intro-caffe-torch-theano/Intro to DL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Deep Learning on GPUs\n",
    "\n",
    "Lab created by Jon Barker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following timer counts down to a five minute warning before the lab instance shuts down.  You should get a pop up at the five minute warning reminding you to save your work!  If you are about to run out of time, please see the [Post-Lab](#Post-Lab-Summary) section for saving this lab to view offline later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<iframe id=\"timer\" src=\"timer/timer.html\" width=\"100%\" height=\"120px\"></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Before we begin, let's verify [WebSockets](http://en.wikipedia.org/wiki/WebSocket) are working on your system.  To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar above.  If all goes well, you should see some output returned below the grey cell.  If not, please consult the [Self-paced Lab Troubleshooting FAQ](https://developer.nvidia.com/self-paced-labs-faq#Troubleshooting) to debug the issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The answer should be three: 3\n"
     ]
    }
   ],
   "source": [
    "print \"The answer should be three: \" + str(1+2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's execute the cell below to display information about the GPUs running on the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sat Dec  2 09:47:36 2017       \r\n",
      "+------------------------------------------------------+                       \r\n",
      "| NVIDIA-SMI 346.46     Driver Version: 346.46         |                       \r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  GRID K520           On   | 0000:00:03.0     Off |                  N/A |\r\n",
      "| N/A   49C    P0    43W / 125W |     83MiB /  4095MiB |      0%      Default |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                       GPU Memory |\r\n",
      "|  GPU       PID  Type  Process name                               Usage      |\r\n",
      "|=============================================================================|\r\n",
      "|    0     15870    C   ./build/tools/caffe                             70MiB |\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In this class we will introduce the topic of Deep Learning, a rapidly growing segment of Artificial Intelligence.  Deep Learning is increasingly being used to deliver near-human level accuracy in image classification, voice recognition, natural language processing, and more.  In this class we will cover the basics of Deep Learning through some live examples, we will introduce the three major Deep Learning software frameworks and demonstrate why Deep Learning excels when run on GPUs.\n",
    "\n",
    "This introductory class is intended to serve as a first introduction to the concept of Deep Learning and a live tour of the major software frameworks.  There is some complex looking code presented, but it is not necessary to understand this code to complete the class.  There are some times where you will be waiting a couple of minutes for the Deep Learning algorithms to run - feel free to use this time to explore the code.\n",
    "\n",
    "By the end of this class you will hopefully be excited by the potential applications of Deep Learning and have a better idea of which of the frameworks you may want to learn more about in one of our [upcoming follow-on classes](https://developer.nvidia.com/deep-learning-courses)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo\n",
    "\n",
    "We will begin with a demonstration that will use a neural network to identify the subject of an image.  Click the button below to upload an image file from your local machine.  If you don't wish to upload an image just skip past this step and we'll randomly choose one of our images for you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<iframe id=\"upload\" src=\"upload\" width=\"100%\" height=\"200px\"></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you uploaded your own image, change the `your_image_name` to the new file name in the code below.  \n",
    "\n",
    "To run or execute the cell, click in it and either press the play button in the toolbar at the top, or use Ctrl + Enter on your keyboard.  For this demo and for the rest of the lab, you will know the lab is processing when you see a solid circle in the top-right of the window that looks like this: ![](jupyter_executing.png)\n",
    "Otherwise, when it is idle, you will see the following: ![](jupyter_idle.png)\n",
    "For troubleshooting, please see [Self-paced Lab Troubleshooting FAQ](https://developer.nvidia.com/self-paced-labs-faq#Troubleshooting) to debug the issue.\n",
    "\n",
    "After executing the below cell, it will load the image into memory and display it for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Bv9dBW00hCioV32CwAqDe86p9E8tzAPmZlXUhBbOHZ4kbTZ4+8AzJoIcQksFg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S61aoVXv/pVjAyPUKtVOXfmNHe97MWYXDC/ME+SptTrVaIoYGVlhTtf9jKOHD5Io17Fl3ZR2a6B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2+xtaEqy9ARG4OzJkjacRhbYU0IFRZYhKvo93tU/Q619xhng7xdBSWiAzpVSRVxXSkDhJFE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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ea867c850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "\n",
    "images_upload_dir = \"/home/ubuntu/images/\"\n",
    "your_image_name = \"image3.jpg\"\n",
    "image = images_upload_dir + your_image_name\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "im = Image.open(image)\n",
    "plt.imshow(im)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the image is displayed above, execute the below cell to get some predictions about what the image is showing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "library 0.113547\r\n",
      "bookshop, bookstore, bookstall 0.106699\r\n",
      "shoe shop, shoe-shop, shoe store 0.103562\r\n",
      "barbershop 0.0826674\r\n",
      "bakery, bakeshop, bakehouse 0.075637\r\n",
      "turnstile 0.0520235\r\n",
      "tobacco shop, tobacconist shop, tobacconist 0.0489212\r\n",
      "restaurant, eating house, eating place, eatery 0.0453319\r\n",
      "barber chair 0.0387792\r\n",
      "grocery store, grocery, food market, market 0.0349023\r\n"
     ]
    }
   ],
   "source": [
    "!/home/ubuntu/notebook/DLIntro/overfeat/bin/linux_64/cuda/overfeat_cuda -l -n 10 $image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What you see as output are 10 predictions (in order) about the contents of the image produced by a Deep Learning (DL) system called [Overfeat](http://cilvr.nyu.edu/doku.php?id=code:start). Each prediction is of an object which may be present in the image along with the confidence in the predictions.  In machine learning we call these predictions 'classifications'.\n",
    "\n",
    "If you were tasked with writing a computer program to make predictions like this, how would you do it?\n",
    "\n",
    "The traditional approach to this problem taken by the computer vision research community was to hand-craft functions which would look for particular features in the image that were believed to be indicative of certain objects or scenes.  For example, hard corners and straight edges might be believed to indicate the presence of manmade objects in the scene.  The responses from these feature extraction functions would then be fed into another function which would decide whether to declare a particular object had been detected in the image.\n",
    "\n",
    "![Traditional approach](files/DLIntro/tradition.png)\n",
    "\n",
    "<div align=\"center\">*Figure 1: Traditional approach to machine perception - hand-crafted features are extracted from the raw data and is then independently passed to a classification function. This typically leads to brittle and false-alarm prone detectors.*</div>\n",
    "\n",
    "Unfortunately there are a number of problems with this approach.  Firstly, it is very hard to think of robust, reliable features which map to specific object types.  Secondly, it is a massive task to come up with the right combination of features for every type of object you want to be able to classify.  Thirdly, it is very difficult to design functions that are robust to translations, rotations and scalings of objects in the image.  Together these problems resulted in traditional computer vision struggling to develop high accuracy object detectors and classifiers for a broad range of objects.\n",
    "\n",
    "The DL system that you saw demonstrated above was created in a very different way.  No human knowledge was encoded in the DL model about the types and combinations of features that are important for labelling different types of object or scene.  Instead, a combined feature extraction and classification model was learned by allowing the computer to examine millions of ground-truth labelled images to discover which features and combinations of features were most discriminative for each of the object classes (In machine learning we call this 'training' the model).  Furthermore, this was done in such a way that the model didn't just learn to classify the specific objects it was trained on; instead, it abstracted out the essence of those objects in such a way that it could recognize previously unseen but visually similar objects.  This learning process refined tens of millions of free parameters in the DL model above to be able to accurately classify 1000 different object types.  On a traditional CPU based server this training would take weeks to complete.  This network was instead trained in just hours by exploiting GPU acceleration.  Almost all DL systems today exploit massive GPU acceleration to make training practical.\n",
    "\n",
    "![Machine learning approach](files/DLIntro/DL.png)\n",
    "\n",
    "<div align=\"center\">*Figure 2: In the Deep Learning approach the feature extraction and classification functions are simultaneously learned using large amounts of training data.  The learned model can then be deployed in a new workflow where previously unseen data samples are classified.*</div>\n",
    "\n",
    "In this class we are going to take a tour of the technology behind DL and the popular open-source frameworks which make it accessible to researchers and software developers today.  Hopefully by the end of the class you will have an appreciation for the varied applications of DL, the benefits of some of the available DL frameworks and the essential nature of GPU acceleration for training real-world networks in reasonable timeframes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Deep Learning?\n",
    "\n",
    "Deep Learning (DL) is a branch of artificial intelligence research that is attempting to develop the techniques that will allow computers to learn complex perception tasks such as seeing and hearing at human levels of performance.  Recent advances in DL have yielded startling performance gains in fields such as computer vision, speech recognition and natural language understanding.  DL is already in use today to understand data and user inputs in technologies such as virtual personal assistants and online image search.  DL is an active area of ongoing research where it is envisaged that human level perception of unstructured data will enable technologies such as self-driving cars and truly intelligent machines.\n",
    "\n",
    "DL attempts to use large volumes of unstructured data, such as images and audio clips, to learn hierarchical models which capture the complex structure in the data and then use these models to predict properties of previously unseen data.  For example, DL has proven extremely successful at learning hierarchical models of the visual features and concepts represented in handheld camera images and then using those models to automatically label previously unseen images with the objects present in them.\n",
    "\n",
    "The models learned through DL are biologically inspired artificial neural networks (ANNs).  An ANN is an interconnected group of nodes, akin to the vast network of neurons in a brain. In the image below, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another.  Input data is fed into the red nodes, and dependent on the weights on the connections between nodes, causes varying levels of activation of the subsequent hidden and output nodes.  In our example above the input nodes would be connected to image pixels and the output nodes would have a one-to-one correspondence with the possible object classes; the job of the hidden nodes is to learn the complex function which maps pixels to object labels. \n",
    "\n",
    "![ANN](files/DLIntro/ANN.png)\n",
    "\n",
    "<div align=\"center\">*Figure 3:  Fully-connected Artificial Neural Network with one hidden layer*</div>\n",
    "\n",
    "For the advanced reader, the activation of a neuron is just a function of a variable which is the weighted sum of the inputs.  For basic neural networks the function is a sigmoid function.  The idea is that if the weighted sum of the inputs exceeds a threshold value, the neuron gives a positive output.\n",
    "\n",
    "For ANNs to be effective in difficult perception tasks, such as object labelling in images, these networks usually have many stacked layers of artificial neurons each with many neurons in the layer.  It is these many wide layers that lead to these networks being labelled Deep Neural Networks (DNNs).  \n",
    "\n",
    "One particular class of DNN which has shown great capability in visual perception tasks is called the Convolutional Neural Network (CNN).  CNNs have a structure which loosely resembles the structure the human visual cortex where lower levels of the model hierarchy are focused on small and local visual details, such as oriented line segments, which aggregate together into higher levels of the model which correspond to complex human concepts, such as faces and animals.\n",
    "\n",
    "![CNN](files/DLIntro/cnn.png)\n",
    "\n",
    "<div align=\"center\">*Figure 4:  Deep Neural Networks learn a hierarchical model of the input data in which the layers correspond to increasingly complex real-world concepts.  The number of parameters in a network is a function of the number of neurons in the network and the architecture of the network connectivity.*</div>\n",
    "\n",
    "The [Imagenet Challenge](http://image-net.org/challenges/LSVRC/2014/index) is an annual competition where competitors are provided with 1.2 million natural images from the internet which are labelled with the objects that appear in those images using 1000 different class labels.  Competitors must create a model using this data which will then be tested against a further 100,000 images to see how accurately the model can identify and localize objects within them.  Over the past few years CNN based approaches have come to dominate the competition with accuracy in the object identification task recently exceeding 95% - which is comparable with human performance in labelling the objects in the test dataset.\n",
    "\n",
    "The mathematics that underpins DL training is predominantly linear matrix algebra.  Computation of this type of mathematics is highly parallelizable making it a perfect fit for acceleration using GPUs.  Training a DNN that can be competitive in the Imagenet challenge is computationally very intensive and would take weeks, months or even years of computation on a CPU based system.  Through massive parallelization, GPUs can reduce this training time to days or even hours. Now almost all entrants into the Imagenet Challenge use GPUs, sometimes many of them, to train CNNs with billions of trainable parameters.  The graph below shows how significant recent improvements in accuracy in the Imagenet challenge correlate with the explosion in the use of GPUs for training DNN entries.\n",
    "\n",
    "![](files/DLIntro/gpuimagenet.png)\n",
    "\n",
    "<div align=\"center\">*Figure 5: The introduction of GPU accelerated Deep Learning into the ImageNet challenge began a period of unprecedented performance improvements.*</div>\n",
    "\n",
    "GPUs are not only far more computationally efficient for training DNNs - they are also far more cost effective at scale.  In 2013 Google built it's \"Google Brain\" - a 1000 server, 16,000 core CPU based cluster for training a state-of-the-art DNN for image understanding.  It cost an estimated \\$5,000,000 to build.  Shortly afterwards a team from the Stanford AI Lab showed that using 3 GPU accelerated servers with 12 GPUs per server - a total of 18,432 cores - they could train the same DNN and achieve the same performance.  Their system cost approximately $33,000 - 150th of the hardware cost and energy usage ([Wired Article](http://www.wired.com/2013/06/andrew_ng/)).\n",
    "\n",
    "![Google Brain vs. Stanford GPUs](files/DLIntro/gbrain.png)\n",
    "\n",
    "<div align=\"center\">*Figure 6: GPUs are the most cost effective and size and power efficient means for training large Deep Neural Networks.*</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Learning Frameworks\n",
    "\n",
    "In recent years a number of software development frameworks have been created to make researching and applying DL more accessible and efficient.  In this class we will introduce three of the current most popular frameworks: Caffe, Theano and Torch.  They all share the common benefits of providing highly optimized GPU enabled code specific to the computations required for training DNNs whilst providing access to that code through simple command line or scripting language interfaces such as Python.  Many powerful DNNs can be trained and deployed using these frameworks without ever having to write any GPU or complex compiled code but whilst still benefiting from the huge training speed-up afforded by GPU acceleration.\n",
    "\n",
    "In order to compare and contrast these frameworks we will look at how you train a CNN in each of the frameworks to automatically classify images of different types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Framework 1: Caffe\n",
    "\n",
    "[Caffe](http://caffe.berkeleyvision.org/) is a DL framework created by PhD student Yangqing Jia from UC Berkley.  Some of benefits of Caffe to the DL practitioner and researcher:\n",
    "* Caffe is fast due to it's highly optimized C/CUDA backend which integrates GPU acceleration\n",
    "* Caffe is still very accessible due to command line, Python and Matlab interfaces  \n",
    "* The wrapper interfaces make it very easy to integrate DNN training and deployment into larger data analytics workflows  \n",
    "* Caffe has a large open-source development community adding new features all time\n",
    "* Caffe has an associated model-zoo where researchers can upload trained models for others to fine tune or use for inference using their own data\n",
    "\n",
    "You may choose to use Caffe as your DL framework if you fit the following profile:\n",
    "\n",
    "* You are interested in quickly getting up and running training and testing DNNs against your own image datasets without writing a lot of new code\n",
    "* You are a C/CUDA developer wishing to extend DL functionality within a framework\n",
    "\n",
    "One of the key benefits of Caffe for those wishing to apply deep learning to their own data is that defining and building many different types of DNN is possible without ever writing a line of code.  In this section we'll guide you through doing just that.\n",
    "\n",
    "#### Caffe example\n",
    "\n",
    "We're going to work with the well-known [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar.html) dataset which consists of 60000 32x32 color images split into ten classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship and truck.  Here are some examples:\n",
    "\n",
    "![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane1.png)![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile10.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/bird7.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/cat9.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/deer6.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/dog4.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/horse6.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/ship1.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/truck5.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane4.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile4.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/bird1.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/cat1.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/deer4.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/deer8.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/dog10.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/ship7.png) ![](http://www.cs.toronto.edu/~kriz/cifar-10-sample/horse4.png)\n",
    "\n",
    "We're working with the smaller CIFAR-10 dataset so that you can actually train a network from start to finish within the time available in this class; however, the CNN we're defining and training exhibits all the essential features of the much larger networks used in the ImageNet challenge.\n",
    "\n",
    "Training a DNN in Caffe requires you to provide three things: a [network definition file](files/caffe/examples/cifar10/cifar10_quick.prototxt) specifying the neural network architecture, a [solver definition file](files/caffe/examples/cifar10/cifar10_quick_solver.prototxt) specifying the training parameters and a dataset.  We've prepared each of these for you and there's no need to change them but you can take a look at them using the links provided.  The network and solver definition files are human readable text files.  Our dataset of images has been preloaded into a database to make it faster to read into Caffe.  \n",
    "\n",
    "In the network definition file we specify the inputs, outputs and structure of each of the layers in the DNN.  Here is an example of how a convolutional layer is defined, which is representative of how all layers are defined:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "layer {\n",
    "  name: \"conv1\"\n",
    "  type: \"Convolution\"\n",
    "  bottom: \"data\"\n",
    "  top: \"conv1\"\n",
    "  param {\n",
    "    lr_mult: 1\n",
    "  }\n",
    "  param {\n",
    "    lr_mult: 2\n",
    "  }\n",
    "  convolution_param {\n",
    "    num_output: 32\n",
    "    pad: 2\n",
    "    kernel_size: 5\n",
    "    stride: 1\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The solver definition file is simply a list of parameters which are passed to the DNN training algorithm.  Here is how the parameters are set in this example:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# The train/test net protocol buffer definition\n",
    "net: \"examples/cifar10/cifar10_quick_train_test.prototxt\"\n",
    "# test_iter specifies how many forward passes the test should carry out.\n",
    "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\n",
    "# covering the full 10,000 testing images.\n",
    "test_iter: 100\n",
    "# Carry out testing every 500 training iterations.\n",
    "test_interval: 500\n",
    "# The base learning rate, momentum and the weight decay of the network.\n",
    "base_lr: 0.001\n",
    "momentum: 0.9\n",
    "weight_decay: 0.004\n",
    "# The learning rate policy\n",
    "lr_policy: \"fixed\"\n",
    "# Display every 100 iterations\n",
    "display: 100\n",
    "# The maximum number of iterations\n",
    "max_iter: 4000\n",
    "# snapshot intermediate results\n",
    "snapshot: 4000\n",
    "snapshot_prefix: \"examples/cifar10/cifar10_quick\"\n",
    "# solver mode: CPU or GPU\n",
    "solver_mode: GPU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training process consists of first randomly initializing the weights on the connections between neurons in the network whose structure is defined in the network definition file.  Then, one at a time, each image in the training dataset is fed into the network along with its label.  The network guesses the label based purely on the input pixels - this is called classification.  If the guess is wrong then a small modification is made to the weights so that the network will guess more accurately for that image next time it sees it.  This process is repeated many thousands of times until the network converges to a stable average classification accuracy across all the training images.\n",
    "\n",
    "Training the model is as simple as running the cell below.  When you do so, you will see a large number of messages flying by.  First the network will be initialized layer by layer and then training will begin.  After each 100 training iterations you will get an update on the training loss which should be decreasing and after each 500 you will get an update on the accuracy against a test set (Test net output #0) which should be increasing.  An iteration is when the network trains on each image in the training set exactly once.  **Training the network takes a couple of minutes**, so spend the time familiarizing yourself with Caffe's output - keep scrolling to the bottom of the output window to see the latest updates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "libdc1394 error: Failed to initialize libdc1394\n",
      "I1202 09:47:54.355943 16239 caffe.cpp:99] Use GPU with device ID 0\n",
      "I1202 09:47:54.496587 16239 caffe.cpp:107] Starting Optimization\n",
      "I1202 09:47:54.496724 16239 solver.cpp:32] Initializing solver from parameters: \n",
      "test_iter: 100\n",
      "test_interval: 500\n",
      "base_lr: 0.001\n",
      "display: 100\n",
      "max_iter: 4000\n",
      "lr_policy: \"fixed\"\n",
      "momentum: 0.9\n",
      "weight_decay: 0.004\n",
      "snapshot: 4000\n",
      "snapshot_prefix: \"examples/cifar10/cifar10_quick\"\n",
      "solver_mode: GPU\n",
      "net: \"examples/cifar10/cifar10_quick_train_test.prototxt\"\n",
      "I1202 09:47:54.496804 16239 solver.cpp:70] Creating training net from net file: examples/cifar10/cifar10_quick_train_test.prototxt\n",
      "I1202 09:47:54.497236 16239 net.cpp:253] The NetState phase (0) differed from the phase (1) specified by a rule in layer cifar\n",
      "I1202 09:47:54.497272 16239 net.cpp:253] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\n",
      "I1202 09:47:54.497386 16239 net.cpp:42] Initializing net from parameters: \n",
      "name: \"CIFAR10_quick\"\n",
      "state {\n",
      "  phase: TRAIN\n",
      "}\n",
      "layer {\n",
      "  name: \"cifar\"\n",
      "  type: \"Data\"\n",
      "  top: \"data\"\n",
      "  top: \"label\"\n",
      "  include {\n",
      "    phase: TRAIN\n",
      "  }\n",
      "  transform_param {\n",
      "    mean_file: \"examples/cifar10/mean.binaryproto\"\n",
      "  }\n",
      "  data_param {\n",
      "    source: \"examples/cifar10/cifar10_train_lmdb\"\n",
      "    batch_size: 100\n",
      "    backend: LMDB\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"conv1\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"data\"\n",
      "  top: \"conv1\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 32\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.0001\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"pool1\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv1\"\n",
      "  top: \"pool1\"\n",
      "  pooling_param {\n",
      "    pool: MAX\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu1\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"pool1\"\n",
      "  top: \"pool1\"\n",
      "}\n",
      "layer {\n",
      "  name: \"conv2\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"pool1\"\n",
      "  top: \"conv2\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 32\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.01\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu2\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"conv2\"\n",
      "  top: \"conv2\"\n",
      "}\n",
      "layer {\n",
      "  name: \"pool2\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv2\"\n",
      "  top: \"pool2\"\n",
      "  pooling_param {\n",
      "    pool: AVE\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"conv3\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"pool2\"\n",
      "  top: \"conv3\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 64\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.01\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu3\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"conv3\"\n",
      "  top: \"conv3\"\n",
      "}\n",
      "layer {\n",
      "  name: \"pool3\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv3\"\n",
      "  top: \"pool3\"\n",
      "  pooling_param {\n",
      "    pool: AVE\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"ip1\"\n",
      "  type: \"InnerProduct\"\n",
      "  bottom: \"pool3\"\n",
      "  top: \"ip1\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  inner_product_param {\n",
      "    num_output: 64\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.1\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"ip2\"\n",
      "  type: \"InnerProduct\"\n",
      "  bottom: \"ip1\"\n",
      "  top: \"ip2\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  inner_product_param {\n",
      "    num_output: 10\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.1\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"loss\"\n",
      "  type: \"SoftmaxWithLoss\"\n",
      "  bottom: \"ip2\"\n",
      "  bottom: \"label\"\n",
      "  top: \"loss\"\n",
      "}\n",
      "I1202 09:47:54.498025 16239 layer_factory.hpp:74] Creating layer cifar\n",
      "I1202 09:47:54.498054 16239 net.cpp:76] Creating Layer cifar\n",
      "I1202 09:47:54.498070 16239 net.cpp:334] cifar -> data\n",
      "I1202 09:47:54.498111 16239 net.cpp:334] cifar -> label\n",
      "I1202 09:47:54.498129 16239 net.cpp:105] Setting up cifar\n",
      "I1202 09:47:54.498208 16239 db.cpp:34] Opened lmdb examples/cifar10/cifar10_train_lmdb\n",
      "I1202 09:47:54.498276 16239 data_layer.cpp:67] output data size: 100,3,32,32\n",
      "I1202 09:47:54.498296 16239 data_transformer.cpp:22] Loading mean file from: examples/cifar10/mean.binaryproto\n",
      "I1202 09:47:54.498924 16239 net.cpp:112] Top shape: 100 3 32 32 (307200)\n",
      "I1202 09:47:54.498947 16239 net.cpp:112] Top shape: 100 1 1 1 (100)\n",
      "I1202 09:47:54.498960 16239 layer_factory.hpp:74] Creating layer conv1\n",
      "I1202 09:47:54.499028 16239 net.cpp:76] Creating Layer conv1\n",
      "I1202 09:47:54.499043 16239 net.cpp:372] conv1 <- data\n",
      "I1202 09:47:54.499063 16239 net.cpp:334] conv1 -> conv1\n",
      "I1202 09:47:54.499081 16239 net.cpp:105] Setting up conv1\n",
      "I1202 09:47:54.558872 16239 net.cpp:112] Top shape: 100 32 32 32 (3276800)\n",
      "I1202 09:47:54.558954 16239 layer_factory.hpp:74] Creating layer pool1\n",
      "I1202 09:47:54.558984 16239 net.cpp:76] Creating Layer pool1\n",
      "I1202 09:47:54.558995 16239 net.cpp:372] pool1 <- conv1\n",
      "I1202 09:47:54.559010 16239 net.cpp:334] pool1 -> pool1\n",
      "I1202 09:47:54.559025 16239 net.cpp:105] Setting up pool1\n",
      "I1202 09:47:54.559185 16239 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:47:54.559203 16239 layer_factory.hpp:74] Creating layer relu1\n",
      "I1202 09:47:54.559216 16239 net.cpp:76] Creating Layer relu1\n",
      "I1202 09:47:54.559226 16239 net.cpp:372] relu1 <- pool1\n",
      "I1202 09:47:54.559238 16239 net.cpp:323] relu1 -> pool1 (in-place)\n",
      "I1202 09:47:54.559249 16239 net.cpp:105] Setting up relu1\n",
      "I1202 09:47:54.559300 16239 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:47:54.559314 16239 layer_factory.hpp:74] Creating layer conv2\n",
      "I1202 09:47:54.559330 16239 net.cpp:76] Creating Layer conv2\n",
      "I1202 09:47:54.559340 16239 net.cpp:372] conv2 <- pool1\n",
      "I1202 09:47:54.559352 16239 net.cpp:334] conv2 -> conv2\n",
      "I1202 09:47:54.559367 16239 net.cpp:105] Setting up conv2\n",
      "I1202 09:47:54.560411 16239 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:47:54.560444 16239 layer_factory.hpp:74] Creating layer relu2\n",
      "I1202 09:47:54.560458 16239 net.cpp:76] Creating Layer relu2\n",
      "I1202 09:47:54.560467 16239 net.cpp:372] relu2 <- conv2\n",
      "I1202 09:47:54.560478 16239 net.cpp:323] relu2 -> conv2 (in-place)\n",
      "I1202 09:47:54.560490 16239 net.cpp:105] Setting up relu2\n",
      "I1202 09:47:54.560537 16239 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:47:54.560550 16239 layer_factory.hpp:74] Creating layer pool2\n",
      "I1202 09:47:54.560562 16239 net.cpp:76] Creating Layer pool2\n",
      "I1202 09:47:54.560571 16239 net.cpp:372] pool2 <- conv2\n",
      "I1202 09:47:54.560582 16239 net.cpp:334] pool2 -> pool2\n",
      "I1202 09:47:54.560593 16239 net.cpp:105] Setting up pool2\n",
      "I1202 09:47:54.560724 16239 net.cpp:112] Top shape: 100 32 8 8 (204800)\n",
      "I1202 09:47:54.560740 16239 layer_factory.hpp:74] Creating layer conv3\n",
      "I1202 09:47:54.560755 16239 net.cpp:76] Creating Layer conv3\n",
      "I1202 09:47:54.560765 16239 net.cpp:372] conv3 <- pool2\n",
      "I1202 09:47:54.560776 16239 net.cpp:334] conv3 -> conv3\n",
      "I1202 09:47:54.560789 16239 net.cpp:105] Setting up conv3\n",
      "I1202 09:47:54.562707 16239 net.cpp:112] Top shape: 100 64 8 8 (409600)\n",
      "I1202 09:47:54.562741 16239 layer_factory.hpp:74] Creating layer relu3\n",
      "I1202 09:47:54.562758 16239 net.cpp:76] Creating Layer relu3\n",
      "I1202 09:47:54.562768 16239 net.cpp:372] relu3 <- conv3\n",
      "I1202 09:47:54.562780 16239 net.cpp:323] relu3 -> conv3 (in-place)\n",
      "I1202 09:47:54.562791 16239 net.cpp:105] Setting up relu3\n",
      "I1202 09:47:54.562840 16239 net.cpp:112] Top shape: 100 64 8 8 (409600)\n",
      "I1202 09:47:54.562852 16239 layer_factory.hpp:74] Creating layer pool3\n",
      "I1202 09:47:54.562865 16239 net.cpp:76] Creating Layer pool3\n",
      "I1202 09:47:54.562875 16239 net.cpp:372] pool3 <- conv3\n",
      "I1202 09:47:54.562885 16239 net.cpp:334] pool3 -> pool3\n",
      "I1202 09:47:54.562896 16239 net.cpp:105] Setting up pool3\n",
      "I1202 09:47:54.562944 16239 net.cpp:112] Top shape: 100 64 4 4 (102400)\n",
      "I1202 09:47:54.562957 16239 layer_factory.hpp:74] Creating layer ip1\n",
      "I1202 09:47:54.562973 16239 net.cpp:76] Creating Layer ip1\n",
      "I1202 09:47:54.562983 16239 net.cpp:372] ip1 <- pool3\n",
      "I1202 09:47:54.562996 16239 net.cpp:334] ip1 -> ip1\n",
      "I1202 09:47:54.563012 16239 net.cpp:105] Setting up ip1\n",
      "I1202 09:47:54.565166 16239 net.cpp:112] Top shape: 100 64 1 1 (6400)\n",
      "I1202 09:47:54.565192 16239 layer_factory.hpp:74] Creating layer ip2\n",
      "I1202 09:47:54.565205 16239 net.cpp:76] Creating Layer ip2\n",
      "I1202 09:47:54.565215 16239 net.cpp:372] ip2 <- ip1\n",
      "I1202 09:47:54.565256 16239 net.cpp:334] ip2 -> ip2\n",
      "I1202 09:47:54.565270 16239 net.cpp:105] Setting up ip2\n",
      "I1202 09:47:54.565304 16239 net.cpp:112] Top shape: 100 10 1 1 (1000)\n",
      "I1202 09:47:54.565320 16239 layer_factory.hpp:74] Creating layer loss\n",
      "I1202 09:47:54.565333 16239 net.cpp:76] Creating Layer loss\n",
      "I1202 09:47:54.565345 16239 net.cpp:372] loss <- ip2\n",
      "I1202 09:47:54.565354 16239 net.cpp:372] loss <- label\n",
      "I1202 09:47:54.565366 16239 net.cpp:334] loss -> loss\n",
      "I1202 09:47:54.565381 16239 net.cpp:105] Setting up loss\n",
      "I1202 09:47:54.565398 16239 layer_factory.hpp:74] Creating layer loss\n",
      "I1202 09:47:54.565469 16239 net.cpp:112] Top shape: 1 1 1 1 (1)\n",
      "I1202 09:47:54.565482 16239 net.cpp:118]     with loss weight 1\n",
      "I1202 09:47:54.565511 16239 net.cpp:163] loss needs backward computation.\n",
      "I1202 09:47:54.565521 16239 net.cpp:163] ip2 needs backward computation.\n",
      "I1202 09:47:54.565531 16239 net.cpp:163] ip1 needs backward computation.\n",
      "I1202 09:47:54.565539 16239 net.cpp:163] pool3 needs backward computation.\n",
      "I1202 09:47:54.565548 16239 net.cpp:163] relu3 needs backward computation.\n",
      "I1202 09:47:54.565557 16239 net.cpp:163] conv3 needs backward computation.\n",
      "I1202 09:47:54.565565 16239 net.cpp:163] pool2 needs backward computation.\n",
      "I1202 09:47:54.565574 16239 net.cpp:163] relu2 needs backward computation.\n",
      "I1202 09:47:54.565582 16239 net.cpp:163] conv2 needs backward computation.\n",
      "I1202 09:47:54.565592 16239 net.cpp:163] relu1 needs backward computation.\n",
      "I1202 09:47:54.565599 16239 net.cpp:163] pool1 needs backward computation.\n",
      "I1202 09:47:54.565608 16239 net.cpp:163] conv1 needs backward computation.\n",
      "I1202 09:47:54.565618 16239 net.cpp:165] cifar does not need backward computation.\n",
      "I1202 09:47:54.565626 16239 net.cpp:201] This network produces output loss\n",
      "I1202 09:47:54.565644 16239 net.cpp:446] Collecting Learning Rate and Weight Decay.\n",
      "I1202 09:47:54.565659 16239 net.cpp:213] Network initialization done.\n",
      "I1202 09:47:54.565668 16239 net.cpp:214] Memory required for data: 31978804\n",
      "I1202 09:47:54.566045 16239 solver.cpp:154] Creating test net (#0) specified by net file: examples/cifar10/cifar10_quick_train_test.prototxt\n",
      "I1202 09:47:54.566083 16239 net.cpp:253] The NetState phase (1) differed from the phase (0) specified by a rule in layer cifar\n",
      "I1202 09:47:54.566205 16239 net.cpp:42] Initializing net from parameters: \n",
      "name: \"CIFAR10_quick\"\n",
      "state {\n",
      "  phase: TEST\n",
      "}\n",
      "layer {\n",
      "  name: \"cifar\"\n",
      "  type: \"Data\"\n",
      "  top: \"data\"\n",
      "  top: \"label\"\n",
      "  include {\n",
      "    phase: TEST\n",
      "  }\n",
      "  transform_param {\n",
      "    mean_file: \"examples/cifar10/mean.binaryproto\"\n",
      "  }\n",
      "  data_param {\n",
      "    source: \"examples/cifar10/cifar10_test_lmdb\"\n",
      "    batch_size: 100\n",
      "    backend: LMDB\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"conv1\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"data\"\n",
      "  top: \"conv1\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 32\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.0001\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"pool1\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv1\"\n",
      "  top: \"pool1\"\n",
      "  pooling_param {\n",
      "    pool: MAX\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu1\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"pool1\"\n",
      "  top: \"pool1\"\n",
      "}\n",
      "layer {\n",
      "  name: \"conv2\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"pool1\"\n",
      "  top: \"conv2\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 32\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.01\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu2\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"conv2\"\n",
      "  top: \"conv2\"\n",
      "}\n",
      "layer {\n",
      "  name: \"pool2\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv2\"\n",
      "  top: \"pool2\"\n",
      "  pooling_param {\n",
      "    pool: AVE\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"conv3\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"pool2\"\n",
      "  top: \"conv3\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 64\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.01\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu3\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"conv3\"\n",
      "  top: \"conv3\"\n",
      "}\n",
      "layer {\n",
      "  name: \"pool3\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv3\"\n",
      "  top: \"pool3\"\n",
      "  pooling_param {\n",
      "    pool: AVE\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"ip1\"\n",
      "  type: \"InnerProduct\"\n",
      "  bottom: \"pool3\"\n",
      "  top: \"ip1\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  inner_product_param {\n",
      "    num_output: 64\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.1\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"ip2\"\n",
      "  type: \"InnerProduct\"\n",
      "  bottom: \"ip1\"\n",
      "  top: \"ip2\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  inner_product_param {\n",
      "    num_output: 10\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.1\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"accuracy\"\n",
      "  type: \"Accuracy\"\n",
      "  bottom: \"ip2\"\n",
      "  bottom: \"label\"\n",
      "  top: \"accuracy\"\n",
      "  include {\n",
      "    phase: TEST\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"loss\"\n",
      "  type: \"SoftmaxWithLoss\"\n",
      "  bottom: \"ip2\"\n",
      "  bottom: \"label\"\n",
      "  top: \"loss\"\n",
      "}\n",
      "I1202 09:47:54.566879 16239 layer_factory.hpp:74] Creating layer cifar\n",
      "I1202 09:47:54.566896 16239 net.cpp:76] Creating Layer cifar\n",
      "I1202 09:47:54.566908 16239 net.cpp:334] cifar -> data\n",
      "I1202 09:47:54.566923 16239 net.cpp:334] cifar -> label\n",
      "I1202 09:47:54.566936 16239 net.cpp:105] Setting up cifar\n",
      "I1202 09:47:54.566982 16239 db.cpp:34] Opened lmdb examples/cifar10/cifar10_test_lmdb\n",
      "I1202 09:47:54.567023 16239 data_layer.cpp:67] output data size: 100,3,32,32\n",
      "I1202 09:47:54.567036 16239 data_transformer.cpp:22] Loading mean file from: examples/cifar10/mean.binaryproto\n",
      "I1202 09:47:54.567437 16239 net.cpp:112] Top shape: 100 3 32 32 (307200)\n",
      "I1202 09:47:54.567459 16239 net.cpp:112] Top shape: 100 1 1 1 (100)\n",
      "I1202 09:47:54.567472 16239 layer_factory.hpp:74] Creating layer label_cifar_1_split\n",
      "I1202 09:47:54.567487 16239 net.cpp:76] Creating Layer label_cifar_1_split\n",
      "I1202 09:47:54.567497 16239 net.cpp:372] label_cifar_1_split <- label\n",
      "I1202 09:47:54.567512 16239 net.cpp:334] label_cifar_1_split -> label_cifar_1_split_0\n",
      "I1202 09:47:54.567526 16239 net.cpp:334] label_cifar_1_split -> label_cifar_1_split_1\n",
      "I1202 09:47:54.567538 16239 net.cpp:105] Setting up label_cifar_1_split\n",
      "I1202 09:47:54.567550 16239 net.cpp:112] Top shape: 100 1 1 1 (100)\n",
      "I1202 09:47:54.567559 16239 net.cpp:112] Top shape: 100 1 1 1 (100)\n",
      "I1202 09:47:54.567569 16239 layer_factory.hpp:74] Creating layer conv1\n",
      "I1202 09:47:54.567581 16239 net.cpp:76] Creating Layer conv1\n",
      "I1202 09:47:54.567592 16239 net.cpp:372] conv1 <- data\n",
      "I1202 09:47:54.567603 16239 net.cpp:334] conv1 -> conv1\n",
      "I1202 09:47:54.567616 16239 net.cpp:105] Setting up conv1\n",
      "I1202 09:47:54.567951 16239 net.cpp:112] Top shape: 100 32 32 32 (3276800)\n",
      "I1202 09:47:54.567986 16239 layer_factory.hpp:74] Creating layer pool1\n",
      "I1202 09:47:54.568001 16239 net.cpp:76] Creating Layer pool1\n",
      "I1202 09:47:54.568013 16239 net.cpp:372] pool1 <- conv1\n",
      "I1202 09:47:54.568024 16239 net.cpp:334] pool1 -> pool1\n",
      "I1202 09:47:54.568037 16239 net.cpp:105] Setting up pool1\n",
      "I1202 09:47:54.568176 16239 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:47:54.568192 16239 layer_factory.hpp:74] Creating layer relu1\n",
      "I1202 09:47:54.568207 16239 net.cpp:76] Creating Layer relu1\n",
      "I1202 09:47:54.568217 16239 net.cpp:372] relu1 <- pool1\n",
      "I1202 09:47:54.568229 16239 net.cpp:323] relu1 -> pool1 (in-place)\n",
      "I1202 09:47:54.568240 16239 net.cpp:105] Setting up relu1\n",
      "I1202 09:47:54.568291 16239 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:47:54.568305 16239 layer_factory.hpp:74] Creating layer conv2\n",
      "I1202 09:47:54.568318 16239 net.cpp:76] Creating Layer conv2\n",
      "I1202 09:47:54.568328 16239 net.cpp:372] conv2 <- pool1\n",
      "I1202 09:47:54.568339 16239 net.cpp:334] conv2 -> conv2\n",
      "I1202 09:47:54.568352 16239 net.cpp:105] Setting up conv2\n",
      "I1202 09:47:54.569463 16239 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:47:54.569495 16239 layer_factory.hpp:74] Creating layer relu2\n",
      "I1202 09:47:54.569509 16239 net.cpp:76] Creating Layer relu2\n",
      "I1202 09:47:54.569533 16239 net.cpp:372] relu2 <- conv2\n",
      "I1202 09:47:54.569545 16239 net.cpp:323] relu2 -> conv2 (in-place)\n",
      "I1202 09:47:54.569556 16239 net.cpp:105] Setting up relu2\n",
      "I1202 09:47:54.569614 16239 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:47:54.569628 16239 layer_factory.hpp:74] Creating layer pool2\n",
      "I1202 09:47:54.569643 16239 net.cpp:76] Creating Layer pool2\n",
      "I1202 09:47:54.569651 16239 net.cpp:372] pool2 <- conv2\n",
      "I1202 09:47:54.569663 16239 net.cpp:334] pool2 -> pool2\n",
      "I1202 09:47:54.569674 16239 net.cpp:105] Setting up pool2\n",
      "I1202 09:47:54.569726 16239 net.cpp:112] Top shape: 100 32 8 8 (204800)\n",
      "I1202 09:47:54.569739 16239 layer_factory.hpp:74] Creating layer conv3\n",
      "I1202 09:47:54.569754 16239 net.cpp:76] Creating Layer conv3\n",
      "I1202 09:47:54.569764 16239 net.cpp:372] conv3 <- pool2\n",
      "I1202 09:47:54.569775 16239 net.cpp:334] conv3 -> conv3\n",
      "I1202 09:47:54.569788 16239 net.cpp:105] Setting up conv3\n",
      "I1202 09:47:54.571722 16239 net.cpp:112] Top shape: 100 64 8 8 (409600)\n",
      "I1202 09:47:54.571753 16239 layer_factory.hpp:74] Creating layer relu3\n",
      "I1202 09:47:54.571765 16239 net.cpp:76] Creating Layer relu3\n",
      "I1202 09:47:54.571775 16239 net.cpp:372] relu3 <- conv3\n",
      "I1202 09:47:54.571789 16239 net.cpp:323] relu3 -> conv3 (in-place)\n",
      "I1202 09:47:54.571801 16239 net.cpp:105] Setting up relu3\n",
      "I1202 09:47:54.571933 16239 net.cpp:112] Top shape: 100 64 8 8 (409600)\n",
      "I1202 09:47:54.571949 16239 layer_factory.hpp:74] Creating layer pool3\n",
      "I1202 09:47:54.571960 16239 net.cpp:76] Creating Layer pool3\n",
      "I1202 09:47:54.571970 16239 net.cpp:372] pool3 <- conv3\n",
      "I1202 09:47:54.571985 16239 net.cpp:334] pool3 -> pool3\n",
      "I1202 09:47:54.571996 16239 net.cpp:105] Setting up pool3\n",
      "I1202 09:47:54.572051 16239 net.cpp:112] Top shape: 100 64 4 4 (102400)\n",
      "I1202 09:47:54.572063 16239 layer_factory.hpp:74] Creating layer ip1\n",
      "I1202 09:47:54.572075 16239 net.cpp:76] Creating Layer ip1\n",
      "I1202 09:47:54.572084 16239 net.cpp:372] ip1 <- pool3\n",
      "I1202 09:47:54.572098 16239 net.cpp:334] ip1 -> ip1\n",
      "I1202 09:47:54.572110 16239 net.cpp:105] Setting up ip1\n",
      "I1202 09:47:54.574296 16239 net.cpp:112] Top shape: 100 64 1 1 (6400)\n",
      "I1202 09:47:54.574323 16239 layer_factory.hpp:74] Creating layer ip2\n",
      "I1202 09:47:54.574335 16239 net.cpp:76] Creating Layer ip2\n",
      "I1202 09:47:54.574345 16239 net.cpp:372] ip2 <- ip1\n",
      "I1202 09:47:54.574358 16239 net.cpp:334] ip2 -> ip2\n",
      "I1202 09:47:54.574371 16239 net.cpp:105] Setting up ip2\n",
      "I1202 09:47:54.574407 16239 net.cpp:112] Top shape: 100 10 1 1 (1000)\n",
      "I1202 09:47:54.574422 16239 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
      "I1202 09:47:54.574435 16239 net.cpp:76] Creating Layer ip2_ip2_0_split\n",
      "I1202 09:47:54.574445 16239 net.cpp:372] ip2_ip2_0_split <- ip2\n",
      "I1202 09:47:54.574456 16239 net.cpp:334] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
      "I1202 09:47:54.574468 16239 net.cpp:334] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
      "I1202 09:47:54.574481 16239 net.cpp:105] Setting up ip2_ip2_0_split\n",
      "I1202 09:47:54.574489 16239 net.cpp:112] Top shape: 100 10 1 1 (1000)\n",
      "I1202 09:47:54.574498 16239 net.cpp:112] Top shape: 100 10 1 1 (1000)\n",
      "I1202 09:47:54.574508 16239 layer_factory.hpp:74] Creating layer accuracy\n",
      "I1202 09:47:54.574523 16239 net.cpp:76] Creating Layer accuracy\n",
      "I1202 09:47:54.574534 16239 net.cpp:372] accuracy <- ip2_ip2_0_split_0\n",
      "I1202 09:47:54.574544 16239 net.cpp:372] accuracy <- label_cifar_1_split_0\n",
      "I1202 09:47:54.574555 16239 net.cpp:334] accuracy -> accuracy\n",
      "I1202 09:47:54.574568 16239 net.cpp:105] Setting up accuracy\n",
      "I1202 09:47:54.574580 16239 net.cpp:112] Top shape: 1 1 1 1 (1)\n",
      "I1202 09:47:54.574589 16239 layer_factory.hpp:74] Creating layer loss\n",
      "I1202 09:47:54.574599 16239 net.cpp:76] Creating Layer loss\n",
      "I1202 09:47:54.574609 16239 net.cpp:372] loss <- ip2_ip2_0_split_1\n",
      "I1202 09:47:54.574618 16239 net.cpp:372] loss <- label_cifar_1_split_1\n",
      "I1202 09:47:54.574631 16239 net.cpp:334] loss -> loss\n",
      "I1202 09:47:54.574642 16239 net.cpp:105] Setting up loss\n",
      "I1202 09:47:54.574653 16239 layer_factory.hpp:74] Creating layer loss\n",
      "I1202 09:47:54.574725 16239 net.cpp:112] Top shape: 1 1 1 1 (1)\n",
      "I1202 09:47:54.574739 16239 net.cpp:118]     with loss weight 1\n",
      "I1202 09:47:54.574766 16239 net.cpp:163] loss needs backward computation.\n",
      "I1202 09:47:54.574777 16239 net.cpp:165] accuracy does not need backward computation.\n",
      "I1202 09:47:54.574786 16239 net.cpp:163] ip2_ip2_0_split needs backward computation.\n",
      "I1202 09:47:54.574795 16239 net.cpp:163] ip2 needs backward computation.\n",
      "I1202 09:47:54.574805 16239 net.cpp:163] ip1 needs backward computation.\n",
      "I1202 09:47:54.574813 16239 net.cpp:163] pool3 needs backward computation.\n",
      "I1202 09:47:54.574822 16239 net.cpp:163] relu3 needs backward computation.\n",
      "I1202 09:47:54.574831 16239 net.cpp:163] conv3 needs backward computation.\n",
      "I1202 09:47:54.574839 16239 net.cpp:163] pool2 needs backward computation.\n",
      "I1202 09:47:54.574847 16239 net.cpp:163] relu2 needs backward computation.\n",
      "I1202 09:47:54.574858 16239 net.cpp:163] conv2 needs backward computation.\n",
      "I1202 09:47:54.574867 16239 net.cpp:163] relu1 needs backward computation.\n",
      "I1202 09:47:54.574875 16239 net.cpp:163] pool1 needs backward computation.\n",
      "I1202 09:47:54.574884 16239 net.cpp:163] conv1 needs backward computation.\n",
      "I1202 09:47:54.574893 16239 net.cpp:165] label_cifar_1_split does not need backward computation.\n",
      "I1202 09:47:54.574903 16239 net.cpp:165] cifar does not need backward computation.\n",
      "I1202 09:47:54.574910 16239 net.cpp:201] This network produces output accuracy\n",
      "I1202 09:47:54.574920 16239 net.cpp:201] This network produces output loss\n",
      "I1202 09:47:54.574939 16239 net.cpp:446] Collecting Learning Rate and Weight Decay.\n",
      "I1202 09:47:54.574950 16239 net.cpp:213] Network initialization done.\n",
      "I1202 09:47:54.574959 16239 net.cpp:214] Memory required for data: 31987608\n",
      "I1202 09:47:54.575008 16239 solver.cpp:42] Solver scaffolding done.\n",
      "I1202 09:47:54.575038 16239 solver.cpp:222] Solving CIFAR10_quick\n",
      "I1202 09:47:54.575048 16239 solver.cpp:223] Learning Rate Policy: fixed\n",
      "I1202 09:47:54.575064 16239 solver.cpp:266] Iteration 0, Testing net (#0)\n",
      "I1202 09:47:55.259501 16239 solver.cpp:315]     Test net output #0: accuracy = 0.1117\n",
      "I1202 09:47:55.259569 16239 solver.cpp:315]     Test net output #1: loss = 2.30263 (* 1 = 2.30263 loss)\n",
      "I1202 09:47:55.268280 16239 solver.cpp:189] Iteration 0, loss = 2.30299\n",
      "I1202 09:47:55.268321 16239 solver.cpp:204]     Train net output #0: loss = 2.30299 (* 1 = 2.30299 loss)\n",
      "I1202 09:47:55.268343 16239 solver.cpp:470] Iteration 0, lr = 0.001\n",
      "I1202 09:47:57.144580 16239 solver.cpp:189] Iteration 100, loss = 1.68621\n",
      "I1202 09:47:57.144644 16239 solver.cpp:204]     Train net output #0: loss = 1.68621 (* 1 = 1.68621 loss)\n",
      "I1202 09:47:57.144659 16239 solver.cpp:470] Iteration 100, lr = 0.001\n",
      "I1202 09:47:59.024019 16239 solver.cpp:189] Iteration 200, loss = 1.57451\n",
      "I1202 09:47:59.024081 16239 solver.cpp:204]     Train net output #0: loss = 1.57451 (* 1 = 1.57451 loss)\n",
      "I1202 09:47:59.024096 16239 solver.cpp:470] Iteration 200, lr = 0.001\n",
      "I1202 09:48:00.903507 16239 solver.cpp:189] Iteration 300, loss = 1.30906\n",
      "I1202 09:48:00.903573 16239 solver.cpp:204]     Train net output #0: loss = 1.30906 (* 1 = 1.30906 loss)\n",
      "I1202 09:48:00.903589 16239 solver.cpp:470] Iteration 300, lr = 0.001\n",
      "I1202 09:48:02.784032 16239 solver.cpp:189] Iteration 400, loss = 1.30634\n",
      "I1202 09:48:02.784098 16239 solver.cpp:204]     Train net output #0: loss = 1.30634 (* 1 = 1.30634 loss)\n",
      "I1202 09:48:02.784113 16239 solver.cpp:470] Iteration 400, lr = 0.001\n",
      "I1202 09:48:04.644973 16239 solver.cpp:266] Iteration 500, Testing net (#0)\n",
      "I1202 09:48:05.336899 16239 solver.cpp:315]     Test net output #0: accuracy = 0.5578\n",
      "I1202 09:48:05.336962 16239 solver.cpp:315]     Test net output #1: loss = 1.27543 (* 1 = 1.27543 loss)\n",
      "I1202 09:48:05.344282 16239 solver.cpp:189] Iteration 500, loss = 1.18502\n",
      "I1202 09:48:05.344349 16239 solver.cpp:204]     Train net output #0: loss = 1.18502 (* 1 = 1.18502 loss)\n",
      "I1202 09:48:05.344367 16239 solver.cpp:470] Iteration 500, lr = 0.001\n",
      "I1202 09:48:07.224591 16239 solver.cpp:189] Iteration 600, loss = 1.30285\n",
      "I1202 09:48:07.224655 16239 solver.cpp:204]     Train net output #0: loss = 1.30285 (* 1 = 1.30285 loss)\n",
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      "I1202 09:49:13.345809 16239 solver.cpp:470] Iteration 3900, lr = 0.001\n",
      "I1202 09:49:15.217684 16239 solver.cpp:334] Snapshotting to examples/cifar10/cifar10_quick_iter_4000.caffemodel\n",
      "I1202 09:49:15.220386 16239 solver.cpp:342] Snapshotting solver state to examples/cifar10/cifar10_quick_iter_4000.solverstate\n",
      "I1202 09:49:15.228559 16239 solver.cpp:248] Iteration 4000, loss = 0.65829\n",
      "I1202 09:49:15.228598 16239 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
      "I1202 09:49:15.911067 16239 solver.cpp:315]     Test net output #0: accuracy = 0.7137\n",
      "I1202 09:49:15.911123 16239 solver.cpp:315]     Test net output #1: loss = 0.868198 (* 1 = 0.868198 loss)\n",
      "I1202 09:49:15.911136 16239 solver.cpp:253] Optimization Done.\n",
      "I1202 09:49:15.911146 16239 caffe.cpp:121] Optimization Done.\n"
     ]
    }
   ],
   "source": [
    "!cd /home/ubuntu/caffe && ./build/tools/caffe train --solver=examples/cifar10/cifar10_quick_solver.prototxt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations - you just trained your first DNN!  After 4000 iterations you will see the accuracy against the test set was ~71% - not bad for a couple of minutes training!  Each iteration took less than 0.02 seconds to train using the GPU.\n",
    "\n",
    "The learned network weights get saved into a binary file called cifar10_quick_iter_4000.caffemodel\n",
    "\n",
    "Classifying new images using the newly trained dataset is just as simple as training was - this time we will use Caffe's Python interface.  Activate the cell below as many times as you like to randomly choose an image from the test set and have it classified - there is no need to edit any of the code. Remember - the test set is images that were never used in training the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S+tAJyMknqul3Lh/nwzeLuMjXzu7krJWrbpI+QD+ti2hjdfLIeiapZBRaLWgVDD/F2FSN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\nBvx7vMcvNP4Pl7HMgYrfRm0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e8c57b290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "automobile\n"
     ]
    },
    {
     "data": {
      "image/png": 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XizTW8aSIOAP4bF6eA+W0qAtJq7af1cA9wB9yjDeQEtcb81lhqQpxPQT8OCJ+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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e5a6c8f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import required Python libraries\n",
    "%matplotlib inline\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import caffe\n",
    "import random\n",
    "\n",
    "# Choose network definition file and pretrained network binary\n",
    "MODEL_FILE = '/home/ubuntu/caffe/examples/cifar10/cifar10_quick.prototxt'\n",
    "PRETRAINED = '/home/ubuntu/caffe/examples/cifar10/cifar10_quick_iter_4000.caffemodel'\n",
    "\n",
    "# Load a random image\n",
    "x = caffe.io.load_image('/home/ubuntu/caffe/examples/images/' + str(random.randint(1,18)) + '.png')\n",
    "\n",
    "# Display the chosen image\n",
    "plt.imshow(x)\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "# Load the pretrained model and select to use the GPU for computation\n",
    "caffe.set_mode_gpu()\n",
    "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
    "                       mean=np.load('/home/ubuntu/caffe/caffe/examples/cifar10/cifar10_mean.npy').mean(1).mean(1),\n",
    "                       raw_scale=255,\n",
    "                       image_dims=(32, 32))\n",
    "\n",
    "# Run the image through the pretrained network\n",
    "prediction = net.predict([x])\n",
    "\n",
    "# List of class labels\n",
    "classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "\n",
    "# Display the predicted probability for each class\n",
    "plt.plot(prediction[0])\n",
    "plt.xticks(range(0,10), classes, rotation=45)\n",
    "# Display the most probable class\n",
    "print classes[prediction[0].argmax()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even for this relatively small dataset and network architecture GPU acceleration is still highly desirable.  Running the cell below will train the same network for 100 iterations using the CPU - as you will see, it is considerably slower than training on the GPU was above. Training for 0.025 times as many iterations takes about 50% more time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "libdc1394 error: Failed to initialize libdc1394\n",
      "I1202 09:54:14.267436 21396 caffe.cpp:103] Use CPU.\n",
      "I1202 09:54:14.404559 21396 caffe.cpp:107] Starting Optimization\n",
      "I1202 09:54:14.404692 21396 solver.cpp:32] Initializing solver from parameters: \n",
      "test_iter: 100\n",
      "test_interval: 100\n",
      "base_lr: 0.001\n",
      "display: 100\n",
      "max_iter: 100\n",
      "lr_policy: \"fixed\"\n",
      "momentum: 0.9\n",
      "weight_decay: 0.004\n",
      "snapshot: 100\n",
      "snapshot_prefix: \"examples/cifar10/cifar10_quick_cpu\"\n",
      "solver_mode: CPU\n",
      "net: \"examples/cifar10/cifar10_quick_train_test.prototxt\"\n",
      "I1202 09:54:14.404770 21396 solver.cpp:70] Creating training net from net file: examples/cifar10/cifar10_quick_train_test.prototxt\n",
      "I1202 09:54:14.405244 21396 net.cpp:253] The NetState phase (0) differed from the phase (1) specified by a rule in layer cifar\n",
      "I1202 09:54:14.405280 21396 net.cpp:253] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\n",
      "I1202 09:54:14.405400 21396 net.cpp:42] Initializing net from parameters: \n",
      "name: \"CIFAR10_quick\"\n",
      "state {\n",
      "  phase: TRAIN\n",
      "}\n",
      "layer {\n",
      "  name: \"cifar\"\n",
      "  type: \"Data\"\n",
      "  top: \"data\"\n",
      "  top: \"label\"\n",
      "  include {\n",
      "    phase: TRAIN\n",
      "  }\n",
      "  transform_param {\n",
      "    mean_file: \"examples/cifar10/mean.binaryproto\"\n",
      "  }\n",
      "  data_param {\n",
      "    source: \"examples/cifar10/cifar10_train_lmdb\"\n",
      "    batch_size: 100\n",
      "    backend: LMDB\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"conv1\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"data\"\n",
      "  top: \"conv1\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 32\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.0001\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"pool1\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv1\"\n",
      "  top: \"pool1\"\n",
      "  pooling_param {\n",
      "    pool: MAX\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu1\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"pool1\"\n",
      "  top: \"pool1\"\n",
      "}\n",
      "layer {\n",
      "  name: \"conv2\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"pool1\"\n",
      "  top: \"conv2\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 32\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.01\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu2\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"conv2\"\n",
      "  top: \"conv2\"\n",
      "}\n",
      "layer {\n",
      "  name: \"pool2\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv2\"\n",
      "  top: \"pool2\"\n",
      "  pooling_param {\n",
      "    pool: AVE\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"conv3\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"pool2\"\n",
      "  top: \"conv3\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 64\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.01\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu3\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"conv3\"\n",
      "  top: \"conv3\"\n",
      "}\n",
      "layer {\n",
      "  name: \"pool3\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv3\"\n",
      "  top: \"pool3\"\n",
      "  pooling_param {\n",
      "    pool: AVE\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"ip1\"\n",
      "  type: \"InnerProduct\"\n",
      "  bottom: \"pool3\"\n",
      "  top: \"ip1\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  inner_product_param {\n",
      "    num_output: 64\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.1\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"ip2\"\n",
      "  type: \"InnerProduct\"\n",
      "  bottom: \"ip1\"\n",
      "  top: \"ip2\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  inner_product_param {\n",
      "    num_output: 10\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.1\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"loss\"\n",
      "  type: \"SoftmaxWithLoss\"\n",
      "  bottom: \"ip2\"\n",
      "  bottom: \"label\"\n",
      "  top: \"loss\"\n",
      "}\n",
      "I1202 09:54:14.406047 21396 layer_factory.hpp:74] Creating layer cifar\n",
      "I1202 09:54:14.406075 21396 net.cpp:76] Creating Layer cifar\n",
      "I1202 09:54:14.406092 21396 net.cpp:334] cifar -> data\n",
      "I1202 09:54:14.406131 21396 net.cpp:334] cifar -> label\n",
      "I1202 09:54:14.406150 21396 net.cpp:105] Setting up cifar\n",
      "I1202 09:54:14.406249 21396 db.cpp:34] Opened lmdb examples/cifar10/cifar10_train_lmdb\n",
      "I1202 09:54:14.406304 21396 data_layer.cpp:67] output data size: 100,3,32,32\n",
      "I1202 09:54:14.406323 21396 data_transformer.cpp:22] Loading mean file from: examples/cifar10/mean.binaryproto\n",
      "I1202 09:54:14.406947 21396 net.cpp:112] Top shape: 100 3 32 32 (307200)\n",
      "I1202 09:54:14.406971 21396 net.cpp:112] Top shape: 100 1 1 1 (100)\n",
      "I1202 09:54:14.406985 21396 layer_factory.hpp:74] Creating layer conv1\n",
      "I1202 09:54:14.407057 21396 net.cpp:76] Creating Layer conv1\n",
      "I1202 09:54:14.407073 21396 net.cpp:372] conv1 <- data\n",
      "I1202 09:54:14.407094 21396 net.cpp:334] conv1 -> conv1\n",
      "I1202 09:54:14.407110 21396 net.cpp:105] Setting up conv1\n",
      "I1202 09:54:14.461632 21396 net.cpp:112] Top shape: 100 32 32 32 (3276800)\n",
      "I1202 09:54:14.461706 21396 layer_factory.hpp:74] Creating layer pool1\n",
      "I1202 09:54:14.461733 21396 net.cpp:76] Creating Layer pool1\n",
      "I1202 09:54:14.461745 21396 net.cpp:372] pool1 <- conv1\n",
      "I1202 09:54:14.461758 21396 net.cpp:334] pool1 -> pool1\n",
      "I1202 09:54:14.461773 21396 net.cpp:105] Setting up pool1\n",
      "I1202 09:54:14.461936 21396 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:54:14.461954 21396 layer_factory.hpp:74] Creating layer relu1\n",
      "I1202 09:54:14.461967 21396 net.cpp:76] Creating Layer relu1\n",
      "I1202 09:54:14.461977 21396 net.cpp:372] relu1 <- pool1\n",
      "I1202 09:54:14.461987 21396 net.cpp:323] relu1 -> pool1 (in-place)\n",
      "I1202 09:54:14.461998 21396 net.cpp:105] Setting up relu1\n",
      "I1202 09:54:14.462049 21396 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:54:14.462061 21396 layer_factory.hpp:74] Creating layer conv2\n",
      "I1202 09:54:14.462077 21396 net.cpp:76] Creating Layer conv2\n",
      "I1202 09:54:14.462087 21396 net.cpp:372] conv2 <- pool1\n",
      "I1202 09:54:14.462098 21396 net.cpp:334] conv2 -> conv2\n",
      "I1202 09:54:14.462112 21396 net.cpp:105] Setting up conv2\n",
      "I1202 09:54:14.463249 21396 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:54:14.463286 21396 layer_factory.hpp:74] Creating layer relu2\n",
      "I1202 09:54:14.463300 21396 net.cpp:76] Creating Layer relu2\n",
      "I1202 09:54:14.463310 21396 net.cpp:372] relu2 <- conv2\n",
      "I1202 09:54:14.463321 21396 net.cpp:323] relu2 -> conv2 (in-place)\n",
      "I1202 09:54:14.463332 21396 net.cpp:105] Setting up relu2\n",
      "I1202 09:54:14.463378 21396 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:54:14.463390 21396 layer_factory.hpp:74] Creating layer pool2\n",
      "I1202 09:54:14.463402 21396 net.cpp:76] Creating Layer pool2\n",
      "I1202 09:54:14.463410 21396 net.cpp:372] pool2 <- conv2\n",
      "I1202 09:54:14.463425 21396 net.cpp:334] pool2 -> pool2\n",
      "I1202 09:54:14.463440 21396 net.cpp:105] Setting up pool2\n",
      "I1202 09:54:14.463567 21396 net.cpp:112] Top shape: 100 32 8 8 (204800)\n",
      "I1202 09:54:14.463584 21396 layer_factory.hpp:74] Creating layer conv3\n",
      "I1202 09:54:14.463598 21396 net.cpp:76] Creating Layer conv3\n",
      "I1202 09:54:14.463608 21396 net.cpp:372] conv3 <- pool2\n",
      "I1202 09:54:14.463618 21396 net.cpp:334] conv3 -> conv3\n",
      "I1202 09:54:14.463631 21396 net.cpp:105] Setting up conv3\n",
      "I1202 09:54:14.465595 21396 net.cpp:112] Top shape: 100 64 8 8 (409600)\n",
      "I1202 09:54:14.465631 21396 layer_factory.hpp:74] Creating layer relu3\n",
      "I1202 09:54:14.465646 21396 net.cpp:76] Creating Layer relu3\n",
      "I1202 09:54:14.465654 21396 net.cpp:372] relu3 <- conv3\n",
      "I1202 09:54:14.465665 21396 net.cpp:323] relu3 -> conv3 (in-place)\n",
      "I1202 09:54:14.465675 21396 net.cpp:105] Setting up relu3\n",
      "I1202 09:54:14.465731 21396 net.cpp:112] Top shape: 100 64 8 8 (409600)\n",
      "I1202 09:54:14.465744 21396 layer_factory.hpp:74] Creating layer pool3\n",
      "I1202 09:54:14.465754 21396 net.cpp:76] Creating Layer pool3\n",
      "I1202 09:54:14.465764 21396 net.cpp:372] pool3 <- conv3\n",
      "I1202 09:54:14.465775 21396 net.cpp:334] pool3 -> pool3\n",
      "I1202 09:54:14.465787 21396 net.cpp:105] Setting up pool3\n",
      "I1202 09:54:14.465843 21396 net.cpp:112] Top shape: 100 64 4 4 (102400)\n",
      "I1202 09:54:14.465855 21396 layer_factory.hpp:74] Creating layer ip1\n",
      "I1202 09:54:14.465872 21396 net.cpp:76] Creating Layer ip1\n",
      "I1202 09:54:14.465880 21396 net.cpp:372] ip1 <- pool3\n",
      "I1202 09:54:14.465895 21396 net.cpp:334] ip1 -> ip1\n",
      "I1202 09:54:14.465911 21396 net.cpp:105] Setting up ip1\n",
      "I1202 09:54:14.468149 21396 net.cpp:112] Top shape: 100 64 1 1 (6400)\n",
      "I1202 09:54:14.468180 21396 layer_factory.hpp:74] Creating layer ip2\n",
      "I1202 09:54:14.468194 21396 net.cpp:76] Creating Layer ip2\n",
      "I1202 09:54:14.468230 21396 net.cpp:372] ip2 <- ip1\n",
      "I1202 09:54:14.468245 21396 net.cpp:334] ip2 -> ip2\n",
      "I1202 09:54:14.468258 21396 net.cpp:105] Setting up ip2\n",
      "I1202 09:54:14.468294 21396 net.cpp:112] Top shape: 100 10 1 1 (1000)\n",
      "I1202 09:54:14.468309 21396 layer_factory.hpp:74] Creating layer loss\n",
      "I1202 09:54:14.468327 21396 net.cpp:76] Creating Layer loss\n",
      "I1202 09:54:14.468336 21396 net.cpp:372] loss <- ip2\n",
      "I1202 09:54:14.468345 21396 net.cpp:372] loss <- label\n",
      "I1202 09:54:14.468358 21396 net.cpp:334] loss -> loss\n",
      "I1202 09:54:14.468371 21396 net.cpp:105] Setting up loss\n",
      "I1202 09:54:14.468386 21396 layer_factory.hpp:74] Creating layer loss\n",
      "I1202 09:54:14.468472 21396 net.cpp:112] Top shape: 1 1 1 1 (1)\n",
      "I1202 09:54:14.468485 21396 net.cpp:118]     with loss weight 1\n",
      "I1202 09:54:14.468515 21396 net.cpp:163] loss needs backward computation.\n",
      "I1202 09:54:14.468524 21396 net.cpp:163] ip2 needs backward computation.\n",
      "I1202 09:54:14.468533 21396 net.cpp:163] ip1 needs backward computation.\n",
      "I1202 09:54:14.468544 21396 net.cpp:163] pool3 needs backward computation.\n",
      "I1202 09:54:14.468552 21396 net.cpp:163] relu3 needs backward computation.\n",
      "I1202 09:54:14.468560 21396 net.cpp:163] conv3 needs backward computation.\n",
      "I1202 09:54:14.468569 21396 net.cpp:163] pool2 needs backward computation.\n",
      "I1202 09:54:14.468576 21396 net.cpp:163] relu2 needs backward computation.\n",
      "I1202 09:54:14.468585 21396 net.cpp:163] conv2 needs backward computation.\n",
      "I1202 09:54:14.468593 21396 net.cpp:163] relu1 needs backward computation.\n",
      "I1202 09:54:14.468600 21396 net.cpp:163] pool1 needs backward computation.\n",
      "I1202 09:54:14.468608 21396 net.cpp:163] conv1 needs backward computation.\n",
      "I1202 09:54:14.468617 21396 net.cpp:165] cifar does not need backward computation.\n",
      "I1202 09:54:14.468626 21396 net.cpp:201] This network produces output loss\n",
      "I1202 09:54:14.468642 21396 net.cpp:446] Collecting Learning Rate and Weight Decay.\n",
      "I1202 09:54:14.468655 21396 net.cpp:213] Network initialization done.\n",
      "I1202 09:54:14.468664 21396 net.cpp:214] Memory required for data: 31978804\n",
      "I1202 09:54:14.469068 21396 solver.cpp:154] Creating test net (#0) specified by net file: examples/cifar10/cifar10_quick_train_test.prototxt\n",
      "I1202 09:54:14.469106 21396 net.cpp:253] The NetState phase (1) differed from the phase (0) specified by a rule in layer cifar\n",
      "I1202 09:54:14.469238 21396 net.cpp:42] Initializing net from parameters: \n",
      "name: \"CIFAR10_quick\"\n",
      "state {\n",
      "  phase: TEST\n",
      "}\n",
      "layer {\n",
      "  name: \"cifar\"\n",
      "  type: \"Data\"\n",
      "  top: \"data\"\n",
      "  top: \"label\"\n",
      "  include {\n",
      "    phase: TEST\n",
      "  }\n",
      "  transform_param {\n",
      "    mean_file: \"examples/cifar10/mean.binaryproto\"\n",
      "  }\n",
      "  data_param {\n",
      "    source: \"examples/cifar10/cifar10_test_lmdb\"\n",
      "    batch_size: 100\n",
      "    backend: LMDB\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"conv1\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"data\"\n",
      "  top: \"conv1\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 32\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.0001\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"pool1\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv1\"\n",
      "  top: \"pool1\"\n",
      "  pooling_param {\n",
      "    pool: MAX\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu1\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"pool1\"\n",
      "  top: \"pool1\"\n",
      "}\n",
      "layer {\n",
      "  name: \"conv2\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"pool1\"\n",
      "  top: \"conv2\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 32\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.01\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu2\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"conv2\"\n",
      "  top: \"conv2\"\n",
      "}\n",
      "layer {\n",
      "  name: \"pool2\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv2\"\n",
      "  top: \"pool2\"\n",
      "  pooling_param {\n",
      "    pool: AVE\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"conv3\"\n",
      "  type: \"Convolution\"\n",
      "  bottom: \"pool2\"\n",
      "  top: \"conv3\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  convolution_param {\n",
      "    num_output: 64\n",
      "    pad: 2\n",
      "    kernel_size: 5\n",
      "    stride: 1\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.01\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"relu3\"\n",
      "  type: \"ReLU\"\n",
      "  bottom: \"conv3\"\n",
      "  top: \"conv3\"\n",
      "}\n",
      "layer {\n",
      "  name: \"pool3\"\n",
      "  type: \"Pooling\"\n",
      "  bottom: \"conv3\"\n",
      "  top: \"pool3\"\n",
      "  pooling_param {\n",
      "    pool: AVE\n",
      "    kernel_size: 3\n",
      "    stride: 2\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"ip1\"\n",
      "  type: \"InnerProduct\"\n",
      "  bottom: \"pool3\"\n",
      "  top: \"ip1\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  inner_product_param {\n",
      "    num_output: 64\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.1\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"ip2\"\n",
      "  type: \"InnerProduct\"\n",
      "  bottom: \"ip1\"\n",
      "  top: \"ip2\"\n",
      "  param {\n",
      "    lr_mult: 1\n",
      "  }\n",
      "  param {\n",
      "    lr_mult: 2\n",
      "  }\n",
      "  inner_product_param {\n",
      "    num_output: 10\n",
      "    weight_filler {\n",
      "      type: \"gaussian\"\n",
      "      std: 0.1\n",
      "    }\n",
      "    bias_filler {\n",
      "      type: \"constant\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"accuracy\"\n",
      "  type: \"Accuracy\"\n",
      "  bottom: \"ip2\"\n",
      "  bottom: \"label\"\n",
      "  top: \"accuracy\"\n",
      "  include {\n",
      "    phase: TEST\n",
      "  }\n",
      "}\n",
      "layer {\n",
      "  name: \"loss\"\n",
      "  type: \"SoftmaxWithLoss\"\n",
      "  bottom: \"ip2\"\n",
      "  bottom: \"label\"\n",
      "  top: \"loss\"\n",
      "}\n",
      "I1202 09:54:14.469909 21396 layer_factory.hpp:74] Creating layer cifar\n",
      "I1202 09:54:14.469929 21396 net.cpp:76] Creating Layer cifar\n",
      "I1202 09:54:14.469940 21396 net.cpp:334] cifar -> data\n",
      "I1202 09:54:14.469954 21396 net.cpp:334] cifar -> label\n",
      "I1202 09:54:14.469966 21396 net.cpp:105] Setting up cifar\n",
      "I1202 09:54:14.470016 21396 db.cpp:34] Opened lmdb examples/cifar10/cifar10_test_lmdb\n",
      "I1202 09:54:14.470051 21396 data_layer.cpp:67] output data size: 100,3,32,32\n",
      "I1202 09:54:14.470062 21396 data_transformer.cpp:22] Loading mean file from: examples/cifar10/mean.binaryproto\n",
      "I1202 09:54:14.470630 21396 net.cpp:112] Top shape: 100 3 32 32 (307200)\n",
      "I1202 09:54:14.470654 21396 net.cpp:112] Top shape: 100 1 1 1 (100)\n",
      "I1202 09:54:14.470664 21396 layer_factory.hpp:74] Creating layer label_cifar_1_split\n",
      "I1202 09:54:14.470682 21396 net.cpp:76] Creating Layer label_cifar_1_split\n",
      "I1202 09:54:14.470691 21396 net.cpp:372] label_cifar_1_split <- label\n",
      "I1202 09:54:14.470702 21396 net.cpp:334] label_cifar_1_split -> label_cifar_1_split_0\n",
      "I1202 09:54:14.470715 21396 net.cpp:334] label_cifar_1_split -> label_cifar_1_split_1\n",
      "I1202 09:54:14.470729 21396 net.cpp:105] Setting up label_cifar_1_split\n",
      "I1202 09:54:14.470742 21396 net.cpp:112] Top shape: 100 1 1 1 (100)\n",
      "I1202 09:54:14.470752 21396 net.cpp:112] Top shape: 100 1 1 1 (100)\n",
      "I1202 09:54:14.470760 21396 layer_factory.hpp:74] Creating layer conv1\n",
      "I1202 09:54:14.470773 21396 net.cpp:76] Creating Layer conv1\n",
      "I1202 09:54:14.470782 21396 net.cpp:372] conv1 <- data\n",
      "I1202 09:54:14.470795 21396 net.cpp:334] conv1 -> conv1\n",
      "I1202 09:54:14.470808 21396 net.cpp:105] Setting up conv1\n",
      "I1202 09:54:14.471148 21396 net.cpp:112] Top shape: 100 32 32 32 (3276800)\n",
      "I1202 09:54:14.471184 21396 layer_factory.hpp:74] Creating layer pool1\n",
      "I1202 09:54:14.471197 21396 net.cpp:76] Creating Layer pool1\n",
      "I1202 09:54:14.471206 21396 net.cpp:372] pool1 <- conv1\n",
      "I1202 09:54:14.471227 21396 net.cpp:334] pool1 -> pool1\n",
      "I1202 09:54:14.471248 21396 net.cpp:105] Setting up pool1\n",
      "I1202 09:54:14.471408 21396 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:54:14.471426 21396 layer_factory.hpp:74] Creating layer relu1\n",
      "I1202 09:54:14.471438 21396 net.cpp:76] Creating Layer relu1\n",
      "I1202 09:54:14.471447 21396 net.cpp:372] relu1 <- pool1\n",
      "I1202 09:54:14.471462 21396 net.cpp:323] relu1 -> pool1 (in-place)\n",
      "I1202 09:54:14.471473 21396 net.cpp:105] Setting up relu1\n",
      "I1202 09:54:14.471524 21396 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:54:14.471535 21396 layer_factory.hpp:74] Creating layer conv2\n",
      "I1202 09:54:14.471549 21396 net.cpp:76] Creating Layer conv2\n",
      "I1202 09:54:14.471560 21396 net.cpp:372] conv2 <- pool1\n",
      "I1202 09:54:14.471570 21396 net.cpp:334] conv2 -> conv2\n",
      "I1202 09:54:14.471582 21396 net.cpp:105] Setting up conv2\n",
      "I1202 09:54:14.472671 21396 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:54:14.472703 21396 layer_factory.hpp:74] Creating layer relu2\n",
      "I1202 09:54:14.472717 21396 net.cpp:76] Creating Layer relu2\n",
      "I1202 09:54:14.472741 21396 net.cpp:372] relu2 <- conv2\n",
      "I1202 09:54:14.472755 21396 net.cpp:323] relu2 -> conv2 (in-place)\n",
      "I1202 09:54:14.472767 21396 net.cpp:105] Setting up relu2\n",
      "I1202 09:54:14.472822 21396 net.cpp:112] Top shape: 100 32 16 16 (819200)\n",
      "I1202 09:54:14.472834 21396 layer_factory.hpp:74] Creating layer pool2\n",
      "I1202 09:54:14.472846 21396 net.cpp:76] Creating Layer pool2\n",
      "I1202 09:54:14.472853 21396 net.cpp:372] pool2 <- conv2\n",
      "I1202 09:54:14.472867 21396 net.cpp:334] pool2 -> pool2\n",
      "I1202 09:54:14.472877 21396 net.cpp:105] Setting up pool2\n",
      "I1202 09:54:14.472929 21396 net.cpp:112] Top shape: 100 32 8 8 (204800)\n",
      "I1202 09:54:14.472941 21396 layer_factory.hpp:74] Creating layer conv3\n",
      "I1202 09:54:14.472957 21396 net.cpp:76] Creating Layer conv3\n",
      "I1202 09:54:14.472966 21396 net.cpp:372] conv3 <- pool2\n",
      "I1202 09:54:14.472980 21396 net.cpp:334] conv3 -> conv3\n",
      "I1202 09:54:14.472992 21396 net.cpp:105] Setting up conv3\n",
      "I1202 09:54:14.474956 21396 net.cpp:112] Top shape: 100 64 8 8 (409600)\n",
      "I1202 09:54:14.474987 21396 layer_factory.hpp:74] Creating layer relu3\n",
      "I1202 09:54:14.475003 21396 net.cpp:76] Creating Layer relu3\n",
      "I1202 09:54:14.475013 21396 net.cpp:372] relu3 <- conv3\n",
      "I1202 09:54:14.475023 21396 net.cpp:323] relu3 -> conv3 (in-place)\n",
      "I1202 09:54:14.475033 21396 net.cpp:105] Setting up relu3\n",
      "I1202 09:54:14.475167 21396 net.cpp:112] Top shape: 100 64 8 8 (409600)\n",
      "I1202 09:54:14.475183 21396 layer_factory.hpp:74] Creating layer pool3\n",
      "I1202 09:54:14.475196 21396 net.cpp:76] Creating Layer pool3\n",
      "I1202 09:54:14.475206 21396 net.cpp:372] pool3 <- conv3\n",
      "I1202 09:54:14.475217 21396 net.cpp:334] pool3 -> pool3\n",
      "I1202 09:54:14.475229 21396 net.cpp:105] Setting up pool3\n",
      "I1202 09:54:14.475283 21396 net.cpp:112] Top shape: 100 64 4 4 (102400)\n",
      "I1202 09:54:14.475296 21396 layer_factory.hpp:74] Creating layer ip1\n",
      "I1202 09:54:14.475307 21396 net.cpp:76] Creating Layer ip1\n",
      "I1202 09:54:14.475317 21396 net.cpp:372] ip1 <- pool3\n",
      "I1202 09:54:14.475327 21396 net.cpp:334] ip1 -> ip1\n",
      "I1202 09:54:14.475338 21396 net.cpp:105] Setting up ip1\n",
      "I1202 09:54:14.477552 21396 net.cpp:112] Top shape: 100 64 1 1 (6400)\n",
      "I1202 09:54:14.477579 21396 layer_factory.hpp:74] Creating layer ip2\n",
      "I1202 09:54:14.477597 21396 net.cpp:76] Creating Layer ip2\n",
      "I1202 09:54:14.477607 21396 net.cpp:372] ip2 <- ip1\n",
      "I1202 09:54:14.477618 21396 net.cpp:334] ip2 -> ip2\n",
      "I1202 09:54:14.477630 21396 net.cpp:105] Setting up ip2\n",
      "I1202 09:54:14.477668 21396 net.cpp:112] Top shape: 100 10 1 1 (1000)\n",
      "I1202 09:54:14.477682 21396 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
      "I1202 09:54:14.477692 21396 net.cpp:76] Creating Layer ip2_ip2_0_split\n",
      "I1202 09:54:14.477701 21396 net.cpp:372] ip2_ip2_0_split <- ip2\n",
      "I1202 09:54:14.477713 21396 net.cpp:334] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
      "I1202 09:54:14.477725 21396 net.cpp:334] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
      "I1202 09:54:14.477736 21396 net.cpp:105] Setting up ip2_ip2_0_split\n",
      "I1202 09:54:14.477746 21396 net.cpp:112] Top shape: 100 10 1 1 (1000)\n",
      "I1202 09:54:14.477754 21396 net.cpp:112] Top shape: 100 10 1 1 (1000)\n",
      "I1202 09:54:14.477762 21396 layer_factory.hpp:74] Creating layer accuracy\n",
      "I1202 09:54:14.477776 21396 net.cpp:76] Creating Layer accuracy\n",
      "I1202 09:54:14.477784 21396 net.cpp:372] accuracy <- ip2_ip2_0_split_0\n",
      "I1202 09:54:14.477794 21396 net.cpp:372] accuracy <- label_cifar_1_split_0\n",
      "I1202 09:54:14.477807 21396 net.cpp:334] accuracy -> accuracy\n",
      "I1202 09:54:14.477819 21396 net.cpp:105] Setting up accuracy\n",
      "I1202 09:54:14.477831 21396 net.cpp:112] Top shape: 1 1 1 1 (1)\n",
      "I1202 09:54:14.477841 21396 layer_factory.hpp:74] Creating layer loss\n",
      "I1202 09:54:14.477851 21396 net.cpp:76] Creating Layer loss\n",
      "I1202 09:54:14.477859 21396 net.cpp:372] loss <- ip2_ip2_0_split_1\n",
      "I1202 09:54:14.477869 21396 net.cpp:372] loss <- label_cifar_1_split_1\n",
      "I1202 09:54:14.477879 21396 net.cpp:334] loss -> loss\n",
      "I1202 09:54:14.477890 21396 net.cpp:105] Setting up loss\n",
      "I1202 09:54:14.477900 21396 layer_factory.hpp:74] Creating layer loss\n",
      "I1202 09:54:14.477972 21396 net.cpp:112] Top shape: 1 1 1 1 (1)\n",
      "I1202 09:54:14.477984 21396 net.cpp:118]     with loss weight 1\n",
      "I1202 09:54:14.478014 21396 net.cpp:163] loss needs backward computation.\n",
      "I1202 09:54:14.478024 21396 net.cpp:165] accuracy does not need backward computation.\n",
      "I1202 09:54:14.478032 21396 net.cpp:163] ip2_ip2_0_split needs backward computation.\n",
      "I1202 09:54:14.478041 21396 net.cpp:163] ip2 needs backward computation.\n",
      "I1202 09:54:14.478049 21396 net.cpp:163] ip1 needs backward computation.\n",
      "I1202 09:54:14.478057 21396 net.cpp:163] pool3 needs backward computation.\n",
      "I1202 09:54:14.478065 21396 net.cpp:163] relu3 needs backward computation.\n",
      "I1202 09:54:14.478073 21396 net.cpp:163] conv3 needs backward computation.\n",
      "I1202 09:54:14.478082 21396 net.cpp:163] pool2 needs backward computation.\n",
      "I1202 09:54:14.478091 21396 net.cpp:163] relu2 needs backward computation.\n",
      "I1202 09:54:14.478097 21396 net.cpp:163] conv2 needs backward computation.\n",
      "I1202 09:54:14.478106 21396 net.cpp:163] relu1 needs backward computation.\n",
      "I1202 09:54:14.478113 21396 net.cpp:163] pool1 needs backward computation.\n",
      "I1202 09:54:14.478121 21396 net.cpp:163] conv1 needs backward computation.\n",
      "I1202 09:54:14.478130 21396 net.cpp:165] label_cifar_1_split does not need backward computation.\n",
      "I1202 09:54:14.478138 21396 net.cpp:165] cifar does not need backward computation.\n",
      "I1202 09:54:14.478147 21396 net.cpp:201] This network produces output accuracy\n",
      "I1202 09:54:14.478154 21396 net.cpp:201] This network produces output loss\n",
      "I1202 09:54:14.478173 21396 net.cpp:446] Collecting Learning Rate and Weight Decay.\n",
      "I1202 09:54:14.478184 21396 net.cpp:213] Network initialization done.\n",
      "I1202 09:54:14.478193 21396 net.cpp:214] Memory required for data: 31987608\n",
      "I1202 09:54:14.478241 21396 solver.cpp:42] Solver scaffolding done.\n",
      "I1202 09:54:14.478269 21396 solver.cpp:222] Solving CIFAR10_quick\n",
      "I1202 09:54:14.478278 21396 solver.cpp:223] Learning Rate Policy: fixed\n",
      "I1202 09:54:14.478293 21396 solver.cpp:266] Iteration 0, Testing net (#0)\n",
      "I1202 09:54:46.824185 21396 solver.cpp:315]     Test net output #0: accuracy = 0.1\n",
      "I1202 09:54:46.824304 21396 solver.cpp:315]     Test net output #1: loss = 2.30274 (* 1 = 2.30274 loss)\n",
      "I1202 09:54:47.674502 21396 solver.cpp:189] Iteration 0, loss = 2.30311\n",
      "I1202 09:54:47.674579 21396 solver.cpp:204]     Train net output #0: loss = 2.30311 (* 1 = 2.30311 loss)\n",
      "I1202 09:54:47.674605 21396 solver.cpp:470] Iteration 0, lr = 0.001\n",
      "I1202 09:56:08.884909 21396 solver.cpp:334] Snapshotting to examples/cifar10/cifar10_quick_cpu_iter_100.caffemodel\n",
      "I1202 09:56:08.886989 21396 solver.cpp:342] Snapshotting solver state to examples/cifar10/cifar10_quick_cpu_iter_100.solverstate\n",
      "I1202 09:56:09.199126 21396 solver.cpp:248] Iteration 100, loss = 1.68159\n",
      "I1202 09:56:09.199187 21396 solver.cpp:266] Iteration 100, Testing net (#0)\n",
      "I1202 09:56:41.508707 21396 solver.cpp:315]     Test net output #0: accuracy = 0.3855\n",
      "I1202 09:56:41.508826 21396 solver.cpp:315]     Test net output #1: loss = 1.72014 (* 1 = 1.72014 loss)\n",
      "I1202 09:56:41.508841 21396 solver.cpp:253] Optimization Done.\n",
      "I1202 09:56:41.508850 21396 caffe.cpp:121] Optimization Done.\n"
     ]
    }
   ],
   "source": [
    "!cd /home/ubuntu/caffe && ./build/tools/caffe train --solver=examples/cifar10/cifar10_quick_solver_cpu.prototxt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each training iteration this time took about 0.8 seconds - approximately forty times slower."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Framework 2: Theano\n",
    "\n",
    "Theano is a Python DL library created at the University of Montreal.  Some benefits of Theano to the DL practioner and researcher are:\n",
    "* Theano provides a highly expressive way of defining and training DNNs as it is built as a general symbolic math library and offers automatic function differentiation - this means the sky's the limit for DNN architectures and training methods that can be defined without leaving Python  \n",
    "* Theano is still very fast due to a CUDA backend that enables GPU acceleration  \n",
    "* Theano is tightly integrated with Python's popular NumPy library making integration into data analytics workflows a breeze.\n",
    "* A number of lightweight Python libraries have been written on top of Theano to further simplify the DNN training process, e.g. [Lasagne](https://github.com/Lasagne/Lasagne) and [Keras](https://github.com/fchollet/keras)\n",
    "\n",
    "You may choose to use Theano as your DL framework if you fit the following profile:\n",
    "* You are a Python developer looking for a highly expressive DL framework\n",
    "* You wish to develop a wider range of DL applications than just CNNs for imagery by defining new layer types, training algorithms and objective functions without leaving Python\n",
    "\n",
    "#### Theano example\n",
    "\n",
    "In this example we're going to work with the well know [MNIST](http://yann.lecun.com/exdb/mnist/) dataset which consists of 28x28 pixel black and white images of handwritten digits.  Here are some examples:\n",
    "\n",
    "![](http://deeplearning.net/tutorial/_images/mnist_0.png) ![](http://deeplearning.net/tutorial/_images/mnist_1.png) ![](http://deeplearning.net/tutorial/_images/mnist_2.png) ![](http://deeplearning.net/tutorial/_images/mnist_3.png) ![](http://deeplearning.net/tutorial/_images/mnist_4.png) ![](http://deeplearning.net/tutorial/_images/mnist_5.png) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing some Python dependencies and then defining a Python class for Convolutional Layers with Max Pooling.  Don't worry too much about the details - we have included this here so you can see the symbolic math nature of Theano syntax.\n",
    "\n",
    "**WARNING!** It is important that you execute each of the following cells in order as they build upon each other. It is suggested you execute the cells before parsing through the code - some of them can take a minute or two to run.  Remember to watch the circle in the top-right of the window - while it is solid the cell is still executing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 0: GRID K520\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('/home/ubuntu/notebook/DLIntro')\n",
    "import sys\n",
    "import timeit\n",
    "import numpy\n",
    "import theano\n",
    "import theano.tensor as T\n",
    "from theano.tensor.signal import downsample\n",
    "from theano.tensor.nnet import conv\n",
    "from logistic_sgd import LogisticRegression, load_data\n",
    "from mlp import HiddenLayer\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from utils import tile_raster_images\n",
    "from IPython import display\n",
    "\n",
    "class LeNetConvPoolLayer(object):\n",
    "    \"\"\"Pool Layer of a convolutional network \"\"\"\n",
    "\n",
    "    def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):\n",
    "\n",
    "        assert image_shape[1] == filter_shape[1]\n",
    "        self.input = input\n",
    "\n",
    "        # there are \"num input feature maps * filter height * filter width\"\n",
    "        # inputs to each hidden unit\n",
    "        fan_in = numpy.prod(filter_shape[1:])\n",
    "        # each unit in the lower layer receives a gradient from:\n",
    "        # \"num output feature maps * filter height * filter width\" /\n",
    "        #   pooling size\n",
    "        fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /\n",
    "                   numpy.prod(poolsize))\n",
    "        # initialize weights with random weights\n",
    "        W_bound = numpy.sqrt(6. / (fan_in + fan_out))\n",
    "        self.W = theano.shared(\n",
    "            numpy.asarray(\n",
    "                rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),\n",
    "                dtype=theano.config.floatX\n",
    "            ),\n",
    "            borrow=True\n",
    "        )\n",
    "\n",
    "        # the bias is a 1D tensor -- one bias per output feature map\n",
    "        b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)\n",
    "        self.b = theano.shared(value=b_values, borrow=True)\n",
    "\n",
    "        # convolve input feature maps with filters\n",
    "        conv_out = conv.conv2d(\n",
    "            input=input,\n",
    "            filters=self.W,\n",
    "            filter_shape=filter_shape,\n",
    "            image_shape=image_shape\n",
    "        )\n",
    "\n",
    "        # downsample each feature map individually, using maxpooling\n",
    "        pooled_out = downsample.max_pool_2d(\n",
    "            input=conv_out,\n",
    "            ds=poolsize,\n",
    "            ignore_border=True\n",
    "        )\n",
    "\n",
    "        # add the bias term. Since the bias is a vector (1D array), we first\n",
    "        # reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will\n",
    "        # thus be broadcasted across mini-batches and feature map\n",
    "        # width & height\n",
    "        self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))\n",
    "\n",
    "        # store parameters of this layer\n",
    "        self.params = [self.W, self.b]\n",
    "\n",
    "        # keep track of model input\n",
    "        self.input = input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we define our CNN training parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "learning_rate=0.1\n",
    "dataset='/home/ubuntu/notebook/mnist.pkl.gz'\n",
    "nkerns=[100, 50]\n",
    "batch_size=128"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now run some pre-built scripts to load the MNIST dataset and compute how many training batches we will have given our chosen batch size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... loading data\n",
      "... data loaded.\n"
     ]
    }
   ],
   "source": [
    "from logistic_sgd import load_data\n",
    "\n",
    "rng = numpy.random.RandomState(23455)\n",
    "\n",
    "datasets = load_data(dataset)\n",
    "\n",
    "train_set_x, train_set_y = datasets[0]\n",
    "valid_set_x, valid_set_y = datasets[1]\n",
    "test_set_x, test_set_y = datasets[2]\n",
    "\n",
    "# compute number of minibatches for training, validation and testing\n",
    "n_train_batches = train_set_x.get_value(borrow=True).shape[0]\n",
    "n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]\n",
    "n_test_batches = test_set_x.get_value(borrow=True).shape[0]\n",
    "n_train_batches /= batch_size\n",
    "n_valid_batches /= batch_size\n",
    "n_test_batches /= batch_size\n",
    "\n",
    "n_epochs=10000/n_train_batches\n",
    "\n",
    "# allocate symbolic variables for the data\n",
    "index = T.lscalar()  # index to a [mini]batch\n",
    "x = T.matrix('x')   # the data is presented as rasterized images\n",
    "y = T.ivector('y')  # the labels are presented as 1D vector of\n",
    "                        # [int] labels\n",
    "    \n",
    "print '... data loaded.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define our actual CNN layer-by-layer - again, spend as much time as like reviewing the code to understand Theano's syntax, but don't worry if you don't understand all the details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... building the model\n",
      "... model built\n"
     ]
    }
   ],
   "source": [
    "print '... building the model'\n",
    "\n",
    "# Reshape matrix of rasterized images of shape (batch_size, 28 * 28)\n",
    "# to a 4D tensor, compatible with our LeNetConvPoolLayer\n",
    "# (28, 28) is the size of MNIST images.\n",
    "layer0_input = x.reshape((batch_size, 1, 28, 28))\n",
    "\n",
    "# Construct the first convolutional pooling layer:\n",
    "# filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)\n",
    "# maxpooling reduces this further to (24/2, 24/2) = (12, 12)\n",
    "# 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)\n",
    "layer0 = LeNetConvPoolLayer(\n",
    "    rng,\n",
    "    input=layer0_input,\n",
    "    image_shape=(batch_size, 1, 28, 28),\n",
    "    filter_shape=(nkerns[0], 1, 25, 25),\n",
    "    poolsize=(2, 2)\n",
    ")\n",
    "\n",
    "# Construct the second convolutional pooling layer\n",
    "# filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)\n",
    "# maxpooling reduces this further to (8/2, 8/2) = (4, 4)\n",
    "# 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)\n",
    "# layer1 = LeNetConvPoolLayer(\n",
    "#    rng,\n",
    "#    input=layer0.output,\n",
    "#    image_shape=(batch_size, nkerns[0], 9, 9),\n",
    "#    filter_shape=(nkerns[1], nkerns[0], 4, 4),\n",
    "#    poolsize=(2, 2)\n",
    "# )\n",
    "\n",
    "# the HiddenLayer being fully-connected, it operates on 2D matrices of\n",
    "# shape (batch_size, num_pixels) (i.e matrix of rasterized images).\n",
    "# This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),\n",
    "# or (500, 50 * 4 * 4) = (500, 800) with the default values.\n",
    "layer2_input = layer0.output.flatten(2)\n",
    "\n",
    "# construct a fully-connected sigmoidal layer\n",
    "layer2 = HiddenLayer(\n",
    "    rng,\n",
    "    input=layer2_input,\n",
    "    n_in=nkerns[0] * 2 * 2,\n",
    "    n_out=50,\n",
    "    activation=T.tanh\n",
    ")\n",
    "\n",
    "# classify the values of the fully-connected sigmoidal layer\n",
    "layer3 = LogisticRegression(input=layer2.output, n_in=50, n_out=10)\n",
    "\n",
    "# the cost we minimize during training is the NLL of the model\n",
    "cost = layer3.negative_log_likelihood(y)\n",
    "\n",
    "# create a function to compute the mistakes that are made by the model\n",
    "test_model = theano.function(\n",
    "    [index],\n",
    "    layer3.errors(y),\n",
    "    givens={\n",
    "        x: test_set_x[index * batch_size: (index + 1) * batch_size],\n",
    "        y: test_set_y[index * batch_size: (index + 1) * batch_size]\n",
    "    }\n",
    ")\n",
    "\n",
    "validate_model = theano.function(\n",
    "    [index],\n",
    "    layer3.errors(y),\n",
    "    givens={\n",
    "        x: valid_set_x[index * batch_size: (index + 1) * batch_size],\n",
    "        y: valid_set_y[index * batch_size: (index + 1) * batch_size]\n",
    "    }\n",
    ")\n",
    "\n",
    "# create a list of all model parameters to be fit by gradient descent\n",
    "params = layer3.params + layer2.params + layer0.params\n",
    "\n",
    "# create a list of gradients for all model parameters\n",
    "grads = T.grad(cost, params)\n",
    "\n",
    "# train_model is a function that updates the model parameters by\n",
    "# SGD Since this model has many parameters, it would be tedious to\n",
    "# manually create an update rule for each model parameter. We thus\n",
    "# create the updates list by automatically looping over all\n",
    "# (params[i], grads[i]) pairs.\n",
    "updates = [\n",
    "    (param_i, param_i - learning_rate * grad_i)\n",
    "    for param_i, grad_i in zip(params, grads)\n",
    "]\n",
    "\n",
    "train_model = theano.function(\n",
    "    [index],\n",
    "    cost,\n",
    "    updates=updates,\n",
    "    givens={\n",
    "        x: train_set_x[index * batch_size: (index + 1) * batch_size],\n",
    "        y: train_set_y[index * batch_size: (index + 1) * batch_size]\n",
    "    }\n",
    ")\n",
    "print '... model built'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can actually train the CNN. After each iteration you will see an updated visualization of the learned feature layers in the network - this will allow you to see what the network is learning to look for to differentiate the digit classes.  Note that the training cost displayed during training is not easily interpretable in terms of the actual network performance, but as a relative measure you should see it decreasing as the networks learns.  After executing this cell, scroll down to see the training in action!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MHqBFt7a25N622220Wi0Ui0UBF91uF/V6XYAOaUSuW1JomqbBbreLM2Dehmje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TbigtXVVQvGZMl+f90+ulqtjwd1HTV9Pp+KxaKGh4dtNCEwLEkmHb/uPoFM5nDa39/XzMyMSTHR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sSGp78dBN7Dqpvu7rjQR8t+HCzazIQBjBxyKkMQP4h8yKTJcOSzxy3JKRlnRKQbIk6Qr2oOx0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eWlCc3m5uY0Nzen8/Nz7e/vy+u98q73er3a3t42uMnl4ZPpw7Tg705PT5VKpZROp23Buc1l\nIAYa18Vi0bx93EYUCt3+/n6zaAgEAva7rn87XGvuG1UQA1C6uq4Gyc/Pz+v111+3cZTgpX6/37jm\nw8PDunfvnmXBVEB4E6FyZBNTGZGEUBGRgNCb6e7uVjQaVbPZNOGVJKMVsm6azab1F1wVKiMKMTjD\n6x+aJgNvJNl1oAc4OzuzPggBkmy2VqupVCppd3dXjx8/ViaTUblcNty4o6PDlLX1et0q5XA4rPv3\n79uMg6OjI+XzeWO9uWraUqlkQz3oexFsgR949kdHR3rw4IH+53/+x4ImFRLY9sHBgXkc0VsaGxuT\nJKNMu81bKi8YU/S4CNw0q0mcurqurMfX1tb03nvv6T/+4z8UDAY1OTmp3t5eY49dXl5qdXVVxWJR\nw8PD1p+DDQh9ulgsSpJVmdwXBGcQHLD/YFTk+fm5yuWyMpmMCUfHxsbk9/sNZhocHDSIFbEmUBvq\nXnpiJCBf5PVKAj7T6Hk4NGLhsQOlwLphxNfu7q4CgYCWlpb0jW98Q41GQ++//75+/OMf6xe/+IV+\n93d/Vz09PUomk0ZRpJFCZkQmzQZmw8IQcocwUF56PFcTcJLJpJ49e6aNjQ1NTU0ZvNLf3698Pi+p\n3YcFnLSjo0MzMzM2n5fB2XiReDwexeNxhcNhW8D1el3j4+PWXPvjP/5jo3XBGHF7HkxNmp6e1pMn\nTwzeGhgYsJ9pNpsGTQFrkRUfHh4qlUqZChSWgc/nUz6ftxJUkvmd0EjDBwWF6tDQkHntxGIxo8dx\nMJLRc0BR1Uiyaga4ZGhoyKAITMdCoZCmpqZMVBQOh/X7v//7lgnBiSdYuqrG3t5ek8bD9iGRoEEH\nuwYHUEr5ZrNpvQkOcFTOHHhvvPGGwuGwUqmUzTWOx+OWfV5eXmpqasoMv1hfaE5cx1QCDupvV3QH\n2yObzSqbzer58+dGWT45OVEwGFQoFLJmN1BDIpEw2My9h8wpLpVKhoXD0GLOMVResGtssv+/9s7m\np6mui+KrrTQFLoGGG9qapnw0IDFxYnSAcaDGf13jSAdqCJqWQouhvTSEUqCitM+A/LanDh+ehPfN\n3SsxMaD9OPec/bH22vswRHA8HqvRaOj9+/dqt9uq1WpaW1uziZXQlJJMZktPBpkMNNLl5aXNu6dW\nAO2LJDebzarRaJiNoKC6sbFh2Wkcx3rz5o1Rt4xKwSGSAdFlS0B4fn5uNgMZJsEf0lwcF5+Vmg9K\nuSiKTMX2/Plz/fr1S3Eca3Fx0c7m2dmZDe2DGqWvJpzRE3b23wX3dok5TTdfvnyxSJAFhOeVbq/e\na7VaqtfrU11upI7fv3+3QV9nZ2cmKYzjWJPJ7Y04pOEHBwcm5/v9+7eazaZFkkSlHz58sEJLJnN7\n6TBRDNQI81NwTF+/fjWen9oEhSpG/9JxCVVEJEsxmFEGZDn8oZiXzWZVrVanOnbhLTFmhUJBnz9/\ntoIZBS6oJ+gO5IxIRyWZsctkMtbUQ2RbLpeN81xbW1O5XFan07HDP5lMTI2RJIml45VKxWgclCg0\nz0iyaBnJLMoINj6FXQzc/Py8SqWSBoOBisWi3r59a80+9Gt0Oh19+/bNOHQKqgxOg0Kjexpnz+eA\nP+aydg43KX2j0bBWd66AZFQGkeDl5aVlQvRsEOGORiMtLCxY9iTJKDPkj1wfSUTMeeE5IwHkfarV\nqp4+faqNjQ0lSaJms6kkSVQul1Wv17W1tWURLVEw9Gar1VKSJOp2u3bXK9M7yQyhKYhq4eHpjudG\nq3a7rY8fP9r67+zs6NmzZ8a7o0SJokj1el2dTsekjSjhOBfUVKDXcCzlctkKpCcnJ1OyR7Kfd+/e\nGW9eqVT06tUrDYdDHR0dWWBCkT98xmQNGPww02Iw4tnZmfb29nR6emoOmnPDnQE0akrS5uamXb1K\nYBM21CFgoNCPHWH/sB5hrewuuLfGK3hYZk48fvzYutzQTxNtrK+vmxfO5/Nqt9va29vTjx8/1O12\nFcextre3LZKheapYLFqxDYqBqAEjPjMzY1V1agNIzaIo0tramkWlpL5wwhi5ubk5m0hIusegMElW\nRKY4x8hjHnw4QE76M88f5U44DEqSdSgj2aKoc3p6qmazadkThvbk5MQaOzDQfF44QZwQxpqCK5rz\nly9fmvGhLhE2Iy0tLenTp0+mbaZZBfUQWQlFeugLhtJxANDFM29kOBxOjX/u9/tTV+YNBgN1Oh3r\nKsUx7O7uGoXDYcrn85Y5YIQ5sBjCUI5IkZfPOhqN7M5RulUREaBQYjJkqVSyu3zZY6VSyfb0aDSa\nkvwxm51+DBw6NAZKlHq9bo1eSZLo+vpa1WpVr1+/tvcbDAY2b2lubs6K3ldXVzo8PLSmIGo6KEWQ\nG5N5U9MimgfZbNY4fGoQKIry+bxevHihSqWira0tLS8vT4khkDNL0vb2tjlzMjwCExxvWOeiI39l\nZUWXl5c2wbRYLNrcqlarZc6cbJZZWZK0vr6uWq2mnz9/Wsf+zc2Njf6GCiQjYT/QDxDSWDwjSVaj\nqlQqiuPY1o6mO7JIKKuwW5hAAHtA0Ty8djOc53QX3IvB7/f7phIgjWIhibKQWVKpp+hJ9Xo0Gml3\nd9f0vcwKJ1WbnZ2dKlJBr5CuSn9UGOHgrFC1QXROqzQeN1TJLC8v6/j42NK/XC5ns8opSiMD5f1Q\nBLCZ8eDIwBhYdnR0NKUgubq6shkgGOpwnsf19bU6nY51k6K24cYoXovCW2jMQlURUxRpiOIAkUXQ\noRp2SmcyGT158sQoErqaoc/CIjtqFBpxWHvWBueazWaN6uMgHBwcmMyNjIFol+wJ/jVJEhsfwRgC\nDCh9IOw3Rl3gwMJxEtRmwkZBGoAw3LOzs/Z3jDoFd+StNCYxewkqQ9JUExUOHkcKD05Rb2VlRfV6\n3bpn2cMECPl83kbutlot7e7uWoCAAcHQhRQSBo1aBOOM6ezGMWIQ6Ugej8fq9XpKkkQLCwva2dlR\nLpezBrRcLmcBHgPCzs/PbT+GnyWs5aD8gUZhTAESXRqqVldX9eDBA2tCpNOajAlZp/RnLk6329Xx\n8bF1LkM3stfYGzhdIny6wBk4F86Cgg5mlMvp6an29/dtvdk77HHWlK5u9mc4ogGnQMaPw/i3uLdZ\nOqQ44VRAlAJw+YxAIGqBx2OkLmOS0Udj+IgW+v2+HSiKV2juMT4oDEhbQw9MNMrNRsPhUDMzM8at\nQ0PgmVG0EOFCUSDF4iBRLwjVSdJtlNDv9y36Ojw8tKIjen8iO7jMULvMv6VWwCFBv83GHwwGdnAo\niKFyoWZAvQPnGUWRGd/JZKJ+v2/vzaaEmsOBhQ04cOkMKyNFx9mTNV1cXExRRTgy5KatVkvVatWK\nYhRwoZiI6rhmEuNJxMVrMzuF4imGh+cOp8w68X3Ifqjv0BTG915aWlK/3zeuHdUFPHxoZMNLLTCE\nYaaH8SPSZHIrEy4xuBgkeg1oAiKyJ5gaDodWNGTfMa9eunU6cNmh44miaOoaREYw4Lg5U/Pz8zbo\njywBRwr1yLweDDrrh+MNGxSR6/Lz8JxAi2FHWDtJVgAmomafE7CQ5ePEeeZIL5HrIpHGRvGa7Av2\nFVlaoVDQYDCwabvMDAqHFCIQCe8d4Htik6CF2feSpmpfd8G9zcMPNc6SzMuhjImiyLg5us9IxYk4\nqXLzMzwuzRPMJieauri4MF3/eDxWsVi0zUaBJJSaIX8jI1lZWTHvS+RPtEDb/OLiohUiSaPZGEga\nx+OxURShMaBQh0Pc3Ny0w0FmEcexzcBniiXZwXA41OLioul5iTg45Gw+isWoCiRZtE2WBLVFJE+0\nAV8JHYIx4dmRJVAYpQAMJ4kRxvEw14Z1Qo5JEPB39ysTHjEEUCaPHj0yLjg0kkRpaPzDphkOEAcL\nx010TQTJoScKZm+wh5nDDr1Rq9WsZ4NnwPyUUOPNXlpaWrJImWYzDEz4uZCHcj9COCoE5QzrhDgg\npBPy+bwqlcpUkIAsGoP8t2ED/A4KjH3EM8D5oBwieEKjj2INOTbPNVSeQBdipAkEMOTUiqgHYCMw\nqLlczhqb+B7Ip3lP6bZPhj0WOhYMfRzHZnMQAZD58dzjOLZMbXZ21poWsW1RFKlWq1mweHNzY93F\nYWAKbQVDEBbLyYqheHj9u+BeDD6bjcgXrTRfDg0x3htjFY4k4MBSbOt2u1OLReGtVCpZkSfsnpNu\nDxNFYrpdKRCxCXFARObMTyHSo7iMFBSDubq6ate9MciLblNqBtASFIDK5bJRLKTx1C3499ywg7Eg\nNSYVZS1ZXxQiXLTOvJFwU2UyGaMfMpmMVldXrdmGdu+/byMKPwuHJ2wyIZvp9XpTzUsYOiJDqAEi\naJpfeK1QqksBezwem547iiI9fPhQkuwwFAoFuwkL1RG/x4DSDMYVc6hHcA6h8oSxFdB9rJMkG9gl\n3Y7dQHDA2lAvQV4I3RVmDxTQ4YaJDHGAUCnUNobDoRkiKA+cEc+UZjxoHqSGZFFkzjMzMyb/u7m5\nsTHK/F9JRtPxnOC0ea2Q/qBhMTROYZ8JWRtOO3TMXGOJcwiN23g8tiI+N7Lt7+9b7woqHKJt6U+G\nhAOfTCY2pgJajnVnlDS3exHU5XK3Yxd6vZ4ZXmSfPAPWg05wsnqeDWc4dOIhtUYDIc8bJRn9R/QU\nwB7cBZn/Ik1wOBwOx/8+7nZ9isPhcDj+b+AG3+FwOFICN/gOh8ORErjBdzgcjpTADb7D4XCkBG7w\nHQ6HIyVwg+9wOBwpgRt8h8PhSAnc4DscDkdK4Abf4XA4UgI3+A6Hw5ESuMF3OByOlMANvsPhcKQE\nbvAdDocjJXCD73A4HCmBG3yHw+FICdzgOxwOR0rgBt/hcDhSAjf4DofDkRK4wXc4HI6U4B9LvmDD\ngsYdugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e46530310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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isUgul5NW+czMDLFYjGq1yo0bN3C5XHR2dkql7/F46OvrI5PJAOB2uxkeHubF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\neb53/h+2m4XqRawxJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e518e7090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VW1Uq3u9XyQnHfv6OykDr61SO4UAlr3sWvxvGifIdkr4xlfTV16r0HP8flRkrv6tyrvB0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\nfomppJRqcOBz3/UMOEiae4y3exsxZTZmKyGbfh7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e4cee42d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization complete.\n",
      "Best validation score of 98.427484 % obtained at iteration 9750, with test performance 98.377404 %\n"
     ]
    }
   ],
   "source": [
    "###############\n",
    "# TRAIN MODEL #\n",
    "###############\n",
    "%matplotlib inline\n",
    "print '... training'\n",
    "# early-stopping parameters\n",
    "patience = 10000  # look as this many examples regardless\n",
    "patience_increase = 2  # wait this much longer when a new best is\n",
    "                           # found\n",
    "improvement_threshold = 0.995  # a relative improvement of this much is\n",
    "                                   # considered significant\n",
    "validation_frequency = min(n_train_batches, patience / 2)\n",
    "                                  # go through this many\n",
    "                                  # minibatche before checking the network\n",
    "                                  # on the validation set; in this case we\n",
    "                                  # check every epoch\n",
    "\n",
    "best_validation_loss = numpy.inf\n",
    "best_iter = 0\n",
    "test_score = 0.\n",
    "start_time = timeit.default_timer()\n",
    "\n",
    "epoch = 0\n",
    "done_looping = False\n",
    "\n",
    "while (epoch < n_epochs) and (not done_looping):\n",
    "    epoch = epoch + 1\n",
    "    for minibatch_index in xrange(n_train_batches):\n",
    "\n",
    "        iter = (epoch - 1) * n_train_batches + minibatch_index\n",
    "\n",
    "        if iter % 100 == 0:\n",
    "            print 'training @ iter = ', iter\n",
    "        cost_ij = train_model(minibatch_index)\n",
    "\n",
    "        if (iter + 1) % validation_frequency == 0:\n",
    "\n",
    "            # compute zero-one loss on validation set\n",
    "            validation_losses = [validate_model(i) for i\n",
    "                                    in xrange(n_valid_batches)]\n",
    "            this_validation_loss = numpy.mean(validation_losses)\n",
    "            print('epoch %i, minibatch %i/%i, validation error %f %%' %\n",
    "                    (epoch, minibatch_index + 1, n_train_batches,\n",
    "                    this_validation_loss * 100.))\n",
    "\n",
    "            # if we got the best validation score until now\n",
    "            if this_validation_loss < best_validation_loss:\n",
    "\n",
    "                #improve patience if loss improvement is good enough\n",
    "                if this_validation_loss < best_validation_loss *  \\\n",
    "                    improvement_threshold:\n",
    "                    patience = max(patience, iter * patience_increase)\n",
    "\n",
    "                # save best validation score and iteration number\n",
    "                best_validation_loss = this_validation_loss\n",
    "                best_iter = iter\n",
    "\n",
    "                # test it on the test set\n",
    "                test_losses = [\n",
    "                    test_model(i)\n",
    "                    for i in xrange(n_test_batches)\n",
    "                ]\n",
    "                test_score = numpy.mean(test_losses)\n",
    "                print(('     epoch %i, minibatch %i/%i, test error of '\n",
    "                        'best model %f %%') %\n",
    "                        (epoch, minibatch_index + 1, n_train_batches,\n",
    "                        test_score * 100.))\n",
    "                \n",
    "    display.clear_output()\n",
    "    plt.imshow(tile_raster_images(\n",
    "    X = layer0.W.get_value(borrow=True),\n",
    "    img_shape=(25,25),\n",
    "    tile_shape=(10,10),\n",
    "    tile_spacing=(1,1)), \n",
    "    cmap= cm.Greys_r,\n",
    "    aspect='auto')\n",
    "    plt.axis('off')\n",
    "    plt.title('Layer 0 convolutional filters, training cost: ' + str(test_score * 100))\n",
    "    plt.show()\n",
    "    plt.imshow(layer2.W.get_value(borrow=True)[:,:].T, \n",
    "    cmap= cm.Greys_r)\n",
    "    plt.axis('off')\n",
    "    plt.title('Layer 1 fully connected weights, training cost: ' + str(test_score * 100))   \n",
    "    plt.show()\n",
    "    plt.imshow(layer3.W.get_value(borrow=True)[:,:].T, \n",
    "    cmap= cm.Greys_r)\n",
    "    plt.axis('off')\n",
    "    plt.title('Layer 2 fully connected weights, training cost: ' + str(test_score * 100))   \n",
    "    plt.show()\n",
    "\n",
    "    if patience <= iter:\n",
    "        done_looping = True\n",
    "        break\n",
    "\n",
    "end_time = timeit.default_timer()\n",
    "print('Optimization complete.')\n",
    "print('Best validation score of %f %% obtained at iteration %i, '\n",
    "        'with test performance %f %%' %\n",
    "        ((1 - best_validation_loss) * 100., best_iter + 1, (1 - test_score) * 100.))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will see that after 10000 training iterations the classification accuracy against the test set is over 98%.  You may have noticed during training that the convolutional filters came to much more closely resemble line segments that make up the hand written digits and the layer 2 connection weights became more polarized suggesting that the network was learning specific grouping of filters which were significant for each of the digits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read much more about this example and others in the excellent tutorials created by the Theano developers available [here](http://deeplearning.net/tutorial/). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Framework 3: Torch\n",
    "\n",
    "\n",
    "Torch is a scientific computing framework based on the programming language Lua but with a highly optimized C/CUDA backend.  Torch is maintained by a group of research scientists from Facebook, Twitter and Google but also has an community of contributors to the open-source project.\n",
    "\n",
    "Benefits of of Torch:\n",
    "* Efficient Tensor library (like NumPy) with an efficient CUDA backend which lends itself well to DNN computations\n",
    "* Dedicated Neural Networks package -- build arbitrary acyclic computation graphs with automatic differentiation\n",
    "also with fast CUDA and CPU backends\n",
    "* Good community and industry support - several hundred community-built and maintained packages - active contribution from web scale companies, e.g. Facebook\n",
    "* Easy to use Multi-GPU support and distribution of neural network training\n",
    "\n",
    "The Lua scripting language is fast due to Just In Time (JIT) compilation and is a lot like Javascript in syntax.\n",
    "\n",
    "You may choose to use Torch as your DL framework if you fit the following profile:\n",
    "* You are very concerned about run-time speed, but still want the convenience of a scripting interface\n",
    "* You wish to use multiple GPUs for training\n",
    "\n",
    "#### Torch example\n",
    "\n",
    "DL is not only applicable to image data, there have been many recent successes applying DL to understanding natural language text.    One important class of DNNs for natural language understanding is called Recurrent Neural Networks (RNNs).  RNNs extend the standard multi-layer DNN with a temporal memory component that means the network can learn sequential patterns in input data.  This works by having the hidden layers in the network receive both a new data sample as well as their state at the previous time step as input at each iteration. This means that RNNs can learn functions such as time-series forecasting, music generation, natural language word sequences and character level text sequence prediction.  In the example below you see an RNN receiving the sequence \"Hello world!\" as training input in three character windows with a stride of three characters.\n",
    "\n",
    "![RNN](files/DLIntro/rnn.png)\n",
    "\n",
    "<div align=\"center\">*Figure 7:  Recurrent Neural Networks introduce a self-connection to the hidden nodes; this allows the network to remember it's previous states and subsequently learn significant predictive patterns in the input data.*</div> \n",
    "\n",
    "In this example we will show that an RNN built in Torch and trained on a variety of corpora of different styles of text is able to learn to generate random character sequence that retain the appropriate style, vocabulary, punctuation and formatting.  Given a large volume of training text (between 1.1 and 6.2 million training characters per model) the model learns to predict the next character in a partial character sequence; this means the model can generate original text conforming to the same style as the text it has been trained on.\n",
    "\n",
    "Training the RNN capable of generating this style appropriate text takes about an hour per text corpus on an NVIDIA GRID K520 GPU running Torch.  Training the same network on a CPU would take about 100 times as long.  We have already done that GPU training for you. We made use of the excellent [code](https://github.com/karpathy/char-rnn) written by Andrej Karpathy, a graduate student at Stanford. We highly recommend reading Andrej's fascinating blog post associated with this code describing [The Unreasonable Effectiveness of RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/).  \n",
    "\n",
    "Edit the style variable in the cell below to choose a trained network and then run it to see the Torch based RNN randomly generate text in one of the following styles:  William Shakespeare, Leo Tolstoy, President Barack Obama's speech writers or the Linux kernal source code authors.  You can also try increasing the `-length` input to get more text, but it will take longer to generate.\n",
    "\n",
    "**NOTE** If you try the `linux` style, the output will be randomly generated text (character by character) that looks like kernel source code. This is not an error!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0musing CUDA on GPU 0...\u001b[0m\t\n",
      "\u001b[0mcreating an LSTM...\u001b[0m\t\n",
      "\u001b[0mseeding with  \u001b[0m\t\n",
      "\u001b[0m--------------------------\u001b[0m\t\n",
      " Rome, my dear fellow look at\r\n",
      "one along this something devoted and clearly, went on before all his\r\n",
      "conversation of the old man's battery, and althtallies cannot help grow, because it\r\n",
      "was always directing as if they saw the grandeurans\r\n",
      "who had glumted his head and husband who had rode off to himself. Everybody was\r\n",
      "beaten as much way of laughter.\r\n",
      "\r\n",
      "\"That's all they've let the regiment. And my time onto, go beggn it in front!\"\r\n",
      "\r\n",
      "Princess Mary could not call at that love of being a few incompr\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "style='tolstoy'  # Choose from: tinyshakespeare, tolstoy, obama, linux\n",
    "cd /home/ubuntu/notebook/DLIntro/char-rnn/\n",
    "th sample.lua checkpoints/$style/checkpoint.t7 -gpuid 0 -length 500 -primetext ' '"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember there was nothing changed in the structure of the RNN to learn each of these different styles except for the data that was use for training.  This generated text is entirely random (and meaningless) in the specific words chosen and it is not generate from complete sentences sampled from the original input; rather it is a sequence of characters generated one at a time, which makes it astonishing that it conforms to style so well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DIGITS\n",
    "\n",
    "DIGITS is a **D**eep Learning **G**PU **T**raining **S**ystem developed by NVIDIA to help researchers and analysts to easily develop, train and test CNNs.  DIGITS is also a visualization tool that enables real-time visualization of important metrics during CNN training.  DIGITS uses the Caffe framework which we saw above for CNN training.\n",
    "\n",
    "The DIGITS dashboard lets researchers manage their training datasets and training of DNNs.  It also makes it easy to explore and test what your DNN has learned.\n",
    "\n",
    "Most recently in DIGITS 2, DIGITS can manage multiple GPUs in a single machine and distribute training of a single DNN across those GPUs for significant training speed increases.\n",
    "\n",
    "![](files/DLIntro/digits_dashboard.png)\n",
    "\n",
    "<div align=\"center\">*Figure 8:  Interface to the NVIDIA DIGITS Deep Learning tool.*</div>\n",
    "\n",
    "DIGITS is a great place to get started learning more about Deep Learning and the practicalities of training DNNs.  There is a webinar and associated Qwiklab focused on DIGITS coming soon.  Register [here](https://developer.nvidia.com/deep-learning-courses)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More information\n",
    "\n",
    "To learn more about these topics, please visit:\n",
    "* GPU accelerated machine learning: [http://www.nvidia.com/object/machine-learning.html](http://www.nvidia.com/object/machine-learning.html)\n",
    "* Caffe: [http://caffe.berkeleyvision.org/](http://caffe.berkeleyvision.org/)\n",
    "* Theano: [http://deeplearning.net/software/theano/](http://deeplearning.net/software/theano/)\n",
    "* Torch: [http://torch.ch/](http://torch.ch/)\n",
    "* DIGITS: [https://developer.nvidia.com/digits](https://developer.nvidia.com/digits)\n",
    "* cuDNN: [https://developer.nvidia.com/cudnn](https://developer.nvidia.com/cudnn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post-Lab Summary\n",
    "\n",
    "If you would like to download this lab for later viewing, it is recommend you go to your browsers File menu (not the Jupyter notebook file menu) and save the complete web page.  This will ensure the images are copied down as well.\n",
    "\n",
    "### Deep Learning Lab Series\n",
    "\n",
    "Make sure to check out the rest of the classes in this Deep Learning lab series.  You can find them [here](https://developer.nvidia.com/deep-learning-courses)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this class we have shown that there are a variety of exciting applications of DL that are accesible today through the open-source frameworks Caffe, Theano and Torch.  Each of these frameworks has their own unique advantages and are better suited to different types of user and application.  Hopefully this tour has given you some insight into which frameworks may best suit your research or development needs.  In the coming weeks we will be releasing more classes which dive deeper into the specific workings of each of these frameworks."
   ]
  }
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