---
# System prepended metadata

title: Lesson 13 | 神經網路概論2

---

# Lesson 13 | 神經網路概論2

## 第一節：圖像辨識基礎(1)

* 大家看一下下面這些圖像，你是否發覺你能瞬間辨認這些圖像分別代表0-9之中哪一個數字?

![L13-1](https://i.imgur.com/JjCnS9n.png)

* 人工智慧的核心原理就是找出一個預測函數f(x)，而對於手寫數字辨識而言，y是一個10類別的多類別分類，那你是否有辦法想像x的樣子呢？

## 第一節：圖像辨識基礎(2)

* 讓我們利用MNIST的手寫數字資料進行神經網路的實作吧！

    * 請在[這裡](https://linchin.ndmctsgh.edu.tw/data/MNIST.csv)下載[MNIST](https://www.kaggle.com/c/digit-recognizer)的手寫數字資料。
    * 一個28×28的黑白圖片的其實可以被表示成784個介於0至255的數字，這樣我們就又能把問題轉換為單純的預測問題了。

* 這邊的範例包含了訓練集以及測試集的準備。

    ```python=
    import random

    import numpy as np
    import pandas as pd

    data = pd.read_csv('MNIST.csv', header = None)

    # Split Dataset
    train_idx = np.array(random.sample(range(42000), 35000))

    train_data = data[np.isin(range(42000), train_idx) == True]
    train_x = train_data[list(range(1, 785))]
    train_y = train_data[0]

    test_data = data[np.isin(range(42000), train_idx) == False]
    test_x = test_data[list(range(1, 785))]
    test_y = test_data[0]
    ```
    
    * 視覺化呈現部分資料。
    
        ```python=
        import matplotlib.pyplot as plt

        # Show image
        sub_x_plot = train_x.to_numpy()
        sub_x_plot = sub_x_plot.reshape((35000, 28, 28), order='F')

        fig, ax = plt.subplots(2, 2)
        ax[0, 0].imshow(sub_x_plot[7, :, :])
        ax[0, 0].text(0, 2.5, train_y.tolist()[7], c="white")

        ax[0, 1].imshow(sub_x_plot[126, :, :])
        ax[0, 1].text(0, 2.5, train_y.tolist()[126], c="white")

        ax[1, 0].imshow(sub_x_plot[247, :, :])
        ax[1, 0].text(0, 2.5, train_y.tolist()[247], c="white")

        ax[1, 1].imshow(sub_x_plot[871, :, :])
        ax[1, 1].text(0, 2.5, train_y.tolist()[871], c="white")
        ```

## 第一節：圖像辨識基礎(3)

* 圖像資料由於數據特性的問題，將會導致資料量大上很多，尤其在未來我們很可能會面對到百萬級別的ImageNet資料集。

* 這個時候我們會面對到一個硬體的問題，那就是我們不可能預先把所有檔案都讀到RAM內。比較好的解決方法是每次訓練時只讀取小批量的訓練樣本，這樣就能有效降低記憶體的使用。

* 我們先把Training/Test的資料寫出：

    ```python=
    pd.DataFrame(train_data).to_csv("train_data.csv", index = None)
    pd.DataFrame(test_data).to_csv("test_data.csv", index = None)
    ```

* 在Pytorch裡面可以這樣撰寫Dataset(註：這邊的範例依舊把圖讀進記憶體，但你應該能看出如何更換成每筆資料個別讀取）：

    * Windows下cmd的安裝指令
    
        ```
        pip3 install torch torchvision torchaudio
        ```

    ```python=
    import os
    import pandas as pd
    from torch.utils.data import Dataset
    from torchvision.io import read_image

    class CustomImageDataset(Dataset):
        def __init__(self, file):
            self.img_labels = pd.read_csv(file, header = None)

        def __len__(self):
            return len(self.img_labels)

        def __getitem__(self, idx):
            # img_path = os.path.join(self.img_labels.iloc[idx, 0])
            # image = read_image(img_path)

            image = self.img_labels.iloc[idx, list(range(1, 785))]
            label = self.img_labels.iloc[idx, 0]

            image = np.array(image, dtype = 'float32').reshape(-1, 28, 28, order='F')
            label = np.array(label)

            return image, label

    training_data = CustomImageDataset("train_data.csv")
    ```

* 這個Dataloader和之前的並沒有甚麼太大的不同，同樣是透過一樣的方式產生Data：

    ```python=
    from torch.utils.data import DataLoader

    train_dataloader = DataLoader(training_data, batch_size=32, shuffle=True)
    ```
    
## 第一節：圖像辨識基礎(4)

* 可以透過這樣的方式去取出樣本：

    ```python=
    train_features, train_labels = next(iter(train_dataloader))
    ```
    
* 試著視覺化得到看x跟y是否正確：

    ```python=
    import matplotlib.pyplot as plt

    # Show image
    sub_x_plot = np.array(train_features)
    sub_x_plot = sub_x_plot.reshape((32, 28, 28), order='F')

    fig, ax = plt.subplots(2, 2)
    ax[0, 0].imshow(sub_x_plot[0, :, :])
    ax[0, 0].text(0, 2.5, train_labels.tolist()[0], c="white")

    ax[0, 1].imshow(sub_x_plot[1, :, :])
    ax[0, 1].text(0, 2.5, train_labels.tolist()[1], c="white")

    ax[1, 0].imshow(sub_x_plot[2, :, :])
    ax[1, 0].text(0, 2.5, train_labels.tolist()[2], c="white")

    ax[1, 1].imshow(sub_x_plot[3, :, :])
    ax[1, 1].text(0, 2.5, train_labels.tolist()[3], c="white")
    ```

* 如果你願意的話，你可以更改```CustomImageDataset```，在需要的時候再讀取jpeg檔案。

## 第二節：卷積神經網路介紹(1)

* 神經網路最初的構想是利用電腦模擬大腦運作的過程，而在多層感知器中的「全連接層」所模擬的是腦細胞傳遞訊息的思考過程。

* 但回到我們的手寫數字分類問題，當我們看到這些手寫數字時，我們一眼就能認出他們了，但從「圖片」到「概念」的過程真的這麼簡單嗎?

* 現在我們面對的是視覺問題，看來除了模擬大腦思考運作的過程之外，我們還需要模擬眼睛的作用！

    * 1962年時David H. Hubel與Torsten Wiesel共同發表了一篇研究：[Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/pdf/jphysiol01247-0121.pdf)，這篇研究旨在探討生物視覺系統的運作方式，並獲得了1981年的Nobel prize

        ![L13-2](https://i.imgur.com/LOns6bG.png)

    * 他們的研究發現，貓咪在受到不同形狀的圖像刺激時，感受野的腦部細胞會產生不同反應

        ![L13-3](https://i.imgur.com/IoSslOs.png)

* 這樣的研究給了我們什麼啟示呢?也就是在最開始接觸光影訊號的腦神經細胞，每個細胞會辨認一種特定的簡單特徵，而且這些簡單特徵「無論出現在視野範圍的哪裡」，他們都會被激活。

## 第二節：卷積神經網路介紹(2)

* 電腦學家受到了上面生物研究的啟發，因此在多層感知器(網路中的全連接層)之前增加了一個構造，叫做「卷積器」(Convolution filter)

    * 卷積器模擬了感受野最初的細胞，他們負責用來辨認特定特徵，他們的數學模式如下：

        ![L13-4](data:image/gif;base64,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)

* 簡單來說，卷積器透過一個「濾鏡」對圖片進行全局的搜索，卷積後的新圖片稱為「特徵圖」。

    * 「特徵圖」的意義是什麼呢?卷積器就像是最初級的視覺細胞，他們專門辨認某一種簡單特徵，那這個「特徵圖」上面數字越大的，就代表那個地方越符合該細胞所負責的特徵。

    ![L13-5](https://i.imgur.com/y1rrKle.png)

## 第二節：卷積神經網路介紹(3)

* 那假定我們有3個卷積器，分別負責「＼」、「╳」、「／」，那將會產生3個特徵圖：

    ![L13-6](https://i.imgur.com/l3lQAit.png)

* 獲得特徵圖之後，還記得感知機的activated function嗎？我們為了增加神經網路的數學複雜性，會添加一些非線性函數做轉換，因此在經過卷積層後的特徵圖會再經過非線性轉換。

* 接著，由於連續卷積的特徵圖造成了訊息的重複，這時候我們經常會使用「池化層」(pooling layer)進行圖片降維，事實上他等同於把圖片的解析度調低，這同時也能節省計算量。

    ![L13-7](https://i.imgur.com/96hXUwH.png)

## 第二節：卷積神經網路介紹(4)

* 接著我們思考一下，連續的堆疊「卷積器」會產生什麼效果?

    ![L13-8](https://i.imgur.com/8U6TwCp.png)

* 我們想像有一張人的圖片，假定第一個卷積器是辨認眼睛的特徵，第二個卷積器是在辨認鼻子的特徵，第三個卷積器是在辨認耳朵的特徵，第四個卷積器是在辨認手掌的特徵，第五個卷積器是在辨認手臂的特徵

    * 第1.2.3張特徵圖中數值越高的地方，就分別代表眼睛、鼻子、耳朵最有可能在的位置，那將這3張特徵圖合在一起看再一次卷積，是否就能辨認出人臉的位置?

    * 第4.5張特徵圖中數值越高的地方，就分別代表手掌、手臂最有可能在的位置，那將這2張特徵圖合在一起看再一次卷積，是否就能辨認出手的位置?

    * 第4.5張特徵圖對人臉辨識同樣能起到作用，因為人臉不包含手掌、手臂，因此如果有個卷積器想要辨認人臉，他必須對第1.2.3張特徵圖做正向加權，而對第4.5張特徵圖做負向加權

* 所以，我們可以在神經網路中堆疊卷積器，而越淺層的卷積器所辨認的特徵越簡單，越深層的卷積器所辨認的特徵越複雜。

    ![L13-9](https://hackmd.io/_uploads/rykjFc4E2.png)

## 第三節：利用卷積神經網路做手寫數字辨識(1)

* 讓我們一起來實現1989年Yann LeCun所發展的世界上第一個成功的卷積神經網路(Convolutional Neural Networks，CNN)，這個CNN被命名為LeNet。

    ![L13-10](https://i.imgur.com/c2pDzeR.png)

## 第三節：利用卷積神經網路做手寫數字辨識(2)


* 接著讓我們來定義Model architecture：這是一個閹割版的LeNet，原版的LeNet其第一、二層的卷積器數量分別是20以及50，而第一個全連接層具有500個神經元，這個網路結構如下：

    * 第一層卷積組合

        * 原始圖片(28x28x1)要先經過10個5x5的「卷積器」(5x5x1x10)處理，將使圖片變成10張「一階特徵圖」(24x24x10)

        * 接著這10張「一階特徵圖」(24x24x10)會經過ReLU，產生10張「轉換後的一階特徵圖」(24x24x10)

        * 接著這10張「轉換後的一階特徵圖」(24x24x10)再經過2x2「池化器」(2x2)處理，將使圖片變成10張「降維後的一階特徵圖」(12x12x10)

    * 第二層卷積組合

        * 再將10張「降維後的一階特徵圖」(12x12x10)經過20個5x5的「卷積器」(5x5x10x20)處理，將使圖片變成20張「二階特徵圖」(8x8x20)

        * 接著這20張「二階特徵圖」(8x8x20)會經過ReLU，產生20張「轉換後的二階特徵圖」(8x8x20)

        * 接著這20張「轉換後的二階特徵圖」(8x8x20)再經過2x2「池化器」(2x2)處理，將使圖片變成20張「降維後的二階特徵圖」(4x4x20)

    * 全連接層

        * 將「降維後的二階特徵圖」(4x4x20)重新排列，壓製成「一階高級特徵」(320)

        * 讓「一階高級特徵」(320)進入「隱藏層」，輸出「二階高級特徵」(150)

        * 「二階高級特徵」(150)經過ReLU，輸出「轉換後的二階高級特徵」(150)

        * 「轉換後的二階高級特徵」(150)進入「輸出層」，產生「原始輸出」(10)

        * 「原始輸出」(10)經過Softmax函數轉換，判斷圖片是哪個類別

        ```python=
        from torch import nn

        class NeuralNetwork(nn.Module):
            def __init__(self):
                super().__init__()
                self.main_arch = nn.Sequential(
                    # first conv
                    nn.Conv2d(in_channels = 1, out_channels = 20, kernel_size = 5),
                    nn.ReLU(),
                    nn.MaxPool2d(kernel_size = 2, stride = 2),
                    # second conv
                    nn.Conv2d(in_channels = 20, out_channels = 20, kernel_size = 5),
                    nn.ReLU(),
                    nn.MaxPool2d(kernel_size = 2, stride = 2),
                    # flatten
                    nn.Flatten(),
                    # first fully connected layer
                    nn.Linear(20 * 4 * 4, 150),
                    nn.ReLU(),
                    # first fully connected layer
                    nn.Linear(150, 10),
                )
                self.final_pred = nn.LogSoftmax(1)

            def forward(self, x):
                main_network = self.main_arch(x)
                logits = self.final_pred(main_network)

                return logits
        ```
    
* 宣告網路以及損失函數：

    ```python=
    model = NeuralNetwork().to("cpu")
    loss_fn = nn.NLLLoss()
    ```

## 第三節：利用卷積神經網路做手寫數字辨識(3)

* 這是我們上週使用過的函式「train」：

    ```python=
    def train(dataloader, model, loss_fn, optimizer):
        size = len(dataloader.dataset)

        for batch, (X, y) in enumerate(dataloader):

            X, y = X.to('cpu'), y.to('cpu')

            # Compute prediction error
            pred = model(X)
            loss = loss_fn(pred, y)

            # Backpropagation
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            if batch % 100 == 0:
                loss, current = loss.item(), (batch + 1) * len(X)
                print('loss: ', round(loss, 4), '[', current, '/', size, ']')
    ```

* 一樣，我們必須準備優化器

    ```python=
    import torch

    optimizer = torch.optim.Adam(model.parameters(), lr = 1e-4)
    ```

* 開始訓練吧：

    ```python=
    epochs = 5
    model.train() # 記得要將模型宣告成訓練模式。
    for t in range(epochs):
        print(f"Epoch {t+1}\n-------------------------------")
        train(train_dataloader, model, loss_fn, optimizer)
    print("Done!")
    ```

## 第三節：利用卷積神經網路做手寫數字辨識(4)

* 這是我們上週使用過的函式「test」，有稍微進行修改：

```python=
def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.eval() # 測試模式這個函式是重點
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to('cpu'), y.to('cpu')
            pred = model(X)
            test_loss += loss_fn(pred, y)
            pred = torch.argmax(pred, dim=1)
            correct += np.array([1 if pred[i] == y[i] else 0 for i in range(pred.shape[0])]).sum()
    test_loss /= num_batches
    correct /= size
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
```

* 讓我們讀取測試集的資料看看效果：

```python=
testing_data = CustomImageDataset("test_data.csv")
test_dataloader = DataLoader(testing_data, batch_size=32, shuffle=False)

test(test_dataloader, model, loss_fn)
```

* 你是否覺得卷積神經網路非常厲害！

## 總結：

* 為什麼卷積神經網路在圖像辨識上相較於多層感知器這麼強大呢?卷積網路最大的賣點就是卷積層，讓我們仔細思考一下他的厲害之處：

    * 他會考慮各像素之間真實的相關性，而非像多層感知器一樣把每個像素視為完全獨立的特徵。這一點可以參考人類的視覺系統，我想你應該能認同你的眼睛具有平移不變性(shift invariance)的特色，因此CNN因為完美的模仿了視覺系統造就了在測試集中的高準確性。

    * CNN由於權重共享的特性，導致相當的節省參數量。從過擬合的角度思考，這暗示著我們可以用較小的參數量完成複雜的網路，因此較不容易過擬合；從參數量固定的狀況下思考，CNN可以拼湊出較為複雜的結構。因此CNN的結構在影像辨識上具有相當強大的優勢！

* 回顧一下我們的課程，並檢視一下自己的學習效果。你對Python應該有了一定的熟悉度，在醫學可能的實務程式應用上，你或許還會遇到一些困難，但基於本堂課的基礎，同學應該已有能力查詢相關資料並解決，在應用方面，對於基本的數據分析，資料視覺化，甚至到演算法模型的建立，都有了一定的概念。在人工智慧的部分，儘管還有一些任務我們沒有帶各位實現，但你應該能想像到「看圖說話」、「聊天對答」等是怎樣做到的了吧?


