Autoencoder_Fasion_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For  20NG TF as an autoencoder with a desired hidden layer size (try K=20, 100, 200). Verify the obtained reencoding of data (the new feature representation) in several ways: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "import mnist \n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import random\n",
    "import mnist\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shantam Gupta\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting C:/Users/Shantam Gupta/Desktop/MS in DS/Unsupervised ML- DS 5230/Fashion-mnist\\train-images-idx3-ubyte.gz\n",
      "Extracting C:/Users/Shantam Gupta/Desktop/MS in DS/Unsupervised ML- DS 5230/Fashion-mnist\\train-labels-idx1-ubyte.gz\n",
      "Extracting C:/Users/Shantam Gupta/Desktop/MS in DS/Unsupervised ML- DS 5230/Fashion-mnist\\t10k-images-idx3-ubyte.gz\n",
      "Extracting C:/Users/Shantam Gupta/Desktop/MS in DS/Unsupervised ML- DS 5230/Fashion-mnist\\t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "#training data \n",
    "\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "data = input_data.read_data_sets('C:/Users/Shantam Gupta/Documents/GitHub/Autoencoders/data/Fashion-mnist')\n",
    "\n",
    "\n",
    "#Train data \n",
    "X_train = data.train.images\n",
    "Y_train = data.train.labels\n",
    "\n",
    "#Test Data \n",
    "X_test = data.test.images\n",
    "Y_test= data.test.labels\n",
    "\n",
    "Y_train = np.eye(10)[data.train.labels]\n",
    "Y_test = np.eye(10)[data.test.labels]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((55000, 784), (55000, 10), (10000, 784), (10000, 10))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, Y_train.shape, X_test.shape,Y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bulding Model  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy using Decision Trees: 1.0\n",
      "Test Accuracy using Decision Trees: 0.7916\n"
     ]
    }
   ],
   "source": [
    "#Building the Decision Tree Model\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf.fit(X_train,Y_train)\n",
    "\n",
    "#Accuracy on test data\n",
    "print(\"Train Accuracy using Decision Trees:\",clf.score(X_train,Y_train))\n",
    "\n",
    "#Accuracy on test data\n",
    "print(\"Test Accuracy using Decision Trees:\",clf.score(X_test,Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input & Output Placeholders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputs = 784\n",
    "hidden_layer1_units = 20\n",
    "output_layer_units = inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = tf.placeholder(tf.float32, shape = [None, inputs])#flattened shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Softmax Activation\n",
    "$\\Pr{(Y=j | X)}  = \\frac{e^{(X.W_j^T + B_j)}}{\\sum\\limits_{i = 1}^n {e^{(X.W_i^T + B_i)}}} $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.set_random_seed(42)\n",
    "W = {\n",
    "    'encoder_h1': tf.Variable(tf.random_normal([inputs, hidden_layer1_units])),\n",
    "    'decoder_h1':tf.Variable(tf.random_normal([hidden_layer1_units, output_layer_units]))\n",
    "}\n",
    "\n",
    "B = {\n",
    "    'encoder_h1':tf.Variable(tf.random_normal([hidden_layer1_units])),\n",
    "    'decoder_h1':tf.Variable(tf.random_normal([output_layer_units]))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder = tf.matmul(X,W['encoder_h1']) +  B['encoder_h1']\n",
    "encoder = tf.sigmoid(encoder) # using sigmoid activation\n",
    "decoder = tf.matmul(encoder,W['decoder_h1'])  + B['decoder_h1']\n",
    "decoder = tf.sigmoid(decoder) # using sigmoid activation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cost function: Cross Entropy\n",
    "## Optimizer : Adam "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "batch_size = 256\n",
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y = X\n",
    "loss = tf.reduce_mean(tf.pow(decoder - Y, 2))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_batches(batch_size,x):\n",
    "        indexes = list(range(x.shape[0]))\n",
    "        random.shuffle(indexes)\n",
    "        ind = indexes[:batch_size]\n",
    "        return(x[ind])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Epoch: 1 \t Average Train Eror: 0.05305397757911123\n",
      "\n",
      " Test Error 0.05266911\n",
      "\n",
      " Epoch: 6 \t Average Train Eror: 0.033206059015356004\n",
      "\n",
      " Test Error 0.033421453\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for epoch in range(epochs):\n",
    "        train_c = 0                                                       #cost\n",
    "        total_batch = int(X_train.shape[0]/batch_size)\n",
    "        for i in range(total_batch):\n",
    "            batch_x = get_batches(batch_size, X_train)\n",
    "            _,train_c = sess.run([optimizer, loss], feed_dict = {X: batch_x})\n",
    "            train_c += train_c/batch_size    \n",
    "        if epoch % 5 == 0:\n",
    "            print(\"\\n Epoch:\",epoch+1,\"\\t Average Train Eror:\",train_c)  \n",
    "            test_c = sess.run(loss,feed_dict = {X:X_test})\n",
    "            print(\"\\n Test Error\", test_c)    \n",
    "    encoder_train, decoder_train = sess.run([encoder,decoder],feed_dict = {X:X_train})\n",
    "    encoder_test, decoder_test = sess.run([encoder,decoder],feed_dict = {X:X_test}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((55000, 20), (55000, 784), (10000, 20), (10000, 784))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " encoder_train.shape, decoder_train.shape, encoder_test.shape, decoder_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b8b7e99b00>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1SS7ikCw5DtBIWdepGVmiT4SOTrREUjd4jtlIjnLr7QuF7aGjM6kjfEYsdoTL/D3nJwAc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\nZAQZdCGEyAgy6EIIkRH+H6+4bzOxWmGbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b8b7db9c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Test Data \n",
    "i = 2\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Original Image\")\n",
    "plt.imshow(X_train[i].reshape(28,28))\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Encoded - Decoded Image\")\n",
    "img = decoder_train[i].reshape(28,28)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b8b7f0ba90>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RbYXQsbo9kjv9jxaEOdn9td3TZ8rotjddnJF+WCUsyz7fsV7sD/WlUPCnpq930kdsHZjr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\nCCES4f8BAkRTPeq1GwUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b8b7db9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Test Data \n",
    "i = 2\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Original Image\")\n",
    "plt.imshow(X_test[i].reshape(28,28))\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Encoded - Decoded Image\")\n",
    "img = decoder_test[i].reshape(28,28)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Model with reduced dimensions k = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy using Decision Trees: 1.0\n",
      "Test Accuracy using Decision Trees: 0.6737\n"
     ]
    }
   ],
   "source": [
    "#Building the Decision Tree Model\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf.fit(encoder_train,Y_train)\n",
    "\n",
    "#Accuracy on test data\n",
    "print(\"Train Accuracy using Decision Trees:\",clf.score(encoder_train,Y_train))\n",
    "\n",
    "#Accuracy on test data\n",
    "print(\"Test Accuracy using Decision Trees:\",clf.score(encoder_test,Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Epoch: 1 \t Average Train Eror: 0.09033134788274765\n",
      "\n",
      " Test Error 0.08698132\n",
      "\n",
      " Epoch: 6 \t Average Train Eror: 0.045268278494477274\n",
      "\n",
      " Test Error 0.045523237\n"
     ]
    }
   ],
   "source": [
    "inputs = 784\n",
    "hidden_layer1_units = 100\n",
    "output_layer_units = inputs\n",
    "epochs = 10\n",
    "batch_size = 100\n",
    "learning_rate = 0.001\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape = [None, inputs]) #flattened shape\n",
    "\n",
    "tf.set_random_seed(42)\n",
    "W = {\n",
    "    'encoder_h1': tf.Variable(tf.random_normal([inputs, hidden_layer1_units])),\n",
    "    'decoder_h1':tf.Variable(tf.random_normal([hidden_layer1_units, output_layer_units]))\n",
    "}\n",
    "\n",
    "B = {\n",
    "    'encoder_h1':tf.Variable(tf.random_normal([hidden_layer1_units])),\n",
    "    'decoder_h1':tf.Variable(tf.random_normal([output_layer_units]))\n",
    "}\n",
    "\n",
    "\n",
    "encoder = tf.matmul(X,W['encoder_h1']) +  B['encoder_h1']\n",
    "encoder = tf.sigmoid(encoder) # using sigmoid activation\n",
    "decoder = tf.matmul(encoder,W['decoder_h1'])  + B['decoder_h1']\n",
    "decoder = tf.sigmoid(decoder) # using sigmoid activation\n",
    "\n",
    "Y = X\n",
    "loss = tf.reduce_mean(tf.pow(decoder - Y, 2))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\n",
    "\n",
    "def get_batches(batch_size,x):\n",
    "        indexes = list(range(x.shape[0]))\n",
    "        random.shuffle(indexes)\n",
    "        ind = indexes[:batch_size]\n",
    "        return(x[ind])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for epoch in range(epochs):\n",
    "        train_c = 0                                                       #cost\n",
    "        total_batch = int(X_train.shape[0]/batch_size)\n",
    "        for i in range(total_batch):\n",
    "            batch_x = get_batches(batch_size, X_train)\n",
    "            _,train_c = sess.run([optimizer, loss], feed_dict = {X: batch_x})\n",
    "            train_c += train_c/batch_size    \n",
    "        if epoch % 5 == 0:\n",
    "            print(\"\\n Epoch:\",epoch+1,\"\\t Average Train Eror:\",train_c)  \n",
    "            test_c = sess.run(loss,feed_dict = {X:X_test})\n",
    "            print(\"\\n Test Error\", test_c)    \n",
    "    encoder_train_100, decoder_train_100 = sess.run([encoder,decoder],feed_dict = {X:X_train})\n",
    "    encoder_test_100, decoder_test_100 = sess.run([encoder,decoder],feed_dict = {X:X_test}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((55000, 100), (55000, 784), (10000, 100), (10000, 784))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_train_100.shape, decoder_train_100.shape,encoder_test_100.shape, decoder_test_100.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Model with reduced dimensions k = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy using Decision Trees: 1.0\n",
      "Test Accuracy using Decision Trees: 0.7143\n"
     ]
    }
   ],
   "source": [
    "#Building the Decision Tree Model\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf.fit(encoder_train_100,Y_train)\n",
    "\n",
    "#Accuracy on test data\n",
    "print(\"Train Accuracy using Decision Trees:\",clf.score(encoder_train_100,Y_train))\n",
    "\n",
    "#Accuracy on test data\n",
    "print(\"Test Accuracy using Decision Trees:\",clf.score(encoder_test_100,Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b8b8641400>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WDzqh2ADQti/0nll1y56gjGX/WlYMQ5PHl/qb7g4fE+4Tmo+MwaPqmfwBjC4Py/NjvpdE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\n8inbgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b8b7db99e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train Data\n",
    "i = 1\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Original Image\")\n",
    "plt.imshow(X_train[i].reshape(28,28))\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Encoded - Decoded Image\")\n",
    "img = decoder_train_100[i].reshape(28,28)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b8bb8bc208>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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brbMXAOrr7LXZlfGdwPmmeCUJAL/2+dw63xE9vcH2V1et9w88PX7hTskMMc22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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b8b85dbdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test Data\n",
    "i = 1\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Original Image\")\n",
    "plt.imshow(X_test[i].reshape(28,28))\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Encoded - Decoded Image\")\n",
    "img = decoder_test_100[i].reshape(28,28)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Epoch: 1 \t Average Train Eror: 0.09868791870772839\n",
      "\n",
      " Test Error 0.09685414\n",
      "\n",
      " Epoch: 6 \t Average Train Eror: 0.04993384335190058\n",
      "\n",
      " Test Error 0.055533092\n"
     ]
    }
   ],
   "source": [
    "inputs = 784\n",
    "hidden_layer1_units = 200\n",
    "output_layer_units = inputs\n",
    "epochs = 10\n",
    "batch_size = 100\n",
    "learning_rate = 0.001\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape = [None, inputs]) #flattened shape\n",
    "\n",
    "tf.set_random_seed(42)\n",
    "W = {\n",
    "    'encoder_h1': tf.Variable(tf.random_normal([inputs, hidden_layer1_units])),\n",
    "    'decoder_h1':tf.Variable(tf.random_normal([hidden_layer1_units, output_layer_units]))\n",
    "}\n",
    "\n",
    "B = {\n",
    "    'encoder_h1':tf.Variable(tf.random_normal([hidden_layer1_units])),\n",
    "    'decoder_h1':tf.Variable(tf.random_normal([output_layer_units]))\n",
    "}\n",
    "\n",
    "\n",
    "encoder = tf.matmul(X,W['encoder_h1']) +  B['encoder_h1']\n",
    "encoder = tf.sigmoid(encoder) # using softmax activation\n",
    "decoder = tf.matmul(encoder,W['decoder_h1'])  + B['decoder_h1']\n",
    "decoder = tf.sigmoid(decoder) # using softmax activation\n",
    "\n",
    "Y = X\n",
    "loss = tf.reduce_mean(tf.pow(decoder - Y, 2))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\n",
    "\n",
    "def get_batches(batch_size,x):\n",
    "        indexes = list(range(x.shape[0]))\n",
    "        random.shuffle(indexes)\n",
    "        ind = indexes[:batch_size]\n",
    "        return(x[ind])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for epoch in range(epochs):\n",
    "        train_c = 0                                                       #cost\n",
    "        total_batch = int(X_train.shape[0]/batch_size)\n",
    "        for i in range(total_batch):\n",
    "            batch_x = get_batches(batch_size, X_train)\n",
    "            _,train_c = sess.run([optimizer, loss], feed_dict = {X: batch_x})\n",
    "            train_c += train_c/batch_size    \n",
    "        if epoch % 5 == 0:\n",
    "            print(\"\\n Epoch:\",epoch+1,\"\\t Average Train Eror:\",train_c)  \n",
    "            test_c = sess.run(loss,feed_dict = {X:X_test})\n",
    "            print(\"\\n Test Error\", test_c)    \n",
    "    encoder_train_200, decoder_train_200 = sess.run([encoder,decoder],feed_dict = {X:X_train})\n",
    "    encoder_test_200, decoder_test_200 = sess.run([encoder,decoder],feed_dict = {X:X_test}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((55000, 200), (55000, 784))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_train_200.shape, decoder_train_200.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Model with reduced dimensions k = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy using Decision Trees: 1.0\n",
      "Test Accuracy using Decision Trees: 0.721\n"
     ]
    }
   ],
   "source": [
    "#Building the Decision Tree Model\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf.fit(encoder_train_200,Y_train)\n",
    "\n",
    "#Accuracy on test data\n",
    "print(\"Train Accuracy using Decision Trees:\",clf.score(encoder_train_200,Y_train))\n",
    "\n",
    "#Accuracy on test data\n",
    "print(\"Test Accuracy using Decision Trees:\",clf.score(encoder_test_200,Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b8bcac29e8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ArEi/C1FPSLdFphjWoJvZJjN7JP3cCWA1gAUAzgZwe1rsdgDnjFcjhRgPpNsi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\nReUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b8bb90b7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train Data\n",
    "i = 1\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Original Image\")\n",
    "plt.imshow(X_train[i].reshape(28,28))\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Encoded - Decoded Image\")\n",
    "img = decoder_train_200[i].reshape(28,28)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b8bbaf8e80>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/sLszb4/3lKf8AKxXon/WMr9v+fhMu5HNueWfXd1T3/q0kT3VcaCr6wWonVMA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\nJCHQoRNCSEKgQyeEkIRAh04IIQnhfwAccA8Nzl5kqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b8bb90b860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test Data\n",
    "i = 1\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Original Image\")\n",
    "plt.imshow(X_test[i].reshape(28,28))\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Encoded - Decoded Image\")\n",
    "img = decoder_test_200[i].reshape(28,28)\n",
    "plt.imshow(img)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}