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409 líneas
13 KiB
409 líneas
13 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from google.colab import drive\n",
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"\n",
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"drive.mount('/content/drive', force_remount=True)\n",
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"\n",
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"# 输入daseCV所在的路径\n",
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"# 'daseCV' 文件夹包括 '.py', 'classifiers' 和'datasets'文件夹\n",
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"# 例如 'CV/assignments/assignment1/daseCV/'\n",
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"FOLDERNAME = None\n",
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"\n",
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"assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n",
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"\n",
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"%cd drive/My\\ Drive\n",
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"%cp -r $FOLDERNAME ../../\n",
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"%cd ../../\n",
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"%cd daseCV/datasets/\n",
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"!bash get_datasets.sh\n",
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"%cd ../../"
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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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"tags": [
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"pdf-title"
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]
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},
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"source": [
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"# 图像特征练习\n",
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"*补充并完成本练习。*\n",
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"\n",
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"我们已经看到，通过在输入图像的像素上训练线性分类器，从而在图像分类任务上达到一个合理的性能。在本练习中，我们将展示我们可以通过对线性分类器（不是在原始像素上，而是在根据原始像素计算出的特征上）进行训练来改善分类性能。\n",
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"\n",
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"你将在此notebook中完成本练习的所有工作。"
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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": null,
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"metadata": {
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"tags": [
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"pdf-ignore"
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]
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},
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"outputs": [],
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"source": [
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"import random\n",
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"import numpy as np\n",
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"from daseCV.data_utils import load_CIFAR10\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"%matplotlib inline\n",
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"plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
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"plt.rcParams['image.interpolation'] = 'nearest'\n",
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"plt.rcParams['image.cmap'] = 'gray'\n",
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"\n",
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"# for auto-reloading extenrnal modules\n",
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"# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
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"%load_ext autoreload\n",
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"%autoreload 2"
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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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"tags": [
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"pdf-ignore"
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]
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},
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"source": [
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"## 数据加载\n",
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"与之前的练习类似，我们将从磁盘加载CIFAR-10数据。"
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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": null,
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"metadata": {
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"tags": [
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"pdf-ignore"
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]
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},
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"outputs": [],
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"source": [
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"from daseCV.features import color_histogram_hsv, hog_feature\n",
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"\n",
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"def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
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" # Load the raw CIFAR-10 data\n",
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" cifar10_dir = 'daseCV/datasets/cifar-10-batches-py'\n",
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"\n",
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" # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
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" try:\n",
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" del X_train, y_train\n",
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" del X_test, y_test\n",
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" print('Clear previously loaded data.')\n",
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" except:\n",
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" pass\n",
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"\n",
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" X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
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" \n",
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" # Subsample the data\n",
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" mask = list(range(num_training, num_training + num_validation))\n",
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" X_val = X_train[mask]\n",
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" y_val = y_train[mask]\n",
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" mask = list(range(num_training))\n",
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" X_train = X_train[mask]\n",
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" y_train = y_train[mask]\n",
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" mask = list(range(num_test))\n",
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" X_test = X_test[mask]\n",
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" y_test = y_test[mask]\n",
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" \n",
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" return X_train, y_train, X_val, y_val, X_test, y_test\n",
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"\n",
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"X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_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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"tags": [
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"pdf-ignore"
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]
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},
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"source": [
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"## 特征提取\n",
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"对于每一张图片我们将会计算它的方向梯度直方图（英語：Histogram of oriented gradient，简称HOG）以及在HSV颜色空间使用色相通道的颜色直方图。\n",
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"\n",
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"简单来讲，HOG能提取图片的纹理信息而忽略颜色信息，颜色直方图则提取出颜色信息而忽略纹理信息。\n",
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"因此，我们希望将两者结合使用而不是单独使用任一个。去实现这个设想是一个十分有趣的事情。\n",
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"\n",
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"`hog_feature` 和 `color_histogram_hsv`两个函数都可以对单个图像进行运算并返回改图像的一个特征向量。\n",
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"extract_features函数输入一个图像集合和一个特征函数列表然后对每张图片运行每个特征函数，\n",
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"然后将结果存储在一个矩阵中，矩阵的每一列是单个图像的所有特征向量的串联。"
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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": null,
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"metadata": {
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"scrolled": true,
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"tags": [
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"pdf-ignore"
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]
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},
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"outputs": [],
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"source": [
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"from daseCV.features import *\n",
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"\n",
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"num_color_bins = 10 # Number of bins in the color histogram\n",
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"feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
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"X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
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"X_val_feats = extract_features(X_val, feature_fns)\n",
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"X_test_feats = extract_features(X_test, feature_fns)\n",
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"\n",
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"# Preprocessing: Subtract the mean feature\n",
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"mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
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"X_train_feats -= mean_feat\n",
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"X_val_feats -= mean_feat\n",
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"X_test_feats -= mean_feat\n",
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"\n",
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"# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
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"# has roughly the same scale.\n",
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"std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
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"X_train_feats /= std_feat\n",
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"X_val_feats /= std_feat\n",
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"X_test_feats /= std_feat\n",
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"\n",
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"# Preprocessing: Add a bias dimension\n",
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"X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
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"X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
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"X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
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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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"## 使用特征训练SVM\n",
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"使用之前作业完成的多分类SVM代码来训练上面提取的特征。这应该比原始数据直接在SVM上训练会去的更好的效果。"
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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": null,
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"metadata": {
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"tags": [
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"code"
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]
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},
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"outputs": [],
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"source": [
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"# 使用验证集调整学习率和正则化强度\n",
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"\n",
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"from daseCV.classifiers.linear_classifier import LinearSVM\n",
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"\n",
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"learning_rates = [1e-9, 1e-8, 1e-7]\n",
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"regularization_strengths = [5e4, 5e5, 5e6]\n",
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"\n",
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"results = {}\n",
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"best_val = -1\n",
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"best_svm = None\n",
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"\n",
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"################################################################################\n",
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"# 你需要做的: \n",
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"# 使用验证集设置学习率和正则化强度。\n",
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"# 这应该与你对SVM所做的验证相同；\n",
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"# 将训练最好的的分类器保存在best_svm中。\n",
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"# 你可能还想在颜色直方图中使用不同数量的bins。\n",
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"# 如果你细心一点应该能够在验证集上获得接近0.44的准确性。 \n",
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"################################################################################\n",
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"# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
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"\n",
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"pass\n",
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"\n",
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"# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
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"\n",
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"# Print out results.\n",
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"for lr, reg in sorted(results):\n",
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" train_accuracy, val_accuracy = results[(lr, reg)]\n",
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" print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
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" lr, reg, train_accuracy, val_accuracy))\n",
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" \n",
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"print('best validation accuracy achieved during cross-validation: %f' % best_val)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Evaluate your trained SVM on the test set\n",
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"y_test_pred = best_svm.predict(X_test_feats)\n",
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"test_accuracy = np.mean(y_test == y_test_pred)\n",
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"print(test_accuracy)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 直观了解算法工作原理的一种重要方法是可视化它所犯的错误。\n",
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"# 在此可视化中，我们显示了当前系统未正确分类的图像示例。\n",
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"# 第一列显示的图像是我们的系统标记为“ plane”，但其真实标记不是“ plane”。\n",
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"\n",
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"examples_per_class = 8\n",
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"classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
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"for cls, cls_name in enumerate(classes):\n",
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" idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
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" idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
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" for i, idx in enumerate(idxs):\n",
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" plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
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" plt.imshow(X_test[idx].astype('uint8'))\n",
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" plt.axis('off')\n",
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" if i == 0:\n",
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" plt.title(cls_name)\n",
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"plt.show()"
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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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"tags": [
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"pdf-inline"
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]
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},
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"source": [
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"**问题 1:**\n",
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"\n",
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"描述你看到的错误分类结果。你认为他们有道理吗？\n",
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"\n",
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"$\\color{blue}{\\textit 答:}$ **在这里写上你的回答**"
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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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"## 图像特征神经网络\n",
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"在之前的练习中，我们看到在原始像素上训练两层神经网络比线性分类器具有更好的分类精度。在这里，我们已经看到使用图像特征的线性分类器优于使用原始像素的线性分类器。\n",
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"为了完整起见，我们还应该尝试在图像特征上训练神经网络。这种方法应优于以前所有的方法：你应该能够轻松地在测试集上达到55％以上的分类精度；我们最好的模型可达到约60％的精度。"
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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": null,
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"metadata": {
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"tags": [
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"pdf-ignore"
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]
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},
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"outputs": [],
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"source": [
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"# Preprocessing: Remove the bias dimension\n",
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"# Make sure to run this cell only ONCE\n",
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"print(X_train_feats.shape)\n",
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"X_train_feats = X_train_feats[:, :-1]\n",
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"X_val_feats = X_val_feats[:, :-1]\n",
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"X_test_feats = X_test_feats[:, :-1]\n",
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"\n",
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"print(X_train_feats.shape)"
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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": null,
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"metadata": {
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"tags": [
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"code"
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]
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},
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"outputs": [],
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"source": [
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"from daseCV.classifiers.neural_net import TwoLayerNet\n",
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"\n",
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"input_dim = X_train_feats.shape[1]\n",
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"hidden_dim = 500\n",
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"num_classes = 10\n",
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"best_acc = 0.0\n",
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"\n",
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"net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
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"best_net = None\n",
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"\n",
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"################################################################################\n",
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"# TODO: 使用图像特征训练两层神经网络。\n",
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"# 您可能希望像上一节中那样对各种参数进行交叉验证。\n",
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"# 将最佳的模型存储在best_net变量中。 \n",
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"################################################################################\n",
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"# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
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"\n",
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"pass\n",
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"\n",
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"# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 在测试集上运行得到的最好的神经网络分类器，应该能够获得55％以上的准确性。\n",
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"\n",
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"test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n",
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"print(test_acc)"
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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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"---\n",
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"# 重要\n",
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"\n",
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"这里是作业的结尾处，请执行以下步骤:\n",
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"\n",
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"1. 点击`File -> Save`或者用`control+s`组合键，确保你最新的的notebook的作业已经保存到谷歌云。\n",
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"2. 执行以下代码确保 `.py` 文件保存回你的谷歌云。"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"FOLDER_TO_SAVE = os.path.join('drive/My Drive/', FOLDERNAME)\n",
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"FILES_TO_SAVE = []\n",
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"\n",
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"for files in FILES_TO_SAVE:\n",
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" with open(os.path.join(FOLDER_TO_SAVE, '/'.join(files.split('/')[1:])), 'w') as f:\n",
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" f.write(''.join(open(files).readlines()))"
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]
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}
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|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
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},
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|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
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},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.0"
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}
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|
},
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"nbformat": 4,
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"nbformat_minor": 1
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|
}
|