08_Autoencoder/5_Basic_Autoencoder_TSNE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoencoder t-SNE\n",
    "- MNIST\n",
    "- Neural Network\n",
    "- 1 hidden layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Settings\n",
    "### 1) Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.init as init\n",
    "import torchvision.datasets as dset\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.autograd import Variable\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
    "from matplotlib.cbook import get_sample_data\n",
    "from PIL import ImageFile\n",
    "ImageFile.LOAD_TRUNCATED_IMAGES = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Set hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "learning_rate = 0.0002\n",
    "num_epoch = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data\n",
    "\n",
    "### 1) Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "[torch.FloatTensor of size 1x28x28]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_train = dset.MNIST(\"./\", train=True, transform=transforms.ToTensor(), target_transform=None, download=True)\n",
    "mnist_test = dset.MNIST(\"./\", train=False, transform=transforms.ToTensor(), target_transform=None, download=True)\n",
    "\n",
    "mnist_test[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Set DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(mnist_train,batch_size=batch_size, shuffle=True,num_workers=2,drop_last=True)\n",
    "test_loader = torch.utils.data.DataLoader(mnist_test,batch_size=batch_size, shuffle=False,num_workers=2,drop_last=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Model & Optimizer\n",
    "### 1) Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Autoencoder(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Autoencoder,self).__init__()\n",
    "        self.encoder = nn.Linear(28*28,20)\n",
    "        self.decoder = nn.Linear(20,28*28)   \n",
    "                \n",
    "    def forward(self,x):\n",
    "        x = x.view(batch_size,-1)\n",
    "        encoded = self.encoder(x)\n",
    "        out = self.decoder(encoded).view(batch_size,1,28,28)\n",
    "                \n",
    "        return encoded,out\n",
    "    \n",
    "model = Autoencoder().cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Loss func & Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss_func = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Train "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss_arr =[]\n",
    "\n",
    "for i in range(num_epoch):\n",
    "    for j,[image,label] in enumerate(train_loader):\n",
    "        x = Variable(image).cuda()\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        _,output = model.forward(x)\n",
    "        loss = loss_func(output,x)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "    if j % 1000 == 0:\n",
    "        print(loss)\n",
    "        loss_arr.append(loss.cpu().data.numpy()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Check with Train Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": 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ggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01bfb4d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01bc036f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01bfb52be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TSZsl3aKJVwY/muo4d9/p7oPuPphdTw2ge1oKv5n1aSL4v3D330iSu3/o7uPu/qmkn0m6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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01bc067c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01b517b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01bc055588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01bc02fa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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6WH1+xsx2S1rX64EB6K3P9drFzDZI+qKkP1YXPWJmb5jZdjO7psUxW81sp5ntzE4FBdA/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01b4059438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f018077c128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f018057ccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01805a1048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01807d24e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01807de3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f018054bac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01803a0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+uulRMztkZvvMbFmLY/aY2QEzO5A9zQPQPdMOv5ktlPRrST9w9xFJP5W0RdKAJp4Z/Giq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\nCkX4gUIRfqBQ/w/cQyrwjLTI8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01bc0a56d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0180511908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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tM7P5TY7ZYmZDZjaU/aoFoHtaDr+ZDUj6jaQfuvsZST+T9CVJd2jsN4MfT3Scu29190F3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\nCD9QKMIPFOr/AMGdlfd/AM+YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01803137b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01807dfba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2laTfSPqhu38k6WeSbpJ0hyZeGfx4quPcfbO7D7n7UNVz3AG0z7TCb2Z9mgj+L939t5Lk\n7mPufsndP5b0c0kbOjdMAO2Wht8mPq59WtIed//JpMsnbw37LUm72j88AJ0ynU/7vyzpO5J2mtmO\nxmWPS3rYzO7QRPvvgKTvd2SEPSJqBWZtoazdltWz24+mvma3nU2bzY6vsuR5lS22pyO6byW08jLT\n+bT/D5Km+i31bE8fQI4z/IBCEX6gUIQfKBThBwpF+IFCEX6gUJfN0t11qtqvznrO2dTVqstQ16Vq\nr73q+RWl45kfKBThBwpF+IFCEX6gUIQfKBThBwpF+IFCWTfnNZvZe5IOTrpomaT3uzaAL6ZXx9ar\n45IYW6vaObbr3P2a6Vyxq+H/3A83G3b3odoGEOjVsfXquCTG1qq6xsbLfqBQhB8oVN3h31zzz4/0\n6th6dVwSY2tVLWOr9T0/gPrU/cwPoCa1hN/M7jOzvWb2lpk9VscYmjGzA2a208x2mNlwzWPZYmZH\nzWzXpMuWmNlLZrav8feU26TVNLYnzGy08djtMLP7axrbajP7LzP7o5ntNrO/bVxe62MXjKuWx63r\nL/vNbJakNyV9TdKIpO2SHnb3P3Z1IE2Y2QFJQ+5ee0/YzP5K0ilJz7j7+sZl/yjpmLs/2fiP82p3\n/7seGdsTkk7VvXNzY0OZwck7S0t6UNJ3VeNjF4zrIdXwuNXxzL9B0lvuvt/dL0j6laQHahhHz3P3\nbZKOfebiByRtbXy9VRP/eLquydh6grsfdvfXG1+flPTJztK1PnbBuGpRR/hXSnp30vcj6q0tv13S\n78zsNTPbVPdgpjDQ2DZdko5IGqhzMFNId27ups/sLN0zj10rO163Gx/4fd697v4Xkr4h6QeNl7c9\nySfes/VSu2ZaOzd3yxQ7S/9JnY9dqztet1sd4R+VtHrS96sal/UEdx9t/H1U0nPqvd2Hxz7ZJLXx\n99Gax/MnvbRz81Q7S6sHHrte2vG6jvBvl7TGzG4ws9mSvi3phRrG8TlmNr/xQYzMbL6kr6v3dh9+\nQdLGxtcbJT1f41g+pVd2bm62s7Rqfux6bsdrd+/6H0n3a+IT/7cl/X0dY2gyrhsl/U/jz+66xybp\nWU28DBzXxGcjj0haKullSfsk/V7Skh4a279K2inpDU0EbbCmsd2riZf0b0ja0fhzf92PXTCuWh43\nzvADCsUHfkChCD9QKMIPFIrwA4Ui/EChCD9QKMIPFIrwA4X6PwTaZ18F6BdJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f018075d128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f018076ba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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yyeeqpJ+4+1tmNl/Sm2b2aqP2c3f/x84ND0CnpOF39xFJI423x8xsr6TVnR4YgM76Us+7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\nKBThBwpF+IFC/R9YalN/3dRWCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f018077b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01807c74e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01807cc3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01b4068b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01bc086908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01809d62b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01b4062780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01b51fed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01b4057ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01807e1128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uvtXdR9x9pO5+eQA6p600mtkcTQb/F+7+G0ly90PuPuHuVyX9TNJ93RsmgE5Lw2+Tb5k+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\nhSL8QKEIP1Aowg8UivADhfo/8GOi4ZSihswAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0180317cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_img = torch.squeeze(output.cpu().data)\n",
    "print(out_img.size())\n",
    "\n",
    "for i in range(out_img.size()[0]):\n",
    "    plt.imshow(torch.squeeze(image[i]).numpy(),cmap='gray')\n",
    "    plt.show()\n",
    "    plt.imshow(out_img[i].numpy(),cmap='gray')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2032\n"
     ]
    }
   ],
   "source": [
    "total_arr = []\n",
    "for i in range(1):\n",
    "    for j,[image,label] in enumerate(test_loader):\n",
    "        x = Variable(image).cuda()\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        encoded,output = model.forward(x)\n",
    "        for k in range(batch_size):\n",
    "            total_arr.append(encoded[k].view(-1).cpu().data.numpy())\n",
    "        \n",
    "        if j >125:\n",
    "            break\n",
    "            \n",
    "print(len(total_arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------Starting TSNE------\n",
      "\n",
      "\n",
      "------TSNE Done------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n------Starting TSNE------\\n\")\n",
    "\n",
    "tsne_model = TSNE(n_components=2, init='pca',random_state=0)\n",
    "result = tsne_model.fit_transform(total_arr)\n",
    "\n",
    "print(\"\\n------TSNE Done------\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def imscatter(x, y, image, ax=None, zoom=1):\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    try:\n",
    "        image = image\n",
    "    except TypeError:\n",
    "        # Likely already an array...\n",
    "        pass\n",
    "    im = OffsetImage(image)\n",
    "    x, y = np.atleast_1d(x, y)\n",
    "    artists = []\n",
    "    for x0, y0 in zip(x, y):\n",
    "        ab = AnnotationBbox(im, (x0, y0), xycoords='data', frameon=False)\n",
    "        artists.append(ax.add_artist(ab))\n",
    "    ax.update_datalim(np.column_stack([x, y]))\n",
    "    ax.autoscale()\n",
    "    return artists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
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     "data": {
      "image/png": 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YZubENjOn/1dvoUHkieijJc739P8NI2DjiMoQvdZgsMIXBMFbEIS/BUE4IwjC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\nn8P1JsT6bAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01717cd2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n------Starting to plot------\\n\")\n",
    "\n",
    "for i in range(len(result)):\n",
    "    print(\"{}/{}\".format(i,len(result)))\n",
    "    image = mnist_test[i][0][0].numpy()\n",
    "    imscatter(result[i,0],result[i,1], image=image ,zoom=0.2)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}