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使用chatGPT搭建一個(gè)CNN卷積神經(jīng)網(wǎng)絡(luò)

發(fā)布時(shí)間：2023-03-03 16:17:10 稿源：互聯(lián)網(wǎng) 閱讀： 626 問(wèn)大家

chatGPT的火熱依然持續(xù)，我們上期使用chatGPT搭建一個(gè)CNN卷積神經(jīng)網(wǎng)絡(luò)

CNN卷積神經(jīng)網(wǎng)絡(luò)

關(guān)于CNN卷積神經(jīng)網(wǎng)絡(luò)的基礎(chǔ)知識(shí)，我們?cè)谕诘膱D文教程中都有過(guò)詳細(xì)的介紹，這里我們就使用chatGPT來(lái)簡(jiǎn)單介紹一下CNN卷積神經(jīng)網(wǎng)絡(luò)。

Q: 圖文并茂介紹一下CNN卷積神經(jīng)網(wǎng)絡(luò)A：I'm sorry, as a text-based AI language model, I cannot provide images. However, I can explain Convolutional Neural Network (CNN) in text form:A Convolutional Neural Network (CNN) is a type of deep learning neural network commonly used in image and video recognition tasks. It is called "convolutional" because it uses a mathematical operation called convolution to extract features from the input data.The basic building block of a CNN is a convolutional layer, which performs the convolution operation on the input data and generates a feature map. This feature map is then passed through multiple layers, including pooling layers, activation functions, and fully connected layers, to produce the final output.The convolutional layer uses a set of filters, also known as kernels, to scan the input data and detect features such as edges, corners, and textures. The filters are designed to be small and local, allowing them to capture the local relationships in the data. The pooling layer reduces the spatial size of the feature map and helps to reduce the computational cost and overfitting. The activation function introduces non-linearity into the network, allowing it to learn complex representations of the data. The fully connected layer connects all the neurons in the previous layer and performs the final classification or regression.CNNs have been very successful in a wide range of image and video recognition tasks, including object classification, object detection, and image segmentation. They have also been applied to other domains, such as natural language processing and speech recognition.

我開始使用的是中文，讓chatGPT圖文并茂的介紹一下CNN卷積神經(jīng)網(wǎng)絡(luò)，但是chatGPT是一個(gè)基于文本的AI語(yǔ)言模型，不能提供圖像。然而，chatGPT可以用文字形式介紹卷積神經(jīng)網(wǎng)絡(luò)（CNN）。真不知道有人使用chatGPT生成圖像是如何生成的？

由于chatGPT輸出的是英文，我就讓chatGPT重新翻譯了一下以上英文

卷積神經(jīng)網(wǎng)絡(luò)（CNN）是一種常用于圖像和視頻識(shí)別任務(wù)的深度學(xué)習(xí)神經(jīng)網(wǎng)絡(luò)。它被稱為“卷積”是因?yàn)樗褂靡环N稱為卷積的數(shù)學(xué)運(yùn)算來(lái)從輸入數(shù)據(jù)中提取特征。

CNN的基本構(gòu)建塊是卷積層，它對(duì)輸入數(shù)據(jù)執(zhí)行卷積運(yùn)算并生成特征映射。然后將該特征映射通過(guò)多個(gè)層，包括池化層，激活函數(shù)和完全連接層，以生成最終輸出。

卷積層使用一組過(guò)濾器（也稱為內(nèi)核）掃描輸入數(shù)據(jù)，并檢測(cè)諸如邊緣、角落和紋理等特征。過(guò)濾器設(shè)計(jì)得很小，也很局部，因此它們能夠捕獲數(shù)據(jù)中的局部關(guān)系。池化層減小了特征映射的空間大小，有助于減少計(jì)算成本和過(guò)擬合。激活函數(shù)在網(wǎng)絡(luò)中引入非線性，使其能夠?qū)W習(xí)數(shù)據(jù)的復(fù)雜表示。全連接層將前一層的所有神經(jīng)元連接起來(lái)，并執(zhí)行最終的分類或回歸。

卷積神經(jīng)網(wǎng)絡(luò)在廣泛的圖像和視頻識(shí)別任務(wù)中取得了巨大的成功，包括物體分類、物體檢測(cè)和圖像分割。它們也已經(jīng)應(yīng)用于其他領(lǐng)域，如自然語(yǔ)言處理和語(yǔ)音識(shí)別。

chatGPT CNN卷積神經(jīng)網(wǎng)絡(luò)代碼實(shí)現(xiàn)

from keras.models import Sequentialfrom keras.layers import Conv2D, MaxPooling2D, Flatten, Dense# 定義模型model = Sequential()# 添加卷積層model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))# 添加池化層model.add(MaxPooling2D((2, 2)))# 添加卷積層model.add(Conv2D(64, (3, 3), activation='relu'))# 添加池化層model.add(MaxPooling2D((2, 2)))# 添加卷積層model.add(Conv2D(128, (3, 3), activation='relu'))# 展平層model.add(Flatten())# 添加全連接層model.add(Dense(64, activation='relu'))model.add(Dense(10, activation='softmax'))# 編譯模型model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

該代碼定義了一個(gè)包含多個(gè)卷積層、池化層和全連接層的簡(jiǎn)單 CNN 模型，并使用 adam 優(yōu)化器和 categorical_crossentropy 損失函數(shù)編譯模型。

chatGPT不僅代碼寫的飛快，還添加了完整的注釋，且在最后，還進(jìn)行了代碼的介紹，不愧是好的一個(gè)程序員。且以上代碼是沒(méi)有語(yǔ)法錯(cuò)誤的，完全可以正常運(yùn)行。難道程序員要被AI取代了嗎？

ok，下期我們就使用chatGPT搭建的CNN卷積神經(jīng)網(wǎng)絡(luò)，利用實(shí)際的數(shù)據(jù)集進(jìn)行CNN卷積神經(jīng)網(wǎng)絡(luò)的訓(xùn)練，看看chatGPT搭建的CNN卷積神經(jīng)網(wǎng)絡(luò)的訓(xùn)練效果如何