Mnist_MLP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import argparse\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "class Args:\n",
    "        pass\n",
    "    \n",
    "args = Args()\n",
    "args.batch_size = 12\n",
    "args.cuda = True\n",
    "args.lr = 0.001\n",
    "args.momentum = 0.01\n",
    "args.epochs = 10\n",
    "args.log_interval = 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=True, download=True,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "    batch_size=args.batch_size, shuffle=True, **kwargs)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=False, \n",
    "                    transform=transforms.Compose([\n",
    "                        transforms.ToTensor(),\n",
    "                        transforms.Normalize((0.1307,), (0.3081,))\n",
    "                    ])),\n",
    "    batch_size=args.batch_size, shuffle=True, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_set = [i for i in enumerate(train_loader)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_set[45][1][1]\n",
    "#After tinkering a lot with this variable I can figure out the strcuture\n",
    "# train_set = ((1, batch1), (2, batch2), (3, batch3))\n",
    "# batchX = ((X1, X2, X3, .. Xn), (Y1, Y2, Y3, ... Yn))  where n -> batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i, (d, t) in enumerate(train_loader):\n",
    "    if i > 2:\n",
    "        break\n",
    "    print(d.size())\n",
    "    b = d.view(12, -1)\n",
    "    print(b.size())\n",
    "    print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Model0(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(Model0, self).__init__()\n",
    "        \n",
    "        self.input_layer = nn.Linear(28*28, 100)\n",
    "        self.output_layer = nn.Linear(100, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        x = self.input_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        \n",
    "        return F.log_softmax(x)\n",
    "\n",
    "class Model1(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(Model1, self).__init__()\n",
    "        \n",
    "        self.input_layer = nn.Linear(28*28, 100)\n",
    "        \n",
    "        self.hidden_layer = nn.Linear(100, 100)\n",
    "        \n",
    "        self.output_layer = nn.Linear(100, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        x = self.input_layer(x)\n",
    "        x = self.hidden_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        \n",
    "        return F.log_softmax(x)\n",
    "\n",
    "class Model2(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(Model2, self).__init__()\n",
    "        \n",
    "        self.input_layer = nn.Linear(28*28, 100)\n",
    "\n",
    "        self.hidden_layer = nn.Linear(100, 100)\n",
    "        self.hidden_layer1 = nn.Linear(100, 100)\n",
    "        \n",
    "        self.output_layer = nn.Linear(100, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        x = self.input_layer(x)\n",
    " \n",
    "        x = self.hidden_layer(x)\n",
    "        x = self.hidden_layer1(x)\n",
    "\n",
    "        x = self.output_layer(x)\n",
    "\n",
    "        \n",
    "        return F.log_softmax(x)\n",
    "    \n",
    "\n",
    "class Model3(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(Model3, self).__init__()\n",
    "        \n",
    "        self.input_layer = nn.Linear(28*28, 100)\n",
    "        \n",
    "        self.hidden_layer = nn.Linear(100, 100)\n",
    "        self.hidden_layer1 = nn.Linear(100, 100)\n",
    "        self.hidden_layer2 = nn.Linear(100, 100)\n",
    "\n",
    "        self.output_layer = nn.Linear(100, 10)\n",
    "        \n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        x = self.input_layer(x)\n",
    "        \n",
    "        x = self.hidden_layer(x)\n",
    "        x = self.hidden_layer1(x)\n",
    "        x = self.hidden_layer2(x)\n",
    "\n",
    "        x = self.output_layer(x)\n",
    "\n",
    "        \n",
    "        return F.log_softmax(x)\n",
    "    \n",
    "       \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models = Model0(), Model1(), Model2(), Model3() \n",
    "if args.cuda:\n",
    "    for model in models:\n",
    "        model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-76bd12eacaac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSGD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmomentum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmomentum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(epoch, model):\n",
    "    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n",
    "    for i in range(epoch):\n",
    "        model.train()\n",
    "        for batch_idx, (data, target) in enumerate(train_loader):\n",
    "            if args.cuda:\n",
    "                data, target = data.cuda(), target.cuda()\n",
    "            data = data.view(args.batch_size , -1)\n",
    "            data, target = Variable(data), Variable(target)\n",
    "            #print(data.size())\n",
    "            #print(target.size())\n",
    "            optimizer.zero_grad()\n",
    "            output = model(data)\n",
    "            #print(output.size())\n",
    "\n",
    "            loss = F.nll_loss(output, target)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            if batch_idx % args.log_interval == 0:\n",
    "                print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                    i, batch_idx * len(data), len(train_loader.dataset),\n",
    "                    100. * batch_idx / len(train_loader), loss.data[0]))\n",
    "                \n",
    "         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'MNIST_MLP.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test(epoch):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(test_loader):\n",
    "        if args.cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "            \n",
    "        data = data.view(data.size()[0] , -1)\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        #print('after:', data.size())\n",
    "        #print(target.size())\n",
    "        output = model(data)\n",
    "        print(output.data.max(1)[1], target.data)\n",
    "\n",
    "        loss = F.nll_loss(output, target)\n",
    "        \n",
    "        if batch_idx % args.log_interval == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.data[0]))\n",
    "\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Train Epoch: 1 [120/60000 (0%)]\tLoss: 2.305364\n",
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     ]
    },
    {
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      " 3\n",
      "[torch.cuda.LongTensor of size 12 (GPU 0)]\n",
      "\n",
      "\n",
      "    7\n",
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      "    3\n",
      "[torch.cuda.LongTensor of size 12x1 (GPU 0)]\n",
      " \n",
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      " 0\n",
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      " 5\n",
      "[torch.cuda.LongTensor of size 12 (GPU 0)]\n",
      "\n",
      "\n",
      "    9\n",
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      "[torch.cuda.LongTensor of size 12x1 (GPU 0)]\n",
      " \n",
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      "[torch.cuda.LongTensor of size 12 (GPU 0)]\n",
      "\n",
      "\n",
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      "[torch.cuda.LongTensor of size 12 (GPU 0)]\n",
      "\n",
      "\n",
      "    3\n",
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      "[torch.cuda.LongTensor of size 12x1 (GPU 0)]\n",
      " \n",
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      "[torch.cuda.LongTensor of size 12 (GPU 0)]\n",
      "\n",
      "\n",
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      "[torch.cuda.LongTensor of size 12x1 (GPU 0)]\n",
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      "[torch.cuda.LongTensor of size 12 (GPU 0)]\n",
      "\n",
      "\n",
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      "    1\n",
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      "    9\n",
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      "    7\n",
      "    4\n",
      "[torch.cuda.LongTensor of size 12x1 (GPU 0)]\n",
      " \n",
      " 3\n",
      " 2\n",
      " 4\n",
      " 5\n",
      " 2\n",
      " 4\n",
      " 7\n",
      " 0\n",
      " 3\n",
      " 2\n",
      " 1\n",
      " 6\n",
      "[torch.cuda.LongTensor of size 12 (GPU 0)]\n",
      "\n",
      "\n",
      "    1\n",
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      "    4\n",
      "    5\n",
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      "    1\n",
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      "    3\n",
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      "    7\n",
      "[torch.cuda.LongTensor of size 12x1 (GPU 0)]\n",
      " \n",
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      " 9\n",
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      " 2\n",
      "[torch.cuda.LongTensor of size 12 (GPU 0)]\n",
      "\n",
      "Train Epoch: 1 [9960/60000 (17%)]\tLoss: 2.442720\n",
      "\n",
      "    3\n",
      "    1\n",
      "    1\n",
      "    1\n",
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      "[torch.cuda.LongTensor of size 12x1 (GPU 0)]\n",
      " \n",
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      "[torch.cuda.LongTensor of size 12 (GPU 0)]\n",
      "\n",
      "\n",
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      "    1\n",
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      "    1\n",
      "    3\n",
      "[torch.cuda.LongTensor of size 12x1 (GPU 0)]\n",
      " \n",
      " 8\n",
      " 4\n",
      " 3\n",
      " 1\n",
      " 9\n",
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      " 6\n",
      " 2\n",
      " 9\n",
      " 8\n",
      " 5\n",
      " 9\n",
      "[torch.cuda.LongTensor of size 12 (GPU 0)]\n",
      "\n",
      "\n",
      " 3\n",
      " 1\n",
      " 1\n",
      " 3\n",
      "[torch.cuda.LongTensor of size 4x1 (GPU 0)]\n",
      " \n",
      " 6\n",
      " 6\n",
      " 9\n",
      " 0\n",
      "[torch.cuda.LongTensor of size 4 (GPU 0)]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_tuts(epoch):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    for data, target in test_loader:\n",
    "        if args.cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        \n",
    "        data = data.view(data.size()[0], -1)\n",
    "        data, target = Variable(data, volatile=True), Variable(target)\n",
    "        output = model(data)\n",
    "        test_loss += F.nll_loss(output, target).data[0]\n",
    "        pred = output.data.max(1)[1] # get the index of the max log-probability\n",
    "        correct += pred.eq(target.data).cpu().sum()\n",
    "\n",
    "    test_loss = test_loss\n",
    "    test_loss /= len(test_loader) # loss function already averages over batch size\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct / len(test_loader.dataset)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_tuts(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qqRr0tthM9dgHvheyow6+MGSjvhJr0+dxq7vOtuSJuKubJld+HrWEK1gAABJggQUAIAEW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\nWxgWq97itVWzx1vBtf/4WE8H//V9Idt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29dd6e6198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.  2.  1.]\n",
      " [ 3.  3.  5.]\n",
      " [ 1.  8.  0.]]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "from PIL import Image\n",
    "import pprint\n",
    "import numpy \n",
    "\n",
    "fig = plt.figure(1,(8., 8.))\n",
    "grid = ImageGrid(fig, 111,  # similar to subplot(111)\n",
    "                 nrows_ncols=(3, 3),  # creates 2x2 grid of axes\n",
    "                 axes_pad=0.1,  # pad between axes in inch.\n",
    "                 )\n",
    "\n",
    "model.load_state_dict(torch.load('MNIST_MLP.pth'))\n",
    "\n",
    "output = numpy.zeros(9)\n",
    "for i, (data, target) in enumerate(test_loader):\n",
    "    if i < 1:\n",
    "        \n",
    "        data1 = data.cuda()\n",
    "        data1 = data1.view(data.size()[0], -1)\n",
    "        out = model(Variable(data1))\n",
    "        #data = data.view(data.size()[0], -1)\n",
    "        for j in range(9):\n",
    "            \n",
    "            grid[j].matshow(Image.fromarray(data[j][0].numpy()))\n",
    "            output[j] = out.data.max(1)[1][j].cpu().numpy()[0]\n",
    "            \n",
    "    else:\n",
    "        break\n",
    "            \n",
    "output = output.reshape(3,3)\n",
    "plt.show()\n",
    "print(output)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}