{
 "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": [
    "# Dropout\n",
    "Dropout [1] 是一种通过在正向传播中将一些输出随机设置为零，神经网络正则化的方法。在这个练习中，你将实现一个dropout层，并修改你的全连接网络使其可选择的使用dropout\n",
    "\n",
    "[1] [Geoffrey E. Hinton et al, \"Improving neural networks by preventing co-adaptation of feature detectors\", arXiv 2012](https://arxiv.org/abs/1207.0580)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from daseCV.classifiers.fc_net import *\n",
    "from daseCV.data_utils import get_CIFAR10_data\n",
    "from daseCV.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from daseCV.solver import Solver\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 external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropout 正向传播\n",
    "在文件 `daseCV/layers.py` 中完成dropout的正向传播过程。由于dropout在训练和测试期间的行为是不同的，因此请确保两种模式下都实现完成。\n",
    "\n",
    "完成此操作后，运行下面的cell以测试你的代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(500, 500) + 10\n",
    "\n",
    "for p in [0.25, 0.4, 0.7]:\n",
    "  out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n",
    "  out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n",
    "\n",
    "  print('Running tests with p = ', p)\n",
    "  print('Mean of input: ', x.mean())\n",
    "  print('Mean of train-time output: ', out.mean())\n",
    "  print('Mean of test-time output: ', out_test.mean())\n",
    "  print('Fraction of train-time output set to zero: ', (out == 0).mean())\n",
    "  print('Fraction of test-time output set to zero: ', (out_test == 0).mean())\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropout 反向传播\n",
    "在文件 `daseCV/layers.py` 中完成dropout的反向传播。完成之后运行以下cell以对你的实现代码进行梯度检查。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10) + 10\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dropout_param = {'mode': 'train', 'p': 0.2, 'seed': 123}\n",
    "out, cache = dropout_forward(x, dropout_param)\n",
    "dx = dropout_backward(dout, cache)\n",
    "dx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)\n",
    "\n",
    "# Error should be around e-10 or less\n",
    "print('dx relative error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## 问题 1:\n",
    "如果我们不利用inverted dropout，在训练的时候直接将dropout后的值除以 `p`,会发生什么？为什么会这样呢？\n",
    "\n",
    "\n",
    "\n",
    "## 回答:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 全连接网络的Dropout\n",
    "\n",
    "修改`daseCV/classifiers/fc_net.py`文件完成使用dropout的部分。具体来说，如果网络的构造函数收到的`dropout`参数值不为1，则应在每个ReLU之后添加一个dropout层。完成之后，运行以下命令以对你的代码进行梯度检查。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for dropout in [1, 0.75, 0.5]:\n",
    "  print('Running check with dropout = ', dropout)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            weight_scale=5e-2, dtype=np.float64,\n",
    "                            dropout=dropout, seed=123)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "  \n",
    "  # Relative errors should be around e-6 or less; Note that it's fine\n",
    "  # if for dropout=1 you have W2 error be on the order of e-5.\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 正则化实验\n",
    "作为实验，我们将在500个样本上训练一对双层网络：一个不使用dropout，另一个使用概率为0.25的dropout。之后，我们将可视化这两个网络训练和验证的准确度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Train two identical nets, one with dropout and one without\n",
    "np.random.seed(231)\n",
    "num_train = 500\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "dropout_choices = [1, 0.25]\n",
    "for dropout in dropout_choices:\n",
    "  model = FullyConnectedNet([500], dropout=dropout)\n",
    "  print(dropout)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=25, batch_size=100,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 5e-4,\n",
    "                  },\n",
    "                  verbose=True, print_every=100)\n",
    "  solver.train()\n",
    "  solvers[dropout] = solver\n",
    "  print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot train and validation accuracies of the two models\n",
    "\n",
    "train_accs = []\n",
    "val_accs = []\n",
    "for dropout in dropout_choices:\n",
    "  solver = solvers[dropout]\n",
    "  train_accs.append(solver.train_acc_history[-1])\n",
    "  val_accs.append(solver.val_acc_history[-1])\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "for dropout in dropout_choices:\n",
    "  plt.plot(solvers[dropout].train_acc_history, 'o', label='%.2f dropout' % dropout)\n",
    "plt.title('Train accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "  \n",
    "plt.subplot(3, 1, 2)\n",
    "for dropout in dropout_choices:\n",
    "  plt.plot(solvers[dropout].val_acc_history, 'o', label='%.2f dropout' % dropout)\n",
    "plt.title('Val accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## 问题 2:\n",
    "对比有无dropout的验证和训练的精度，你对使用dropout作为正则化有何建议？\n",
    "\n",
    "## 回答:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## 问题三 3:\n",
    "假设我们正在训练一个深层的全连接网络用以进行图像分类，并隐层之后dropout（通过使用概率p进行参数化）。如果我们担心过度拟合而决定减小隐层的大小（即每层中的节点数）时，应该如何修改p（如果有的话）？\n",
    "\n",
    "## 回答:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "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/cnn.py', 'daseCV/classifiers/fc_net.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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