11_CNN_CIFAR-10_CUDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11_CNN_CIFAR-10_CUDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T06:43:02.089657Z",
     "start_time": "2017-08-07T06:42:59.603534Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# Pytorch Library\n",
    "import torch\n",
    "import torch.nn.init\n",
    "import torchvision\n",
    "from torch.autograd import Variable\n",
    "import torchvision.utils as utils\n",
    "import torchvision.datasets as dsets\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "########################################################################\n",
    "# The output of torchvision datasets are PILImage images of range [0, 1].\n",
    "# We transform them to Tensors of normalized range [-1, 1]\n",
    "\n",
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,\n",
    "                                          shuffle=True, num_workers=10)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
    "                                         shuffle=False, num_workers=10)\n",
    "\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T06:43:02.657316Z",
     "start_time": "2017-08-07T06:43:02.091997Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DwzCJWYLlrTaappI040zN11nZEyIVjcvOXkHgVth16Bib17UzVWiYNxRVpzRf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xfANHRK+TzT8wWmcgUmc1Wy8zkqVZ3fBsazFYs5w8KpOtgeP/H6JQxM53BVBW97GE84gOm1yJSn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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01d73000b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " frog plane   cat plane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-2:\n",
      "Process Process-8:\n",
      "Process Process-4:\n",
      "Process Process-7:\n",
      "Process Process-6:\n",
      "Process Process-3:\n",
      "Process Process-10:\n",
      "Process Process-1:\n",
      "Process Process-9:\n",
      "Process Process-5:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 342, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "# Let us show some of the training images, for fun.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T06:43:05.463993Z",
     "start_time": "2017-08-07T06:43:02.659857Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net (\n",
       "  (conv1): Conv2d(3, 24, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (b1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (pool): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n",
       "  (conv2): Conv2d(24, 64, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (b2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (fc1): Linear (1600 -> 240)\n",
       "  (fc2): Linear (240 -> 84)\n",
       "  (fc3): Linear (84 -> 10)\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "########################################################################\n",
    "# 2. Define a Convolution Neural Network\n",
    "# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "\n",
    "        # take 3-channel images (instead of 1-channel images as it was defined).\n",
    "        self.conv1 = nn.Conv2d(3, 24, 5)\n",
    "        self.b1 = nn.BatchNorm2d(24)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        \n",
    "        self.conv2 = nn.Conv2d(24, 64, 5)\n",
    "        self.b2 = nn.BatchNorm2d(64)\n",
    "        \n",
    "        self.fc1 = nn.Linear(64 * 5 * 5, 240)\n",
    "        self.fc2 = nn.Linear(240, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.b1(self.conv1(x))))\n",
    "        x = self.pool(F.relu(self.b2(self.conv2(x))))\n",
    "        x = x.view(-1, 64 * 5 * 5)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "net = Net()\n",
    "net.load_state_dict(torch.load('CNN.pkl'))  # Load the Trained Model\n",
    "net.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T06:43:05.475538Z",
     "start_time": "2017-08-07T06:43:05.467224Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "########################################################################\n",
    "# 3. Define a Loss function and optimizer\n",
    "# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "# Let's use a Classification Cross-Entropy loss and SGD with momentum\n",
    "\n",
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(net.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-04T07:12:53.483624Z",
     "start_time": "2017-08-04T07:07:56.941026Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,   128] loss: 0.008\n",
      "[1,   256] loss: 0.009\n",
      "[1,   384] loss: 0.008\n",
      "Accuracy of the network on the 10000 test images: 66 %\n",
      "[2,   128] loss: 0.004\n",
      "[2,   256] loss: 0.006\n",
      "[2,   384] loss: 0.012\n",
      "Accuracy of the network on the 10000 test images: 66 %\n",
      "[3,   128] loss: 0.010\n",
      "[3,   256] loss: 0.012\n",
      "[3,   384] loss: 0.009\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-61:\n",
      "Process Process-70:\n",
      "Process Process-62:\n",
      "Process Process-69:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 342, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "Process Process-68:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "Process Process-63:\n",
      "Process Process-64:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Process Process-66:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "Process Process-65:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "Process Process-67:\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-d0a40d6d4a02>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtestloader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\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[0m\u001b[1;32m     40\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0mtotal\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/autograd/variable.py\u001b[0m in \u001b[0;36mcuda\u001b[0;34m(self, device_id, async)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masync\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mCudaTransfer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masync\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/autograd/_functions/tensor.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(ctx, i, device_id, async)\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masync\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masync\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masync\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masync\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/_utils.py\u001b[0m in \u001b[0;36m_cuda\u001b[0;34m(self, device, async)\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0mnew_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnew_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masync\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "Traceback (most recent call last):\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "########################################################################\n",
    "# 4. Train the network\n",
    "# ^^^^^^^^^^^^^^^^^^^^\n",
    "#\n",
    "# This is when things start to get interesting.\n",
    "# We simply have to loop over our data iterator, and feed the inputs to the\n",
    "# network and optimize\n",
    "\n",
    "for epoch in range(250):  # loop over the dataset multiple times\n",
    "\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs\n",
    "        inputs, labels = data\n",
    "\n",
    "        # wrap them in Variable\n",
    "        inputs, labels = Variable(inputs).cuda(), Variable(labels).cuda()\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.data[0]\n",
    "        if i % 128 == 127:    # print every 2000 mini-batches\n",
    "            print('[%d, %5d] loss: %.3f' %\n",
    "                  (epoch + 1, i + 1, running_loss / 128))\n",
    "            running_loss = 0.0\n",
    "            \n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs = net(Variable(images).cuda())\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted.cpu() == labels).sum()\n",
    "\n",
    "    print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
    "        100 * correct / total))\n",
    "\n",
    "print('Finished Training')\n",
    "\n",
    "torch.save(net.state_dict(), 'CNN.pkl')  # Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-04T07:07:22.866878Z",
     "start_time": "2017-08-04T07:07:22.372891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p8sfYlDJvJEWtDUkt7x0S4rG3wj6geVGtD7DJpcwpe//4E3+a7qvxtRbrQqbV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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe658699908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GroundTruth:    cat  ship  ship plane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-50:\n",
      "Process Process-49:\n",
      "Process Process-43:\n",
      "Process Process-48:\n",
      "Process Process-42:\n",
      "Process Process-47:\n",
      "Process Process-44:\n",
      "Process Process-41:\n",
      "Process Process-46:\n",
      "Process Process-45:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    r = index_queue.get()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 341, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/queues.py\", line 342, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/home/isjeon/anaconda3/envs/lab_conda/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "\n",
    "########################################################################\n",
    "# 5. Test the network on the test data\n",
    "# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "#\n",
    "# We have trained the network for 2 passes over the training dataset.\n",
    "# But we need to check if the network has learnt anything at all.\n",
    "#\n",
    "# We will check this by predicting the class label that the neural network\n",
    "# outputs, and checking it against the ground-truth. If the prediction is\n",
    "# correct, we add the sample to the list of correct predictions.\n",
    "#\n",
    "# Okay, first step. Let us display an image from the test set to get familiar.\n",
    "\n",
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# print images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-04T07:07:23.087892Z",
     "start_time": "2017-08-04T07:07:23.062316Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:    cat   car   cat  bird\n"
     ]
    }
   ],
   "source": [
    "\n",
    "########################################################################\n",
    "# Okay, now let us see what the neural network thinks these examples above are:\n",
    "\n",
    "outputs = net(Variable(images).cuda())\n",
    "\n",
    "########################################################################\n",
    "# The outputs are energies for the 10 classes.\n",
    "# Higher the energy for a class, the more the network\n",
    "# thinks that the image is of the particular class.\n",
    "# So, let's get the index of the highest energy:\n",
    "_, predicted = torch.max(outputs.data.cpu(), 1)\n",
    "\n",
    "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n",
    "                              for j in range(4) ))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-04T07:07:37.842795Z",
     "start_time": "2017-08-04T07:07:31.852915Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 66 %\n"
     ]
    }
   ],
   "source": [
    "\n",
    "########################################################################\n",
    "# The results seem pretty good.\n",
    "#\n",
    "# Let us look at how the network performs on the whole dataset.\n",
    "\n",
    "correct = 0\n",
    "total = 0\n",
    "for data in testloader:\n",
    "    images, labels = data\n",
    "    outputs = net(Variable(images).cuda())\n",
    "    _, predicted = torch.max(outputs.data, 1)\n",
    "    total += labels.size(0)\n",
    "    correct += (predicted.cpu() == labels).sum()\n",
    "\n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
    "    100 * correct / total))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of plane : 65 %\n",
      "Accuracy of   car : 75 %\n",
      "Accuracy of  bird : 49 %\n",
      "Accuracy of   cat : 43 %\n",
      "Accuracy of  deer : 54 %\n",
      "Accuracy of   dog : 48 %\n",
      "Accuracy of  frog : 71 %\n",
      "Accuracy of horse : 66 %\n",
      "Accuracy of  ship : 75 %\n",
      "Accuracy of truck : 71 %\n"
     ]
    }
   ],
   "source": [
    "\n",
    "########################################################################\n",
    "# That looks waaay better than chance, which is 10% accuracy (randomly picking\n",
    "# a class out of 10 classes).\n",
    "# Seems like the network learnt something.\n",
    "#\n",
    "# Hmmm, what are the classes that performed well, and the classes that did\n",
    "# not perform well:\n",
    "\n",
    "class_correct = list(0. for i in range(10))\n",
    "class_total = list(0. for i in range(10))\n",
    "for data in testloader:\n",
    "    images, labels = data\n",
    "    outputs = net(Variable(images.cuda()))\n",
    "    _, predicted = torch.max(outputs.data, 1)\n",
    "    c = (predicted.cpu() == labels).squeeze()\n",
    "    for i in range(4):\n",
    "        label = labels[i]\n",
    "        class_correct[label] += c[i]\n",
    "        class_total[label] += 1\n",
    "\n",
    "\n",
    "for i in range(10):\n",
    "    print('Accuracy of %5s : %2d %%' % (\n",
    "        classes[i], 100 * class_correct[i] / class_total[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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