python/ConvolutionalAutoencoder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D, UpSampling2D\n",
    "import matplotlib.pyplot as plt\n",
    "from keras import backend as K\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1) #transform 2D 28x28 matrix to 3D (28x28x1) matrix\n",
    "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "\n",
    "x_train /= 255 #inputs have to be between [0, 1]\n",
    "x_test /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 16)        160       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 28, 28, 16)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 14, 14, 2)         290       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 14, 14, 2)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 2)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 7, 7, 2)           38        \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 7, 7, 2)           0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 14, 14, 2)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 14, 14, 16)        304       \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 14, 14, 16)        0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 28, 28, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 28, 28, 1)         145       \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 28, 28, 1)         0         \n",
      "=================================================================\n",
      "Total params: 937\n",
      "Trainable params: 937\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "#1st convolution layer\n",
    "model.add(Conv2D(16, (3, 3) #16 is number of filters and (3, 3) is the size of the filter.\n",
    "    , padding='same', input_shape=(28,28,1)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2), padding='same'))\n",
    "\n",
    "#2nd convolution layer\n",
    "model.add(Conv2D(2,(3, 3), padding='same')) # apply 2 filters sized of (3x3)\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2), padding='same'))\n",
    "\n",
    "#-------------------------\n",
    "\n",
    "#3rd convolution layer\n",
    "model.add(Conv2D(2,(3, 3), padding='same')) # apply 2 filters sized of (3x3)\n",
    "model.add(Activation('relu'))\n",
    "model.add(UpSampling2D((2, 2)))\n",
    "\n",
    "#4rd convolution layer\n",
    "model.add(Conv2D(16,(3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(UpSampling2D((2, 2)))\n",
    "\n",
    "#-------------------------\n",
    "\n",
    "model.add(Conv2D(1,(3, 3), padding='same'))\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/3\n",
      "60000/60000 [==============================] - 93s 2ms/step - loss: 0.1378 - val_loss: 0.1055\n",
      "Epoch 2/3\n",
      "60000/60000 [==============================] - 95s 2ms/step - loss: 0.1017 - val_loss: 0.1000\n",
      "Epoch 3/3\n",
      "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0968 - val_loss: 0.0926\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1aba1680b38>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
    "\n",
    "model.fit(x_train, x_train\n",
    "    , epochs=3\n",
    "    , validation_data=(x_test, x_test)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "restored_imgs = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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CIvxAUIQfCIrwA0ERfiCo/wKuzBi/F4C8NQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba1fe5400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba1fbb160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/2moirTTksMzd75T0gKRvZW9vUZ0fSPqyhpdx65f0vTKbyVaWfkXSt939dJm9jDRKX6W8bmWEv09S54jr8yQdKaGPUbn7kezngKTXNPwxpZUcu7pIavZzoOR+/p+7H3P3K+4+JOmHKvG1y1aWfkXSFnd/Nbu59NdutL7Ket3KCP/7km43sy+ZWYekb0jaWkIfX2BmU7IvYmRmUyTdr9ZbfXirpHXZ5XWSXi+xl89plZWbK60srZJfu1Zb8bqUg3yyoYx/k9QmaZO7f7fpTYzCzP5cw3t7aXjG40/K7M3MXpZ0r4ZnfR2T9B1Jv5D0c0m3Sjos6evu3vQv3ir0dq+uc+XmBvVWaWXpd1Xia1fkiteF9MMRfkBMHOEHBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCo/wOnKsZLiDr8UAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba1eba0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADCFJREFUeJzt3WGoXPWZx/Hvs1n7wrQvDDUarGu6RVdLxGS5iBBZXarFFSHmRaUKS2RL0xcNWNgXK76psBREtt1dfFFIaWgqrbVEs2pdbYsspguLGjVU21grcre9a8hVFGoVKSbPvrgn5VbvnLmZOTNnkuf7gTAz55kz52HI7/7PzDlz/pGZSKrnz/puQFI/DL9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFWX4paL+fJobiwhPJ5QmLDNjNc8ba+SPiOsi4lcR8UpE3D7Oa0marhj13P6IWAO8DFwLLADPADdn5i9b1nHklyZsGiP/5cArmflqZv4B+AGwbYzXkzRF44T/POC3yx4vNMv+RETsjIiDEXFwjG1J6tg4X/ittGvxod36zNwN7AZ3+6VZMs7IvwCcv+zxJ4DXxmtH0rSME/5ngAsj4pMR8RHg88DD3bQladJG3u3PzPcjYhfwY2ANsCczf9FZZ5ImauRDfSNtzM/80sRN5SQfSacuwy8VZfilogy/VJThl4oy/FJRhl8qyvBLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFWX4paIMv1SU4ZeKmuoU3arnoosuGlh76aWXWte97bbbWuv33HPPSD1piSO/VJThl4oy/FJRhl8qyvBLRRl+qSjDLxU11nH+iJgH3gaOAe9n5lwXTen0sWXLloG148ePt667sLDQdTtapouTfP42M9/o4HUkTZG7/VJR44Y/gZ9ExLMRsbOLhiRNx7i7/Vsz87WIWA/8NCJeyswDy5/Q/FHwD4M0Y8Ya+TPzteZ2EdgPXL7Cc3Zn5pxfBkqzZeTwR8TaiPjYifvAZ4EXu2pM0mSNs9t/DrA/Ik68zvcz8/FOupI0cSOHPzNfBS7rsBedhjZv3jyw9s4777Suu3///q7b0TIe6pOKMvxSUYZfKsrwS0UZfqkowy8V5aW7NZZNmza11nft2jWwdu+993bdjk6CI79UlOGXijL8UlGGXyrK8EtFGX6pKMMvFeVxfo3l4osvbq2vXbt2YO3+++/vuh2dBEd+qSjDLxVl+KWiDL9UlOGXijL8UlGGXyoqMnN6G4uY3sY0FU8//XRr/eyzzx5YG3YtgGGX9tbKMjNW8zxHfqkowy8VZfilogy/VJThl4oy/FJRhl8qaujv+SNiD3ADsJiZm5pl64D7gY3APHBTZr41uTbVl40bN7bW5+bmWusvv/zywJrH8fu1mpH/O8B1H1h2O/BEZl4IPNE8lnQKGRr+zDwAvPmBxduAvc39vcCNHfclacJG/cx/TmYeAWhu13fXkqRpmPg1/CJiJ7Bz0tuRdHJGHfmPRsQGgOZ2cdATM3N3Zs5lZvs3Q5KmatTwPwzsaO7vAB7qph1J0zI0/BFxH/A/wF9FxEJEfAG4C7g2In4NXNs8lnQKGfqZPzNvHlD6TMe9aAZdddVVY63/+uuvd9SJuuYZflJRhl8qyvBLRRl+qSjDLxVl+KWinKJbrS699NKx1r/77rs76kRdc+SXijL8UlGGXyrK8EtFGX6pKMMvFWX4paKcoru4K664orX+6KOPttbn5+db61u3bh1Ye++991rX1WicoltSK8MvFWX4paIMv1SU4ZeKMvxSUYZfKsrf8xd3zTXXtNbXrVvXWn/88cdb6x7Ln12O/FJRhl8qyvBLRRl+qSjDLxVl+KWiDL9U1NDj/BGxB7gBWMzMTc2yO4EvAifmX74jM/9zUk1qci677LLW+rDrPezbt6/LdjRFqxn5vwNct8Lyf83Mzc0/gy+dYoaGPzMPAG9OoRdJUzTOZ/5dEfHziNgTEWd11pGkqRg1/N8EPgVsBo4AXx/0xIjYGREHI+LgiNuSNAEjhT8zj2bmscw8DnwLuLzlubszcy4z50ZtUlL3Rgp/RGxY9nA78GI37UialtUc6rsPuBr4eEQsAF8Fro6IzUAC88CXJtijpAnwuv2nuXPPPbe1fujQodb6W2+91Vq/5JJLTronTZbX7ZfUyvBLRRl+qSjDLxVl+KWiDL9UlJfuPs3deuutrfX169e31h977LEOu9EsceSXijL8UlGGXyrK8EtFGX6pKMMvFWX4paI8zn+au+CCC8Zaf9hPenXqcuSXijL8UlGGXyrK8EtFGX6pKMMvFWX4paI8zn+au+GGG8Za/5FHHumoE80aR36pKMMvFWX4paIMv1SU4ZeKMvxSUYZfKmrocf6IOB/4LnAucBzYnZn/HhHrgPuBjcA8cFNm+uPvHlx55ZUDa8Om6FZdqxn53wf+MTMvAa4AvhwRnwZuB57IzAuBJ5rHkk4RQ8OfmUcy87nm/tvAYeA8YBuwt3naXuDGSTUpqXsn9Zk/IjYCW4CngHMy8wgs/YEA2ud9kjRTVn1uf0R8FHgA+Epm/i4iVrveTmDnaO1JmpRVjfwRcQZLwf9eZj7YLD4aERua+gZgcaV1M3N3Zs5l5lwXDUvqxtDwx9IQ/23gcGZ+Y1npYWBHc38H8FD37UmalNXs9m8F/h54ISIONcvuAO4CfhgRXwB+A3xuMi1qmO3btw+srVmzpnXd559/vrV+4MCBkXrS7Bsa/sz8b2DQB/zPdNuOpGnxDD+pKMMvFWX4paIMv1SU4ZeKMvxSUV66+xRw5plnttavv/76kV973759rfVjx46N/NqabY78UlGGXyrK8EtFGX6pKMMvFWX4paIMv1RUZOb0NhYxvY2dRs4444zW+pNPPjmwtri44gWW/uiWW25prb/77rutdc2ezFzVNfYc+aWiDL9UlOGXijL8UlGGXyrK8EtFGX6pKI/zS6cZj/NLamX4paIMv1SU4ZeKMvxSUYZfKsrwS0UNDX9EnB8R/xURhyPiFxFxW7P8zoj4v4g41Pwb/eLxkqZu6Ek+EbEB2JCZz0XEx4BngRuBm4DfZ+a/rHpjnuQjTdxqT/IZOmNPZh4BjjT3346Iw8B547UnqW8n9Zk/IjYCW4CnmkW7IuLnEbEnIs4asM7OiDgYEQfH6lRSp1Z9bn9EfBR4EvhaZj4YEecAbwAJ/DNLHw3+YchruNsvTdhqd/tXFf6IOAP4EfDjzPzGCvWNwI8yc9OQ1zH80oR19sOeiAjg28Dh5cFvvgg8YTvw4sk2Kak/q/m2/0rgZ8ALwPFm8R3AzcBmlnb754EvNV8Otr2WI780YZ3u9nfF8EuT5+/5JbUy/FJRhl8qyvBLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFTX0Ap4dewP432WPP94sm0Wz2tus9gX2Nqoue7tgtU+c6u/5P7TxiIOZOddbAy1mtbdZ7QvsbVR99eZuv1SU4ZeK6jv8u3vefptZ7W1W+wJ7G1UvvfX6mV9Sf/oe+SX1pJfwR8R1EfGriHglIm7vo4dBImI+Il5oZh7udYqxZhq0xYh4cdmydRHx04j4dXO74jRpPfU2EzM3t8ws3et7N2szXk99tz8i1gAvA9cCC8AzwM2Z+cupNjJARMwDc5nZ+zHhiPgb4PfAd0/MhhQRdwNvZuZdzR/OszLzn2aktzs5yZmbJ9TboJmlb6XH967LGa+70MfIfznwSma+mpl/AH4AbOuhj5mXmQeANz+weBuwt7m/l6X/PFM3oLeZkJlHMvO55v7bwImZpXt971r66kUf4T8P+O2yxwvM1pTfCfwkIp6NiJ19N7OCc07MjNTcru+5nw8aOnPzNH1gZumZee9GmfG6a32Ef6XZRGbpkMPWzPxr4O+ALze7t1qdbwKfYmkatyPA1/tspplZ+gHgK5n5uz57WW6Fvnp53/oI/wJw/rLHnwBe66GPFWXma83tIrCfpY8ps+ToiUlSm9vFnvv5o8w8mpnHMvM48C16fO+amaUfAL6XmQ82i3t/71bqq6/3rY/wPwNcGBGfjIiPAJ8HHu6hjw+JiLXNFzFExFrgs8ze7MMPAzua+zuAh3rs5U/MyszNg2aWpuf3btZmvO7lJJ/mUMa/AWuAPZn5tak3sYKI+EuWRntY+sXj9/vsLSLuA65m6VdfR4GvAv8B/BD4C+A3wOcyc+pfvA3o7WpOcubmCfU2aGbpp+jxvetyxutO+vEMP6kmz/CTijL8UlGGXyrK8EtFGX6pKMMvFWX4paIMv1TU/wNPnZK3k8+kHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba1ef0ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba1e85cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba2320278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba2359ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba2381ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7zXhdyUE+2VDGv0vqkNTr7j9ueRMTMLPrNLa1l8bOePxVlb2Z2XZJd2rsrK9BSZsk/Yek30q6RtJhSevcveVfvOX0dqcucubmJvWWN7P0m6rwvStzxutS+uEIPyAmjvADgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxDU/wOtS2VJL3oRYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba1f0e470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    plt.imshow(x_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    plt.show()\n",
    "    \n",
    "    plt.imshow(restored_imgs[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"----------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 .  (?, 28, 28, 16)\n",
      "1 .  (?, 28, 28, 16)\n",
      "2 .  (?, 14, 14, 16)\n",
      "3 .  (?, 14, 14, 2)\n",
      "4 .  (?, 14, 14, 2)\n",
      "5 .  (?, 7, 7, 2)\n",
      "6 .  (?, 7, 7, 2)\n",
      "7 .  (?, 7, 7, 2)\n",
      "8 .  (?, 14, 14, 2)\n",
      "9 .  (?, 14, 14, 16)\n",
      "10 .  (?, 14, 14, 16)\n",
      "11 .  (?, 28, 28, 16)\n",
      "12 .  (?, 28, 28, 1)\n",
      "13 .  (?, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "layers = len(model.layers)\n",
    "\n",
    "for i in range(layers):\n",
    "    print(i, \". \", model.layers[i].output.get_shape())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#layer[7] is activation_3 (Activation), it is compressed representation\n",
    "get_3rd_layer_output = K.function([model.layers[0].input], [model.layers[7].output])\n",
    "compressed = get_3rd_layer_output([x_test])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#layer[7] is size of (None, 7, 7, 2). this means 2 different 7x7 sized matrixes. We will flatten these matrixes.\n",
    "compressed = compressed.reshape(10000,7*7*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.contrib.factorization.python.ops import clustering_ops\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-13-d950a9d154d3>:4: KMeansClustering.__init__ (from tensorflow.contrib.learn.python.learn.estimators.kmeans) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.contrib.factorization.KMeansClustering instead of tf.contrib.learn.KMeansClustering. It has a similar interface, but uses the tf.estimator.Estimator API instead of tf.contrib.learn.Estimator.\n",
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: C:\\Users\\IS96273\\AppData\\Local\\Temp\\tmp0yqpstfv\n",
      "INFO:tensorflow:Using config: {'_log_step_count_steps': 100, '_tf_random_seed': None, '_keep_checkpoint_max': 5, '_save_checkpoints_secs': 600, '_save_checkpoints_steps': None, '_save_summary_steps': 100, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1\n",
      "}\n",
      ", '_task_type': None, '_master': '', '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x000001ABB3D170B8>, '_environment': 'local', '_evaluation_master': '', '_keep_checkpoint_every_n_hours': 10000, '_is_chief': True, '_num_ps_replicas': 0, '_session_config': None, '_model_dir': 'C:\\\\Users\\\\IS96273\\\\AppData\\\\Local\\\\Temp\\\\tmp0yqpstfv', '_num_worker_replicas': 0, '_task_id': 0}\n"
     ]
    }
   ],
   "source": [
    "unsupervised_model = tf.contrib.learn.KMeansClustering(\n",
    "    10 #num of clusters\n",
    "    , distance_metric = clustering_ops.SQUARED_EUCLIDEAN_DISTANCE\n",
    "    , initial_clusters=tf.contrib.learn.KMeansClustering.RANDOM_INIT\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_input_fn():\n",
    "    data = tf.constant(compressed, tf.float32)\n",
    "    return (data, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\IS96273\\AppData\\Local\\Continuum\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\factorization\\python\\ops\\clustering_ops.py:196: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into C:\\Users\\IS96273\\AppData\\Local\\Temp\\tmp0yqpstfv\\model.ckpt.\n",
      "INFO:tensorflow:step = 1, loss = 135367.0\n",
      "INFO:tensorflow:global_step/sec: 172.736\n",
      "INFO:tensorflow:step = 101, loss = 77365.0 (0.581 sec)\n",
      "INFO:tensorflow:global_step/sec: 347.942\n",
      "INFO:tensorflow:step = 201, loss = 77066.8 (0.287 sec)\n",
      "INFO:tensorflow:global_step/sec: 346.51\n",
      "INFO:tensorflow:step = 301, loss = 76946.8 (0.288 sec)\n",
      "INFO:tensorflow:global_step/sec: 288.412\n",
      "INFO:tensorflow:step = 401, loss = 76875.8 (0.348 sec)\n",
      "INFO:tensorflow:global_step/sec: 294.093\n",
      "INFO:tensorflow:step = 501, loss = 76829.3 (0.338 sec)\n",
      "INFO:tensorflow:global_step/sec: 302.266\n",
      "INFO:tensorflow:step = 601, loss = 76795.9 (0.332 sec)\n",
      "INFO:tensorflow:global_step/sec: 342.49\n",
      "INFO:tensorflow:step = 701, loss = 76769.0 (0.292 sec)\n",
      "INFO:tensorflow:global_step/sec: 340.934\n",
      "INFO:tensorflow:step = 801, loss = 76746.6 (0.293 sec)\n",
      "INFO:tensorflow:global_step/sec: 322.785\n",
      "INFO:tensorflow:step = 901, loss = 76728.8 (0.310 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 1000 into C:\\Users\\IS96273\\AppData\\Local\\Temp\\tmp0yqpstfv\\model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 76713.7.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KMeansClustering(params={'distance_metric': 'squared_euclidean', 'num_clusters': 10, 'kmeans_plus_plus_num_retries': 2, 'mini_batch_steps_per_iteration': 1, 'training_initial_clusters': 'random', 'use_mini_batch': True, 'random_seed': 0, 'relative_tolerance': None})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unsupervised_model.fit(input_fn=train_input_fn, steps=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\IS96273\\AppData\\Local\\Temp\\tmp0yqpstfv\\model.ckpt-1000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba1d4fda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb46515c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba23ec1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba23ce470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb425ff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb46adbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb46e84a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb46b0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb47598d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb4780ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb47cea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb4808470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb47a1cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb487b898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb48c1eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb48f0940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb492b240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb4963550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb499f860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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38//cfY+7f+PuhyT9UiU+d9nK0i9LWuDur2R3l/7c9ddXWc9bGeFfLek8M/u+mbVJ+rGkZSX08S1mdlL2QYzM7CRJP1DrrT68TNLU7PpUSUtL7OVvtMrKzXkrS6vk567VVrwu5Us+2VTGf0oaIGmuu89qehP9MLO/U8/RXuo543Fhmb2Z2SJJneo562uPpJ9LelXSi5LOkvShpBvcvekfvOX01qkjXLm5Qb3lrSz9jkp87uq54nVd+uEbfkBMfMMPCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQ/wdIuH1E36BMiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb49e3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb5951908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VOeN1Kf1whh8QE2f4AUERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8I6r/Lh2kFKuWcgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb7130400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb49e8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb71a37f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb71e9f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb7217f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb72337b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb71703c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb7211198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb71cf7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb718ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb7163f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb59621d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb49e44e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb49ce588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb4924f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BvZylWUZuLjWytAp+75ptxOtCTvLJDmX8j6T+kpa6+8KGN9EDM7tcXVt7qeuKx5VF9mZmqyRNVtdVXwckzZe0TtLvJP2LpL9J+rG7N/yLtxK9TVYvR26uU2+lRpZ+XwW+d3mOeJ1LP5zhB8TEGX5AUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4L6J06kUyRBc/GjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb72559e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb47efb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb48f4dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb4886a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb495bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb48c69b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb468f358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADYFJREFUeJzt3X2oXPWdx/HPR21RbP+I1DyQZFdTZLVGtMtV1wfEVSzZEIgRIlGQLJRGpIKFIMaARpFVWdtkC0IlxdAr1jSFNiZ/VI2KElcWMcZg0mS1lyTb3E1IGlLS+ID14bt/3ONyG+/8ZpynM8n3/YIwM+d7zpwvQz5zztzfmfk5IgQgn1PqbgBAPQg/kBThB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkTuvnzmxzOSHQYxHhVtbr6Mhve47td22P2F7WyXMB6C+3e22/7VMlvSfpBkmjkt6UdEtE7Cxsw5Ef6LF+HPkvkzQSEbsj4q+SfiVpfgfPB6CPOgn/dEn7xj0erZb9DdtLbG+xvaWDfQHosk7+4DfRqcWXTusjYrWk1RKn/cAg6eTIPypp5rjHMyTt76wdAP3SSfjflHSe7XNtf13SIkkbu9MWgF5r+7Q/Ij61faekFySdKmlNRPy+a50B6Km2h/ra2hmf+YGe68tFPgBOXIQfSIrwA0kRfiApwg8kRfiBpAg/kBThB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8k1fYU3ZJke6+kY5I+k/RpRAx1oykAvddR+Cv/HBGHu/A8APqI034gqU7DH5I22X7L9pJuNASgPzo97b8qIvbbnizpRdv/HRGbx69QvSnwxgAMGEdEd57IfkDS+xHx48I63dkZgIYiwq2s1/Zpv+0zbX/zi/uSvidpR7vPB6C/OjntnyJpve0vnueZiHi+K10B6Lmunfa3tLOT9LR/0qRJxfqtt95arC9btqxYnzFjxlfuqVXPPvtssT48PNzR9ui/np/2AzixEX4gKcIPJEX4gaQIP5AU4QeSYqivRWeccUbD2nPPPVfc9pprrulo36+++mqx/s477zSsvfvuu8VtFyxYUKxfccUVxfptt91WrDMU2H8M9QEoIvxAUoQfSIrwA0kRfiApwg8kRfiBpBjnb9Fdd93VsLZq1aritnv27CnWX3nllWL9jjvuKNY/+eSTYr3klFPK7//PPPNMsd7sOoFFixY1rK1fv764LdrDOD+AIsIPJEX4gaQIP5AU4QeSIvxAUoQfSIpx/haNjIw0rM2aNau47fnnn1+sv/fee2311A+l3zGQpKeffrpYv+iiixrWrr766uK2hw4dKtYxMcb5ARQRfiApwg8kRfiBpAg/kBThB5Ii/EBSpzVbwfYaSfMkHYqI2dWysyStk3SOpL2Sbo6IP/euzRPb5ZdfXqwP8jj/Rx99VKzfd999xfpLL73UsNbsN/2vvPLKYh2daeXI/wtJc45btkzSyxFxnqSXq8cATiBNwx8RmyUdOW7xfEnD1f1hSTd2uS8APdbuZ/4pEXFAkqrbyd1rCUA/NP3M3ynbSyQt6fV+AHw17R75D9qeJknVbcNvYETE6ogYioihNvcFoAfaDf9GSYur+4slbehOOwD6pWn4ba+V9F+S/sH2qO3vS3pU0g22/yDphuoxgBMI3+dv0bx58xrW1q1bV9z26NGjxfrcuXOL9W3bthXrg+zGGxsPBD3xxBPFbc8999xivdk1CFnxfX4ARYQfSIrwA0kRfiApwg8kRfiBpBjq64K77767WH/wwQeL9WZDgbfffnuxvnHjxmK9E7Nnzy7WH3nkkWK99JXfF154objtQw89VKw//vjjxXpWDPUBKCL8QFKEH0iK8ANJEX4gKcIPJEX4gaQY5++D0teBJWnt2rXFerNpskvbr1ixorjt7t27i/Vm02hv3ry5WF+5cmXDWrOv5N5zzz3F+tSpU4v1I0eO/93ZHBjnB1BE+IGkCD+QFOEHkiL8QFKEH0iK8ANJMc4/AC688MJi/f777y/WFy5c2LD2wQcfFLd9++23i/XXXnutWL/33nuL9U2bNjWsLVtWntx569atxfrkyeUpIg8fPlysn6wY5wdQRPiBpAg/kBThB5Ii/EBShB9IivADSTUd57e9RtI8SYciYna17AFJP5D0p2q15RHxu6Y7Y5y/LXZ52PaCCy5oWBseHi5u22ysfObMmcV6M6X/X+vXry9ue9NNNxXrCxYsKNY3bNhQrJ+sujnO/wtJcyZYvioiLqn+NQ0+gMHSNPwRsVlSzp9EAU5inXzmv9P2O7bX2J7UtY4A9EW74f+ZpG9LukTSAUk/abSi7SW2t9je0ua+APRAW+GPiIMR8VlEfC7p55IuK6y7OiKGImKo3SYBdF9b4bc9bdzDBZJ2dKcdAP1yWrMVbK+VdK2kb9kelbRC0rW2L5EUkvZKKs8hDWDgNA1/RNwyweIne9ALGmh2LcbOnTsb1i699NLitmeffXaxPn369GL94YcfLtbnzJlolHjMrl27its2U7q+Qco7zt8qrvADkiL8QFKEH0iK8ANJEX4gKcIPJMVPd6MjS5cuLdYfe+yxhrVmQ3Xr1q0r1vfv31+sz507t1g/WfHT3QCKCD+QFOEHkiL8QFKEH0iK8ANJEX4gqaZf6QV65cMPPyzW9+3bV6zv2MFvyHSCIz+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJMU4P05YR48erbuFExpHfiApwg8kRfiBpAg/kBThB5Ii/EBShB9Iquk4v+2Zkp6SNFXS55JWR8RPbZ8laZ2kcyTtlXRzRPy5d63iZDNlypRi/frrry/WX3/99W62k04rR/5PJS2NiAsk/ZOkH9r+jqRlkl6OiPMkvVw9BnCCaBr+iDgQEVur+8ck7ZI0XdJ8ScPVasOSbuxVkwC67yt95rd9jqTvSnpD0pSIOCCNvUFImtzt5gD0TsvX9tv+hqTfSPpRRPzFbmk6MNleImlJe+0B6JWWjvy2v6ax4P8yIn5bLT5oe1pVnybp0ETbRsTqiBiKiKFuNAygO5qG32OH+Ccl7YqIleNKGyUtru4vlrSh++0B6JVWTvuvknSbpO22t1XLlkt6VNKvbX9f0h8lLexNizhZzZo1q1g//fTTi/Xnn3++m+2k0zT8EfGfkhp9wC8PxAIYWFzhByRF+IGkCD+QFOEHkiL8QFKEH0iKn+5GbZYvX97R9qOjo13qJCeO/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOP8qM3FF19crO/bt69Y//jjj7vZTjoc+YGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcb5UZujR48W69ddd12xfuzYsW62kw5HfiApwg8kRfiBpAg/kBThB5Ii/EBShB9Iquk4v+2Zkp6SNFXS55JWR8RPbT8g6QeS/lStujwifterRjGYtm/fXqzv2bOnYW3Tpk3FbUdGRtrqCa1p5SKfTyUtjYittr8p6S3bL1a1VRHx4961B6BXmoY/Ig5IOlDdP2Z7l6TpvW4MQG99pc/8ts+R9F1Jb1SL7rT9ju01tic12GaJ7S22t3TUKYCuajn8tr8h6TeSfhQRf5H0M0nflnSJxs4MfjLRdhGxOiKGImKoC/0C6JKWwm/7axoL/i8j4reSFBEHI+KziPhc0s8lXda7NgF0W9Pw27akJyXtioiV45ZPG7faAkk7ut8egF5xRJRXsK+W9Jqk7Rob6pOk5ZJu0dgpf0jaK+n26o+Dpecq7wxAxyLCrazXNPzdRPiB3ms1/FzhByRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSKrfU3QflvQ/4x5/q1o2iAa1t0HtS6K3dnWzt79vdcW+fp//Szu3twzqb/sNam+D2pdEb+2qqzdO+4GkCD+QVN3hX13z/ksGtbdB7Uuit3bV0lutn/kB1KfuIz+AmtQSfttzbL9re8T2sjp6aMT2XtvbbW+re4qxahq0Q7Z3jFt2lu0Xbf+hup1wmrSaenvA9v9Wr90223Nr6m2m7Vds77L9e9t3Vctrfe0KfdXyuvX9tN/2qZLek3SDpFFJb0q6JSJ29rWRBmzvlTQUEbWPCdu+RtL7kp6KiNnVsn+XdCQiHq3eOCdFxD0D0tsDkt6ve+bmakKZaeNnlpZ0o6R/VY2vXaGvm1XD61bHkf8ySSMRsTsi/irpV5Lm19DHwIuIzZKOHLd4vqTh6v6wxv7z9F2D3gZCRByIiK3V/WOSvphZutbXrtBXLeoI/3RJ+8Y9HtVgTfkdkjbZfsv2krqbmcCUL2ZGqm4n19zP8ZrO3NxPx80sPTCvXTszXndbHeGfaDaRQRpyuCoi/lHSv0j6YXV6i9a0NHNzv0wws/RAaHfG626rI/yjkmaOezxD0v4a+phQROyvbg9JWq/Bm3344BeTpFa3h2ru5/8N0szNE80srQF47QZpxus6wv+mpPNsn2v765IWSdpYQx9fYvvM6g8xsn2mpO9p8GYf3ihpcXV/saQNNfbyNwZl5uZGM0ur5tdu0Ga8ruUin2oo4z8knSppTUT8W9+bmIDtWRo72ktj33h8ps7ebK+VdK3GvvV1UNIKSc9K+rWkv5P0R0kLI6Lvf3hr0Nu1+oozN/eot0YzS7+hGl+7bs543ZV+uMIPyIkr/ICkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJPV/GS4kHocYj+MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb4685dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADNtJREFUeJzt3W+IXPW9x/HP59pENC2oBNOYbrVt9KKo2LJIIKVY/1RbirFCTQUxhdrtgwqNBjTqgwThSpT+fVTY2NAIrWmh7TUPyr0R8U8vqLhqibYxqYZNuzVsWqLWJZqi+fbBnly2cec3k9kzc2bzfb8gzMz5njPny5DPnnP2d2Z/jggByOc/mm4AQDMIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpD7Uz53Z5nZCoMciwp2sN6cjv+1rbO+2/art9XN5LwD95W7v7bd9kqQ9kq6SNCHpOUk3RsQfC9tw5Ad6rB9H/kslvRoReyPin5K2SVo1h/cD0EdzCf8ySX+Z8XqiWvZvbI/YHrM9Nod9AajZXH7hN9upxQdO6yNiVNKoxGk/MEjmcuSfkDQ04/XHJL0+t3YA9Mtcwv+cpHNtf8L2Qklfk7S9nrYA9FrXp/0R8Z7tWyX9r6STJG2JiD/U1hmAnup6qK+rnXHND/RcX27yATB/EX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kBThB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5BU11N0S5LtcUlvS3pf0nsRMVxHUwB6b07hr3w+Iv5ew/sA6CNO+4Gk5hr+kLTD9vO2R+poCEB/zPW0f2VEvG77TEmP2n4lIp6auUL1Q4EfDMCAcUTU80b2RklTEfHdwjr17AxASxHhTtbr+rTf9iLbHzn6XNIXJL3c7fsB6K+5nPYvkfQb20ff5+cR8T+1dAWg52o77e9oZ0lP+9etW1esr169ulgfHi7fPlH9AJ7Vzp07i9uuXbu2WH/88ceLdQyenp/2A5jfCD+QFOEHkiL8QFKEH0iK8ANJ1fGtvhQWLFjQsrZ58+bitldffXWx/uCDDxbr119/fbG+YsWKlrVNmzYVt7355puLdYb6Tlwc+YGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKb7S26FTTz21ZW1qaqq47fnnn1+s7969u6ueOrF+/fpi/bbbbivWlyxZUmc76AO+0gugiPADSRF+ICnCDyRF+IGkCD+QFOEHkmKcv0Mnn3xyy9revXuL21500UXF+sGDB7vqqRNDQ0PF+r59+4r1yy+/vFh/4oknjrcl9Bjj/ACKCD+QFOEHkiL8QFKEH0iK8ANJEX4gqbZ/t9/2FklflnQgIi6slp0h6ReSzpE0LumGiHijd2027/Dhwy1rV1xxRXHbQ4cO1d1O35x99tlNt4Ae6eTI/1NJ1xyzbL2kxyLiXEmPVa8BzCNtwx8RT0k69ha0VZK2Vs+3Srqu5r4A9Fi31/xLImK/JFWPZ9bXEoB+6PlcfbZHJI30ej8Ajk+3R/5J20slqXo80GrFiBiNiOGIGO5yXwB6oNvwb5e0pnq+RtIj9bQDoF/aht/2w5KelvSftidsf0PSJklX2f6TpKuq1wDmkbbX/BFxY4tSeXA7kVdeeaXpFloq/R0C5MYdfkBShB9IivADSRF+ICnCDyRF+IGken57L5q1evXqOW0/Pj5eTyMYOBz5gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApxvlPcIsXLy7Wn3766WL9ySefrLMdDBCO/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOP8J4DzzjuvZe2mm24qbnvffffV3Q7mCY78QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5CUI6K8gr1F0pclHYiIC6tlGyV9U9LfqtXujojftt2ZXd4ZurJjx46WtSuvvLK47Ysvvlisv/nmm8X6W2+9Vaw/8MADLWvPPPNMcVt0JyLcyXqdHPl/KumaWZb/ICIuqf61DT6AwdI2/BHxlKSDfegFQB/N5Zr/Vts7bW+xfXptHQHoi27D/2NJn5J0iaT9kr7XakXbI7bHbI91uS8APdBV+CNiMiLej4gjkjZLurSw7mhEDEfEcLdNAqhfV+G3vXTGy69IermedgD0S9uv9Np+WNJlkhbbnpC0QdJlti+RFJLGJX2rhz0C6IG24/y17oxx/p44dOhQy9rk5GRx29dee61Yt8tDxqecckqxvmLFipa1iYmJ4rYXXHBBsT41NVWsZ1XnOD+AExDhB5Ii/EBShB9IivADSRF+ICmG+k4Ay5Yta1lrNxzW7iu57SxYsKBYP+uss1rW7rjjjuK2K1euLNZvueWWYn1sLOcd5Qz1ASgi/EBShB9IivADSRF+ICnCDyRF+IGkGOfHwLrnnnuK9Xb3CVx88cUta/v27euqp/mAcX4ARYQfSIrwA0kRfiApwg8kRfiBpAg/kBTj/Ji32k0vXpq6/M4776y7nYHBOD+AIsIPJEX4gaQIP5AU4QeSIvxAUoQfSKrtOL/tIUkPSfqopCOSRiPiR7bPkPQLSedIGpd0Q0S80ea9GOdHbe66665ifcOGDS1rp512WnHbd999t6ueBkGd4/zvSVoXEedLWiHp27YvkLRe0mMRca6kx6rXAOaJtuGPiP0R8UL1/G1JuyQtk7RK0tZqta2SrutVkwDqd1zX/LbPkfRpSc9KWhIR+6XpHxCSzqy7OQC986FOV7T9YUm/krQ2Iv5hd3RZIdsjkka6aw9Ar3R05Le9QNPB/1lE/LpaPGl7aVVfKunAbNtGxGhEDEfEcB0NA6hH2/B7+hD/E0m7IuL7M0rbJa2pnq+R9Ej97QHolU6G+j4r6XeSXtL0UJ8k3a3p6/5fSvq4pD9L+mpEHGzzXgz1oTbLly8v1vfs2dOytmjRouK277zzTlc9DYJOh/raXvNHxP9JavVmVxxPUwAGB3f4AUkRfiApwg8kRfiBpAg/kBThB5Lq+PZeYNC88UbxG+RogyM/kBThB5Ii/EBShB9IivADSRF+ICnCDyTFOD/mrWuvvbbpFuY1jvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kBTj/BhYCxcuLNZvv/32Yv3+++9vWTt8+HBXPZ1IOPIDSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFJtx/ltD0l6SNJHJR2RNBoRP7K9UdI3Jf2tWvXuiPhtrxrFiWdoaKhYv/fee4v15cuXF+vbtm1rWTty5Ehx2ww6ucnnPUnrIuIF2x+R9LztR6vaDyLiu71rD0CvtA1/ROyXtL96/rbtXZKW9boxAL11XNf8ts+R9GlJz1aLbrW90/YW26e32GbE9pjtsTl1CqBWHYff9ocl/UrS2oj4h6QfS/qUpEs0fWbwvdm2i4jRiBiOiOEa+gVQk47Cb3uBpoP/s4j4tSRFxGREvB8RRyRtlnRp79oEULe24bdtST+RtCsivj9j+dIZq31F0sv1twegVxwR5RXsz0r6naSXND3UJ0l3S7pR06f8IWlc0reqXw6W3qu8MwBzFhHuZL224a8T4Qd6r9Pwc4cfkBThB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gqX5P0f13SftmvF5cLRtEg9rboPYl0Vu36uzt7E5X7Ov3+T+wc3tsUP+236D2Nqh9SfTWraZ647QfSIrwA0k1Hf7RhvdfMqi9DWpfEr11q5HeGr3mB9Ccpo/8ABrSSPhtX2N7t+1Xba9voodWbI/bfsn275ueYqyaBu2A7ZdnLDvD9qO2/1Q9zjpNWkO9bbT91+qz+73tLzXU25Dtx23vsv0H29+pljf62RX6auRz6/tpv+2TJO2RdJWkCUnPSboxIv7Y10ZasD0uaTgiGh8Ttv05SVOSHoqIC6tlD0g6GBGbqh+cp0fEnQPS20ZJU03P3FxNKLN05szSkq6T9HU1+NkV+rpBDXxuTRz5L5X0akTsjYh/StomaVUDfQy8iHhK0sFjFq+StLV6vlXT/3n6rkVvAyEi9kfEC9XztyUdnVm60c+u0Fcjmgj/Mkl/mfF6QoM15XdI2mH7edsjTTcziyVHZ0aqHs9suJ9jtZ25uZ+OmVl6YD67bma8rlsT4Z9tNpFBGnJYGRGfkfRFSd+uTm/RmY5mbu6XWWaWHgjdznhdtybCPyFpaMbrj0l6vYE+ZhURr1ePByT9RoM3+/Dk0UlSq8cDDffz/wZp5ubZZpbWAHx2gzTjdRPhf07SubY/YXuhpK9J2t5AHx9ge1H1ixjZXiTpCxq82Ye3S1pTPV8j6ZEGe/k3gzJzc6uZpdXwZzdoM143cpNPNZTxQ0knSdoSEf/V9yZmYfuTmj7aS9PfePx5k73ZfljSZZr+1tekpA2S/lvSLyV9XNKfJX01Ivr+i7cWvV2m45y5uUe9tZpZ+lk1+NnVOeN1Lf1whx+QE3f4AUkRfiApwg8kRfiBpAg/kBThB5Ii/EBShB9I6l+ac903POsoaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb4254470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb478fef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = unsupervised_model.predict(input_fn=train_input_fn)\n",
    "\n",
    "index = 0\n",
    "for i in clusters:\n",
    "    current_cluster = i['cluster_idx']\n",
    "    features = x_test[index]\n",
    "    \n",
    "    if index < 200 and current_cluster == 5:\n",
    "        plt.imshow(x_test[index].reshape(28, 28))\n",
    "        plt.gray()\n",
    "        plt.show()\n",
    "    index = index + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}