【原创深度学习与TensorFlow 动手实践系列 - 3】第三课：卷积神经网络 - 基础篇...

╰半橙微兮° 2022-01-13 06:43 180阅读 0赞

【原创 深度学习与TensorFlow 动手实践系列 - 3】第三课：卷积神经网络 - 基础篇

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**提纲：**

**1. 链式反向梯度传到**

**2. 卷积神经网络 - 卷积层**

**3. 卷积神经网络 - 功能层**

**4. 实例：卷积神经网络MNIST分类**

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**期待目标：**

**1. 清楚神经网络优化原理，掌握反向传播计算。**

**2. 掌握卷积神经网络卷积层的结构特点，关键参数，层间的连接方式。**

**3. 了解不同卷积神经网络功能层的作用，会进行简单的卷积神经网络结构设计。**

**4. 能够运行TensorFlow卷积神经网络 MNIST。 **

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f(x, y, z) = (x + y) \* z

(3.00 + 1.00) \* -2.00 = -8.00

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1. 什么是卷积层

2. 有什么组成

3. 受什么影响，有何特点

4. 怎么工作

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**卷积层**

**1. 卷积神经网络基本结构**

**2. 由多个卷积核组合形成**

**3. 每个卷积核同输入数据卷积运算，形成新的特征“图”**

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**非线性激励层（None-linear activation layer）：卷积是线性运算，增加非线性描述能力**

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**降维：特征图稀疏，减少数据运算量，保持精度**

**池化层（Pooling Layer）- 降维：**

**1. 数据降维，方便计算，存储（max，ave）**

**2. 池化过程中，每张特征图单独降维**

**![4915-20170606191815856-1675941327.png][]**

** 归一化：特征的scale保持一致。**

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**卷积神经网络（CNN） - 基础 演示代码：**

**https://github.com/wiibrew/DeepLearningCourseCodes**

**https://github.com/wiibrew/DeepLearningCourseCodes/blob/master/course\_3\_tf\_mnist\_cnn.py**

**https://github.com/wiibrew/DeepLearningCourseCodes/blob/master/course\_3\_tf\_mnist\_cnn.ipynb**

**卷积神经网络运行情况：**

![4915-20170609105056387-1095881789.png][]

转载于:https://www.cnblogs.com/licheng/p/6952997.html

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