LeNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import os\n",
    "import numpy as np\n",
    "from collections import OrderedDict\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.datasets.mnist import MNIST\n",
    "import torch.optim as optim\n",
    "import cv2\n",
    "import scipy.misc\n",
    "from torch.utils.data import Dataset\n",
    "from skimage import io, transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeNet(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(LeNet, self).__init__()\n",
    "        \n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        \n",
    "        n_size = self.get_shape((3, 81, 81))\n",
    "        \n",
    "        self.fc1 = nn.Linear(n_size, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 4)\n",
    "        \n",
    "    def get_shape(self, input_shape):\n",
    "        bs = 1\n",
    "        input_tensor = Variable(torch.rand(bs, *input_shape))\n",
    "        output_feat = self.forward_feature(input_tensor)\n",
    "        n_size = output_feat.data.view(bs, -1).size(1)\n",
    "        return n_size\n",
    "    \n",
    "    def forward_feature(self, x):\n",
    "        out = F.relu(self.conv1(x))\n",
    "        out = F.max_pool2d(out, 2)\n",
    "        out = F.relu(self.conv2(out))\n",
    "        out = F.max_pool2d(out, 2)\n",
    "        return out\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.conv1(x))\n",
    "        out = F.max_pool2d(out, 2)\n",
    "        out = F.relu(self.conv2(out))\n",
    "        out = F.max_pool2d(out, 2)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = F.relu(self.fc1(out))\n",
    "        out = F.relu(self.fc2(out))\n",
    "        out = F.softmax(self.fc3(out))\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = LeNet()\n",
    "# print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CellsDataset(Dataset):\n",
    "    \n",
    "    def __init__(self, transform=None):\n",
    "        self.folders = ['wbcs/', 'rbcs/', 'platelets/', 'background/']\n",
    "        self.inverse_transform = {'background':0, 'rbcs':1, 'wbcs':2, 'platelets':3}\n",
    "        self.root = 'Patches/'\n",
    "        self.files = []\n",
    "        for folder in self.folders:\n",
    "            temp = os.listdir(self.root + folder)\n",
    "            for i in range(len(temp)):\n",
    "                self.files.append(self.root + folder + temp[i])\n",
    "        self.transform = transform\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.files)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        img_name = self.files[idx]\n",
    "        img_file = io.imread(img_name)\n",
    "        \n",
    "        if img_file is not None:\n",
    "            img_file = np.array(img_file, dtype=np.float32)\n",
    "            img_file /= 255.\n",
    "            \n",
    "            if self.transform:\n",
    "                img_file = self.transform(img_file)\n",
    "                \n",
    "        sample = {'image': img_file, 'label': self.inverse_transform[img_name.split('/')[1]]}\n",
    "        \n",
    "        return sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_dataset = CellsDataset(transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_loader = DataLoader(transformed_dataset, batch_size = 64, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LeNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(epoch):\n",
    "    model.train()\n",
    "    loss_list, batch_list = [], []\n",
    "    for i, sample in enumerate(data_train_loader):\n",
    "        \n",
    "        image = sample['image']\n",
    "        label = sample['label']\n",
    "        image, label = Variable(image), Variable(label)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        output = model(image)\n",
    "        \n",
    "        loss = criterion(output, label)\n",
    "        \n",
    "        loss_list.append(loss.data[0])\n",
    "        batch_list.append(i+1)\n",
    "        \n",
    "        if i % 100 == 0:\n",
    "            print('Train - Epoch: %d, Batch: %d, Loss: %f' % (epoch, i, loss.data[0]))\n",
    "        \n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "#         _, predicted = output.max(1)\n",
    "#         correct = predicted.eq(label).sum().item()\n",
    "#         print('correct = ' + str(correct / 64.0 * 100.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:37: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:15: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n",
      "  from ipykernel import kernelapp as app\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:19: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train - Epoch: 0, Batch: 0, Loss: 1.385581\n",
      "Train - Epoch: 0, Batch: 100, Loss: 1.174902\n",
      "Train - Epoch: 0, Batch: 200, Loss: 1.121642\n",
      "Train - Epoch: 0, Batch: 300, Loss: 1.200180\n",
      "Train - Epoch: 0, Batch: 400, Loss: 1.080833\n",
      "Train - Epoch: 0, Batch: 500, Loss: 1.097698\n",
      "Train - Epoch: 0, Batch: 600, Loss: 1.108816\n",
      "Train - Epoch: 0, Batch: 700, Loss: 1.085970\n",
      "Train - Epoch: 0, Batch: 800, Loss: 1.195457\n",
      "Train - Epoch: 0, Batch: 900, Loss: 1.057411\n",
      "Train - Epoch: 0, Batch: 1000, Loss: 1.148726\n",
      "Train - Epoch: 0, Batch: 1100, Loss: 1.064498\n",
      "Train - Epoch: 0, Batch: 1200, Loss: 1.036080\n",
      "Train - Epoch: 1, Batch: 0, Loss: 1.162360\n",
      "Train - Epoch: 1, Batch: 100, Loss: 1.083358\n",
      "Train - Epoch: 1, Batch: 200, Loss: 1.188177\n",
      "Train - Epoch: 1, Batch: 300, Loss: 1.156106\n",
      "Train - Epoch: 1, Batch: 400, Loss: 1.032188\n",
      "Train - Epoch: 1, Batch: 500, Loss: 1.073159\n",
      "Train - Epoch: 1, Batch: 600, Loss: 1.007075\n",
      "Train - Epoch: 1, Batch: 700, Loss: 0.946754\n",
      "Train - Epoch: 1, Batch: 800, Loss: 1.155814\n",
      "Train - Epoch: 1, Batch: 900, Loss: 1.072318\n",
      "Train - Epoch: 1, Batch: 1000, Loss: 0.961421\n",
      "Train - Epoch: 1, Batch: 1100, Loss: 1.017938\n",
      "Train - Epoch: 1, Batch: 1200, Loss: 0.991576\n",
      "Train - Epoch: 2, Batch: 0, Loss: 1.024393\n",
      "Train - Epoch: 2, Batch: 100, Loss: 1.020885\n",
      "Train - Epoch: 2, Batch: 200, Loss: 1.014979\n",
      "Train - Epoch: 2, Batch: 300, Loss: 1.029810\n",
      "Train - Epoch: 2, Batch: 400, Loss: 1.135275\n",
      "Train - Epoch: 2, Batch: 500, Loss: 1.062067\n",
      "Train - Epoch: 2, Batch: 600, Loss: 1.054498\n",
      "Train - Epoch: 2, Batch: 700, Loss: 0.993863\n",
      "Train - Epoch: 2, Batch: 800, Loss: 1.087787\n",
      "Train - Epoch: 2, Batch: 900, Loss: 1.044324\n",
      "Train - Epoch: 2, Batch: 1000, Loss: 1.058865\n",
      "Train - Epoch: 2, Batch: 1100, Loss: 1.026463\n",
      "Train - Epoch: 2, Batch: 1200, Loss: 1.014838\n",
      "Train - Epoch: 3, Batch: 0, Loss: 0.989750\n",
      "Train - Epoch: 3, Batch: 100, Loss: 1.102736\n",
      "Train - Epoch: 3, Batch: 200, Loss: 1.036714\n",
      "Train - Epoch: 3, Batch: 300, Loss: 1.130820\n",
      "Train - Epoch: 3, Batch: 400, Loss: 0.978548\n",
      "Train - Epoch: 3, Batch: 500, Loss: 1.038859\n",
      "Train - Epoch: 3, Batch: 600, Loss: 1.067381\n",
      "Train - Epoch: 3, Batch: 700, Loss: 0.996596\n",
      "Train - Epoch: 3, Batch: 800, Loss: 0.958766\n",
      "Train - Epoch: 3, Batch: 900, Loss: 1.014517\n",
      "Train - Epoch: 3, Batch: 1000, Loss: 1.031947\n",
      "Train - Epoch: 3, Batch: 1100, Loss: 1.026004\n",
      "Train - Epoch: 3, Batch: 1200, Loss: 1.024675\n",
      "Train - Epoch: 4, Batch: 0, Loss: 1.033672\n",
      "Train - Epoch: 4, Batch: 100, Loss: 0.978640\n",
      "Train - Epoch: 4, Batch: 200, Loss: 1.133887\n",
      "Train - Epoch: 4, Batch: 300, Loss: 1.006302\n",
      "Train - Epoch: 4, Batch: 400, Loss: 0.972727\n",
      "Train - Epoch: 4, Batch: 500, Loss: 1.058126\n",
      "Train - Epoch: 4, Batch: 600, Loss: 1.006128\n",
      "Train - Epoch: 4, Batch: 700, Loss: 0.951022\n",
      "Train - Epoch: 4, Batch: 800, Loss: 0.997484\n",
      "Train - Epoch: 4, Batch: 900, Loss: 0.911210\n",
      "Train - Epoch: 4, Batch: 1000, Loss: 0.954928\n",
      "Train - Epoch: 4, Batch: 1100, Loss: 0.967994\n",
      "Train - Epoch: 4, Batch: 1200, Loss: 1.051146\n",
      "Train - Epoch: 5, Batch: 0, Loss: 0.982079\n",
      "Train - Epoch: 5, Batch: 100, Loss: 1.114252\n",
      "Train - Epoch: 5, Batch: 200, Loss: 1.161544\n",
      "Train - Epoch: 5, Batch: 300, Loss: 0.992924\n",
      "Train - Epoch: 5, Batch: 400, Loss: 1.018717\n",
      "Train - Epoch: 5, Batch: 500, Loss: 0.901888\n",
      "Train - Epoch: 5, Batch: 600, Loss: 0.943269\n",
      "Train - Epoch: 5, Batch: 700, Loss: 1.010798\n",
      "Train - Epoch: 5, Batch: 800, Loss: 1.125196\n",
      "Train - Epoch: 5, Batch: 900, Loss: 1.060917\n",
      "Train - Epoch: 5, Batch: 1000, Loss: 1.052064\n",
      "Train - Epoch: 5, Batch: 1100, Loss: 0.965573\n",
      "Train - Epoch: 5, Batch: 1200, Loss: 1.077207\n",
      "Train - Epoch: 6, Batch: 0, Loss: 1.048918\n",
      "Train - Epoch: 6, Batch: 100, Loss: 0.969300\n",
      "Train - Epoch: 6, Batch: 200, Loss: 0.913729\n",
      "Train - Epoch: 6, Batch: 300, Loss: 1.097666\n",
      "Train - Epoch: 6, Batch: 400, Loss: 0.979729\n",
      "Train - Epoch: 6, Batch: 500, Loss: 1.065329\n",
      "Train - Epoch: 6, Batch: 600, Loss: 0.978248\n",
      "Train - Epoch: 6, Batch: 700, Loss: 1.075280\n",
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      "Train - Epoch: 6, Batch: 1100, Loss: 1.077499\n",
      "Train - Epoch: 6, Batch: 1200, Loss: 1.096063\n",
      "Train - Epoch: 7, Batch: 0, Loss: 0.997600\n",
      "Train - Epoch: 7, Batch: 100, Loss: 1.037685\n",
      "Train - Epoch: 7, Batch: 200, Loss: 1.061972\n",
      "Train - Epoch: 7, Batch: 300, Loss: 1.012487\n",
      "Train - Epoch: 7, Batch: 400, Loss: 1.019483\n",
      "Train - Epoch: 7, Batch: 500, Loss: 1.060135\n",
      "Train - Epoch: 7, Batch: 600, Loss: 0.947292\n",
      "Train - Epoch: 7, Batch: 700, Loss: 1.003556\n",
      "Train - Epoch: 7, Batch: 800, Loss: 0.931827\n",
      "Train - Epoch: 7, Batch: 900, Loss: 0.962907\n",
      "Train - Epoch: 7, Batch: 1000, Loss: 0.992030\n",
      "Train - Epoch: 7, Batch: 1100, Loss: 1.069649\n",
      "Train - Epoch: 7, Batch: 1200, Loss: 0.890951\n",
      "Train - Epoch: 8, Batch: 0, Loss: 0.952983\n",
      "Train - Epoch: 8, Batch: 100, Loss: 0.938553\n",
      "Train - Epoch: 8, Batch: 200, Loss: 0.981007\n",
      "Train - Epoch: 8, Batch: 300, Loss: 0.887370\n",
      "Train - Epoch: 8, Batch: 400, Loss: 1.189966\n",
      "Train - Epoch: 8, Batch: 500, Loss: 1.008396\n",
      "Train - Epoch: 8, Batch: 600, Loss: 0.963841\n",
      "Train - Epoch: 8, Batch: 700, Loss: 0.946746\n",
      "Train - Epoch: 8, Batch: 800, Loss: 1.098892\n",
      "Train - Epoch: 8, Batch: 900, Loss: 0.988766\n",
      "Train - Epoch: 8, Batch: 1000, Loss: 1.024579\n",
      "Train - Epoch: 8, Batch: 1100, Loss: 0.993520\n",
      "Train - Epoch: 8, Batch: 1200, Loss: 0.929332\n",
      "Train - Epoch: 9, Batch: 0, Loss: 1.072764\n",
      "Train - Epoch: 9, Batch: 100, Loss: 0.953052\n",
      "Train - Epoch: 9, Batch: 200, Loss: 1.064018\n",
      "Train - Epoch: 9, Batch: 300, Loss: 1.228980\n",
      "Train - Epoch: 9, Batch: 400, Loss: 0.964206\n",
      "Train - Epoch: 9, Batch: 500, Loss: 0.817677\n",
      "Train - Epoch: 9, Batch: 600, Loss: 0.977432\n",
      "Train - Epoch: 9, Batch: 700, Loss: 0.956842\n",
      "Train - Epoch: 9, Batch: 800, Loss: 0.878437\n",
      "Train - Epoch: 9, Batch: 900, Loss: 0.868573\n",
      "Train - Epoch: 9, Batch: 1000, Loss: 0.883057\n",
      "Train - Epoch: 9, Batch: 1100, Loss: 0.971403\n",
      "Train - Epoch: 9, Batch: 1200, Loss: 0.935955\n",
      "Train - Epoch: 10, Batch: 0, Loss: 0.979066\n",
      "Train - Epoch: 10, Batch: 100, Loss: 0.967980\n",
      "Train - Epoch: 10, Batch: 200, Loss: 0.888503\n",
      "Train - Epoch: 10, Batch: 300, Loss: 0.878071\n",
      "Train - Epoch: 10, Batch: 400, Loss: 0.905224\n",
      "Train - Epoch: 10, Batch: 500, Loss: 0.932378\n",
      "Train - Epoch: 10, Batch: 600, Loss: 0.950659\n",
      "Train - Epoch: 10, Batch: 700, Loss: 0.962292\n",
      "Train - Epoch: 10, Batch: 800, Loss: 0.888697\n",
      "Train - Epoch: 10, Batch: 900, Loss: 1.062331\n",
      "Train - Epoch: 10, Batch: 1000, Loss: 0.872489\n",
      "Train - Epoch: 10, Batch: 1100, Loss: 0.959364\n",
      "Train - Epoch: 10, Batch: 1200, Loss: 1.000183\n",
      "Train - Epoch: 11, Batch: 0, Loss: 0.999979\n",
      "Train - Epoch: 11, Batch: 100, Loss: 0.936776\n",
      "Train - Epoch: 11, Batch: 200, Loss: 0.968383\n",
      "Train - Epoch: 11, Batch: 300, Loss: 0.944861\n",
      "Train - Epoch: 11, Batch: 400, Loss: 0.982971\n",
      "Train - Epoch: 11, Batch: 500, Loss: 0.843622\n",
      "Train - Epoch: 11, Batch: 600, Loss: 0.893366\n",
      "Train - Epoch: 11, Batch: 700, Loss: 0.945105\n",
      "Train - Epoch: 11, Batch: 800, Loss: 0.920381\n",
      "Train - Epoch: 11, Batch: 900, Loss: 0.850555\n",
      "Train - Epoch: 11, Batch: 1000, Loss: 0.814813\n",
      "Train - Epoch: 11, Batch: 1100, Loss: 0.799349\n",
      "Train - Epoch: 11, Batch: 1200, Loss: 0.960563\n",
      "Train - Epoch: 12, Batch: 0, Loss: 1.052397\n",
      "Train - Epoch: 12, Batch: 100, Loss: 1.047493\n",
      "Train - Epoch: 12, Batch: 200, Loss: 0.896332\n",
      "Train - Epoch: 12, Batch: 300, Loss: 1.135067\n",
      "Train - Epoch: 12, Batch: 400, Loss: 1.004248\n",
      "Train - Epoch: 12, Batch: 500, Loss: 0.922667\n",
      "Train - Epoch: 12, Batch: 600, Loss: 0.936306\n",
      "Train - Epoch: 12, Batch: 700, Loss: 0.847870\n",
      "Train - Epoch: 12, Batch: 800, Loss: 0.980866\n",
      "Train - Epoch: 12, Batch: 900, Loss: 0.940664\n",
      "Train - Epoch: 12, Batch: 1000, Loss: 0.908431\n",
      "Train - Epoch: 12, Batch: 1100, Loss: 0.870136\n",
      "Train - Epoch: 12, Batch: 1200, Loss: 0.917178\n",
      "Train - Epoch: 13, Batch: 0, Loss: 0.908322\n",
      "Train - Epoch: 13, Batch: 100, Loss: 0.973826\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-23-f55408fba276>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-22-e98a45e86434>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epoch)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Train - Epoch: %d, Batch: %d, Loss: %f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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     22\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/torch/tensor.pyc\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m     91\u001b[0m                 \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m \u001b[0;34m`\u001b[0m\u001b[0;34m`\u001b[0m\u001b[0mFalse\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     92\u001b[0m         \"\"\"\n\u001b[0;32m---> 93\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/torch/autograd/__init__.pyc\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m     88\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m     89\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m         allow_unreachable=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for e in range(50):\n",
    "    train(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/torch/serialization.py:241: UserWarning: Couldn't retrieve source code for container of type LeNet. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    }
   ],
   "source": [
    "torch.save(model, 'model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'model_dict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model1 = LeNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model1.load_state_dict(torch.load('model_dict'))"
   ]
  }
 ],
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