=(1,), activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(2, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.compile(Adam(lr=.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "0s - loss: 0.6933 - acc: 0.6419\n",
      "Epoch 2/20\n",
      "0s - loss: 0.6710 - acc: 0.7857\n",
      "Epoch 3/20\n",
      "0s - loss: 0.6397 - acc: 0.8443\n",
      "Epoch 4/20\n",
      "0s - loss: 0.6061 - acc: 0.8476\n",
      "Epoch 5/20\n",
      "0s - loss: 0.5678 - acc: 0.8505\n",
      "Epoch 6/20\n",
      "0s - loss: 0.5228 - acc: 0.8600\n",
      "Epoch 7/20\n",
      "0s - loss: 0.4681 - acc: 0.8733\n",
      "Epoch 8/20\n",
      "0s - loss: 0.4105 - acc: 0.8962\n",
      "Epoch 9/20\n",
      "0s - loss: 0.3670 - acc: 0.9119\n",
      "Epoch 10/20\n",
      "0s - loss: 0.3329 - acc: 0.9167\n",
      "Epoch 11/20\n",
      "0s - loss: 0.3100 - acc: 0.9243\n",
      "Epoch 12/20\n",
      "0s - loss: 0.2925 - acc: 0.9271\n",
      "Epoch 13/20\n",
      "0s - loss: 0.2820 - acc: 0.9300\n",
      "Epoch 14/20\n",
      "0s - loss: 0.2757 - acc: 0.9338\n",
      "Epoch 15/20\n",
      "0s - loss: 0.2712 - acc: 0.9319\n",
      "Epoch 16/20\n",
      "0s - loss: 0.2681 - acc: 0.9381\n",
      "Epoch 17/20\n",
      "0s - loss: 0.2655 - acc: 0.9376\n",
      "Epoch 18/20\n",
      "0s - loss: 0.2634 - acc: 0.9352\n",
      "Epoch 19/20\n",
      "0s - loss: 0.2618 - acc: 0.9390\n",
      "Epoch 20/20\n",
      "0s - loss: 0.2603 - acc: 0.9352\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa8f1d9fb38>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(scaled_train_samples, train_labels, batch_size=10, epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/epoch/anaconda2/envs/fastai34/lib/python3.4/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# Preprocess test data\n",
    "\n",
    "test_labels =  []\n",
    "test_samples = []\n",
    "\n",
    "for i in range(10):\n",
    "    # The 5% of younger individuals who did experience side effects\n",
    "    random_younger = randint(13,64)\n",
    "    test_samples.append(random_younger)\n",
    "    test_labels.append(1)\n",
    "    \n",
    "    # The 5% of older individuals who did not experience side effects\n",
    "    random_older = randint(65,100)\n",
    "    test_samples.append(random_older)\n",
    "    test_labels.append(0)\n",
    "\n",
    "for i in range(200):\n",
    "    # The 95% of younger individuals who did not experience side effects\n",
    "    random_younger = randint(13,64)\n",
    "    test_samples.append(random_younger)\n",
    "    test_labels.append(0)\n",
    "    \n",
    "    # The 95% of older individuals who did experience side effects\n",
    "    random_older = randint(65,100)\n",
    "    test_samples.append(random_older)\n",
    "    test_labels.append(1)\n",
    "    \n",
    "test_labels = np.array(test_labels)\n",
    "test_samples = np.array(test_samples)\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_test_samples = scaler.fit_transform((test_samples).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(scaled_test_samples, batch_size=10, verbose=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.28293326  0.35764667]\n",
      "[ 0.26525486  0.37844887]\n",
      "[ 0.85707456  0.04403995]\n",
      "[ 0.0979089   0.67903334]\n",
      "[ 0.87152088  0.04061354]\n",
      "[ 0.04943148  0.85192239]\n",
      "[ 0.880009   0.0379883]\n",
      "[ 0.12007324  0.61951774]\n",
      "[ 0.64052224  0.10959   ]\n",
      "[ 0.13958886  0.5766331 ]\n",
      "[ 0.82027066  0.0517699 ]\n",
      "[ 0.04943148  0.85192239]\n",
      "[ 0.87042195  0.04095384]\n",
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      "[ 0.23208363  0.42135212]\n",
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      "[ 0.06808709  0.7811752 ]\n",
      "[ 0.85707456  0.04403995]\n",
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      "[ 0.07451599  0.7569769 ]\n",
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      "[ 0.87477005  0.0396088 ]\n",
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      "[ 0.82027066  0.0517699 ]\n",
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      "[ 0.30130684  0.33736712]\n",
      "[ 0.05176451  0.84311408]\n",
      "[ 0.84330875  0.04692649]\n",
      "[ 0.21662255  0.44330263]\n",
      "[ 0.87042195  0.04095384]\n",
      "[ 0.16233467  0.53246796]\n",
      "[ 0.42364404  0.22938529]\n",
      "[ 0.03793108  0.89261192]\n",
      "[ 0.84821194  0.04590416]\n",
      "[ 0.16233467  0.53246796]\n",
      "[ 0.84821194  0.04590416]\n",
      "[ 0.0430468   0.87568933]\n",
      "[ 0.36020902  0.28021055]\n",
      "[ 0.21662255  0.44330263]\n",
      "[ 0.7485773   0.07064888]\n",
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      "[ 0.87477005  0.0396088 ]\n",
      "[ 0.04120404  0.88207549]\n",
      "[ 0.48969987  0.18540421]\n",
      "[ 0.05176451  0.84311408]\n",
      "[ 0.87477005  0.0396088 ]\n",
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      "[ 0.85298848  0.04490304]\n",
      "[ 0.26525486  0.37844887]\n",
      "[ 0.40210381  0.24557945]\n",
      "[ 0.06217557  0.80358917]\n",
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      "[ 0.86930537  0.04130033]\n",
      "[ 0.1117581   0.64022535]\n",
      "[ 0.82682848  0.05037352]\n",
      "[ 0.16233467  0.53246796]\n",
      "[ 0.48969987  0.18540421]\n",
      "[ 0.06506889  0.7926057 ]\n",
      "[ 0.82027066  0.0517699 ]\n",
      "[ 0.05674605  0.82422394]\n",
      "[ 0.76366335  0.06596307]\n",
      "[ 0.04719836  0.86031818]\n",
      "[ 0.85979176  0.04345375]\n",
      "[ 0.12007324  0.61951774]\n",
      "[ 0.8768968   0.03895227]\n",
      "[ 0.07451599  0.7569769 ]\n",
      "[ 0.55611736  0.14823376]\n",
      "[ 0.04943148  0.85192239]\n",
      "[ 0.55611736  0.14823376]\n",
      "[ 0.0430468   0.87568933]\n",
      "[ 0.33999148  0.29860252]\n",
      "[ 0.03793108  0.89261192]\n",
      "[ 0.86509645  0.04230364]\n",
      "[ 0.23208363  0.42135212]\n",
      "[ 0.4454785   0.21395613]\n",
      "[ 0.08204049  0.72956663]\n",
      "[ 0.82682848  0.05037352]\n",
      "[ 0.24829839  0.39970824]\n",
      "[ 0.86246562  0.04287501]\n",
      "[ 0.21662255  0.44330263]\n",
      "[ 0.87898576  0.03830663]\n",
      "[ 0.0979089   0.67903334]\n",
      "[ 0.82682848  0.05037352]\n",
      "[ 0.08204049  0.72956663]\n",
      "[ 0.880009   0.0379883]\n",
      "[ 0.07808454  0.74381268]\n",
      "[ 0.33999148  0.29860252]\n",
      "[ 0.03793108  0.89261192]\n",
      "[ 0.82027066  0.0517699 ]\n",
      "[ 0.0594027   0.81412745]\n",
      "[ 0.87042195  0.04095384]\n",
      "[ 0.13958886  0.5766331 ]\n",
      "[ 0.87261182  0.04027594]\n",
      "[ 0.05674605  0.82422394]\n",
      "[ 0.78949356  0.05915147]\n",
      "[ 0.09193034  0.69722801]\n",
      "[ 0.38093486  0.26252744]\n",
      "[ 0.12007324  0.61951774]\n",
      "[ 0.87583733  0.03927922]\n",
      "[ 0.0979089   0.67903334]\n",
      "[ 0.8768968   0.03895227]\n",
      "[ 0.04719836  0.86031818]\n",
      "[ 0.8676846   0.04173953]\n",
      "[ 0.05420133  0.83388394]\n",
      "[ 0.55611736  0.14823376]\n",
      "[ 0.06217557  0.80358917]\n",
      "[ 0.42364404  0.22938529]\n",
      "[ 0.21662255  0.44330263]\n",
      "[ 0.76366335  0.06596307]\n",
      "[ 0.05674605  0.82422394]\n",
      "[ 0.880009   0.0379883]\n",
      "[ 0.24829839  0.39970824]\n",
      "[ 0.83827734  0.04797048]\n",
      "[ 0.12925252  0.59830874]\n",
      "[ 0.36020902  0.28021055]\n",
      "[ 0.08204049  0.72956663]\n",
      "[ 0.86509645  0.04230364]\n",
      "[ 0.04506137  0.86831146]\n",
      "[ 0.87261182  0.04027594]\n",
      "[ 0.04506137  0.86831146]\n",
      "[ 0.86509645  0.04230364]\n",
      "[ 0.17478551  0.51014841]\n",
      "[ 0.7155025   0.08153573]\n",
      "[ 0.0430468   0.87568933]\n",
      "[ 0.33999148  0.29860252]\n",
      "[ 0.07808454  0.74381268]\n",
      "[ 0.8736949   0.03994104]\n",
      "[ 0.0594027   0.81412745]\n",
      "[ 0.4454785   0.21395613]\n",
      "[ 0.12925252  0.59830874]\n",
      "[ 0.85707456  0.04403995]\n",
      "[ 0.0430468   0.87568933]\n",
      "[ 0.67980295  0.09405305]\n",
      "[ 0.18797709  0.48778817]\n",
      "[ 0.62010962  0.11817335]\n",
      "[ 0.03953509  0.88745135]\n",
      "[ 0.62010962  0.11817335]\n",
      "[ 0.05420133  0.83388394]\n",
      "[ 0.80109262  0.05631671]\n",
      "[ 0.09193034  0.69722801]\n",
      "[ 0.85298848  0.04490304]\n",
      "[ 0.20192057  0.46547687]\n",
      "[ 0.83300829  0.04906356]\n",
      "[ 0.10429112  0.66016704]\n",
      "[ 0.62010962  0.11817335]\n",
      "[ 0.06506889  0.7926057 ]\n",
      "[ 0.55611736  0.14823376]\n",
      "[ 0.04506137  0.86831146]\n",
      "[ 0.85707456  0.04403995]\n",
      "[ 0.04506137  0.86831146]\n",
      "[ 0.55611736  0.14823376]\n",
      "[ 0.06808709  0.7811752 ]\n",
      "[ 0.8768968   0.03895227]\n",
      "[ 0.08204049  0.72956663]\n",
      "[ 0.64052224  0.10959   ]\n",
      "[ 0.07451599  0.7569769 ]\n",
      "[ 0.8736949   0.03994104]\n",
      "[ 0.0430468   0.87568933]\n",
      "[ 0.76366335  0.06596307]\n",
      "[ 0.06808709  0.7811752 ]\n",
      "[ 0.69801664  0.08753506]\n",
      "[ 0.05674605  0.82422394]\n",
      "[ 0.4454785   0.21395613]\n",
      "[ 0.04719836  0.86031818]\n",
      "[ 0.87152088  0.04061354]\n",
      "[ 0.03638967  0.89756322]\n",
      "[ 0.85979176  0.04345375]\n",
      "[ 0.15060882  0.55465847]\n",
      "[ 0.83827734  0.04797048]\n",
      "[ 0.18797709  0.48778817]\n",
      "[ 0.87477005  0.0396088 ]\n",
      "[ 0.10429112  0.66016704]\n",
      "[ 0.32034045  0.31766897]\n",
      "[ 0.03953509  0.88745135]\n",
      "[ 0.8736949   0.03994104]\n",
      "[ 0.20192057  0.46547687]\n",
      "[ 0.732364    0.07591337]\n",
      "[ 0.03793108  0.89261192]\n",
      "[ 0.880009   0.0379883]\n",
      "[ 0.1117581   0.64022535]\n",
      "[ 0.85707456  0.04403995]\n",
      "[ 0.04943148  0.85192239]\n",
      "[ 0.87794852  0.03862793]\n",
      "[ 0.0430468   0.87568933]\n",
      "[ 0.732364    0.07591337]\n",
      "[ 0.05420133  0.83388394]\n",
      "[ 0.83827734  0.04797048]\n",
      "[ 0.16233467  0.53246796]\n",
      "[ 0.880009   0.0379883]\n",
      "[ 0.07451599  0.7569769 ]\n",
      "[ 0.732364    0.07591337]\n",
      "[ 0.20192057  0.46547687]\n",
      "[ 0.87152088  0.04061354]\n",
      "[ 0.03638967  0.89756322]\n",
      "[ 0.8768968   0.03895227]\n",
      "[ 0.07808454  0.74381268]\n",
      "[ 0.87152088  0.04061354]\n",
      "[ 0.03953509  0.88745135]\n",
      "[ 0.83827734  0.04797048]\n",
      "[ 0.0979089   0.67903334]\n",
      "[ 0.53408301  0.15988748]\n",
      "[ 0.03793108  0.89261192]\n",
      "[ 0.77739257  0.06212683]\n",
      "[ 0.24829839  0.39970824]\n",
      "[ 0.48969987  0.18540421]\n",
      "[ 0.04120404  0.88207549]\n",
      "[ 0.85979176  0.04345375]\n",
      "[ 0.0430468   0.87568933]\n",
      "[ 0.7485773   0.07064888]\n",
      "[ 0.23208363  0.42135212]\n",
      "[ 0.87583733  0.03927922]\n",
      "[ 0.06808709  0.7811752 ]\n",
      "[ 0.84330875  0.04692649]\n",
      "[ 0.12925252  0.59830874]\n",
      "[ 0.28293326  0.35764667]\n",
      "[ 0.04943148  0.85192239]\n",
      "[ 0.86246562  0.04287501]\n",
      "[ 0.03793108  0.89261192]\n",
      "[ 0.46752542  0.1992964 ]\n",
      "[ 0.0430468   0.87568933]\n",
      "[ 0.38093486  0.26252744]\n",
      "[ 0.03953509  0.88745135]\n",
      "[ 0.87477005  0.0396088 ]\n",
      "[ 0.23208363  0.42135212]\n",
      "[ 0.87261182  0.04027594]\n",
      "[ 0.0594027   0.81412745]\n",
      "[ 0.82027066  0.0517699 ]\n",
      "[ 0.12925252  0.59830874]\n",
      "[ 0.7155025   0.08153573]\n",
      "[ 0.03638967  0.89756322]\n",
      "[ 0.86930537  0.04130033]\n",
      "[ 0.12007324  0.61951774]\n",
      "[ 0.53408301  0.15988748]\n",
      "[ 0.21662255  0.44330263]\n",
      "[ 0.7155025   0.08153573]\n",
      "[ 0.07808454  0.74381268]\n",
      "[ 0.67980295  0.09405305]\n",
      "[ 0.05674605  0.82422394]\n",
      "[ 0.84821194  0.04590416]\n",
      "[ 0.0979089   0.67903334]\n",
      "[ 0.84821194  0.04590416]\n",
      "[ 0.05176451  0.84311408]\n",
      "[ 0.87042195  0.04095384]\n",
      "[ 0.20192057  0.46547687]\n",
      "[ 0.67980295  0.09405305]\n",
      "[ 0.18797709  0.48778817]\n",
      "[ 0.85298848  0.04490304]\n",
      "[ 0.0430468   0.87568933]\n",
      "[ 0.77739257  0.06212683]\n",
      "[ 0.0594027   0.81412745]\n",
      "[ 0.36020902  0.28021055]\n",
      "[ 0.24829839  0.39970824]\n",
      "[ 0.84330875  0.04692649]\n",
      "[ 0.26525486  0.37844887]\n",
      "[ 0.40210381  0.24557945]\n",
      "[ 0.06506889  0.7926057 ]\n",
      "[ 0.86509645  0.04230364]\n",
      "[ 0.0594027   0.81412745]\n",
      "[ 0.30130684  0.33736712]\n",
      "[ 0.23208363  0.42135212]\n",
      "[ 0.36020902  0.28021055]\n",
      "[ 0.04506137  0.86831146]\n",
      "[ 0.82027066  0.0517699 ]\n",
      "[ 0.06808709  0.7811752 ]\n",
      "[ 0.48969987  0.18540421]\n",
      "[ 0.05420133  0.83388394]\n",
      "[ 0.4454785   0.21395613]\n",
      "[ 0.08204049  0.72956663]\n",
      "[ 0.32034045  0.31766897]\n",
      "[ 0.0430468   0.87568933]\n",
      "[ 0.880009   0.0379883]\n",
      "[ 0.23208363  0.42135212]\n",
      "[ 0.8121165   0.05363343]\n",
      "[ 0.08658814  0.71420431]\n",
      "[ 0.51191491  0.17227209]\n",
      "[ 0.17478551  0.51014841]\n",
      "[ 0.8121165   0.05363343]\n",
      "[ 0.04120404  0.88207549]\n",
      "[ 0.880009   0.0379883]\n",
      "[ 0.08204049  0.72956663]\n",
      "[ 0.87898576  0.03830663]\n",
      "[ 0.03793108  0.89261192]\n",
      "[ 0.87042195  0.04095384]\n",
      "[ 0.06217557  0.80358917]\n",
      "[ 0.82682848  0.05037352]\n",
      "[ 0.12007324  0.61951774]\n",
      "[ 0.77739257  0.06212683]\n",
      "[ 0.04719836  0.86031818]\n",
      "[ 0.84330875  0.04692649]\n",
      "[ 0.06506889  0.7926057 ]\n",
      "[ 0.87898576  0.03830663]\n",
      "[ 0.07808454  0.74381268]\n",
      "[ 0.8736949   0.03994104]\n",
      "[ 0.07123458  0.76929814]\n",
      "[ 0.33999148  0.29860252]\n",
      "[ 0.0430468   0.87568933]\n",
      "[ 0.85707456  0.04403995]\n",
      "[ 0.07808454  0.74381268]\n",
      "[ 0.83827734  0.04797048]\n",
      "[ 0.04506137  0.86831146]\n",
      "[ 0.85298848  0.04490304]\n",
      "[ 0.07123458  0.76929814]\n",
      "[ 0.42364404  0.22938529]\n",
      "[ 0.17478551  0.51014841]\n"
     ]
    }
   ],
   "source": [
    "for i in predictions:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers.core import Dense\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(16, input_shape=(2,), activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(2, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(Adam(lr=0.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# weight, height\n",
    "train_samples = [[150, 67], [130, 60], [200, 65], [125, 52], [230, 72], [181, 70]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 0: male\n",
    "# 1: female\n",
    "train_labels = [1, 1, 0, 1, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.fit(x=train_samples, y=train_labels, batch_size=3, epochs=10, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, BatchNormalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(16, input_shape=(1,5), activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    BatchNormalization(axis=1),\n",
    "    Dense(2, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#beta_initializer: Initializer for the beta weight.\n",
    "#gamma_initializer: Initializer for the gamma weight."
   ]
  },
  {
   "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": []
  },
  {
   "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": []
  },
  {
   "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": []
  },
  {
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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