[examples] draft Python solving example

author: Jonathan L Long <jonlong@cs.berkeley.edu> 2015-03-12 01:03:51 -0700
committer: Evan Shelhamer <shelhamer@imaginarynumber.net> 2015-06-30 14:50:35 -0700
commit: ccda10250d8adeee82cdc577204069b79a9d7d6f (patch)
tree: d7b9e4ef58c1c5ac46b0457a4fc4e283f84f6717 /examples
parent: 1d6cac298264efc02c0db4b4809a22d8afc0d5a4 (diff)
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+{
+ "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",
+      "--2015-03-12 11:54:02--  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",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 9912422 (9.5M) [application/x-gzip]\n",
+      "Saving to: 'train-images-idx3-ubyte.gz'\n",
+      "\n",
+      "train-images-idx3-u 100%[=====================>]   9.45M   194KB/s   in 51s    \n",
+      "\n",
+      "2015-03-12 11:54:54 (188 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n",
+      "\n",
+      "--2015-03-12 11:54:54--  http://yann.lecun.com/exdb/mnist/train-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: 28881 (28K) [application/x-gzip]\n",
+      "Saving to: 'train-labels-idx1-ubyte.gz'\n",
+      "\n",
+      "train-labels-idx1-u 100%[=====================>]  28.20K   104KB/s   in 0.3s   \n",
+      "\n",
+      "2015-03-12 11:54:55 (104 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n",
+      "\n",
+      "--2015-03-12 11:54:55--  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   224KB/s   in 9.2s   \n",
+      "\n",
+      "2015-03-12 11:55:04 (176 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n",
+      "\n",
+      "--2015-03-12 11:55:04--  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  --.-KB/s   in 0.09s  \n",
+      "\n",
+      "2015-03-12 11:55:04 (50.0 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n",
+      "\n",
+      "Unzipping...\n",
+      "train-images-idx3-ubyte already exists -- do you wish to overwrite (y or n)? ^Ctrain-labels-idx1-ubyte already exists -- do you wish to overwrite (y or n)? \n",
+      "\tnot overwriting\n",
+      "t10k-images-idx3-ubyte already exists -- do you wish to overwrite (y or n)? Creating lmdb...\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!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 solver prototxt, defining the learning parameters\n",
+    "* the net prototxt, defining the architecture and train/test data\n",
+    "\n",
+    "Here are the learning parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# The train/test net protocol buffer definition\r\n",
+      "net: \"examples/mnist/lenet_train_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",
+      "# solver mode: CPU or GPU\r\n",
+      "solver_mode: GPU\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat examples/mnist/lenet_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here is the version of LeNet we'll be using. In future version of Caffe, networks can be defined directly in Python (see [PR #2086](https://github.com/BVLC/caffe/pull/2086)).\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": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name: \"LeNet\"\r\n",
+      "layer {\r\n",
+      "  name: \"mnist\"\r\n",
+      "  type: \"Data\"\r\n",
+      "  top: \"data\"\r\n",
+      "  top: \"label\"\r\n",
+      "  include {\r\n",
+      "    phase: TRAIN\r\n",
+      "  }\r\n",
+      "  transform_param {\r\n",
+      "    scale: 0.00390625\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: \"mnist\"\r\n",
+      "  type: \"Data\"\r\n",
+      "  top: \"data\"\r\n",
+      "  top: \"label\"\r\n",
+      "  include {\r\n",
+      "    phase: TEST\r\n",
+      "  }\r\n",
+      "  transform_param {\r\n",
+      "    scale: 0.00390625\r\n",
+      "  }\r\n",
+      "  data_param {\r\n",
+      "    source: \"examples/mnist/mnist_test_lmdb\"\r\n",
+      "    batch_size: 100\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",
+      "  param {\r\n",
+      "    lr_mult: 1\r\n",
+      "  }\r\n",
+      "  param {\r\n",
+      "    lr_mult: 2\r\n",
+      "  }\r\n",
+      "  convolution_param {\r\n",
+      "    num_output: 20\r\n",
+      "    kernel_size: 5\r\n",
+      "    stride: 1\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "    bias_filler {\r\n",
+      "      type: \"constant\"\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",
+      "  param {\r\n",
+      "    lr_mult: 1\r\n",
+      "  }\r\n",
+      "  param {\r\n",
+      "    lr_mult: 2\r\n",
+      "  }\r\n",
+      "  convolution_param {\r\n",
+      "    num_output: 50\r\n",
+      "    kernel_size: 5\r\n",
+      "    stride: 1\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "    bias_filler {\r\n",
+      "      type: \"constant\"\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",
+      "  param {\r\n",
+      "    lr_mult: 1\r\n",
+      "  }\r\n",
+      "  param {\r\n",
+      "    lr_mult: 2\r\n",
+      "  }\r\n",
+      "  inner_product_param {\r\n",
+      "    num_output: 500\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "    bias_filler {\r\n",
+      "      type: \"constant\"\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",
+      "  param {\r\n",
+      "    lr_mult: 1\r\n",
+      "  }\r\n",
+      "  param {\r\n",
+      "    lr_mult: 2\r\n",
+      "  }\r",
+      "\r\n",
+      "  inner_product_param {\r\n",
+      "    num_output: 10\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "    bias_filler {\r\n",
+      "      type: \"constant\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"accuracy\"\r\n",
+      "  type: \"Accuracy\"\r\n",
+      "  bottom: \"ip2\"\r\n",
+      "  bottom: \"label\"\r\n",
+      "  top: \"accuracy\"\r\n",
+      "  include {\r\n",
+      "    phase: TEST\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_train_test.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": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "solver = caffe.SGDSolver('examples/mnist/lenet_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": 10,
+   "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": 10,
+     "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": 11,
+   "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": 11,
+     "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": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'accuracy': array(0.07000000029802322, dtype=float32),\n",
+       " 'loss': array(2.375746726989746, dtype=float32)}"
+      ]
+     },
+     "execution_count": 12,
+     "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": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 5.  0.  4.  1.  9.  2.  1.  3.]\n"
+     ]
+    },
+    {
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+       "40F9c+84y1w99q1FkiSvtArw3/jdUv5b3VcPq027+v/nYlzFymDfBAqFEDlCCD3wfVbqQH7rEUKY\n",
+       "hBCW1cdm4CgruVjkOpkQZZ3M32Me1DcfAj8QQuiFELlAIXB9C9r3zLBqeGTW5vn51vbVY9Smhedk\n",
+       "XD12PuwnQZKkRSHEPwLOsKIY+WtJku7E4lzPIWnAqdWyWHHA30qS9JkQ4iZPWSfz9wEhxN8BB4AU\n",
+       "IcQQ8G95QA1RSZLahRC/AtqBReAfrs4svxXcp6/+HfCSEGJdnh/41vfVE9WmfZb7Sg1NV1FRUXlO\n",
+       "UCMdVVRUVJ4TVIOtoqKi8pygGmwVFRWV5wTVYKuoqKg8J6gGW0VFReU5QTXYKioqKs8JqsFWUVFR\n",
+       "eU5QDbaKiorKc8L/DzAr6bE92WeRAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x109ed4650>"
+      ]
+     },
+     "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": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 7.  2.  1.  0.  4.  1.  4.  9.]\n"
+     ]
+    },
+    {
+     "data": {
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+       "R5IkbX+PpJ1JCvDOamsxHfD/JEm6IIS4TZj7ZO4GhBA/A44BNiHEMPBXPKGHqCRJ7UKIN4F2IAh8\n",
+       "f3Vm+YXgMWP110CdEOKBOj/whR+rDfWm3cljpaamq6ioqOwS1ExHFRUVlV2C6rBVVFRUdgmqw1ZR\n",
+       "UVHZJagOW0VFRWWXoDpsFRUVlV2C6rBVVFRUdgmqw1ZRUVHZJagOW0VFRWWX8P8BCxPUWfGXxrcA\n",
+       "AAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d221bd0>"
+      ]
+     },
+     "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": 15,
+   "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": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x123c20e90>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "AAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x122785190>"
+      ]
+     },
+     "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": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 13 s, sys: 134 ms, total: 13.1 s\n",
+      "Wall time: 13 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "niter = 200\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = zeros(niter)\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 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]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot the train loss. (Normally we should plot the test loss as well, but we're only testing every 500 iterations, and loss drops very quickly for this example.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x123c2ebd0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+       "hBBCNCBxEEII0YDEQQghRAMSByGEEA1IHIQQQjQgcRBCCNHA/webgPPorxOoYAAAAABJRU5ErkJg\n",
+       "gg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12276b250>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(arange(niter), train_loss)\n",
+    "xlabel('iteration')\n",
+    "ylabel('train loss')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems to have dropped quickly and coverged (except for stochasticity); 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": 19,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x123da73d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1241bfa10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x124252150>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1236dcf10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "YII=\n"
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1243d6c90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1243fea50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1246ea5d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12477ad10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1247e3910>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "ayTdLmmhpBccYVuvkvTlaNksaYWkl6tVcK2KiF9E656INZKGGvybAPw/wBkqAN3u/RFx49gn2vda\n",
+       "7Tho+bWSzomIXbZvltR3hPWGDi3g9p+9embMc3vFsRLAEXCGCkA3GZE09gby70p6r+2pkmT7hbb7\n",
+       "x3nfXElb28XUr0s6Z8xro/vff5BbJb2tfZ/WoKTXSFqlQ4ssADgi/qsLQDfYf2bobkl725fuPivp\n",
+       "U2pdbvuRbUvaLOl32/Fjf6L8HUl/YXudpJ+oddlvv6sl3WP7roh4x/73RcR/2D63vc2Q9OGI2Gz7\n",
+       "9IPWrXGWAeBZmDYBAACgIS75AQAANERBBQAA0BAFFQAAQEMUVAAAAA1RUAEAADREQQUAANAQBRUA\n",
+       "AEBDFFQAAAAN/R9mVy2QoO8DbAAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x124972f50>"
+      ]
+     },
+     "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": 20,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x125bd0550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "TkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x125d7d610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x125e0d510>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x124940ad0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "YII=\n"
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+      "text/plain": [
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+      ],
+      "text/plain": [
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x124ed8bd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1249e5110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1242c3b90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x124765690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x124a05cd0>"
+      ]
+     },
+     "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": {
+  "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
author	Jonathan L Long <jonlong@cs.berkeley.edu>	2015-03-12 01:03:51 -0700
committer	Evan Shelhamer <shelhamer@imaginarynumber.net>	2015-06-30 14:50:35 -0700
commit	ccda10250d8adeee82cdc577204069b79a9d7d6f (patch)
tree	d7b9e4ef58c1c5ac46b0457a4fc4e283f84f6717 /examples
parent	1d6cac298264efc02c0db4b4809a22d8afc0d5a4 (diff)
download	caffeonacl-ccda10250d8adeee82cdc577204069b79a9d7d6f.tar.gz caffeonacl-ccda10250d8adeee82cdc577204069b79a9d7d6f.tar.bz2 caffeonacl-ccda10250d8adeee82cdc577204069b79a9d7d6f.zip