deconv_features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import importlib\n",
    "import torch\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from utils import load_torch, load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "importlib.reload(load_torch)\n",
    "\n",
    "data_folder = \"quickdraw/train_simplified/\"\n",
    "access_file_generator = map(lambda x : data_folder + x + '.csv', load.classes)\n",
    "trainGenerator = load_torch.ImageLoader(load.classes, data_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = trainGenerator.__iter__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X.shape: torch.Size([16, 1, 32, 32])\n",
      "y.shape: torch.Size([16])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "X, y = next(gen)\n",
    "print('X.shape:', X.shape)\n",
    "print('y.shape:', y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.autograd as ag\n",
    "import torch.optim as optim\n",
    "\n",
    "\n",
    "class VanillaDoodle(nn.Module):\n",
    "    # Here we define our network structure\n",
    "    def __init__(self, cdim=8):\n",
    "        super(VanillaDoodle, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 6, 3).double() \n",
    "        self.conv2 = nn.Conv2d(6,16, 3).double()\n",
    "        self.conv3 = nn.Conv2d(16,32,3).double()\n",
    "        self.conv4 = nn.Conv2d(32,64,3).double()\n",
    "        self.fc1   = nn.Linear(128,120).double()\n",
    "        self.fc2   = nn.Linear(120, 84).double() \n",
    "        self.fc3   = nn.Linear(84,cdim).double()\n",
    "        \n",
    "        self.maxunpool1=nn.MaxUnpool2d(2, stride=2)\n",
    "        self.maxunpool2=nn.MaxUnpool2d(2, stride=2)\n",
    "        self.maxunpool3=nn.MaxUnpool2d(2, stride=2)\n",
    "        self.deconv1 = nn.ConvTranspose2d(6, 1, 3).double()\n",
    "        self.deconv2 = nn.ConvTranspose2d(16, 6, 3).double()\n",
    "        self.deconv3 = nn.ConvTranspose2d(32, 16, 3).double()\n",
    "        \n",
    "    # Here we define one forward pass through the network\n",
    "    def forward(self, x):\n",
    "        x, indices1 = F.max_pool2d(F.relu(self.conv1(x)), (2, 2), return_indices = True)\n",
    "        y_unpool1=self.deconv1(F.relu(self.maxunpool1(x,indices1)))\n",
    "        #plt.imshow(y_unpool1[0,...])\n",
    "        \n",
    "        array= y_unpool1.detach().numpy()\n",
    "        array_img = array[127,0,:,:]\n",
    "        print(array_img.shape)\n",
    "        plt.title(\"Layer1\")\n",
    "        plt.imshow(array_img)\n",
    "        plt.show()\n",
    "        #print(array_img.shape)\n",
    "        output_size = self.conv2(x).size()\n",
    "        x, indices2 = F.max_pool2d(F.relu(self.conv2(x)), (2, 2), return_indices = True)\n",
    "        y_unpool2=self.deconv2(F.relu(self.maxunpool2(x,indices2, output_size)))\n",
    "        array= y_unpool2.detach().numpy()\n",
    "        array_img = array[127,0,:,:]\n",
    "        plt.title(\"Layer2\")\n",
    "        plt.imshow(array_img)\n",
    "        plt.show()\n",
    "        \n",
    "        x, indices3 = F.max_pool2d(F.relu(self.conv3(x)), (2, 2),return_indices = True)\n",
    "        y_unpool3=self.deconv3(F.relu(self.maxunpool3(x,indices3)))\n",
    "        x = x.view(-1, int(self.num_flat_features(x)))\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "    \n",
    "    \n",
    "    # Determine the number of features in a batch of tensors\n",
    "    def num_flat_features(self, x): \n",
    "        size = x.size()\n",
    "        return np.prod(size[1:])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "B     = 128              # Minibatch size\n",
    "T     = 1               # Number of epochs\n",
    "gamma = .001             # learning rate\n",
    "rho   = .9               # momentum\n",
    "\n",
    "classes = load.classes\n",
    "    \n",
    "net = VanillaDoodle(cdim=len(classes))\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(\n",
    "    net.parameters(),\n",
    "    lr=gamma,\n",
    "    momentum=rho\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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d4+DpY1Rnl2yZXWt37vNl2GfX5o76O62B1/xmmXL4zTLl8JtlyuE3y5TDb5Yph98sUw6/WaYcfrNMOfxmmXL4zTLl8JtlyuE3y5TDb5Yph98sUw6/WaYcfrNMOfxmmUqaqLPpvn+SFJKmVTM8M6tK6kSdSJoFzAdWlTwmM6tB0kSdhcuAzwADztZjZiNX0nt+SWcCT0TEvSWPx8xqMuRv75U0HrgYOHWQ7c8FzgXonNI91O7MrCIpa/6/AGYD90paCcwE7pY0vVXj5ok6O/bzRJ1mI8WQ1/wRcR9wYN/t4h/AvIjYWOK4zKxiqRN1mtk+LnWizub7DyttNGZWGx/hZ5Yph98sU4qo7xgdSRuAPw5w9zRgJO00HGnjgZE3Jo9nz4ZjPIdGxAGDaVhr+PdE0tKImDfc4+gz0sYDI29MHs+ejbTx9OfNfrNMOfxmmRpJ4b9quAfQz0gbD4y8MXk8ezbSxvNnRsx7fjOr10ha85tZjRx+s0zVHn5Jp0t6WNIKSRe1uH+MpB8U998p6bAKxzJL0q8lLZP0gKTzWrQ5WdJzku4pLp+vajxNfa6UdF/R39IW90vS14pl9AdJx1U4lrlNj/0eSZslnd+vTaXLqNVXyUmaKmmxpOXFz5bni0s6u2izXNLZFY7ni5IeKp6PGyVNGaB2j89trSKitgvQATwKzAFGA/cCR/Rr83HgyuL6WcAPKhzPQcBxxfWJwCMtxnMy8LOal9NKYNoe7j8DuAUQcAJwZ43P33oaB5LUtoyANwLHAfc3/e4/gIuK6xcBl7aomwo8VvzsLq53VzSeU4HO4vqlrcYzmOe2zkvda/7jgRUR8VhE7ASuAxb2a7MQuLq4fgNwiiRVMZiIWBcRdxfXtwDLgBlV9FWyhcB3o2EJMEXSQTX0ewrwaEQMdJRmJaL1V8k1v06uBt7RovQ0YHFEbIqIZ4DFtPg+yjLGExG3RkRPcXMJje+5GNHqDv8MYHXT7TW8NGwvtikW5nPA/lUPrHh78RrgzhZ3nyjpXkm3SDqy6rHQ+F7EWyXdVXwTUn+DWY5VOAu4doD76l5Gr4iIddD4J07Td0w0Ga7l9CEaW2at7O25rc2Qv8yjTa3W4P0/axxMm1JJmgD8CDg/Ijb3u/tuGpu5WyWdAfwEOLzK8QAnRcRaSQcCiyU9VKxtXhxyi5qql9Fo4Ezgsy3uHo5lNBjDsZwuBnqARQM02dtzW5u61/xrgFlNt2cCawdqI6kTmEzrbw8uhaQuGsFfFBE/7n9/RGyOiK3F9ZuBrqrnKYiItcXPp4AbabxdajaY5Vi2twJ3R8ST/e8YjmUEPNn3Vqf4+VSLNrUup2KH4gLgvVG8we9vEM9tbeoO/++AwyXNLtYkZwE39WtzE9C3V/adwG0DLch2FfsSvgksi4gvD9Bmet8+B0nH01hmT1cxnqKP/SRN7LtOY0dS/wlTbgI+UOz1PwF4rm8TuELvZoBN/rqXUaH5dXI28NMWbX4BnCqpu/g04NTid6WTdDpwIXBmRDw/QJvBPLf1qXsPI4091Y/Q2Ot/cfG7f6ax0ADGAtcDK4D/A+ZUOJbX09gM/ANwT3E5A/go8NGizSeAB2h8MrEEeF3Fy2dO0de9Rb99y6h5TAIuL5bhfTS+Q7HKMY2nEebJTb+rbRnR+KezDthFY23+YRr7gX4FLC9+Ti3azgO+0VT7oeK1tAI4p8LxrKCxf6HvddT3idXBwM17em6H6+LDe80y5SP8zDLl8JtlyuE3y5TDb5Yph98sUw6/WaYcfrNM/T+G2sMnKmCsLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZsHvw6cUJ44ALUzekDzvdd1Ta/8bdxyZXydwfppXb8rapyXX2TkkbJrt7TnrfcWBm2vuig2kTmQKM600slzi/51jnnt8sUw6/WaYcfrNMOfxmmXL4zTLl8JtlyuE3y5TDb5Yph98sUw6/WaYcfrNMOfxmmXL4zTJV70Sd49Im6iR9YB77jkofBZYq6TUWXljQUV5DhmnS5vr7gO7tae9LbzungWzjc/RK5J7fLFMOv1mmHH6zTCVN1Nn02N9KCkmzqmmemVUldaJOJB0HvBPYWHKbzKwGSRN1Fr4EfBrvQzU7IiV955d0AfBURKwuuT1mVpMR/84vaRJwJXDOMJdfCiwF6Jo2c6TVmVlFUnr+1wDzgdWSNgBzgQckHd1qYU/UaTY2jbjnj4g1wOyB28U/gEUR8VyJ7TKziqVO1GlmR7jUiTqbH59XWmvMrDY+ws8sUw6/WaYUUd8xOpKeBX4zxMOzgLG003CstQfGXpvcnkMbjfYcHxGvGs6CtYb/UCStiohFo92OAWOtPTD22uT2HNpYa89g3uw3y5TDb5apsRT+a0e7AYOMtfbA2GuT23NoY609v2PMfOc3s3qNpZ7fzGrk8JtlqvbwSzpX0q8krZd0RYvHx0v6VvH4SknzKmzLcZLulbRW0iOSLmmxzFmSdkh6sLh8tqr2NNW5QdKaor5VLR6XpH8v1tFDkk6vsC0nNr32ByXtlHTpoGUqXUetTiUnqUfScknrir8tx4tLuqhYZp2kiypsz+clPVq8H7dImjFE2UO+t7WKiNouQAfwOLAA6AZWAycNWuYTwDXF9SXAtypszzHA6cX1qcBjLdpzFnBbzetpAzDrEI+fB9wBCFgMrKzx/XuaxoEkta0j4EzgdODhpvv+BbiiuH4F8LkW5XqAJ4q/M4vrMytqzzlAZ3H9c63aM5z3ts5L3T3/GcD6iHgiIg4ANwEXDlrmQuCG4vp3gbMlVTLzRkRsiYgHiuu7gLXAnCrqKtmFwDeiYQUwQ9IxNdR7NvB4RAx1lGYlovWp5Jo/JzcA721R9F3A8ojYFhHbgeW0OB9lGe2JiLsioq+4uYLGeS7GtLrDPwd4sun2Jl4etpeWKVbmDuCoqhtWfL04DVjZ4uE3S1ot6Q5JJ1fdFhrnRbxL0v3FmZAGG856rMIS4MYhHqt7Hb06IrZA4584TeeYaDJa6+liGltmrRzuva1NrdN10dhMHWzwb43DWaZUkqYA3wMujYidgx5+gMZm7m5J5wHfBxZW2R7grRGxWdJsYLmkR4ve5qUmtyhT9TrqBi4APtPi4dFYR8MxGuvpSqAPWDbEIod7b2tTd8+/CTiu6fZcYPNQy0jqBKbT+uzBpZDURSP4yyLi5sGPR8TOiNhdXL8d6Kp6noKI2Fz83QrcQuPrUrPhrMeyvRt4ICKeGfzAaKwj4JmBrzrF360tlql1PRU7FM8HPhjFF/zBhvHe1qbu8N8HLJQ0v+hJlgC3DlrmVmBgr+z7gHuGWpHtKvYlfA1YGxFfHGKZowf2OUg6g8Y6e76K9hR1TJY0deA6jR1JgydMuRX4s2Kv/2Jgx8AmcIU+wBCb/HWvo0Lz5+Qi4ActlrkTOEfSzOLXgHOK+0on6VzgcuCCiNgzxDLDeW/rU/ceRhp7qh+jsdf/yuK+f6Sx0gAmAN8B1gP/ByyosC1vo7EZ+BDwYHE5D/g48PFimU8Cj9D4ZWIF8JaK18+Coq7VRb0D66i5TQKuLtbhGhrnUKyyTZNohHl60321rSMa/3S2AL00evOP0tgPdDewrvjbUyy7CPhqU9mLi8/SeuAjFbZnPY39CwOfo4FfrI4Fbj/UeztaFx/ea5YpH+FnlimH3yxTDr9Zphx+s0w5/GaZcvjNMuXwm2Xq/wG3Cbw6cNE71gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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f9Jn4Szux55bKq0xb0Uv2a712uDsi0VFsR7n6u+1Wqn4Zb9dFCDk1ofgJ8RSKnxBPofgJ8RSKnxBPofgJ8ZSIQ32KbCNEIWfZIbbpg92tAAs+ZIe8qktqTds9e2ebtiHldrLN+y3uWnelQ5pMn8OFBaZt0ZszTdvQpXZoaN9UO8aWM8QdYjtsl+LDQz+1w3lT5m4xbS2d9jz2/2qUczx+uj2Pzy962LR9d90c09Z0id2uq7LM/Xrua7abxp7MiT3HQ2/add0vIvUisq7b2O0isltEVgd/l6d1loSQfqc3l/0PAnCdKu9S1WnB3/P9Oy1CSLrpUfyqugTAgQjmQgiJkL4s+N0kImuCrwVl1oNEZJ6I1IhITVeT/d2MEBItqYr/XgDjAEwDUAfgR9YDVXWBqlaranVWsb3IQgiJlpTEr6p7VTWhql0Afg7AXrYmhJyQpBTqE5FKVa0L7n4SwLqwx/8/Kuhsc++y/EU7rLHuMrdPwZv2lcQbZ4w3bVkhFc7q37Xba0mOO/sqofYGs/fkmbbCHbZf86ftDLexA+1w5OY17lZkg9eaLhj4T7tMW/1/jTVt+yfbGYvX3bzYOf7g+rNNn2/96h9NW2JSs2krfN3O6ntvujvEFjvoztAE/Mnq61H8IvIogAsADBaRXQBuA3CBiEwDoABqAXy+zzMhhERKj+JX1bmO4V+mYS6EkAjhz3sJ8RSKnxBPofgJ8RSKnxBPOWGy+rI/Y/+C2Aq+jZu73fRpS9ihnOWbxpi2wlI7u9DqbJaXbRfwbC6wW2uNm7vVtK2qsdPfNuXZoa2Cve7P89a/O2j6TCu2W2gt+Qe7JVdJoX2sHnn0Iud4x2g7E7NjqB2+GpBtH8eOiw6btgojY66lPLrsvFT9Mp7VRwg5NaH4CfEUip8QT6H4CfEUip8QT6H4CfGUSEN9WVmKAQVxp63p5WGm3/lXr3COv/L0h02fsF5sUmyHjdryQhrQ1bkz9OKV7ucEAKdNet+0rVpuh/Nuu/zXtm3plaatdVKbc3zIkyWmz4CvhYTfau2wYskiO1PthSfvdI7PWvB10yeMjlL7raq77ZCYmWkXYXZeqn7s1UcISQsUPyGeQvET4ikUPyGeQvET4imRrvZ3tcXQutGdKJI1067RtuQx96r+uDnbTJ+dj9u154qvcLf/AoCmZytN26Gp7lXxiZX29rbWuNtWAUBXqb1i++g/XGLacj9tJy0l8twrx2/8589Mn8n3f8m0Fe6zV6lH3W0nJl35xZvdPl99z/TZtsw+VgOLW0xbfJwdrbCSZqJM0EnVj4k9hJC0QPET4ikUPyGeQvET4ikUPyGeQvET4im96dgzEsBDAIYB6AKwQFXvFpFyAI8DGI1k155Pq6pdKA4AcrugI9113wY9VWC6xa9115jLj9khnuLddhht585Bpm3KVXZdwJbXRzvH92w8zfTpnGyHf/J32OGfkp/tNW2fyN9o2lZ9c7pz/PTSeabPXVc/bNq+fb/dQmvx8g+ZtpLPuevqHXrWPlZj/naHadu60t2GrCeY2GPTmzN/J4BbVHUSgHMAfElEJgOYD2Cxqo4HsDi4Twg5SehR/Kpap6org9tNADYAqAJwBYCFwcMWArDzTAkhJxzH9Z1fREYDmA5gGYChRzv1Bv+H9PfkCCHpo9fiF5EiAIsA3Kyqdo/ov/SbJyI1IlKTaDqSyhwJIWmgV+IXkRwkhf+Iqj4VDO8VkcrAXgnA+QN3VV2gqtWqWh0rLuyPORNC+oEexS8igmRL7g2q+uNupmcBXB/cvh7AM/0/PUJIuhC1elAdfYDIRwC8DmAtkqE+ALgVye/9TwAYBWAHgKtU1e65BSBvbJVW/eCLblueHbYTK0qyzK5L9/Gr/2jantl8hmnr3GFfnVSfu8k5/vaeKtMnjOICd709AMCTg01Tw0dCstjK3KHUlgN2KDW70N5e52E7HPnx6rdN20tb/so5PqjUzt68dPi7pu2hFeeatrAWa75l9e2cfx/atu6244rd6DHOr6pLAVgbczdkI4Sc8PAXfoR4CsVPiKdQ/IR4CsVPiKdQ/IR4SqQFPLNjXSgvcf/Kr+tpO7T101t/6hyfl3Wt6fPk0rNNm3TakZCuAruV17L145zjpavsgpqHPmyHf9p322HF8/95nWlb+qqdTdeabWWxmS5INOTbxhzbcfk9M2y/y9wFN1tfGGq6PHRGuWnLOhIzbW37B9o2ZvWZ8MxPiKdQ/IR4CsVPiKdQ/IR4CsVPiKdQ/IR4So9Zff1J3qiRWvnNrzhtJaPcBR8BoGVtmXM8a6KdITas1K43kp1lh6+2v2+HHD86brNzfOXeEaZPe6cdojqt3K53uuX9CtOWP8AOAVkMLbaP1eE2O9R3qNHOBqwoazrueYwrcRdjBYC2hB0yXb5pjGljVt+fOZ6sPp75CfEUip8QT6H4CfEUip8QT6H4CfGUSBN7ADup5khLnu1kLJiHJcbsrC2yt5diAsZr7xsJNSE+YfvatM1OSAnza+myV+Atv+1dpSntSyrtOoMH3hxm2krPcbcbe33tRHtfuXYUJnbAfqsysSc1eOYnxFMofkI8heInxFMofkI8heInxFMofkI8pcdQn4iMBPAQgGFIVoJboKp3i8jtAG4E0BA89FZVfT5sWwUFcUyv3uK0bdxnd/huLneHNc6ass30WV9vh6H6OwHjZEgSSXVf+zcOMm3xIQnTZtXqyxluh8M6Q+ondlXYcywojtu2k/g1SyWx53joTZy/E8AtqrpSRIoBrBCRlwPbXap6Z/qmRwhJF73p1VcHoC643SQiGwCk1pmSEHLCcFzf+UVkNIDpSHboBYCbRGSNiNwvIu6ke0LICUmvxS8iRQAWAbhZVRsB3AtgHIBpSF4Z/MjwmyciNSJSEz9kF10ghERLr8QvIjlICv8RVX0KAFR1r6omVLULwM8BzHT5quoCVa1W1eq80vQuYBBCek+P4hcRAfBLABtU9cfdxiu7PeyTAOwWM4SQE47erPbPAnAdgLUisjoYuxXAXBGZBkAB1AL4fE8bamnLxYoN7lpspUPtenDFG9213Wraxps+We0RZm2dDBli+fb2WgfaIaryCQdMW2Gu7fdeljt0e8OsJabPA7+/wLRhvx0qa6u3M0JP6tcszVl9vVntXwrANfPQmD4h5MSGv/AjxFMofkI8heInxFMofkI8heInxFMiLeApMUVuiTsDa87otabfI5vPc45fdI7t8+bu0abtRMmYi9Iv3mG/1E2H7IKg7S/b7ctaQ36zJZPdhT9/9fSFpk/pDLuV18UjNpm2326fYtpO5tcs3Vl9PPMT4ikUPyGeQvET4ikUPyGeQvET4ikUPyGeEm2vvngWdKu7v95DTeeabuWb3BlR7/7R6J0HID7T/lxrG2UXFSkvOWLaGurdPeGycuzCk23bi01baKZdiR02GlTWbNqsOeYV2tuL7bXDUEPm7DRtuw7Y/f9kp/t1jg/rMH2KnrGLhT5R7SwXAQCINRnNHMGsvjB45ifEUyh+QjyF4ifEUyh+QjyF4ifEUyh+Qjwl0lBf8cAWXHzJKqdtxxG758fGfaOd41M+u8H02VvrLhQKAImGfNPWucTOpKowIoQHJ5suGHxmvWkLy/RqarTn0f5ihWmbtNidGbd1rh1GKwvJptv6znDTdubUWtP29sFRzvFJ43ebPnXD3GFKAKjItvsCnqqZmMzqI4SkBYqfEE+h+AnxFIqfEE+h+AnxlB5X+0UkH8ASAHnB43+tqreJyBgAjwEoB7ASwHWqai93Amg8MgAvLD/TaTt76hbTz0qAWVozyfT5xkXPmbYfPzPHtE24bqNp29Aw1Dk+orDF9Hnv3WGmbfBYuxVWa469un34TDs5Zvo1dc7xrDa7Hdqup+zIyL996XHTdue7l5i2s6dsdY7vaS4xfbJjdoKUlbAEAIMr7Odm+aXiE7VfVeVB2+dwkWnrLb0588cBXKiqU5Fsxz1bRM4B8EMAd6nqeAAHAdzQ59kQQiKjR/FrkqM5pDnBnwK4EMCvg/GFAK5MywwJIWmhV9/5RSQWdOitB/AygK0ADqnq0aTiXQCq0jNFQkg66JX4VTWhqtMAjAAwE4Dry7bzi7mIzBORGhGpSTTbhTIIIdFyXKv9qnoIwKsAzgFQKiJHFwxHANhj+CxQ1WpVrY4Vuau7EEKip0fxi0iFiJQGtwcAuBjABgCvAPj74GHXA3gmXZMkhPQ/vUnsqQSwUERiSH5YPKGqz4nIegCPici/A1gF4Jc9bUiyFTnl7jZOq3fbSwaxoe6MGgmpmXbPhgtMW1a7XU+tJMeu79e+2p18NOvKdaZPfaMdkglL3MjJDanR1mC313rt3QnO8c/NWGr6PHye3ZLru29dYdrCahcuqzvdOV5edcj0aW6xE65yC+zwZuOR4/dLxSdqv/4I54XRo/hVdQ2A6Y7xbUh+/yeEnITwF36EeArFT4inUPyEeArFT4inUPyEeIqohrQY6u+diTQAeC+4OxiAXTwuOjiPD8J5fJCTbR6nqapd5LEbkYr/AzsWqVHV6ozsnPPgPDgPXvYT4isUPyGekknxL8jgvrvDeXwQzuODnLLzyNh3fkJIZuFlPyGeQvET4ikZEb+IzBaRjSKyRUTmZ2IOwTxqRWStiKwWkZoI93u/iNSLyLpuY+Ui8rKIbA7+280L0zuP20Vkd3BMVovI5RHMY6SIvCIiG0TkHRH5SjAe6TEJmUekx0RE8kXkTyLydjCP7wXjY0RkWXA8HhcRuwFgb1DVSP8AxJCsATgWQC6AtwFMjnoewVxqAQzOwH7PBzADwLpuY/8JYH5wez6AH2ZoHrcD+HrEx6MSwIzgdjGATQAmR31MQuYR6TEBIACKgts5AJYhWT3rCQBXB+P3AfhCX/aTiTP/TABbVHWbJuv8PwbArhhxCqKqSwAcW7T/CiSrIAMRVUM25hE5qlqnqiuD201IVoqqQsTHJGQekaJJ0l4xOxPirwKws9v9TFb+VQAvicgKEZmXoTkcZaiq1gHJNyGAIRmcy00isib4WpD2rx/dEZHRSBaPWYYMHpNj5gFEfEyiqJidCfG7amhlKt44S1VnALgMwJdE5PwMzeNE4l4A45Bs0FIH4EdR7VhEigAsAnCzqjZGtd9ezCPyY6J9qJjdWzIh/l0ARna7b1b+TTequif4Xw/gaWS2LNleEakEgOB/fSYmoap7gzdeF4CfI6JjIiI5SAruEVV9KhiO/Ji45pGpYxLs+7grZveWTIh/OYDxwcplLoCrATwb9SREpFBEio/eBnApALsSZ/p5FskqyEAGqyEfFVvAJxHBMRERQbIA7AZV/XE3U6THxJpH1McksorZUa1gHrOaeTmSK6lbAXw7Q3MYi2Sk4W0A70Q5DwCPInn52IHkldANAAYBWAxgc/C/PEPzeBjAWgBrkBRfZQTz+AiSl7BrAKwO/i6P+piEzCPSYwLgTCQrYq9B8oPmX7u9Z/8EYAuAJwHk9WU//HkvIZ7CX/gR4ikUPyGeQvET4ikUPyGeQvET4ikUPyGeQvET4in/B+t246c7lPTRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-40ba39f20cf1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;31m# Forward pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-23-e2990b72d141>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Layer2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray_img\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_pool2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreturn_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    251\u001b[0m     \"\"\"\n\u001b[1;32m    252\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mshow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2210\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2211\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2212\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2213\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2214\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    514\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1475\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1477\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2605\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2607\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2609\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m    591\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m             im, l, b, trans = self.make_image(\n\u001b[0;32m--> 593\u001b[0;31m                 renderer, renderer.get_image_magnification())\n\u001b[0m\u001b[1;32m    594\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, renderer, magnification, unsampled)\u001b[0m\n\u001b[1;32m    839\u001b[0m         return self._make_image(\n\u001b[1;32m    840\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransformed_bbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagnification\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 841\u001b[0;31m             unsampled=unsampled)\n\u001b[0m\u001b[1;32m    842\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    843\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_unsampled_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_make_image\u001b[0;34m(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)\u001b[0m\n\u001b[1;32m    487\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m                 \u001b[0;31m# mask and run through the norm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m                 \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmasked_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA_resampled\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_mask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    490\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m                 \u001b[0;31m# Always convert to RGBA, even if only RGB input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, value, clip)\u001b[0m\n\u001b[1;32m    940\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_None\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    941\u001b[0m         \u001b[0;31m# Convert at least to float, without losing precision.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    943\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda:0')\n",
    "#if torch.cuda.is_available():\n",
    "   #net = net.cuda()\n",
    "\n",
    "accList, lossList = list(), list()\n",
    "accuracy = [0]\n",
    "loss_stored = []\n",
    "for epoch in range(T):            \n",
    "    gen = load_torch.ImageLoader(classes=load.classes,root_location=data_folder,read_size=32,batch_size=128)\n",
    "    for i, (images, labels) in enumerate(gen):\n",
    "        images = images\n",
    "        labels = labels\n",
    "        \n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = net(images)\n",
    "        \n",
    "        loss = criterion(outputs.cpu(), labels)\n",
    "        acc = 100 * np.mean(labels.data.numpy() == outputs.cpu().data.numpy ().T.argmax(axis =0))\n",
    "        \n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        del images\n",
    "        del labels\n",
    "        del outputs\n",
    "\n",
    "        if (i+1) % 100 == 0:\n",
    "            print ('Epoch [{}/{}], Step [{}], Loss: {:.4f}, Accuracy:{:.4f}' .format(epoch+1, T, i+1, loss.item(),acc))\n",
    "            #model.save_state_dict('mytraining.pt')\n",
    "        if (i+1)%100==0:\n",
    "            loss_stored.append(loss.item())\n",
    "            accuracy.append((0.1*acc) + (0.9*accuracy[-1]))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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