зеркало из https://github.com/microsoft/caffe.git
948 строки
59 KiB
Plaintext
948 строки
59 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Fine-tuning a Pretrained Network for Style Recognition\n",
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"\n",
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"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",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, we will need to prepare the data. This involves the following parts:\n",
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"(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
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"(2) Download a subset of the overall Flickr style dataset for this demo.\n",
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"(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import os\n",
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"os.chdir('..')\n",
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"import sys\n",
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"sys.path.insert(0, './python')\n",
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"\n",
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"import caffe\n",
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"import numpy as np\n",
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"from pylab import *\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
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"# and a subset of 2000 images for the style recognition task.\n",
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"!data/ilsvrc12/get_ilsvrc_aux.sh\n",
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"!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
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"!python examples/finetune_flickr_style/assemble_data.py \\\n",
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" --workers=-1 --images=2000 --seed=1701 --label=5"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's show what is the difference between the fine-tuning network and the original caffe model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1c1\r\n",
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"< name: \"CaffeNet\"\r\n",
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"---\r\n",
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"> name: \"FlickrStyleCaffeNet\"\r\n",
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"4c4\r\n",
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"< type: \"Data\"\r\n",
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"---\r\n",
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"> type: \"ImageData\"\r\n",
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"15,26c15,19\r\n",
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"< # mean pixel / channel-wise mean instead of mean image\r\n",
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"< # transform_param {\r\n",
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"< # crop_size: 227\r\n",
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"< # mean_value: 104\r\n",
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"< # mean_value: 117\r\n",
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"< # mean_value: 123\r\n",
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"< # mirror: true\r\n",
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"< # }\r\n",
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"< data_param {\r\n",
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"< source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
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"< batch_size: 256\r\n",
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"< backend: LMDB\r\n",
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"---\r\n",
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"> image_data_param {\r\n",
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"> source: \"data/flickr_style/train.txt\"\r\n",
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"> batch_size: 50\r\n",
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"> new_height: 256\r\n",
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"> new_width: 256\r\n",
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"31c24\r\n",
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"< type: \"Data\"\r\n",
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"---\r\n",
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"> type: \"ImageData\"\r\n",
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"42,51c35,36\r\n",
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"< # mean pixel / channel-wise mean instead of mean image\r\n",
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"< # transform_param {\r\n",
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"< # crop_size: 227\r\n",
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"< # mean_value: 104\r\n",
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"< # mean_value: 117\r\n",
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"< # mean_value: 123\r\n",
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"< # mirror: true\r\n",
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"< # }\r\n",
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"< data_param {\r\n",
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"< source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
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"---\r\n",
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"> image_data_param {\r\n",
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"> source: \"data/flickr_style/test.txt\"\r\n",
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"53c38,39\r\n",
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"< backend: LMDB\r\n",
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"---\r\n",
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"> new_height: 256\r\n",
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"> new_width: 256\r\n",
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"323a310\r\n",
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"> # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
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"360c347\r\n",
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"< name: \"fc8\"\r\n",
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"---\r\n",
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"> name: \"fc8_flickr\"\r\n",
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"363c350,351\r\n",
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"< top: \"fc8\"\r\n",
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"---\r\n",
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"> top: \"fc8_flickr\"\r\n",
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"> # 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",
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"365c353\r\n",
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"< lr_mult: 1\r\n",
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"---\r\n",
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"> lr_mult: 10\r\n",
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"369c357\r\n",
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"< lr_mult: 2\r\n",
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"---\r\n",
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"> lr_mult: 20\r\n",
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"373c361\r\n",
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"< num_output: 1000\r\n",
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"---\r\n",
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"> num_output: 20\r\n",
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"384a373,379\r\n",
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"> name: \"loss\"\r\n",
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"> type: \"SoftmaxWithLoss\"\r\n",
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"> bottom: \"fc8_flickr\"\r\n",
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"> bottom: \"label\"\r\n",
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"> top: \"loss\"\r\n",
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"> }\r\n",
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"> layer {\r\n",
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"387c382\r\n",
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"< bottom: \"fc8\"\r\n",
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"---\r\n",
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"> bottom: \"fc8_flickr\"\r\n",
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"393,399d387\r\n",
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"< }\r\n",
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"< layer {\r\n",
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"< name: \"loss\"\r\n",
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"< type: \"SoftmaxWithLoss\"\r\n",
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"< bottom: \"fc8\"\r\n",
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"< bottom: \"label\"\r\n",
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"< top: \"loss\"\r\n"
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]
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}
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],
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"source": [
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"!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For your record, if you want to train the network in pure C++ tools, here is the command:\n",
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"\n",
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"<code>\n",
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"build/tools/caffe train \\\n",
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" -solver models/finetune_flickr_style/solver.prototxt \\\n",
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" -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
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" -gpu 0\n",
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"</code>\n",
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"\n",
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"However, we will train using Python in this example."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"iter 0, finetune_loss=3.360094, scratch_loss=3.136188\n",
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"iter 10, finetune_loss=2.672608, scratch_loss=9.736364\n",
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"iter 20, finetune_loss=2.071996, scratch_loss=2.250404\n",
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"iter 30, finetune_loss=1.758295, scratch_loss=2.049553\n",
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"iter 40, finetune_loss=1.533391, scratch_loss=1.941318\n",
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"iter 50, finetune_loss=1.561658, scratch_loss=1.839706\n",
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"iter 60, finetune_loss=1.461696, scratch_loss=1.880035\n",
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"iter 70, finetune_loss=1.267941, scratch_loss=1.719161\n",
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"iter 80, finetune_loss=1.192778, scratch_loss=1.627453\n",
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"iter 90, finetune_loss=1.541176, scratch_loss=1.822061\n",
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"iter 100, finetune_loss=1.029039, scratch_loss=1.654087\n",
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"iter 110, finetune_loss=1.138547, scratch_loss=1.735837\n",
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"iter 120, finetune_loss=0.917412, scratch_loss=1.851918\n",
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"iter 130, finetune_loss=0.971519, scratch_loss=1.801927\n",
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"iter 140, finetune_loss=0.868252, scratch_loss=1.745545\n",
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"iter 150, finetune_loss=0.790020, scratch_loss=1.844925\n",
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"iter 160, finetune_loss=1.092668, scratch_loss=1.695591\n",
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"iter 170, finetune_loss=1.055344, scratch_loss=1.661715\n",
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"iter 180, finetune_loss=0.969769, scratch_loss=1.823639\n",
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"iter 190, finetune_loss=0.780566, scratch_loss=1.820862\n",
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"done\n"
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]
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}
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],
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"source": [
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"niter = 200\n",
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"# losses will also be stored in the log\n",
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"train_loss = np.zeros(niter)\n",
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"scratch_train_loss = np.zeros(niter)\n",
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"\n",
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"caffe.set_device(0)\n",
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"caffe.set_mode_gpu()\n",
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"# We create a solver that fine-tunes from a previously trained network.\n",
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"solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
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"solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
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"# For reference, we also create a solver that does no finetuning.\n",
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"scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
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"\n",
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"# We run the solver for niter times, and record the training loss.\n",
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"for it in range(niter):\n",
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" solver.step(1) # SGD by Caffe\n",
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" scratch_solver.step(1)\n",
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" # store the train loss\n",
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" train_loss[it] = solver.net.blobs['loss'].data\n",
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" scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
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" if it % 10 == 0:\n",
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" print 'iter %d, finetune_loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
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"print 'done'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's look at the training loss produced by the two training procedures respectively."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false,
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7fbb36f0ad50>,\n",
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" <matplotlib.lines.Line2D at 0x7fbb36f0afd0>]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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],
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7fbb37f20990>"
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]
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},
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"metadata": {},
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|
"output_type": "display_data"
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}
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|
],
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"source": [
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|
"plot(np.vstack([train_loss, scratch_train_loss]).T)"
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]
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|
},
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{
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|
"cell_type": "markdown",
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"metadata": {},
|
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"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:"
|
|
]
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|
},
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{
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"cell_type": "code",
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|
"execution_count": 6,
|
|
"metadata": {
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|
"collapsed": false
|
|
},
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|
"outputs": [
|
|
{
|
|
"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7fbb347a8310>,\n",
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" <matplotlib.lines.Line2D at 0x7fbb347a8590>]"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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],
|
|
"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
|
|
}
|