DaSE-Computer-Vision-2021
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from google.colab import drive\n",
"\n",
"drive.mount('/content/drive', force_remount=True)\n",
"\n",
"# 输入daseCV所在的路径\n",
"# 'daseCV' 文件夹包括 '.py', 'classifiers' 和'datasets'文件夹\n",
"# 例如 'CV/assignments/assignment1/daseCV/'\n",
"FOLDERNAME = None\n",
"\n",
"assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n",
"\n",
"%cd drive/My\\ Drive\n",
"%cp -r $FOLDERNAME ../../\n",
"%cd ../../\n",
"%cd daseCV/datasets/\n",
"!bash get_datasets.sh\n",
"%cd ../../"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"pdf-title"
]
},
"source": [
"# Softmax 练习\n",
"\n",
"*补充并完成本练习。*\n",
"\n",
"本练习类似于SVM练习,你要完成的事情包括:\n",
"\n",
"- 为Softmax分类器实现完全矢量化的**损失函数**\n",
"- 实现其**解析梯度(analytic gradient)**的完全矢量化表达式\n",
"- 用数值梯度**检查你的代码**\n",
"- 使用验证集**调整学习率和正则化强度**\n",
"- 使用**SGD优化**损失函数\n",
"- **可视化**最终学习的权重\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"pdf-ignore"
]
},
"outputs": [],
"source": [
"import random\n",
"import numpy as np\n",
"from daseCV.data_utils import load_CIFAR10\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"# for auto-reloading extenrnal modules\n",
"# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"pdf-ignore"
]
},
"outputs": [],
"source": [
"def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, num_dev=500):\n",
" \"\"\"\n",
" Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
" it for the linear classifier. These are the same steps as we used for the\n",
" SVM, but condensed to a single function. \n",
" \"\"\"\n",
" # Load the raw CIFAR-10 data\n",
" cifar10_dir = 'daseCV/datasets/cifar-10-batches-py'\n",
" \n",
" # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
" try:\n",
" del X_train, y_train\n",
" del X_test, y_test\n",
" print('Clear previously loaded data.')\n",
" except:\n",
" pass\n",
"\n",
" X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
" \n",
" # subsample the data\n",
" mask = list(range(num_training, num_training + num_validation))\n",
" X_val = X_train[mask]\n",
" y_val = y_train[mask]\n",
" mask = list(range(num_training))\n",
" X_train = X_train[mask]\n",
" y_train = y_train[mask]\n",
" mask = list(range(num_test))\n",
" X_test = X_test[mask]\n",
" y_test = y_test[mask]\n",
" mask = np.random.choice(num_training, num_dev, replace=False)\n",
" X_dev = X_train[mask]\n",
" y_dev = y_train[mask]\n",
" \n",
" # Preprocessing: reshape the image data into rows\n",
" X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
" X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
" X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
" X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
" \n",
" # Normalize the data: subtract the mean image\n",
" mean_image = np.mean(X_train, axis = 0)\n",
" X_train -= mean_image\n",
" X_val -= mean_image\n",
" X_test -= mean_image\n",
" X_dev -= mean_image\n",
" \n",
" # add bias dimension and transform into columns\n",
" X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
" X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
" X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
" X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
" \n",
" return X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev\n",
"\n",
"\n",
"# Invoke the above function to get our data.\n",
"X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev = get_CIFAR10_data()\n",
"print('Train data shape: ', X_train.shape)\n",
"print('Train labels shape: ', y_train.shape)\n",
"print('Validation data shape: ', X_val.shape)\n",
"print('Validation labels shape: ', y_val.shape)\n",
"print('Test data shape: ', X_test.shape)\n",
"print('Test labels shape: ', y_test.shape)\n",
"print('dev data shape: ', X_dev.shape)\n",
"print('dev labels shape: ', y_dev.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Softmax 分类器\n",
"\n",
"请在**daseCV/classifiers/softmax.py**中完成本节的代码。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 首先使用嵌套循环实现简单的softmax损失函数。\n",
"# 打开文件 daseCV/classifiers/softmax.py 并补充完成\n",
"# softmax_loss_naive 函数.\n",
"\n",
"from daseCV.classifiers.softmax import softmax_loss_naive\n",
"import time\n",
"\n",
"# 生成一个随机的softmax权重矩阵,并使用它来计算损失。\n",
"W = np.random.randn(3073, 10) * 0.0001\n",
"loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
"\n",
"# As a rough sanity check, our loss should be something close to -log(0.1).\n",
"print('loss: %f' % loss)\n",
"print('sanity check: %f' % (-np.log(0.1)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"pdf-inline"
]
},
"source": [
"**问题 1**\n",
"\n",
"\n",
"为什么我们期望损失接近-log(0.1)?简要说明。\n",
"\n",
"$\\color{blue}{\\textit 答:}$ *在这里写上你的答案* \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 完成softmax_loss_naive,并实现使用嵌套循环的梯度的版本(naive)。\n",
"loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
"\n",
"# 就像SVM那样,请使用数值梯度检查作为调试工具。\n",
"# 数值梯度应接近分析梯度。\n",
"from daseCV.gradient_check import grad_check_sparse\n",
"f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
"grad_numerical = grad_check_sparse(f, W, grad, 10)\n",
"\n",
"# 与SVM情况类似,使用正则化进行另一个梯度检查\n",
"loss, grad = softmax_loss_naive(W, X_dev, y_dev, 5e1)\n",
"f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
"grad_numerical = grad_check_sparse(f, W, grad, 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 现在,我们有了softmax损失函数及其梯度的简单实现,\n",
"# 接下来要在 softmax_loss_vectorized 中完成一个向量化版本.\n",
"# 这两个版本应计算出相同的结果,但矢量化版本应更快。\n",
"tic = time.time()\n",
"loss_naive, grad_naive = softmax_loss_naive(W, X_dev, y_dev, 0.000005)\n",
"toc = time.time()\n",
"print('naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
"\n",
"from daseCV.classifiers.softmax import softmax_loss_vectorized\n",
"tic = time.time()\n",
"loss_vectorized, grad_vectorized = softmax_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
"toc = time.time()\n",
"print('vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
"\n",
"# 正如前面在SVM练习中所做的一样,我们使用Frobenius范数比较两个版本梯度。\n",
"grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
"print('Loss difference: %f' % np.abs(loss_naive - loss_vectorized))\n",
"print('Gradient difference: %f' % grad_difference)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"code"
]
},
"outputs": [],
"source": [
"# 使用验证集调整超参数(正则化强度和学习率)。您应该尝试不同的学习率和正则化强度范围; \n",
"# 如果您小心的话,您应该能够在验证集上获得超过0.35的精度。\n",
"from daseCV.classifiers import Softmax\n",
"results = {}\n",
"best_val = -1\n",
"best_softmax = None\n",
"learning_rates = [1e-7, 5e-7]\n",
"regularization_strengths = [2.5e4, 5e4]\n",
"\n",
"################################################################################\n",
"# 需要完成的事: \n",
"# 对验证集设置学习率和正则化强度。\n",
"# 这与之前SVM中做的类似;\n",
"# 保存训练效果最好的softmax分类器到best_softmax中。\n",
"################################################################################\n",
"# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
"\n",
"pass\n",
"\n",
"# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
" \n",
"# Print out results.\n",
"for lr, reg in sorted(results):\n",
" train_accuracy, val_accuracy = results[(lr, reg)]\n",
" print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
" lr, reg, train_accuracy, val_accuracy))\n",
" \n",
"print('best validation accuracy achieved during cross-validation: %f' % best_val)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 在测试集上评估\n",
"# 在测试集上评估最好的softmax\n",
"y_test_pred = best_softmax.predict(X_test)\n",
"test_accuracy = np.mean(y_test == y_test_pred)\n",
"print('softmax on raw pixels final test set accuracy: %f' % (test_accuracy, ))"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"pdf-inline"
]
},
"source": [
"**问题 2** - *对或错*\n",
"\n",
"假设总训练损失定义为所有训练样本中每个数据点损失的总和。可能会有新的数据点添加到训练集中,同时SVM损失保持不变,但是对于Softmax分类器的损失而言,情况并非如此。\n",
"\n",
"$\\color{blue}{\\textit 你的回答:}$\n",
"\n",
"\n",
"$\\color{blue}{\\textit 你的解释:}$\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 可视化每个类别的学习到的权重\n",
"w = best_softmax.W[:-1,:] # strip out the bias\n",
"w = w.reshape(32, 32, 3, 10)\n",
"\n",
"w_min, w_max = np.min(w), np.max(w)\n",
"\n",
"classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
"for i in range(10):\n",
" plt.subplot(2, 5, i + 1)\n",
" \n",
" # Rescale the weights to be between 0 and 255\n",
" wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
" plt.imshow(wimg.astype('uint8'))\n",
" plt.axis('off')\n",
" plt.title(classes[i])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"# 重要\n",
"\n",
"这里是作业的结尾处,请执行以下步骤:\n",
"\n",
"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",
"2. 执行以下代码确保 `.py` 文件保存回你的谷歌云。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"FOLDER_TO_SAVE = os.path.join('drive/My Drive/', FOLDERNAME)\n",
"FILES_TO_SAVE = ['daseCV/classifiers/softmax.py']\n",
"\n",
"for files in FILES_TO_SAVE:\n",
" with open(os.path.join(FOLDER_TO_SAVE, '/'.join(files.split('/')[1:])), 'w') as f:\n",
" f.write(''.join(open(files).readlines()))"
]
}
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