{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solving in Python with LeNet\n",
    "\n",
    "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Set up the Python environment: we'll use the `pylab` import for numpy and plot inline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pylab import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Import `caffe`, adding it to `sys.path` if needed. Make sure you've built pycaffe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, caffe_root + 'python')\n",
    "import caffe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We'll be using the provided LeNet example data and networks (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",
      "Creating lmdb...\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# run scripts from caffe root\n",
    "import os\n",
    "os.chdir(caffe_root)\n",
    "# Download data\n",
    "!data/mnist/get_mnist.sh\n",
    "# Prepare data\n",
    "!examples/mnist/create_mnist.sh\n",
    "# back to examples\n",
    "os.chdir('examples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Creating the net \n",
    "\n",
    "Now let's make a variant of LeNet, the classic 1989 convnet architecture.\n",
    "\n",
    "We'll 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 by creating 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, 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",
    "    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",
    "    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.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
    "    n.relu1 = L.ReLU(n.fc1, in_place=True)\n",
    "    n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
    "    n.loss =  L.SoftmaxWithLoss(n.score, n.label)\n",
    "    \n",
    "    return n.to_proto()\n",
    "    \n",
    "with open('mnist/lenet_auto_train.prototxt', 'w') as f:\n",
    "    f.write(str(lenet('mnist/mnist_train_lmdb', 64)))\n",
    "    \n",
    "with open('mnist/lenet_auto_test.prototxt', 'w') as f:\n",
    "    f.write(str(lenet('mnist/mnist_test_lmdb', 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The net has been written to disk in a 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: \"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: \"fc1\"\r\n",
      "  type: \"InnerProduct\"\r\n",
      "  bottom: \"pool2\"\r\n",
      "  top: \"fc1\"\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: \"fc1\"\r\n",
      "  top: \"fc1\"\r\n",
      "}\r\n",
      "layer {\r\n",
      "  name: \"score\"\r\n",
      "  type: \"InnerProduct\"\r\n",
      "  bottom: \"fc1\"\r\n",
      "  top: \"score\"\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: \"score\"\r\n",
      "  bottom: \"label\"\r\n",
      "  top: \"loss\"\r\n",
      "}\r\n"
     ]
    }
   ],
   "source": [
    "!cat 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 (already provided on disk). 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: \"mnist/lenet_auto_train.prototxt\"\r\n",
      "test_net: \"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: \"mnist/lenet\"\r\n"
     ]
    }
   ],
   "source": [
    "!cat mnist/lenet_auto_solver.prototxt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Loading and checking the solver\n",
    "\n",
    "* Let's pick a device and load the solver. We'll use SGD (with momentum), but other methods (such as Adagrad and Nesterov's accelerated gradient) are also available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "caffe.set_device(0)\n",
    "caffe.set_mode_gpu()\n",
    "\n",
    "### load the solver and create train and test nets\n",
    "solver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n",
    "solver = caffe.SGDSolver('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",
       " ('fc1', (64, 500)),\n",
       " ('score', (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",
       " ('fc1', (500, 800)),\n",
       " ('score', (10, 500))]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# just print the weight sizes (we'll omit the 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(2.365971088409424, 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": [
      "train labels: [ 5.  0.  4.  1.  9.  2.  1.  3.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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QPKRCoKCggObmZgYHB5PeBEroW/1z4/fC4qy2tpacnJykjWtfC7ZSqcRsNlNT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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51a65ee690>"
      ]
     },
     "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'); axis('off')\n",
    "print 'train labels:', solver.net.blobs['label'].data[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test labels: [ 7.  2.  1.  0.  4.  1.  4.  9.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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cmpqaOHPmDG63W5xS9br3wqEWbKEAob29nb//+7/H6XQSCASYnp7md7/73Qt7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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51a4030a50>"
      ]
     },
     "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'); axis('off')\n",
    "print 'test labels:', solver.test_nets[0].blobs['label'].data[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Stepping the solver\n",
    "\n",
    "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": [
       "(-0.5, 24.5, 19.5, -0.5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jFTwwn93j7J28IyBzl56sTpaurot9FallO3DApo6nS85qMkS9O7qfyl2WnLeCHYvQYu8o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\nLtEpzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519c0eab90>"
      ]
     },
     "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'); axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Writing a custom training loop\n",
    "\n",
    "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 12.6 s, sys: 2.4 s, total: 15 s\n",
      "Wall time: 14.4 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['score'].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['score'].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 0x7f5199b33610>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tVTHRhi302f4ujBpV6KY4juMUE41VdXe4oKofBgVME5HEAnpNRIbWqmlFwmHv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\nJGkQBpAkaRA3ABGGQ9Z+SfjXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519bf16690>"
      ]
     },
     "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')\n",
    "ax2.set_title('Test Accuracy: {:.2f}'.format(test_acc[-1]))"
   ]
  },
  {
   "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": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5199aaab50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5199a4ba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51999dd210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519994c650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51998d0e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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7fDF2TbNS0gZJR25mZvwarKsSKklnS3oqIp6OiIOSviHpXR3uE4YREfdLem7Q05dJ+nL7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\nQE0kVAAAADWRUAEAANREQgUAAFDT/wMd8Optey5//QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519984f250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51997d3ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519bee6f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519c0b0c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519c0d5bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51996ff910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519969d110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5199603550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5199591d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51994fb990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519948af90>"
      ]
     },
     "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": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51991d5790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51990357d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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WaS/5S0T0AyL6OhFd7hV/R8D9fbn2OhqNSknj9/ulpOEmRMC7/WTY+C0Wi8jn8zIomc/n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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5198fc5750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5198f2af90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5198ebf310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5198e2cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GXtPAb2qtvzAY90YY+y9vfX2ddDpNPB4nFApda66K1LmPhCZJRw2HwyQSCZOcJRmG4rAT\nAowmiYlUEasvl8tRLpdNWGPRuJE0+rJbZ3nMW0thB0pALxqNsr6+zsbGBolEglAoZFq2jhv3CSFN\nJBIxlRE2aezk93HZhUKaQqHAwcEBR0dHQwHURWOWDfBvKqX+WCn1NaVUbG4rmhB2kyIhzW0kzX3C\n6/UOkSYejw81V7pJ0pyfnxtJc3h4OESapZA0V+CfAc+47LmXA/7R3FY0Iez67IcmaSQPWCRNNBod\nkjS2fnKdpMlms5yenppzGRa+/mk+pLU2OcFKqd8Cfn9uK5oQotNIInkymSQSiZhg4H2tScxgO4dZ\nEqYSiQTr6+tsbW2RyWSMHhYIBMZaP6P6l/Tnk5jYXVp7MCVplFKbetB4Gvhz3GP/YHF2SasySWjy\n+/33VtIqKaj2GQt2QlgymSSVSg2RRlIfrktBHW1ZctdkEUyTI/xrwE8rpT7PpRW1B/y1ha7yGowr\nWRG94L4kjUgW+3wFaZYk1RLpdJqtrS2ePHliiH6dpLFrqUTS2Jl6d7n1Tpsj/PUFrGUqjEoaqdH2\n+/33uj3ZSWB221nJYRZJ8+TJE6N/XRWctJPdbcKMugzuCstpXkwIW4cQPeI+I8M+n8+U4MqhGlJT\nHolEePr0Kdvb24bgkkhuVxzYhGg0Gqauqlarsbe3Z3wzrVbLHHd4V7GyR0GaZYM4G9fW1ox1ZI9U\nKkUqlSIWiw0RRgg/WmQnllKhUKBYLPLq1StjMUkZzLwTxa6DQ5oFQOqWUqmUOfPSHuFw2BztI6a1\nLR1HKyQqlQr5fJ6DgwNevXrF0dERx8fHxjfT6XTmmiR2ExzSLAB2WW0mk2F7e5utrS0z7C5Y44KV\ndsmLOPLy+Tz7+/u8fPmSQqFgEsXsLudLbXI7GIa0MBFFN5PJ8OTJEzY3N0mlUsTj8aH+xDdFovv9\nviFMs9mkXq9TrVY5Ozvj9PSUs7Mz0zb2PlrGPkrS3LUSHAqFTFt88UrLibrJZJJoNGpaxN4GYikJ\naaSGXBoM1Ot1c0jHQ+oasdS46x8yFAqRSqXY3d1ld3fXHFsoZy2IlLltvq9IGrv+W6SNlPOKz8Yh\nzQPF6uoqqVSKZ8+e8bnPfc7Ej+RUXemGPomksbcn2+QWSTNJE6Z541GSZpxyab832ilr9H3x3IoX\n1/b9jJvz3Xff5Z133uHJkyek0+mhz/r9ftMu5LrSYBvtdptyuUw2m+Xo6IhXr15RKBRMKufS99x7\nSBiXOjl6kyTsEAwGTWrC6Pv29hKLxYba3o+LZ0kDpc3NTRKJhCGJHW+ynXc3EafdbnNycsKrV694\n+fIl2WyW4+Nj0zDpvvGoSDOK6yRNMBg0YQcbbrebra0ttre32d7efuMc7nGhCdvbG4lEhoKVoxHu\n20gaKeA7ODjgww8/pFAomO1p6UmjlMpw2Qo2xWVw8l9orf+JeiB9hEcfy/PrJI3H4yGTyZgt5/nz\n50NnXI6edwAYCSTkuGo9t7XqhDT7+/v88Ic/NH327tKBdx1ukjRd4O9ord9XSq0Cf6SU+hbwSyxJ\nH2HpNNVoNCiXy5yenhKJRNBa4/V68fl8Q/9eKWVM5KdPn74xn9vtNrGh9fV10wBaAopXKbOjVQOy\ntttgtHl1qVQyVpJ0qVgmXEsarfUxcDx4XFdKfQhss0R9hPv9Pu12m0qlQrFYJBaL0e12TUbf6Mlw\nLpeLcDhMOp1Ga004HH7j/fX1ddPYUdIuRYkdR4TRagGYrMVsp9MxZTT1ep1isUi1Wh1qwrhMuLVO\noy4bUH8B+D8sUR/hUdKsrq6a9hx263iBUopwOIzWmlAoRDr946XLjbcj1NLv7jp9xCbMNJJG+gOX\nSiVKpRLFYpFKpbK051neijSDrek/AX9ba12zfzyt77ePcL/fNx0YisXiUKdyaZpow+VyGT9KKpUa\nynyT66gCK1iUpOl2u9RqNU5PTzk+Pn74kkYp5eWSMP9Gay2tX5emj7DoNNJdSvrTSRPEi4uLIR+L\nUmqom6bMYV9nWYsQxz7/abRFrH1I2Pn5OScnJ+TzeZP+IA2tm83mw5M06vJX/Rrw/7TW/9h6a2n6\nCEtJqijCfr+f9fX1ofiMLTkWGZeSue1On5KmaSeDS4TabgApXc/L5TLFYvFO65gmxU2S5kvAXwC+\nr5T63uC1X2WJ+gjLX269XjfnKJ2dnb0R1LvLjlKjOb2jkuX4+JijoyMz7IM55FqtVh8mabTW/4ur\na6OWoo+w1AFJzY/b7aZSqZgD06WrpuSvLHot9vYkEkZiSNI9NJ/Ps7e3x8cff8wnn3xCo9Ew/XCE\n6DIeHGkeAuzOVoBptpjP58lms0YxtmNBtt9lnsnnIslsE3o0v7dWq5mO57lcjkKhQLvdNucvCFHs\nU+mWDY+CNPJXrZSi1WpxenpKNpvF7/fT7XZNaqVc7bGIioXz83MqlQqlUonT01Ojq4jeIo0hT05O\n3sjxtfsSLysePGkAE4+RDt9iRQE0Gg2i0agpsJcqTGnZMer8mwWyPQlp8vk8R0dHpmmSJIaLxKnX\n66b+etTCWoby4avw4Eljm7WiT5RKJQBzOHoymTRVAc1mE8B0k1oEhDR2Vwdp15bL5Ux7NluyyHex\nv9ey4sGTBob9LHa7VVGS7Vzber1uXpPsOEldsHv32qkQdinsOBMahmNP0skhl8uRz+cpFotmm5KT\n5x4yHgVpbEiBfKvVAjBZ/XIqW6lUolarUalUKJfLpFIpkxAuB6OK4uz3+00vO/tgU/v8J5Fctjkv\nJnUul+Pk5MSYz+M81A8Rj5Y0UnQmUqZarbKyskIgEBjK7C8UCibZSq4Sm5K2rEJC0UVEsRXnnA2l\nFKenp0NDSLPoBop3hUdJGtlK2u320FGFkoAl5zcVCgXW1tZIp9NDTQ/FGSjnEYxKqnw+b0ahUDAK\nsMB20tVqNeOjua/qgXnj0ZEGuNZctQ/VEgki3ctF75EjkMXiEokiQ6wgGaOQKgK52r2JHwMeJWmu\ng2xfzWZzqG5aTpArFAqmikB0HPvMbTnexx4C27knyrb4YKRA35E0DxBiUQFmC2s2m5TLZQKBgOni\n4PP5THqn3HyJHdlSRHJe7O1p9IhlGcvssJsE6jrmX5Mj/FVu6CN8nzk2N2FUzxmXCG6/P5rmYB+h\nPI4IVzUaWmaH3ThorcdGeG8izQawYecIA7/AZVS7prX+zWs++3B+HQdjcRVpps0RhiXpI+zg7nHr\nXAErR/h/D15aij7CDu4etyLNYGv6j1zmCNdZoj7CDu4e1+o0YHKE/wvwX0dSPuX9XeD39eCwDet1\nR6d54LhKp7lW0lyVIzxIJhfcax9hB3ePm6ynnwT+APg+lyY3wN8HvsLl1mT6CFt1UPJZR9I8cExl\ncs8ChzQPH1NtTw4cjINDGgcTwyGNg4nhkMbBxHBI42BiOKRxMDEc0jiYGAvz0zh4vHAkjYOJ4ZDG\nwcRYKGmUUu8ppX6klPqTQRfQWefbV0p9Xyn1PaXU/53i819XSuWVUj+wXksopb6llHqplPrmJLlB\nV8z3VaXU68Eav6eUem+C+TJKqf+hlPqhUuoDpdTfmmWN18w39RqB8fms8xiAG/gY2AW8wPvAT8w4\n5x6QmOHzP8VlItkPrNd+A/h7g8e/DPz6jPP9GvB3p1zfBvD5weNV4CPgJ6Zd4zXzTb1GrfVCJc0X\ngY+11vta6y7wu8DPz2HeqdNMtdbfAcojL/8cl21tGVx/Ycb5YMo1aq2PtdbvDx7XAbsF78RrvGa+\nqdcIi92etoFD6/lrfrzgaaGBbyulvquU+qszziVYRHvbmVNh592Cd57puoskzSJs+S9prb8AfBn4\n60qpn5rn5PpSjs+67plTYUdb8M66xnmn6y6SNFkgYz3PcCltpobWOje4FoFvcLkFzor8oFRHMhJn\nam+rtS7oAYDfmnSN17XgnWaN1nz/VuabdY2LJM13gXeVUrtKqRXgF7lsJTsVlFJBpVR48DgE/Czz\nSTOV9rYwh/a2s6TC3qIF70RrXFi67izWzC209y9zqbF/zGUV5ixzPePSAnsf+GCa+YDfAY6ADpf6\n1i8BCeDbwEvgm0Bshvn+EpcVqd8H/nhwc9MTzPeTQH/wHb83GO9Nu8Yr5vvyLGvUWjthBAeTw/EI\nO5gYDmkcTAyHNA4mhkMaBxPDIY2DieGQxsHEcEjjYGI4pHEwMf4/w2zPGHuGeikAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5198dc0d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51994b6550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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KNGajy15tCJBIJAZax2vfmnFzapzDiDMGNmz1pIK6aOXUarWoVCpWNMVi0fbH8T3NFFBP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\nT5UJ3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51994cc7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5199160590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5199196050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51991d5f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Qq9XMqFarA8m8cDh8T9KIFo7kh6RFRUgjKYGriIUkjRRPiTOptaZWq5HL5Uxtiq1BIzUr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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519c085f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519998c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f519c0f7b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5199b2b090>"
      ]
     },
     "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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Experiment with architecture and optimization\n",
    "\n",
    "Now that we've defined, trained, and tested LeNet there are many possible next steps:\n",
    "\n",
    "- Define new architectures for comparison\n",
    "- Tune optimization by setting `base_lr` and the like or simply training longer\n",
    "- Switching the solver type from `SGD` to an adaptive method like `AdaDelta` or `Adam`\n",
    "\n",
    "Feel free to explore these directions by editing the all-in-one example that follows.\n",
    "Look for \"`EDIT HERE`\" comments for suggested choice points.\n",
    "\n",
    "By default this defines a simple linear classifier as a baseline.\n",
    "\n",
    "In case your coffee hasn't kicked in and you'd like inspiration, try out\n",
    "\n",
    "1. Switch the nonlinearity from `ReLU` to `ELU` or a saturing nonlinearity like `Sigmoid`\n",
    "2. Stack more fully connected and nonlinear layers\n",
    "3. Search over learning rate 10x at a time (trying `0.1` and `0.001`)\n",
    "4. Switch the solver type to `Adam` (this adaptive solver type should be less sensitive to hyperparameters, but no guarantees...)\n",
    "5. Solve for longer by setting `niter` higher (to 500 or 1,000 for instance) to better show training differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
      "Iteration 200 testing...\n",
      "Iteration 225 testing...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5199af9f50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ObSIiqKpElpsBC4ETgFXAVOA8VZ0f2Se1Qs3fVXVgMdoXxwV3LzAXKzInWALC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5198f46a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_net_path = 'mnist/custom_auto_train.prototxt'\n",
    "test_net_path = 'mnist/custom_auto_test.prototxt'\n",
    "solver_config_path = 'mnist/custom_auto_solver.prototxt'\n",
    "\n",
    "### define net\n",
    "def custom_net(lmdb, batch_size):\n",
    "    # define your own net!\n",
    "    n = caffe.NetSpec()\n",
    "    \n",
    "    # keep this data layer for all networks\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",
    "    # EDIT HERE to try different networks\n",
    "    # this single layer defines a simple linear classifier\n",
    "    # (in particular this defines a multiway logistic regression)\n",
    "    n.score =   L.InnerProduct(n.data, num_output=10, weight_filler=dict(type='xavier'))\n",
    "    \n",
    "    # EDIT HERE this is the LeNet variant we have already tried\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.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
    "    # EDIT HERE consider L.ELU or L.Sigmoid for the nonlinearity\n",
    "    # n.relu1 = L.ReLU(n.fc1, in_place=True)\n",
    "    # n.score =   L.InnerProduct(n.fc1, num_output=10, weight_filler=dict(type='xavier'))\n",
    "    \n",
    "    # keep this loss layer for all networks\n",
    "    n.loss =  L.SoftmaxWithLoss(n.score, n.label)\n",
    "    \n",
    "    return n.to_proto()\n",
    "\n",
    "with open(train_net_path, 'w') as f:\n",
    "    f.write(str(custom_net('mnist/mnist_train_lmdb', 64)))    \n",
    "with open(test_net_path, 'w') as f:\n",
    "    f.write(str(custom_net('mnist/mnist_test_lmdb', 100)))\n",
    "\n",
    "### define solver\n",
    "from caffe.proto import caffe_pb2\n",
    "s = caffe_pb2.SolverParameter()\n",
    "\n",
    "# Set a seed for reproducible experiments:\n",
    "# this controls for randomization in training.\n",
    "s.random_seed = 0xCAFFE\n",
    "\n",
    "# Specify locations of the train and (maybe) test networks.\n",
    "s.train_net = train_net_path\n",
    "s.test_net.append(test_net_path)\n",
    "s.test_interval = 500  # Test after every 500 training iterations.\n",
    "s.test_iter.append(100) # Test on 100 batches each time we test.\n",
    "\n",
    "s.max_iter = 10000     # no. of times to update the net (training iterations)\n",
    " \n",
    "# EDIT HERE to try different solvers\n",
    "# solver types include \"SGD\", \"Adam\", and \"Nesterov\" among others.\n",
    "s.type = \"SGD\"\n",
    "\n",
    "# Set the initial learning rate for SGD.\n",
    "s.base_lr = 0.01  # EDIT HERE to try different learning rates\n",
    "# Set momentum to accelerate learning by\n",
    "# taking weighted average of current and previous updates.\n",
    "s.momentum = 0.9\n",
    "# Set weight decay to regularize and prevent overfitting\n",
    "s.weight_decay = 5e-4\n",
    "\n",
    "# Set `lr_policy` to define how the learning rate changes during training.\n",
    "# This is the same policy as our default LeNet.\n",
    "s.lr_policy = 'inv'\n",
    "s.gamma = 0.0001\n",
    "s.power = 0.75\n",
    "# EDIT HERE to try the fixed rate (and compare with adaptive solvers)\n",
    "# `fixed` is the simplest policy that keeps the learning rate constant.\n",
    "# s.lr_policy = 'fixed'\n",
    "\n",
    "# Display the current training loss and accuracy every 1000 iterations.\n",
    "s.display = 1000\n",
    "\n",
    "# Snapshots are files used to store networks we've trained.\n",
    "# We'll snapshot every 5K iterations -- twice during training.\n",
    "s.snapshot = 5000\n",
    "s.snapshot_prefix = 'mnist/custom_net'\n",
    "\n",
    "# Train on the GPU\n",
    "s.solver_mode = caffe_pb2.SolverParameter.GPU\n",
    "\n",
    "# Write the solver to a temporary file and return its filename.\n",
    "with open(solver_config_path, 'w') as f:\n",
    "    f.write(str(s))\n",
    "\n",
    "### load the solver and create train and test nets\n",
    "solver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n",
    "solver = caffe.get_solver(solver_config_path)\n",
    "\n",
    "### solve\n",
    "niter = 250  # EDIT HERE increase to train for longer\n",
    "test_interval = niter / 10\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",
    "\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",
    "    # 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['score'].data.argmax(1)\n",
    "                           == solver.test_nets[0].blobs['label'].data)\n",
    "        test_acc[it // test_interval] = correct / 1e4\n",
    "\n",
    "_, 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')\n",
    "ax2.set_title('Custom Test Accuracy: {:.2f}'.format(test_acc[-1]))"
   ]
  }
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