tensorflow-for-deep-learning-py/Deep_Art/deepdream/examples/01-learning-lenet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python solving with LeNet\n",
    "\n",
    "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, './python')\n",
    "import caffe\n",
    "\n",
    "from pylab import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be running the provided LeNet example (make sure you've downloaded the data and created the databases, as below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "--2015-06-30 14:41:56--  http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Resolving yann.lecun.com... 128.122.47.89\n",
      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
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      "Length: 9912422 (9.5M) [application/x-gzip]\n",
      "Saving to: 'train-images-idx3-ubyte.gz'\n",
      "\n",
      "train-images-idx3-u 100%[=====================>]   9.45M   146KB/s   in 57s    \n",
      "\n",
      "2015-06-30 14:42:53 (171 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n",
      "\n",
      "--2015-06-30 14:42:53--  http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Resolving yann.lecun.com... 128.122.47.89\n",
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      "\n",
      "train-labels-idx1-u 100%[=====================>]  28.20K   107KB/s   in 0.3s   \n",
      "\n",
      "2015-06-30 14:42:53 (107 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n",
      "\n",
      "--2015-06-30 14:42:53--  http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Resolving yann.lecun.com... 128.122.47.89\n",
      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1648877 (1.6M) [application/x-gzip]\n",
      "Saving to: 't10k-images-idx3-ubyte.gz'\n",
      "\n",
      "t10k-images-idx3-ub 100%[=====================>]   1.57M   205KB/s   in 8.2s   \n",
      "\n",
      "2015-06-30 14:43:02 (197 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n",
      "\n",
      "--2015-06-30 14:43:02--  http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Resolving yann.lecun.com... 128.122.47.89\n",
      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4542 (4.4K) [application/x-gzip]\n",
      "Saving to: 't10k-labels-idx1-ubyte.gz'\n",
      "\n",
      "t10k-labels-idx1-ub 100%[=====================>]   4.44K  26.9KB/s   in 0.2s   \n",
      "\n",
      "2015-06-30 14:43:02 (26.9 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n",
      "\n",
      "Unzipping...\n",
      "Done.\n",
      "Creating lmdb...\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# Download and prepare data\n",
    "!data/mnist/get_mnist.sh\n",
    "!examples/mnist/create_mnist.sh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need two external files to help out:\n",
    "* the net prototxt, defining the architecture and pointing to the train/test data\n",
    "* the solver prototxt, defining the learning parameters\n",
    "\n",
    "We start with the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n",
    "\n",
    "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from caffe import layers as L\n",
    "from caffe import params as P\n",
    "\n",
    "def lenet(lmdb, batch_size):\n",
    "    # our version of LeNet: a series of linear and simple nonlinear transformations\n",
    "    n = caffe.NetSpec()\n",
    "    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
    "                             transform_param=dict(scale=1./255), ntop=2)\n",
    "    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
    "    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
    "    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
    "    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
    "    n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
    "    n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
    "    return n.to_proto()\n",
    "    \n",
    "with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f:\n",
    "    f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))\n",
    "    \n",
    "with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f:\n",
    "    f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The net has been written to disk in more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer {\r\n",
      "  name: \"data\"\r\n",
      "  type: \"Data\"\r\n",
      "  top: \"data\"\r\n",
      "  top: \"label\"\r\n",
      "  transform_param {\r\n",
      "    scale: 0.00392156862745\r\n",
      "  }\r\n",
      "  data_param {\r\n",
      "    source: \"examples/mnist/mnist_train_lmdb\"\r\n",
      "    batch_size: 64\r\n",
      "    backend: LMDB\r\n",
      "  }\r\n",
      "}\r\n",
      "layer {\r\n",
      "  name: \"conv1\"\r\n",
      "  type: \"Convolution\"\r\n",
      "  bottom: \"data\"\r\n",
      "  top: \"conv1\"\r\n",
      "  convolution_param {\r\n",
      "    num_output: 20\r\n",
      "    kernel_size: 5\r\n",
      "    weight_filler {\r\n",
      "      type: \"xavier\"\r\n",
      "    }\r\n",
      "  }\r\n",
      "}\r\n",
      "layer {\r\n",
      "  name: \"pool1\"\r\n",
      "  type: \"Pooling\"\r\n",
      "  bottom: \"conv1\"\r\n",
      "  top: \"pool1\"\r\n",
      "  pooling_param {\r\n",
      "    pool: MAX\r\n",
      "    kernel_size: 2\r\n",
      "    stride: 2\r\n",
      "  }\r\n",
      "}\r\n",
      "layer {\r\n",
      "  name: \"conv2\"\r\n",
      "  type: \"Convolution\"\r\n",
      "  bottom: \"pool1\"\r\n",
      "  top: \"conv2\"\r\n",
      "  convolution_param {\r\n",
      "    num_output: 50\r\n",
      "    kernel_size: 5\r\n",
      "    weight_filler {\r\n",
      "      type: \"xavier\"\r\n",
      "    }\r\n",
      "  }\r\n",
      "}\r\n",
      "layer {\r\n",
      "  name: \"pool2\"\r\n",
      "  type: \"Pooling\"\r\n",
      "  bottom: \"conv2\"\r\n",
      "  top: \"pool2\"\r\n",
      "  pooling_param {\r\n",
      "    pool: MAX\r\n",
      "    kernel_size: 2\r\n",
      "    stride: 2\r\n",
      "  }\r\n",
      "}\r\n",
      "layer {\r\n",
      "  name: \"ip1\"\r\n",
      "  type: \"InnerProduct\"\r\n",
      "  bottom: \"pool2\"\r\n",
      "  top: \"ip1\"\r\n",
      "  inner_product_param {\r\n",
      "    num_output: 500\r\n",
      "    weight_filler {\r\n",
      "      type: \"xavier\"\r\n",
      "    }\r\n",
      "  }\r\n",
      "}\r\n",
      "layer {\r\n",
      "  name: \"relu1\"\r\n",
      "  type: \"ReLU\"\r\n",
      "  bottom: \"ip1\"\r\n",
      "  top: \"ip1\"\r\n",
      "}\r\n",
      "layer {\r\n",
      "  name: \"ip2\"\r\n",
      "  type: \"InnerProduct\"\r\n",
      "  bottom: \"ip1\"\r\n",
      "  top: \"ip2\"\r\n",
      "  inner_product_param {\r\n",
      "    num_output: 10\r\n",
      "    weight_filler {\r\n",
      "      type: \"xavier\"\r\n",
      "    }\r\n",
      "  }\r\n",
      "}\r\n",
      "layer {\r\n",
      "  name: \"loss\"\r\n",
      "  type: \"SoftmaxWithLoss\"\r\n",
      "  bottom: \"ip2\"\r\n",
      "  bottom: \"label\"\r\n",
      "  top: \"loss\"\r\n",
      "}\r\n"
     ]
    }
   ],
   "source": [
    "!cat examples/mnist/lenet_auto_train.prototxt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see the learning parameters, which are also written as a `prototxt` file. We're using SGD with momentum, weight decay, and a specific learning rate schedule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# The train/test net protocol buffer definition\r\n",
      "train_net: \"examples/mnist/lenet_auto_train.prototxt\"\r\n",
      "test_net: \"examples/mnist/lenet_auto_test.prototxt\"\r\n",
      "# test_iter specifies how many forward passes the test should carry out.\r\n",
      "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n",
      "# covering the full 10,000 testing images.\r\n",
      "test_iter: 100\r\n",
      "# Carry out testing every 500 training iterations.\r\n",
      "test_interval: 500\r\n",
      "# The base learning rate, momentum and the weight decay of the network.\r\n",
      "base_lr: 0.01\r\n",
      "momentum: 0.9\r\n",
      "weight_decay: 0.0005\r\n",
      "# The learning rate policy\r\n",
      "lr_policy: \"inv\"\r\n",
      "gamma: 0.0001\r\n",
      "power: 0.75\r\n",
      "# Display every 100 iterations\r\n",
      "display: 100\r\n",
      "# The maximum number of iterations\r\n",
      "max_iter: 10000\r\n",
      "# snapshot intermediate results\r\n",
      "snapshot: 5000\r\n",
      "snapshot_prefix: \"examples/mnist/lenet\"\r\n"
     ]
    }
   ],
   "source": [
    "!cat examples/mnist/lenet_auto_solver.prototxt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's pick a device and load the solver. We'll use SGD (with momentum), but Adagrad and Nesterov's accelerated gradient are also available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "caffe.set_device(0)\n",
    "caffe.set_mode_gpu()\n",
    "solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('data', (64, 1, 28, 28)),\n",
       " ('label', (64,)),\n",
       " ('conv1', (64, 20, 24, 24)),\n",
       " ('pool1', (64, 20, 12, 12)),\n",
       " ('conv2', (64, 50, 8, 8)),\n",
       " ('pool2', (64, 50, 4, 4)),\n",
       " ('ip1', (64, 500)),\n",
       " ('ip2', (64, 10)),\n",
       " ('loss', ())]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# each output is (batch size, feature dim, spatial dim)\n",
    "[(k, v.data.shape) for k, v in solver.net.blobs.items()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('conv1', (20, 1, 5, 5)),\n",
       " ('conv2', (50, 20, 5, 5)),\n",
       " ('ip1', (500, 800)),\n",
       " ('ip2', (10, 500))]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# just print the weight sizes (not biases)\n",
    "[(k, v[0].data.shape) for k, v in solver.net.params.items()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': array(2.301163673400879, dtype=float32)}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solver.net.forward()  # train net\n",
    "solver.test_nets[0].forward()  # test net (there can be more than one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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       "eU5QDbaKiorKc8L/DzAr6bE92WeRAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7939901710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we use a little trick to tile the first eight images\n",
    "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
    "print solver.net.blobs['label'].data[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.  2.  1.  0.  4.  1.  4.  9.]\n"
     ]
    },
    {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7939901490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
    "print solver.test_nets[0].blobs['label'].data[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both train and test nets seem to be loading data, and to have correct labels.\n",
    "\n",
    "Let's take one step of (minibatch) SGD and see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "solver.step(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do we have gradients propagating through our filters? Let's see the updates to the first layer, shown here as a $4 \\times 5$ grid of $5 \\times 5$ filters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f79383819d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7939901850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n",
    "       .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n",
    "Note that this process will be the same as if training through the `caffe` binary. In particular:\n",
    "* logging will continue to happen as normal\n",
    "* snapshots will be taken at the interval specified in the solver prototxt (here, every 5000 iterations)\n",
    "* testing will happen at the interval specified (here, every 500 iterations)\n",
    "\n",
    "Since we have control of the loop in Python, we're free to compute additional things as we go, as we show below. We can do many other things as well, for example:\n",
    "* write a custom stopping criterion\n",
    "* change the solving process by updating the net in the loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0 testing...\n",
      "Iteration 25 testing...\n",
      "Iteration 50 testing...\n",
      "Iteration 75 testing...\n",
      "Iteration 100 testing...\n",
      "Iteration 125 testing...\n",
      "Iteration 150 testing...\n",
      "Iteration 175 testing...\n",
      "CPU times: user 12.3 s, sys: 3.96 s, total: 16.2 s\n",
      "Wall time: 15.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "niter = 200\n",
    "test_interval = 25\n",
    "# losses will also be stored in the log\n",
    "train_loss = zeros(niter)\n",
    "test_acc = zeros(int(np.ceil(niter / test_interval)))\n",
    "output = zeros((niter, 8, 10))\n",
    "\n",
    "# the main solver loop\n",
    "for it in range(niter):\n",
    "    solver.step(1)  # SGD by Caffe\n",
    "    \n",
    "    # store the train loss\n",
    "    train_loss[it] = solver.net.blobs['loss'].data\n",
    "    \n",
    "    # store the output on the first test batch\n",
    "    # (start the forward pass at conv1 to avoid loading new data)\n",
    "    solver.test_nets[0].forward(start='conv1')\n",
    "    output[it] = solver.test_nets[0].blobs['ip2'].data[:8]\n",
    "    \n",
    "    # run a full test every so often\n",
    "    # (Caffe can also do this for us and write to a log, but we show here\n",
    "    #  how to do it directly in Python, where more complicated things are easier.)\n",
    "    if it % test_interval == 0:\n",
    "        print 'Iteration', it, 'testing...'\n",
    "        correct = 0\n",
    "        for test_it in range(100):\n",
    "            solver.test_nets[0].forward()\n",
    "            correct += sum(solver.test_nets[0].blobs['ip2'].data.argmax(1)\n",
    "                           == solver.test_nets[0].blobs['label'].data)\n",
    "        test_acc[it // test_interval] = correct / 1e4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the train loss and test accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f793878f490>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79387025d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax1 = subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(arange(niter), train_loss)\n",
    "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
    "ax1.set_xlabel('iteration')\n",
    "ax1.set_ylabel('train loss')\n",
    "ax2.set_ylabel('test accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n",
    "\n",
    "Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7938797710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7938323190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7938233050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f793811c290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79380a2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792bc15650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792ba9ddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b9b9550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b97ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b91c3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(8):\n",
    "    figure(figsize=(2, 2))\n",
    "    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n",
    "    figure(figsize=(10, 2))\n",
    "    imshow(output[:50, i].T, interpolation='nearest', cmap='gray')\n",
    "    xlabel('iteration')\n",
    "    ylabel('label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n",
    "\n",
    "Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b65d950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "rncAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b4bf3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b33bd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "JEnq6f8BUrepXiOjd3cAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b2b79d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b23ec90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b1bd290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b1419d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
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      "text/plain": [
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b06c950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
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      "text/plain": [
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792b570150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d792baffb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(8):\n",
    "    figure(figsize=(2, 2))\n",
    "    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n",
    "    figure(figsize=(10, 2))\n",
    "    imshow(exp(output[:50, i].T) / exp(output[:50, i].T).sum(0), interpolation='nearest', cmap='gray')\n",
    "    xlabel('iteration')\n",
    "    ylabel('label')"
   ]
  }
 ],
 "metadata": {
  "description": "Define, train, and test the classic LeNet with the Python interface.",
  "example_name": "Learning LeNet",
  "include_in_docs": true,
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  },
  "priority": 2
 },
 "nbformat": 4,
 "nbformat_minor": 0
}