You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GoogLeNet\n",
    "前面我们讲的 VGG 是 2014 年 ImageNet 比赛的亚军，那么冠军是谁呢？就是我们马上要讲的 GoogLeNet，这是 Google 的研究人员提出的网络结构，在当时取得了非常大的影响，因为网络的结构变得前所未有，它颠覆了大家对卷积网络的串联的印象和固定做法，采用了一种非常有效的 inception 模块，得到了比 VGG 更深的网络结构，但是却比 VGG 的参数更少，因为其去掉了后面的全连接层，所以参数大大减少，同时有了很高的计算效率。\n",
    "\n",
    "![](https://ws2.sinaimg.cn/large/006tNc79ly1fmprhdocouj30qb08vac3.jpg)\n",
    "\n",
    "这是 googlenet 的网络示意图，下面我们介绍一下其作为创新的 inception 模块。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inception 模块\n",
    "在上面的网络中，我们看到了多个四个并行卷积的层，这些四个卷积并行的层就是 inception 模块，可视化如下\n",
    "\n",
    "![](https://ws4.sinaimg.cn/large/006tNc79gy1fmprivb2hxj30dn09dwef.jpg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一个 inception 模块的四个并行线路如下：\n",
    "1.一个 1 x 1 的卷积，一个小的感受野进行卷积提取特征\n",
    "2.一个 1 x 1 的卷积加上一个 3 x 3 的卷积，1 x 1 的卷积降低输入的特征通道，减少参数计算量，然后接一个 3 x 3 的卷积做一个较大感受野的卷积\n",
    "3.一个 1 x 1 的卷积加上一个 5 x 5 的卷积，作用和第二个一样\n",
    "4.一个 3 x 3 的最大池化加上 1 x 1 的卷积，最大池化改变输入的特征排列，1 x 1 的卷积进行特征提取\n",
    "\n",
    "最后将四个并行线路得到的特征在通道这个维度上拼接在一起，下面我们可以实现一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-22T12:51:05.427292Z",
     "start_time": "2017-12-22T12:51:04.924747Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.autograd import Variable\n",
    "from torchvision.datasets import CIFAR10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-22T12:51:08.890890Z",
     "start_time": "2017-12-22T12:51:08.876313Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义一个卷积加一个 relu 激活函数和一个 batchnorm 作为一个基本的层结构\n",
    "def conv_relu(in_channel, out_channel, kernel, stride=1, padding=0):\n",
    "    layer = nn.Sequential(\n",
    "        nn.Conv2d(in_channel, out_channel, kernel, stride, padding),\n",
    "        nn.BatchNorm2d(out_channel, eps=1e-3),\n",
    "        nn.ReLU(True)\n",
    "    )\n",
    "    return layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-22T12:51:09.671474Z",
     "start_time": "2017-12-22T12:51:09.587337Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class inception(nn.Module):\n",
    "    def __init__(self, in_channel, out1_1, out2_1, out2_3, out3_1, out3_5, out4_1):\n",
    "        super(inception, self).__init__()\n",
    "        # 第一条线路\n",
    "        self.branch1x1 = conv_relu(in_channel, out1_1, 1)\n",
    "        \n",
    "        # 第二条线路\n",
    "        self.branch3x3 = nn.Sequential( \n",
    "            conv_relu(in_channel, out2_1, 1),\n",
    "            conv_relu(out2_1, out2_3, 3, padding=1)\n",
    "        )\n",
    "        \n",
    "        # 第三条线路\n",
    "        self.branch5x5 = nn.Sequential(\n",
    "            conv_relu(in_channel, out3_1, 1),\n",
    "            conv_relu(out3_1, out3_5, 5, padding=2)\n",
    "        )\n",
    "        \n",
    "        # 第四条线路\n",
    "        self.branch_pool = nn.Sequential(\n",
    "            nn.MaxPool2d(3, stride=1, padding=1),\n",
    "            conv_relu(in_channel, out4_1, 1)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        f1 = self.branch1x1(x)\n",
    "        f2 = self.branch3x3(x)\n",
    "        f3 = self.branch5x5(x)\n",
    "        f4 = self.branch_pool(x)\n",
    "        output = torch.cat((f1, f2, f3, f4), dim=1)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-22T12:51:10.948630Z",
     "start_time": "2017-12-22T12:51:10.757903Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input shape: 3 x 96 x 96\n",
      "output shape: 256 x 96 x 96\n"
     ]
    }
   ],
   "source": [
    "test_net = inception(3, 64, 48, 64, 64, 96, 32)\n",
    "test_x = Variable(torch.zeros(1, 3, 96, 96))\n",
    "print('input shape: {} x {} x {}'.format(test_x.shape[1], test_x.shape[2], test_x.shape[3]))\n",
    "test_y = test_net(test_x)\n",
    "print('output shape: {} x {} x {}'.format(test_y.shape[1], test_y.shape[2], test_y.shape[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到输入经过了 inception 模块之后，大小没有变化，通道的维度变多了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们定义 GoogLeNet，GoogLeNet 可以看作是很多个 inception 模块的串联，注意，原论文中使用了多个输出来解决梯度消失的问题，这里我们只定义一个简单版本的 GoogLeNet，简化为一个输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-22T12:51:13.149380Z",
     "start_time": "2017-12-22T12:51:12.934110Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class googlenet(nn.Module):\n",
    "    def __init__(self, in_channel, num_classes, verbose=False):\n",
    "        super(googlenet, self).__init__()\n",
    "        self.verbose = verbose\n",
    "        \n",
    "        self.block1 = nn.Sequential(\n",
    "            conv_relu(in_channel, out_channel=64, kernel=7, stride=2, padding=3),\n",
    "            nn.MaxPool2d(3, 2)\n",
    "        )\n",
    "        \n",
    "        self.block2 = nn.Sequential(\n",
    "            conv_relu(64, 64, kernel=1),\n",
    "            conv_relu(64, 192, kernel=3, padding=1),\n",
    "            nn.MaxPool2d(3, 2)\n",
    "        )\n",
    "        \n",
    "        self.block3 = nn.Sequential(\n",
    "            inception(192, 64, 96, 128, 16, 32, 32),\n",
    "            inception(256, 128, 128, 192, 32, 96, 64),\n",
    "            nn.MaxPool2d(3, 2)\n",
    "        )\n",
    "        \n",
    "        self.block4 = nn.Sequential(\n",
    "            inception(480, 192, 96, 208, 16, 48, 64),\n",
    "            inception(512, 160, 112, 224, 24, 64, 64),\n",
    "            inception(512, 128, 128, 256, 24, 64, 64),\n",
    "            inception(512, 112, 144, 288, 32, 64, 64),\n",
    "            inception(528, 256, 160, 320, 32, 128, 128),\n",
    "            nn.MaxPool2d(3, 2)\n",
    "        )\n",
    "        \n",
    "        self.block5 = nn.Sequential(\n",
    "            inception(832, 256, 160, 320, 32, 128, 128),\n",
    "            inception(832, 384, 182, 384, 48, 128, 128),\n",
    "            nn.AvgPool2d(2)\n",
    "        )\n",
    "        \n",
    "        self.classifier = nn.Linear(1024, num_classes)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.block1(x)\n",
    "        if self.verbose:\n",
    "            print('block 1 output: {}'.format(x.shape))\n",
    "        x = self.block2(x)\n",
    "        if self.verbose:\n",
    "            print('block 2 output: {}'.format(x.shape))\n",
    "        x = self.block3(x)\n",
    "        if self.verbose:\n",
    "            print('block 3 output: {}'.format(x.shape))\n",
    "        x = self.block4(x)\n",
    "        if self.verbose:\n",
    "            print('block 4 output: {}'.format(x.shape))\n",
    "        x = self.block5(x)\n",
    "        if self.verbose:\n",
    "            print('block 5 output: {}'.format(x.shape))\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = self.classifier(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-22T12:51:13.614936Z",
     "start_time": "2017-12-22T12:51:13.428383Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "block 1 output: torch.Size([1, 64, 23, 23])\n",
      "block 2 output: torch.Size([1, 192, 11, 11])\n",
      "block 3 output: torch.Size([1, 480, 5, 5])\n",
      "block 4 output: torch.Size([1, 832, 2, 2])\n",
      "block 5 output: torch.Size([1, 1024, 1, 1])\n",
      "output: torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "test_net = googlenet(3, 10, True)\n",
    "test_x = Variable(torch.zeros(1, 3, 96, 96))\n",
    "test_y = test_net(test_x)\n",
    "print('output: {}'.format(test_y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到输入的尺寸不断减小，通道的维度不断增加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-22T12:51:16.387778Z",
     "start_time": "2017-12-22T12:51:15.121350Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from utils import train\n",
    "\n",
    "def data_tf(x):\n",
    "    x = x.resize((96, 96), 2) # 将图片放大到 96 x 96\n",
    "    x = np.array(x, dtype='float32') / 255\n",
    "    x = (x - 0.5) / 0.5 # 标准化，这个技巧之后会讲到\n",
    "    x = x.transpose((2, 0, 1)) # 将 channel 放到第一维，只是 pytorch 要求的输入方式\n",
    "    x = torch.from_numpy(x)\n",
    "    return x\n",
    "     \n",
    "train_set = CIFAR10('./data', train=True, transform=data_tf)\n",
    "train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)\n",
    "test_set = CIFAR10('./data', train=False, transform=data_tf)\n",
    "test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)\n",
    "\n",
    "net = googlenet(3, 10)\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=0.01)\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-22T13:17:25.310685Z",
     "start_time": "2017-12-22T12:51:16.389607Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0. Train Loss: 1.504840, Train Acc: 0.452605, Valid Loss: 1.372426, Valid Acc: 0.514339, Time 00:01:25\n",
      "Epoch 1. Train Loss: 1.046663, Train Acc: 0.630734, Valid Loss: 1.147823, Valid Acc: 0.606309, Time 00:01:02\n",
      "Epoch 2. Train Loss: 0.833869, Train Acc: 0.710618, Valid Loss: 1.017181, Valid Acc: 0.644284, Time 00:00:54\n",
      "Epoch 3. Train Loss: 0.688739, Train Acc: 0.760670, Valid Loss: 0.847099, Valid Acc: 0.712520, Time 00:00:58\n",
      "Epoch 4. Train Loss: 0.576516, Train Acc: 0.801111, Valid Loss: 0.850494, Valid Acc: 0.706487, Time 00:01:01\n",
      "Epoch 5. Train Loss: 0.483854, Train Acc: 0.832241, Valid Loss: 0.802392, Valid Acc: 0.726958, Time 00:01:08\n",
      "Epoch 6. Train Loss: 0.410416, Train Acc: 0.857657, Valid Loss: 0.865246, Valid Acc: 0.721618, Time 00:01:23\n",
      "Epoch 7. Train Loss: 0.346010, Train Acc: 0.881813, Valid Loss: 0.850472, Valid Acc: 0.729430, Time 00:01:28\n",
      "Epoch 8. Train Loss: 0.289854, Train Acc: 0.900815, Valid Loss: 1.313582, Valid Acc: 0.650712, Time 00:01:22\n",
      "Epoch 9. Train Loss: 0.239552, Train Acc: 0.918378, Valid Loss: 0.970173, Valid Acc: 0.726661, Time 00:01:30\n",
      "Epoch 10. Train Loss: 0.212439, Train Acc: 0.927270, Valid Loss: 1.188284, Valid Acc: 0.665843, Time 00:01:29\n",
      "Epoch 11. Train Loss: 0.175206, Train Acc: 0.939758, Valid Loss: 0.736437, Valid Acc: 0.790051, Time 00:01:29\n",
      "Epoch 12. Train Loss: 0.140491, Train Acc: 0.952366, Valid Loss: 0.878171, Valid Acc: 0.764241, Time 00:01:14\n",
      "Epoch 13. Train Loss: 0.127249, Train Acc: 0.956981, Valid Loss: 1.159881, Valid Acc: 0.731309, Time 00:01:00\n",
      "Epoch 14. Train Loss: 0.108748, Train Acc: 0.962836, Valid Loss: 1.234320, Valid Acc: 0.716377, Time 00:01:23\n",
      "Epoch 15. Train Loss: 0.091655, Train Acc: 0.969030, Valid Loss: 0.822575, Valid Acc: 0.790348, Time 00:01:28\n",
      "Epoch 16. Train Loss: 0.086218, Train Acc: 0.970309, Valid Loss: 0.943607, Valid Acc: 0.767306, Time 00:01:24\n",
      "Epoch 17. Train Loss: 0.069979, Train Acc: 0.976822, Valid Loss: 1.038973, Valid Acc: 0.755340, Time 00:01:22\n",
      "Epoch 18. Train Loss: 0.066750, Train Acc: 0.977322, Valid Loss: 0.838827, Valid Acc: 0.801226, Time 00:01:23\n",
      "Epoch 19. Train Loss: 0.052757, Train Acc: 0.982577, Valid Loss: 0.876127, Valid Acc: 0.796479, Time 00:01:25\n"
     ]
    }
   ],
   "source": [
    "train(net, train_data, test_data, 20, optimizer, criterion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GoogLeNet 加入了更加结构化的 Inception 块使得我们能够使用更大的通道，更多的层，同时也控制了计算量。\n",
    "\n",
    "**小练习：GoogLeNet 有很多后续的版本，尝试看看论文，看看有什么不同，实现一下：  \n",
    "v1：最早的版本  \n",
    "v2：加入 batch normalization 加快训练  \n",
    "v3：对 inception 模块做了调整  \n",
    "v4：基于 ResNet 加入了 残差连接  **"
   ]
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