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394 рядки
13 KiB
394 рядки
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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"# Softmax 练习\n",
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"\n",
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"*补充并完成本练习。*\n",
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"\n",
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"本练习类似于SVM练习，你要完成的事情包括:\n",
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"\n",
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"- 为Softmax分类器实现完全矢量化的**损失函数**\n",
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"- 实现其**解析梯度（analytic gradient）**的完全矢量化表达式\n",
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"- 用数值梯度**检查你的代码**\n",
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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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"- 使用**SGD优化**损失函数\n",
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"- **可视化**最终学习的权重\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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"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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"%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": "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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"def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, num_dev=500):\n",
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" \"\"\"\n",
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" Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
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" it for the linear classifier. These are the same steps as we used for the\n",
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" SVM, but condensed to a single function. \n",
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" \"\"\"\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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" mask = np.random.choice(num_training, num_dev, replace=False)\n",
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" X_dev = X_train[mask]\n",
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" y_dev = y_train[mask]\n",
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" \n",
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" # Preprocessing: reshape the image data into rows\n",
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" X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
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" X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
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" X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
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" X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
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" \n",
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" # Normalize the data: subtract the mean image\n",
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" mean_image = np.mean(X_train, axis = 0)\n",
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" X_train -= mean_image\n",
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" X_val -= mean_image\n",
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" X_test -= mean_image\n",
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" X_dev -= mean_image\n",
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" \n",
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" # add bias dimension and transform into columns\n",
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" X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
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" X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
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" X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
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" X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
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" \n",
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" return X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev\n",
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"\n",
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"\n",
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"# Invoke the above function to get our data.\n",
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"X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev = get_CIFAR10_data()\n",
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"print('Train data shape: ', X_train.shape)\n",
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"print('Train labels shape: ', y_train.shape)\n",
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"print('Validation data shape: ', X_val.shape)\n",
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"print('Validation labels shape: ', y_val.shape)\n",
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"print('Test data shape: ', X_test.shape)\n",
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"print('Test labels shape: ', y_test.shape)\n",
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"print('dev data shape: ', X_dev.shape)\n",
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"print('dev labels shape: ', y_dev.shape)"
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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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"## Softmax 分类器\n",
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"\n",
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"请在**daseCV/classifiers/softmax.py**中完成本节的代码。"
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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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"# 首先使用嵌套循环实现简单的softmax损失函数。\n",
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"# 打开文件 daseCV/classifiers/softmax.py 并补充完成\n",
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"# softmax_loss_naive 函数.\n",
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"\n",
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"from daseCV.classifiers.softmax import softmax_loss_naive\n",
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"import time\n",
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"\n",
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"# 生成一个随机的softmax权重矩阵，并使用它来计算损失。\n",
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"W = np.random.randn(3073, 10) * 0.0001\n",
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"loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
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"\n",
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"# As a rough sanity check, our loss should be something close to -log(0.1).\n",
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"print('loss: %f' % loss)\n",
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"print('sanity check: %f' % (-np.log(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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"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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"\n",
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"为什么我们期望损失接近-log（0.1）？简要说明。\n",
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"\n",
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"$\\color{blue}{\\textit 答:}$ *在这里写上你的答案* \n",
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"\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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"# 完成softmax_loss_naive，并实现使用嵌套循环的梯度的版本(naive)。\n",
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"loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
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"\n",
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"# 就像SVM那样，请使用数值梯度检查作为调试工具。\n",
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"# 数值梯度应接近分析梯度。\n",
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"from daseCV.gradient_check import grad_check_sparse\n",
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"f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
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"grad_numerical = grad_check_sparse(f, W, grad, 10)\n",
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"\n",
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"# 与SVM情况类似，使用正则化进行另一个梯度检查\n",
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"loss, grad = softmax_loss_naive(W, X_dev, y_dev, 5e1)\n",
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"f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
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"grad_numerical = grad_check_sparse(f, W, grad, 10)"
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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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"# 现在，我们有了softmax损失函数及其梯度的简单实现，\n",
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"# 接下来要在 softmax_loss_vectorized 中完成一个向量化版本.\n",
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"# 这两个版本应计算出相同的结果，但矢量化版本应更快。\n",
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"tic = time.time()\n",
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"loss_naive, grad_naive = softmax_loss_naive(W, X_dev, y_dev, 0.000005)\n",
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"toc = time.time()\n",
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"print('naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
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"\n",
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"from daseCV.classifiers.softmax import softmax_loss_vectorized\n",
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"tic = time.time()\n",
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"loss_vectorized, grad_vectorized = softmax_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
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"toc = time.time()\n",
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"print('vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
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"\n",
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"# 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",
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"grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
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"print('Loss difference: %f' % np.abs(loss_naive - loss_vectorized))\n",
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"print('Gradient difference: %f' % grad_difference)"
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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",
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"# 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",
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"from daseCV.classifiers import Softmax\n",
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"results = {}\n",
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"best_val = -1\n",
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"best_softmax = None\n",
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"learning_rates = [1e-7, 5e-7]\n",
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"regularization_strengths = [2.5e4, 5e4]\n",
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"\n",
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"################################################################################\n",
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"# 需要完成的事: \n",
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"# 对验证集设置学习率和正则化强度。\n",
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"# 这与之前SVM中做的类似；\n",
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"# 保存训练效果最好的softmax分类器到best_softmax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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" \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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"# 在测试集上评估\n",
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"# 在测试集上评估最好的softmax\n",
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"y_test_pred = best_softmax.predict(X_test)\n",
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"test_accuracy = np.mean(y_test == y_test_pred)\n",
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"print('softmax on raw pixels final test set accuracy: %f' % (test_accuracy, ))"
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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 2** - *对或错*\n",
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"\n",
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"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",
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"\n",
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"$\\color{blue}{\\textit 你的回答:}$\n",
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"\n",
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"\n",
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"$\\color{blue}{\\textit 你的解释:}$\n",
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"\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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"# 可视化每个类别的学习到的权重\n",
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"w = best_softmax.W[:-1,:] # strip out the bias\n",
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"w = w.reshape(32, 32, 3, 10)\n",
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"\n",
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"w_min, w_max = np.min(w), np.max(w)\n",
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"\n",
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"classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
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"for i in range(10):\n",
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" plt.subplot(2, 5, i + 1)\n",
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" \n",
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" # Rescale the weights to be between 0 and 255\n",
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" wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
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" plt.imshow(wimg.astype('uint8'))\n",
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" plt.axis('off')\n",
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" plt.title(classes[i])"
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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 = ['daseCV/classifiers/softmax.py']\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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]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
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"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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}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
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|
}
|