[examples] sequence and revise notebooks

- combine classification + filter visualization - order by classification, learning LeNet, brewing logreg, and fine-tuning to flickr style - improve flow of content in classification + filter visualization - include solver needed for learning LeNet - edit notebook descriptions for site catalogue
2015-03-17 19:01:02 -07:00 · 2015-03-17 19:01:02 -07:00 · a6cb8ec62c
--- a/examples/00-classification.ipynb
+++ b/examples/00-classification.ipynb
--- a/examples/01-learning-lenet.ipynb
+++ b/examples/01-learning-lenet.ipynb
--- a/examples/02-brewing-logreg.ipynb
+++ b/examples/02-brewing-logreg.ipynb
--- a/examples/03-fine-tuning.ipynb
+++ b/examples/03-fine-tuning.ipynb
@ -0,0 +1,947 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-tuning a Pretrained Network for Style Recognition\n",
+    "\n",
+    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n",
+    "\n",
+    "The upside of such approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we will need to prepare the data. This involves the following parts:\n",
+    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
+    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
+    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir('..')\n",
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "\n",
+    "import caffe\n",
+    "import numpy as np\n",
+    "from pylab import *\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
+    "# and a subset of 2000 images for the style recognition task.\n",
+    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
+    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
+    "    --workers=-1 --images=2000 --seed=1701 --label=5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's show what is the difference between the fine-tuning network and the original caffe model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1c1\r\n",
+      "< name: \"CaffeNet\"\r\n",
+      "---\r\n",
+      "> name: \"FlickrStyleCaffeNet\"\r\n",
+      "4c4\r\n",
+      "<   type: \"Data\"\r\n",
+      "---\r\n",
+      ">   type: \"ImageData\"\r\n",
+      "15,26c15,19\r\n",
+      "< # mean pixel / channel-wise mean instead of mean image\r\n",
+      "< #  transform_param {\r\n",
+      "< #    crop_size: 227\r\n",
+      "< #    mean_value: 104\r\n",
+      "< #    mean_value: 117\r\n",
+      "< #    mean_value: 123\r\n",
+      "< #    mirror: true\r\n",
+      "< #  }\r\n",
+      "<   data_param {\r\n",
+      "<     source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
+      "<     batch_size: 256\r\n",
+      "<     backend: LMDB\r\n",
+      "---\r\n",
+      ">   image_data_param {\r\n",
+      ">     source: \"data/flickr_style/train.txt\"\r\n",
+      ">     batch_size: 50\r\n",
+      ">     new_height: 256\r\n",
+      ">     new_width: 256\r\n",
+      "31c24\r\n",
+      "<   type: \"Data\"\r\n",
+      "---\r\n",
+      ">   type: \"ImageData\"\r\n",
+      "42,51c35,36\r\n",
+      "< # mean pixel / channel-wise mean instead of mean image\r\n",
+      "< #  transform_param {\r\n",
+      "< #    crop_size: 227\r\n",
+      "< #    mean_value: 104\r\n",
+      "< #    mean_value: 117\r\n",
+      "< #    mean_value: 123\r\n",
+      "< #    mirror: true\r\n",
+      "< #  }\r\n",
+      "<   data_param {\r\n",
+      "<     source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
+      "---\r\n",
+      ">   image_data_param {\r\n",
+      ">     source: \"data/flickr_style/test.txt\"\r\n",
+      "53c38,39\r\n",
+      "<     backend: LMDB\r\n",
+      "---\r\n",
+      ">     new_height: 256\r\n",
+      ">     new_width: 256\r\n",
+      "323a310\r\n",
+      ">   # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
+      "360c347\r\n",
+      "<   name: \"fc8\"\r\n",
+      "---\r\n",
+      ">   name: \"fc8_flickr\"\r\n",
+      "363c350,351\r\n",
+      "<   top: \"fc8\"\r\n",
+      "---\r\n",
+      ">   top: \"fc8_flickr\"\r\n",
+      ">   # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n",
+      "365c353\r\n",
+      "<     lr_mult: 1\r\n",
+      "---\r\n",
+      ">     lr_mult: 10\r\n",
+      "369c357\r\n",
+      "<     lr_mult: 2\r\n",
+      "---\r\n",
+      ">     lr_mult: 20\r\n",
+      "373c361\r\n",
+      "<     num_output: 1000\r\n",
+      "---\r\n",
+      ">     num_output: 20\r\n",
+      "384a373,379\r\n",
+      ">   name: \"loss\"\r\n",
+      ">   type: \"SoftmaxWithLoss\"\r\n",
+      ">   bottom: \"fc8_flickr\"\r\n",
+      ">   bottom: \"label\"\r\n",
+      ">   top: \"loss\"\r\n",
+      "> }\r\n",
+      "> layer {\r\n",
+      "387c382\r\n",
+      "<   bottom: \"fc8\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc8_flickr\"\r\n",
+      "393,399d387\r\n",
+      "< }\r\n",
+      "< layer {\r\n",
+      "<   name: \"loss\"\r\n",
+      "<   type: \"SoftmaxWithLoss\"\r\n",
+      "<   bottom: \"fc8\"\r\n",
+      "<   bottom: \"label\"\r\n",
+      "<   top: \"loss\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For your record, if you want to train the network in pure C++ tools, here is the command:\n",
+    "\n",
+    "<code>\n",
+    "build/tools/caffe train \\\n",
+    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
+    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
+    "    -gpu 0\n",
+    "</code>\n",
+    "\n",
+    "However, we will train using Python in this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iter 0, finetune_loss=3.360094, scratch_loss=3.136188\n",
+      "iter 10, finetune_loss=2.672608, scratch_loss=9.736364\n",
+      "iter 20, finetune_loss=2.071996, scratch_loss=2.250404\n",
+      "iter 30, finetune_loss=1.758295, scratch_loss=2.049553\n",
+      "iter 40, finetune_loss=1.533391, scratch_loss=1.941318\n",
+      "iter 50, finetune_loss=1.561658, scratch_loss=1.839706\n",
+      "iter 60, finetune_loss=1.461696, scratch_loss=1.880035\n",
+      "iter 70, finetune_loss=1.267941, scratch_loss=1.719161\n",
+      "iter 80, finetune_loss=1.192778, scratch_loss=1.627453\n",
+      "iter 90, finetune_loss=1.541176, scratch_loss=1.822061\n",
+      "iter 100, finetune_loss=1.029039, scratch_loss=1.654087\n",
+      "iter 110, finetune_loss=1.138547, scratch_loss=1.735837\n",
+      "iter 120, finetune_loss=0.917412, scratch_loss=1.851918\n",
+      "iter 130, finetune_loss=0.971519, scratch_loss=1.801927\n",
+      "iter 140, finetune_loss=0.868252, scratch_loss=1.745545\n",
+      "iter 150, finetune_loss=0.790020, scratch_loss=1.844925\n",
+      "iter 160, finetune_loss=1.092668, scratch_loss=1.695591\n",
+      "iter 170, finetune_loss=1.055344, scratch_loss=1.661715\n",
+      "iter 180, finetune_loss=0.969769, scratch_loss=1.823639\n",
+      "iter 190, finetune_loss=0.780566, scratch_loss=1.820862\n",
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "niter = 200\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = np.zeros(niter)\n",
+    "scratch_train_loss = np.zeros(niter)\n",
+    "\n",
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "# We create a solver that fine-tunes from a previously trained network.\n",
+    "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+    "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+    "# For reference, we also create a solver that does no finetuning.\n",
+    "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+    "\n",
+    "# We run the solver for niter times, and record the training loss.\n",
+    "for it in range(niter):\n",
+    "    solver.step(1)  # SGD by Caffe\n",
+    "    scratch_solver.step(1)\n",
+    "    # store the train loss\n",
+    "    train_loss[it] = solver.net.blobs['loss'].data\n",
+    "    scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
+    "    if it % 10 == 0:\n",
+    "        print 'iter %d, finetune_loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
+    "print 'done'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the training loss produced by the two training procedures respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbb36f0ad50>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbb36f0afd0>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "ZGZWR3rl4iMzM+sfDnUzszriUDczqyMOdTOzOuJQNzOrIw51M7M64lA3M6sjDnUzszry/wFBsEB8\n",
+       "UlvRigAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbb37f20990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbb347a8310>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbb347a8590>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+       "jSJIx7K3lcDjwGFJB/KxTZTUP13oZGZWU16T1cysppzgzcxqygnezKymnODNzGrKCd7MrKac4M3M\n",
+       "asoJ3sysppzgzcxq6j+vUsbacqJa4gAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbb37f207d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy for fine-tuning: 0.570000001788\n",
+      "Accuracy for training from scratch: 0.224000000954\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_iters = 10\n",
+    "accuracy = 0\n",
+    "scratch_accuracy = 0\n",
+    "for it in arange(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "    scratch_solver.test_nets[0].forward()\n",
+    "    scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "scratch_accuracy /= test_iters\n",
+    "print 'Accuracy for fine-tuning:', accuracy\n",
+    "print 'Accuracy for training from scratch:', scratch_accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
+    "\n",
+    "http://demo.vislab.berkeleyvision.org/"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Fine-tune the ImageNet-trained CaffeNet on new data.",
+  "example_name": "Fine-tuning for Style Recognition",
+  "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": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
--- a/examples/Finetune
+++ b/examples/Finetune
@ -1,951 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Finetune a Pretrained Network with Flickr Style Data\n",
-    "\n",
-    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network, and finetune the last few layers using your custom data.\n",
-    "\n",
-    "The upside of such approach is that, since pre-trained networks are trained on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we will need to prepare the data. This involves the following parts:\n",
-    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
-    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
-    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/jiayq/Research/caffe\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "os.chdir('..')\n",
-    "import sys\n",
-    "sys.path.insert(0, './python')\n",
-    "print os.getcwd()\n",
-    "\n",
-    "import caffe\n",
-    "import numpy as np\n",
-    "from pylab import *\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading...\n",
-      "--2015-03-17 10:51:07--  http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n",
-      "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n",
-      "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 17858008 (17M) [application/octet-stream]\n",
-      "Saving to: ‘caffe_ilsvrc12.tar.gz’\n",
-      "\n",
-      "100%[======================================>] 17,858,008   287KB/s   in 55s    \n",
-      "\n",
-      "2015-03-17 10:52:02 (318 KB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n",
-      "\n",
-      "Unzipping...\n",
-      "Done.\n",
-      "Model already exists.\n",
-      "Downloading 2000 images with 3 workers...\n",
-      "Writing train/val for 1903 successfully downloaded images.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
-    "# and a subset of 2000 images for the style recognition task.\n",
-    "\n",
-    "# You won't need to run this - we should have already created it for you.\n",
-    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
-    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
-    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
-    "    --workers=-1 --images=2000 --seed=1701 --label=5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's show what is the difference between the finetune network and the original caffe model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1c1\r\n",
-      "< name: \"CaffeNet\"\r\n",
-      "---\r\n",
-      "> name: \"FlickrStyleCaffeNet\"\r\n",
-      "4c4\r\n",
-      "<   type: \"Data\"\r\n",
-      "---\r\n",
-      ">   type: \"ImageData\"\r\n",
-      "15,26c15,19\r\n",
-      "< # mean pixel / channel-wise mean instead of mean image\r\n",
-      "< #  transform_param {\r\n",
-      "< #    crop_size: 227\r\n",
-      "< #    mean_value: 104\r\n",
-      "< #    mean_value: 117\r\n",
-      "< #    mean_value: 123\r\n",
-      "< #    mirror: true\r\n",
-      "< #  }\r\n",
-      "<   data_param {\r\n",
-      "<     source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
-      "<     batch_size: 256\r\n",
-      "<     backend: LMDB\r\n",
-      "---\r\n",
-      ">   image_data_param {\r\n",
-      ">     source: \"data/flickr_style/train.txt\"\r\n",
-      ">     batch_size: 50\r\n",
-      ">     new_height: 256\r\n",
-      ">     new_width: 256\r\n",
-      "31c24\r\n",
-      "<   type: \"Data\"\r\n",
-      "---\r\n",
-      ">   type: \"ImageData\"\r\n",
-      "42,51c35,36\r\n",
-      "< # mean pixel / channel-wise mean instead of mean image\r\n",
-      "< #  transform_param {\r\n",
-      "< #    crop_size: 227\r\n",
-      "< #    mean_value: 104\r\n",
-      "< #    mean_value: 117\r\n",
-      "< #    mean_value: 123\r\n",
-      "< #    mirror: true\r\n",
-      "< #  }\r\n",
-      "<   data_param {\r\n",
-      "<     source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
-      "---\r\n",
-      ">   image_data_param {\r\n",
-      ">     source: \"data/flickr_style/test.txt\"\r\n",
-      "53c38,39\r\n",
-      "<     backend: LMDB\r\n",
-      "---\r\n",
-      ">     new_height: 256\r\n",
-      ">     new_width: 256\r\n",
-      "323a310\r\n",
-      ">   # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
-      "360c347\r\n",
-      "<   name: \"fc8\"\r\n",
-      "---\r\n",
-      ">   name: \"fc8_flickr\"\r\n",
-      "363c350,351\r\n",
-      "<   top: \"fc8\"\r\n",
-      "---\r\n",
-      ">   top: \"fc8_flickr\"\r\n",
-      ">   # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n",
-      "365c353\r\n",
-      "<     lr_mult: 1\r\n",
-      "---\r\n",
-      ">     lr_mult: 10\r\n",
-      "369c357\r\n",
-      "<     lr_mult: 2\r\n",
-      "---\r\n",
-      ">     lr_mult: 20\r\n",
-      "373c361\r\n",
-      "<     num_output: 1000\r\n",
-      "---\r\n",
-      ">     num_output: 20\r\n",
-      "384a373,379\r\n",
-      ">   name: \"loss\"\r\n",
-      ">   type: \"SoftmaxWithLoss\"\r\n",
-      ">   bottom: \"fc8_flickr\"\r\n",
-      ">   bottom: \"label\"\r\n",
-      ">   top: \"loss\"\r\n",
-      "> }\r\n",
-      "> layer {\r\n",
-      "387c382\r\n",
-      "<   bottom: \"fc8\"\r\n",
-      "---\r\n",
-      ">   bottom: \"fc8_flickr\"\r\n",
-      "393,399d387\r\n",
-      "< }\r\n",
-      "< layer {\r\n",
-      "<   name: \"loss\"\r\n",
-      "<   type: \"SoftmaxWithLoss\"\r\n",
-      "<   bottom: \"fc8\"\r\n",
-      "<   bottom: \"label\"\r\n",
-      "<   top: \"loss\"\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For your record, if you want to train the network in pure C++ tools, here is the command:\n",
-    "\n",
-    "<code>\n",
-    "build/tools/caffe train \\\n",
-    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
-    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
-    "    -gpu 0\n",
-    "</code>\n",
-    "\n",
-    "However, we will train using Python in this example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iter 0, loss=3.786610, scratch_loss=3.163587\n",
-      "iter 10, loss=2.556661, scratch_loss=8.774073\n",
-      "iter 20, loss=2.035326, scratch_loss=2.266603\n",
-      "iter 30, loss=1.943101, scratch_loss=1.703273\n",
-      "iter 40, loss=1.982698, scratch_loss=1.831079\n",
-      "iter 50, loss=1.559268, scratch_loss=2.041238\n",
-      "iter 60, loss=1.464433, scratch_loss=1.836157\n",
-      "iter 70, loss=1.481868, scratch_loss=1.705826\n",
-      "iter 80, loss=1.394870, scratch_loss=1.695532\n",
-      "iter 90, loss=1.055422, scratch_loss=1.867379\n",
-      "iter 100, loss=1.407976, scratch_loss=1.881758\n",
-      "iter 110, loss=1.569579, scratch_loss=1.701803\n",
-      "iter 120, loss=0.951682, scratch_loss=1.764299\n",
-      "iter 130, loss=0.905122, scratch_loss=1.879305\n",
-      "iter 140, loss=1.020678, scratch_loss=1.746009\n",
-      "iter 150, loss=0.784985, scratch_loss=1.739624\n",
-      "iter 160, loss=0.911735, scratch_loss=1.673230\n",
-      "iter 170, loss=0.965255, scratch_loss=1.725484\n",
-      "iter 180, loss=1.028102, scratch_loss=1.676103\n",
-      "iter 190, loss=0.905020, scratch_loss=1.885763\n",
-      "done\n"
-     ]
-    }
-   ],
-   "source": [
-    "niter = 200\n",
-    "# losses will also be stored in the log\n",
-    "train_loss = np.zeros(niter)\n",
-    "scratch_train_loss = np.zeros(niter)\n",
-    "\n",
-    "caffe.set_device(0)\n",
-    "caffe.set_mode_gpu()\n",
-    "# We create a solver that finetunes from a previously trained network.\n",
-    "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
-    "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
-    "# For reference, we also create a solver that does no finetuning.\n",
-    "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
-    "\n",
-    "# We run the solver for niter times, and record the training loss.\n",
-    "for it in range(niter):\n",
-    "    solver.step(1)  # SGD by Caffe\n",
-    "    scratch_solver.step(1)\n",
-    "    # store the train loss\n",
-    "    train_loss[it] = solver.net.blobs['loss'].data\n",
-    "    scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
-    "    if it % 10 == 0:\n",
-    "        print 'iter %d, loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
-    "print 'done'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's look at the training loss produced by the two training procedures respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f39dad72390>,\n",
-       " <matplotlib.lines.Line2D at 0x7f39dad72610>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "ra0pAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3a2803d590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(np.vstack([train_loss, scratch_train_loss]).T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f39d50acc90>,\n",
-       " <matplotlib.lines.Line2D at 0x7f39d50acf10>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "jHVuv4LQn9djmY+H2FoLgK84jzfV9zNpoJRSjClI1vwxpIzKHr9hiC+vZZL53Aw8i0yKvBp4C9nH\n",
-       "77+R7+pC4Ly67mnXJ34s3wrcgJTxPo4IkZsP0mOZzVlI65fHCMtMz0e/n4qiKIqiKIqiKIqiKIqi\n",
-       "KIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqidBf+P41gkbjYnj6JAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f39dad22150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy for fine-tuning: 0.570000001788\n",
-      "Accuracy for training from scratch: 0.224000000954\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_iters = 10\n",
-    "accuracy = 0\n",
-    "scratch_accuracy = 0\n",
-    "for it in arange(test_iters):\n",
-    "    solver.test_nets[0].forward()\n",
-    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-    "    scratch_solver.test_nets[0].forward()\n",
-    "    scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n",
-    "accuracy /= test_iters\n",
-    "scratch_accuracy /= test_iters\n",
-    "print 'Accuracy for fine-tuning:', accuracy\n",
-    "print 'Accuracy for training from scratch:', scratch_accuracy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
-    "\n",
-    "http://demo.vislab.berkeleyvision.org/"
-   ]
-  }
- ],
- "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": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@ -8385,7 +8385,7 @@
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  },
-  "priority": 3
+  "priority": 6
 },
 "nbformat": 4,
 "nbformat_minor": 0
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
--- a/examples/hdf5_classification.ipynb
+++ b/examples/hdf5_classification.ipynb
--- a/examples/hdf5_classification/nonlinear_auto_test.prototxt
+++ b/examples/hdf5_classification/nonlinear_auto_test.prototxt
@ -0,0 +1,54 @@
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/test.txt"
+    batch_size: 10
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip1"
+  inner_product_param {
+    num_output: 40
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
--- a/examples/hdf5_classification/nonlinear_auto_train.prototxt
+++ b/examples/hdf5_classification/nonlinear_auto_train.prototxt
@ -0,0 +1,54 @@
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/train.txt"
+    batch_size: 10
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip1"
+  inner_product_param {
+    num_output: 40
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
--- a/examples/hdf5_classification/nonlinear_solver.prototxt
+++ b/examples/hdf5_classification/nonlinear_solver.prototxt
@ -0,0 +1,15 @@
+train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt"
+test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt"
+test_iter: 250
+test_interval: 1000
+base_lr: 0.01
+lr_policy: "step"
+gamma: 0.1
+stepsize: 5000
+display: 1000
+max_iter: 10000
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/hdf5_classification/data/train"
+solver_mode: CPU
--- a/examples/hdf5_classification/nonlinear_train_val.prototxt
+++ b/examples/hdf5_classification/nonlinear_train_val.prototxt
@ -8,7 +8,7 @@ layer {
    phase: TRAIN
  }
  hdf5_data_param {
-    source: "hdf5_classification/data/train.txt"
+    source: "examples/hdf5_classification/data/train.txt"
    batch_size: 10
  }
 }
@ -21,7 +21,7 @@ layer {
    phase: TEST
  }
  hdf5_data_param {
-    source: "hdf5_classification/data/test.txt"
+    source: "examples/hdf5_classification/data/test.txt"
    batch_size: 10
  }
 }
@ -41,8 +41,7 @@ layer {
  inner_product_param {
    num_output: 40
    weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
    }
    bias_filler {
      type: "constant"
@ -72,8 +71,7 @@ layer {
  inner_product_param {
    num_output: 2
    weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
    }
    bias_filler {
      type: "constant"
--- a/examples/hdf5_classification/solver.prototxt
+++ b/examples/hdf5_classification/solver.prototxt
@ -1,4 +1,5 @@
-net: "hdf5_classification/train_val.prototxt"
+train_net: "examples/hdf5_classification/logreg_auto_train.prototxt"
+test_net: "examples/hdf5_classification/logreg_auto_test.prototxt"
 test_iter: 250
 test_interval: 1000
 base_lr: 0.01
@ -10,5 +11,5 @@ max_iter: 10000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "hdf5_classification/data/train"
+snapshot_prefix: "examples/hdf5_classification/data/train"
 solver_mode: CPU
--- a/examples/hdf5_classification/solver2.prototxt
+++ b/examples/hdf5_classification/solver2.prototxt
@ -1,14 +0,0 @@
-net: "hdf5_classification/train_val2.prototxt"
-test_iter: 250
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 5000
-display: 1000
-max_iter: 10000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "hdf5_classification/data/train"
-solver_mode: CPU
--- a/examples/hdf5_classification/train_val.prototxt
+++ b/examples/hdf5_classification/train_val.prototxt
@ -8,7 +8,7 @@ layer {
    phase: TRAIN
  }
  hdf5_data_param {
-    source: "hdf5_classification/data/train.txt"
+    source: "examples/hdf5_classification/data/train.txt"
    batch_size: 10
  }
 }
@ -21,7 +21,7 @@ layer {
    phase: TEST
  }
  hdf5_data_param {
-    source: "hdf5_classification/data/test.txt"
+    source: "examples/hdf5_classification/data/test.txt"
    batch_size: 10
  }
 }
@ -41,8 +41,7 @@ layer {
  inner_product_param {
    num_output: 2
    weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
    }
    bias_filler {
      type: "constant"
--- a/examples/mnist/lenet_auto_solver.prototxt
+++ b/examples/mnist/lenet_auto_solver.prototxt
@ -0,0 +1,24 @@
+# The train/test net protocol buffer definition
+train_net: "examples/mnist/lenet_auto_train.prototxt"
+test_net: "examples/mnist/lenet_auto_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@ -6884,7 +6884,7 @@
  }
 ],
 "metadata": {
-  "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
+  "description": "How to do net surgery and manually change model parameters for custom use.",
  "example_name": "Editing model parameters",
  "include_in_docs": true,
  "kernelspec": {
--- a/examples/siamese/mnist_siamese.ipynb
+++ b/examples/siamese/mnist_siamese.ipynb
@ -1902,7 +1902,7 @@
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  },
-  "priority": 6
+  "priority": 7
 },
 "nbformat": 4,
 "nbformat_minor": 0