Training both Models and Architecture Experiments .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "import h5py\n",
    "from matplotlib import pyplot as plt \n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "from skimage import io\n",
    "\n",
    "def show(img):\n",
    "    io.imshow(img)\n",
    "    io.show()\n",
    "def softmax_c(z):\n",
    "    assert len(z.shape) == 2\n",
    "    s = np.max(z, axis=1)\n",
    "    s = s[:, np.newaxis]\n",
    "    e_x = np.exp(z - s)\n",
    "    div = np.sum(e_x, axis=1)\n",
    "    div = div[:, np.newaxis] \n",
    "    return e_x / div"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Compression/Reduction Techniques Studied\n",
    "\n",
    "#### Logit Regression : NIPS '14\n",
    "- Formulation: (Regression)\n",
    "- Loss Function : \n",
    "- https://arxiv.org/pdf/1312.6184.pdf\n",
    "- Do Deep Nets Really Need to be Deep?\n",
    "\n",
    "#### Song Hans et al. \n",
    "- Deep Compression, Pruning, trained-Quantization and Huffman Coding\n",
    "- Learning both Weights and Connections for Efficient Neural Networks\n",
    "- Formulation: \n",
    "- Loss Function : \n",
    "\n",
    "#### Quanitzation of Dense Layer. \n",
    "- Discretize the continuous Range of Values in a dense Layer to Clusters\n",
    "- Little or no Loss of accuracy. \n",
    "- Dense Layers are reduced.\n",
    "- a series of information theoretical vector quantization methods (Jegou et al., 2011; Chen et al., 2010) for compressing dense connected layers. \n",
    "- SqueezeNet (Implementation Available)\n",
    "\n",
    "\n",
    "- They Found simply applying kmeans-based scalar quantization achieves very impressive results. Than Matrix Factorization \n",
    "- Compression Rate to Accuracy Tradeoff using Kmeans saclar quantization := 32 / log2 (k)\n",
    "- Product Quantization .. breaking matrix into S submatrices... and kmeans clustering on each one of them . Compression Rate (32mn)/(32kn + log2(k)ms).\n",
    "\n",
    "\n",
    "- COMPRESSING DEEP CONVOLUTIONAL NETWORKS\n",
    "- USING VECTOR QUANTIZATION Yunchao Gong, Liu Liu ∗ , Ming Yang, Lubomir Bourdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('x_train shape:', (60000, 28, 28, 1))\n",
      "(60000, 'train samples')\n",
      "(10000, 'test samples')\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D , Activation\n",
    "from keras import backend as K\n",
    "\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 15\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "# the data, shuffled and split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "if K.image_dim_ordering() == 'th':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.np_utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.np_utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32,3,3, activation='relu', input_shape=input_shape))\n",
    "model.add(Conv2D(64,3,3,activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train cumberSome Model "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          nb_epoch=epochs,\n",
    "          verbose=0,\n",
    "          validation_data=(x_test, y_test))\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])\n",
    "\n",
    "\n",
    "print (\"Saving Model weights\")\n",
    "model.save_weights(\"stockweighs.h5\")\n",
    "print (\"Model Weights Saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "convolution2d_1 (Convolution2D)  (None, 26, 26, 32)    320         convolution2d_input_1[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_2 (Convolution2D)  (None, 24, 24, 64)    18496       convolution2d_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling2d_1 (MaxPooling2D)    (None, 12, 12, 64)    0           convolution2d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)              (None, 12, 12, 64)    0           maxpooling2d_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)              (None, 9216)          0           dropout_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense_1 (Dense)                  (None, 128)           1179776     flatten_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)              (None, 128)           0           dense_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_2 (Dense)                  (None, 10)            1290        dropout_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_1 (Activation)        (None, 10)            0           dense_2[0][0]                    \n",
      "====================================================================================================\n",
      "Total params: 1,199,882\n",
      "Trainable params: 1,199,882\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Load pre-trained wieghts and Evaluate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Test loss:', 0.028270393712621807)\n",
      "('Test accuracy:', 0.99139999999999995)\n"
     ]
    }
   ],
   "source": [
    "model.load_weights('stockweighs.h5')\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter Size of Initial Model and Memory Footprint \n",
    "- How many parameters does your initial model contain? \n",
    "- What is the memory footprint? \n",
    "- What is the accuracy on the testing data set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Memory footprint per Image Feed Forward ~= ', 4.5771865844726562, 'Mb')\n"
     ]
    }
   ],
   "source": [
    "convparams = 320 + 18496\n",
    "trainable_params = model.count_params()\n",
    "footprint = trainable_params * 4\n",
    "\n",
    "print (\"Memory footprint per Image Feed Forward ~= \" , footprint / 1024.0 /1024.0 ,\"Mb\") # 2x Backprop\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distillation of Knowledge in Final Dense Layers , Through Logit Regression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Todo:\n",
    "\n",
    "- See if Redundant Conv-Layers Connections from TL,TR,BL,BR should be pruned. \n",
    "- see indices from karpathy slides and learn pruning connections "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intermediate Outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "\n",
    "def prepare_softtargets(model,X):\n",
    "    inp = model.input                                           # input placeholder\n",
    "    outputs = []\n",
    "    for layer in model.layers[:]:\n",
    "        if layer.name == 'flatten_1':\n",
    "            outputs.append(layer.output)\n",
    "        if layer.name == 'dense_2':\n",
    "            outputs.append(layer.output)\n",
    "            \n",
    "    functor = K.function([inp]+ [K.learning_phase()], outputs ) # evaluation function\n",
    "    layer_outs = functor([X, 1.])\n",
    "    return np.array(layer_outs[0]) , np.array(layer_outs[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare Train Data for small model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Batch # :  55\n",
      "Batch # :  56\n",
      "Batch # :  57\n",
      "Batch # :  58\n",
      "Batch # :  59\n",
      "(60000, 9216)\n",
      "(60000, 10)\n",
      "clean up \n"
     ]
    }
   ],
   "source": [
    "lastconv_out = []\n",
    "logit_out = []\n",
    "for i in range(0,60):\n",
    "    print \"Batch # : \",i\n",
    "    l,l2 =  ( prepare_softtargets(model,x_train[i*1000:(i+1)*1000]))\n",
    "    lastconv_out.append(l)\n",
    "    logit_out.append(l2)\n",
    "    #lastconv_out , logit_out = prepare_softtargets(model,x_train[i*1000:(i+1)*1000])\n",
    "\n",
    "# lastconv_out.shape , logit_out.shape\n",
    "lastconv_out = np.array(lastconv_out)\n",
    "logit_out = np.array(logit_out)\n",
    "lastconv_out = lastconv_out.reshape((60000 , 9216))\n",
    "logit_out = logit_out.reshape((60000 , 10))\n",
    "print lastconv_out.shape\n",
    "print logit_out.shape\n",
    "\n",
    "print \"clean up \" \n",
    "x_train = 0 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write transferset to file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h5f = h5py.File('lastconv_out.h5', 'w')\n",
    "h5f.create_dataset('dataset_1', data=lastconv_out)\n",
    "h5f.close()\n",
    "\n",
    "h5f2 = h5py.File('logit_out.h5', 'w')\n",
    "h5f2.create_dataset('dataset_1', data=logit_out)\n",
    "h5f2.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing Soft-targets and inputs for Evalutation of Model on Totally Held out x_test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch # :  0\n",
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      "Batch # :  7\n",
      "Batch # :  8\n",
      "Batch # :  9\n",
      "(10000, 9216)\n",
      "(10000, 10)\n"
     ]
    }
   ],
   "source": [
    "# free up memory\n",
    "lastconv_out = 0\n",
    "logit_out = 0 \n",
    "\n",
    "test_lastconv_out = []\n",
    "test_logit_out = []\n",
    "\n",
    "for i in range(0,10):\n",
    "    print \"Batch # : \",i\n",
    "    l,l2 =  prepare_softtargets(model,x_test[i*1000:(i+1)*1000])\n",
    "    test_lastconv_out.append(l)\n",
    "    test_logit_out.append(l2)\n",
    "    \n",
    "\n",
    "# lastconv_out.shape , logit_out.shape\n",
    "test_lastconv_out = np.array(test_lastconv_out)\n",
    "test_logit_out = np.array(test_logit_out)\n",
    "\n",
    "test_lastconv_out = test_lastconv_out.reshape((10000 , 9216))\n",
    "test_logit_out = test_logit_out.reshape((10000 , 10))\n",
    "\n",
    "print test_lastconv_out.shape\n",
    "print test_logit_out.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h5f = h5py.File('test_lastconv_out.h5', 'w')\n",
    "h5f.create_dataset('dataset_1', data=test_lastconv_out)\n",
    "h5f.close()\n",
    "\n",
    "h5f2 = h5py.File('test_logit_out.h5', 'w')\n",
    "h5f2.create_dataset('dataset_1', data=test_logit_out)\n",
    "h5f2.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment on different Compression Rates and Architectures (number of Hidden layer Neurons) for plotting results "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Logit Regression Method \n",
    "\n",
    "results = []\n",
    "\n",
    "for HiddenNeuron in [4,5,6,7,8,9,10,11,14,16,20]:\n",
    "    \n",
    "    # Load input,target to studentModel \n",
    "    h5f = h5py.File('lastconv_out.h5' , 'r')\n",
    "    lastconv_out = h5f['dataset_1'][:]\n",
    "    h5f.close()\n",
    "    \n",
    "    h5f2 = h5py.File('logit_out.h5' , 'r')\n",
    "    logit_out = h5f2['dataset_1'][:]\n",
    "    h5f2.close()\n",
    "\n",
    "    student_model = Sequential()\n",
    "    student_model.add(Dense(HiddenNeuron,input_dim=9216,activation='relu'))\n",
    "    student_model.add(Dropout(0.5))\n",
    "    student_model.add(Dense(num_classes))\n",
    "\n",
    "    student_model.compile(loss='mse',\n",
    "                  optimizer=keras.optimizers.Adadelta(),\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    student_model.fit(lastconv_out, logit_out,nb_epoch=10,verbose=0)\n",
    "#     student_model.save_weights(\"student_weights_\"+str(HiddenNeuron)+\"hidden_0.5_dropout.h5\")\n",
    "    \n",
    "    # Compression Rate from Number of Parameters Reduced\n",
    "    print \"HiddenNeurons : \" , HiddenNeuron\n",
    "    print \"Initial Model Parameters : \" , model.count_params()\n",
    "    print \"Compressed Model parameters: \", student_model.count_params() + convparams\n",
    "    \n",
    "    compressionRate = model.count_params() / np.float(student_model.count_params() + convparams)\n",
    "    print \"Compression Rate : \" , compressionRate\n",
    "    \n",
    "    lastconv_out = 0\n",
    "    logit_out = 0                \n",
    "    \n",
    "    h5f = h5py.File('test_lastconv_out.h5' , 'r')\n",
    "    test_lastconv_out = h5f['dataset_1'][:]\n",
    "    h5f.close()\n",
    "    h5f2 = h5py.File('test_logit_out.h5' , 'r')\n",
    "    test_logit_out = h5f2['dataset_1'][:]\n",
    "    h5f2.close()\n",
    "    \n",
    "    pred = student_model.predict(test_lastconv_out)\n",
    "    probs = softmax_c(pred)\n",
    "    pred_classes = np.argmax(probs,axis=1)\n",
    "\n",
    "    accuracy_student = metrics.accuracy_score(y_pred=pred_classes,y_true=np.argmax(y_test,axis=1))\n",
    "    print \"Accuracy : \" , accuracy_student\n",
    "                            \n",
    "    out = {\n",
    "        \"HiddenNeuron\" :    HiddenNeuron,\n",
    "        \"compressionRate\" : compressionRate,\n",
    "        \"nparams_student\" : student_model.count_params() + convparams = 320 + 18496,\n",
    "        \"accuracy_student\": accuracy_student\n",
    "    }\n",
    "    \n",
    "    student_model = 0 \n",
    "    lastconv_out = 0\n",
    "    logit_out = 0 \n",
    "    test_lastconv_out = 0\n",
    "    test_logit_out = 0 \n",
    "    \n",
    "    results.append(out)\n",
    "    # Free-up train Set "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HiddenNeurons :  2\n",
      "Initial Model Parameters :  1199882\n",
      "Compressed Model parameters:  37280\n",
      "Compression Rate :  32.1856759657\n",
      "Accuracy :  0.4211\n",
      "HiddenNeurons :  3\n",
      "Initial Model Parameters :  1199882\n",
      "Compressed Model parameters:  46507\n",
      "Compression Rate :  25.800030103\n",
      "Accuracy :  0.7185\n",
      "HiddenNeurons :  4\n",
      "Initial Model Parameters :  1199882\n",
      "Compressed Model parameters:  55734\n",
      "Compression Rate :  21.5287257329\n",
      "Accuracy :  0.9199\n",
      "HiddenNeurons :  5\n",
      "Initial Model Parameters :  1199882\n",
      "Compressed Model parameters:  64961\n",
      "Compression Rate :  18.4708055603\n",
      "Accuracy :  0.9423\n",
      "HiddenNeurons :  6\n",
      "Initial Model Parameters :  1199882\n",
      "Compressed Model parameters:  74188\n",
      "Compression Rate :  16.1735321076\n",
      "Accuracy :  0.9626\n",
      "HiddenNeurons :  7\n",
      "Initial Model Parameters :  1199882\n",
      "Compressed Model parameters:  83415\n",
      "Compression Rate :  14.3844872025\n",
      "Accuracy :  0.9699\n"
     ]
    }
   ],
   "source": [
    "## Logit Regression Method \n",
    "\n",
    "results = []\n",
    "\n",
    "for HiddenNeuron in [2,3,4,5,6,7]:\n",
    "    \n",
    "    # Load input,target to studentModel \n",
    "    h5f = h5py.File('lastconv_out.h5' , 'r')\n",
    "    lastconv_out = h5f['dataset_1'][:]\n",
    "    h5f.close()\n",
    "    \n",
    "    h5f2 = h5py.File('logit_out.h5' , 'r')\n",
    "    logit_out = h5f2['dataset_1'][:]\n",
    "    h5f2.close()\n",
    "\n",
    "    student_model = Sequential()\n",
    "    student_model.add(Dense(HiddenNeuron,input_dim=9216,activation='relu'))\n",
    "    student_model.add(Dropout(0.2))\n",
    "    student_model.add(Dense(num_classes))\n",
    "\n",
    "    student_model.compile(loss='mse',\n",
    "                  optimizer=keras.optimizers.Adadelta(),\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    student_model.fit(lastconv_out, logit_out,nb_epoch=40,verbose=0)\n",
    "#     student_model.save_weights(\"student_weights_\"+str(HiddenNeuron)+\"hidden_0.5_dropout.h5\")\n",
    "    \n",
    "    # Compression Rate from Number of Parameters Reduced\n",
    "    print \"HiddenNeurons : \" , HiddenNeuron\n",
    "    print \"Initial Model Parameters : \" , model.count_params()\n",
    "    print \"Compressed Model parameters: \", student_model.count_params() + convparams\n",
    "    \n",
    "    compressionRate = model.count_params() / np.float(student_model.count_params()  + convparams)\n",
    "    print \"Compression Rate : \" , compressionRate\n",
    "    \n",
    "    lastconv_out = 0\n",
    "    logit_out = 0                \n",
    "    \n",
    "    h5f = h5py.File('test_lastconv_out.h5' , 'r')\n",
    "    test_lastconv_out = h5f['dataset_1'][:]\n",
    "    h5f.close()\n",
    "    h5f2 = h5py.File('test_logit_out.h5' , 'r')\n",
    "    test_logit_out = h5f2['dataset_1'][:]\n",
    "    h5f2.close()\n",
    "    \n",
    "    pred = student_model.predict(test_lastconv_out)\n",
    "    probs = softmax_c(pred)\n",
    "    pred_classes = np.argmax(probs,axis=1)\n",
    "\n",
    "    accuracy_student = metrics.accuracy_score(y_pred=pred_classes,y_true=np.argmax(y_test,axis=1))\n",
    "    print \"Accuracy : \" , accuracy_student\n",
    "                            \n",
    "    out = {\n",
    "        \"HiddenNeuron\" :    HiddenNeuron,\n",
    "        \"compressionRate\" : compressionRate,\n",
    "        \"nparams_student\" : student_model.count_params()  + convparams,\n",
    "        \"accuracy_student\": accuracy_student\n",
    "    }\n",
    "    \n",
    "    student_model = 0 \n",
    "    lastconv_out = 0\n",
    "    logit_out = 0 \n",
    "    test_lastconv_out = 0\n",
    "    test_logit_out = 0 \n",
    "    \n",
    "    results.append(out)\n",
    "    # Free-up train Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'HiddenNeuron': 2,\n",
       "  'accuracy_student': 0.42109999999999997,\n",
       "  'compressionRate': 32.185675965665233,\n",
       "  'nparams_student': 37280},\n",
       " {'HiddenNeuron': 3,\n",
       "  'accuracy_student': 0.71850000000000003,\n",
       "  'compressionRate': 25.800030102995247,\n",
       "  'nparams_student': 46507},\n",
       " {'HiddenNeuron': 4,\n",
       "  'accuracy_student': 0.91990000000000005,\n",
       "  'compressionRate': 21.528725732945777,\n",
       "  'nparams_student': 55734},\n",
       " {'HiddenNeuron': 5,\n",
       "  'accuracy_student': 0.94230000000000003,\n",
       "  'compressionRate': 18.470805560259233,\n",
       "  'nparams_student': 64961},\n",
       " {'HiddenNeuron': 6,\n",
       "  'accuracy_student': 0.96260000000000001,\n",
       "  'compressionRate': 16.173532107618481,\n",
       "  'nparams_student': 74188},\n",
       " {'HiddenNeuron': 7,\n",
       "  'accuracy_student': 0.96989999999999998,\n",
       "  'compressionRate': 14.384487202541509,\n",
       "  'nparams_student': 83415}]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p0YuIiKQwJXoREZEUpkQvIiKSwpToRUREUpgSvYiISApTohcREUlhSvQiIiIpTIleREQk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\nX7gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc51102fd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mc9VjC1n4yfagI4lIBFT0RSQiyYlxFBXkkZ2WxKw5JawprQw6koi0kIq+iEQs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5041d6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_findings(results,save=False):\n",
    "    fig = plt.figure()\n",
    "    fig.suptitle('parameter Size and Accuracy', fontsize=20)\n",
    "    plt.plot([r['nparams_student'] for r in results] , [r['accuracy_student'] for r in results])\n",
    "    plt.xlabel('Parameter Size', fontsize=18)\n",
    "    plt.ylabel('accuracy', fontsize=16)\n",
    "    if save: \n",
    "        fig.savefig('plots/parameterSize_Accuracy.png')\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    fig.suptitle('Compression Rate and Accuracy', fontsize=20)\n",
    "    plt.plot([r['compressionRate'] for r in results] , [r['accuracy_student'] for r in results])\n",
    "    plt.xlabel('compression Rate', fontsize=18)\n",
    "    plt.ylabel('accuracy', fontsize=18)\n",
    "    \n",
    "    if save: \n",
    "        fig.savefig('plots/CompressionRate_Accuracy.png')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_findings(results,save=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Conclusion \n",
    "- Achieved 16.2x compression keeping accuracy at 0.96"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}