【北京大学】4 TensorFlow1.x的反向传播推导与实现

刺骨的言语ヽ痛彻心扉 2022-12-29 02:25 149阅读 0赞

# 1 相关概念 #

（1）反向传播：训练模型参数，在所有参数上用梯度下降，使NN模型在训练数据上的损失函数最小。  
（2）损失函数（loss）：预测值(y)与已知答案（y\_）的差距  
（3）均方误差MSE

> loss = tf.reduce\_mean(tf.square(y\_-y))

（4）反向传播训练方法：以减小loss值为优化目标  
（5）学习率：决定参数每次更新的幅度

# 2 神经网络实现过程 #

（1）准备数据集，提取特征，作为输入喂给神经网络  
（2）搭建NN结构，从输入到输出（先搭建计算图，再用会话执行）  
（3）大量特征数据喂给NN，迭代优化NN参数  
（4）使用训练好的模型预测和分类

# 3 代码实现 #

#coding:utf-8
    #0导入模块，生成模拟数据集。
    #tensorflow学习笔记(北京大学) tf3_6.py 完全解析神经网络搭建学习
    #QQ群：476842922（欢迎加群讨论学习
    import tensorflow as tf
    import numpy as np
    BATCH_SIZE = 8
    SEED = 23455
    rdm = np.random.RandomState(SEED)
    X = rdm.rand(32,2)
    Y_ = [[int(x0 + x1 < 1)] for (x0, x1) in X]
    print("X:\n",X)
    print("Y_:\n",Y_)
    x = tf.placeholder(tf.float32, shape=(None, 2))#用placeholder实现输入定义,2表示体积和重量两个特征
    y_= tf.placeholder(tf.float32, shape=(None, 1))#用placeholder实现占位。1表示输出的特征只有一个特征，就是标签的可能性，只有合格或不合格
    w1= tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))#正态分布随机数。2个输入，对应3个神经元
    w2= tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))#正态分布随机数。3个神经元，对应1个输出
    a = tf.matmul(x, w1)#点积
    y = tf.matmul(a, w2)#点积
    #2定义损失函数及反向传播方法。
    loss_mse = tf.reduce_mean(tf.square(y-y_)) 
    train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss_mse)#0.001是学习率，可以选择不同优化器
    #train_step = tf.train.MomentumOptimizer(0.001,0.9).minimize(loss_mse)不同的优化器
    #train_step = tf.train.AdamOptimizer(0.001).minimize(loss_mse)不同的优化器
    #3生成会话，训练STEPS轮
    with tf.Session() as sess:
        init_op = tf.global_variables_initializer()#初始化
        sess.run(init_op)
        # 输出目前（未经训练）的参数取值。
        print("w1:\n", sess.run(w1))
        print("w2:\n", sess.run(w2))
        print("\n")
        
        # 训练模型。
        STEPS = 3000
        for i in range(STEPS):#3000轮
            start = (i*BATCH_SIZE) % 32 #i*8%32 计算取数据集的下标
            end = start + BATCH_SIZE    #i*8%32+8 计算取标签的下标
            sess.run(train_step, feed_dict={ x: X[start:end], y_: Y_[start:end]})
            if i % 500 == 0:
                total_loss = sess.run(loss_mse, feed_dict={ x: X, y_: Y_})
                print("After %d training step(s), loss_mse on all data is %g" % (i, total_loss))
        
        # 输出训练后的参数取值。
        print("\n")
        print("w1:\n", sess.run(w1))
        print("w2:\n", sess.run(w2))
    #，只搭建承载计算过程的
    #计算图，并没有运算，如果我们想得到运算结果就要用到“会话 Session()”了。 
    #√会话（Session）： 执行计算图中的节点运算 
        print("w1:\n", w1)
        print("w2:\n", w2)
    """ X: [[ 0.83494319 0.11482951] [ 0.66899751 0.46594987] [ 0.60181666 0.58838408] [ 0.31836656 0.20502072] [ 0.87043944 0.02679395] [ 0.41539811 0.43938369] [ 0.68635684 0.24833404] [ 0.97315228 0.68541849] [ 0.03081617 0.89479913] [ 0.24665715 0.28584862] [ 0.31375667 0.47718349] [ 0.56689254 0.77079148] [ 0.7321604 0.35828963] [ 0.15724842 0.94294584] [ 0.34933722 0.84634483] [ 0.50304053 0.81299619] [ 0.23869886 0.9895604 ] [ 0.4636501 0.32531094] [ 0.36510487 0.97365522] [ 0.73350238 0.83833013] [ 0.61810158 0.12580353] [ 0.59274817 0.18779828] [ 0.87150299 0.34679501] [ 0.25883219 0.50002932] [ 0.75690948 0.83429824] [ 0.29316649 0.05646578] [ 0.10409134 0.88235166] [ 0.06727785 0.57784761] [ 0.38492705 0.48384792] [ 0.69234428 0.19687348] [ 0.42783492 0.73416985] [ 0.09696069 0.04883936]] Y_: [[1], [0], [0], [1], [1], [1], [1], [0], [1], [1], [1], [0], [0], [0], [0], [0], [0], [1], [0], [0], [1], [1], [0], [1], [0], [1], [1], [1], [1], [1], [0], [1]] w1: [[-0.81131822 1.48459876 0.06532937] [-2.4427042 0.0992484 0.59122431]] w2: [[-0.81131822] [ 1.48459876] [ 0.06532937]] After 0 training step(s), loss_mse on all data is 5.13118 After 500 training step(s), loss_mse on all data is 0.429111 After 1000 training step(s), loss_mse on all data is 0.409789 After 1500 training step(s), loss_mse on all data is 0.399923 After 2000 training step(s), loss_mse on all data is 0.394146 After 2500 training step(s), loss_mse on all data is 0.390597 w1: [[-0.70006633 0.9136318 0.08953571] [-2.3402493 -0.14641267 0.58823055]] w2: [[-0.06024267] [ 0.91956186] [-0.0682071 ]] """

4 总结  
（1）前向传播中：需要定义输入、参数和输出

x = 
    y_=
    w1 =
    w2 =
    a=
    y=

（2）反向传播：定义损失函数、反向传播方法

loss =
    train_step =
    ```python
    （3）生成会话，训练Steps轮
    ```python
    with tf.Session() as sess:
        init_op = tf.global_variables_initializer()#初始化
        sess.run(init_op)
        # 训练模型。
        STEPS = 3000
        for i in range(STEPS):#3000轮
            start = 
            end = 
            sess.run(train_step, feed_dict:)

# 相关笔记 #

以下所有源码以及更详细PDF笔记请在github下载  
[TensorFolwNotebook-from-Peking-University][]

1.  [【北京大学】1 TensorFlow1.x中Python基础知识][1 TensorFlow1.x_Python]
2.  [【北京大学】2 TensorFlow1.x的张量、计算图、会话][2 TensorFlow1.x]
3.  [【北京大学】3 TensorFlow1.x的前向传播推导与实现][3 TensorFlow1.x]
4.  [【北京大学】4 TensorFlow1.x的反向传播推导与实现][4 TensorFlow1.x]
5.  [【北京大学】5 TensorFlow1.x的损失函数和交叉熵举例讲解及实现][5 TensorFlow1.x]
6.  [【北京大学】6 TensorFlow1.x的学习率、滑动平均和正则化实例及实现][6 TensorFlow1.x]
7.  [【北京大学】7 TensorFlow1.x的神经网络模块设计思想举例及实现][7 TensorFlow1.x]
8.  [【北京大学】8 TensorFlow1.x的Mnist数据集实例实现][8 TensorFlow1.x_Mnist]
9.  [【北京大学】9 TensorFlow1.x的实现自定义Mnist数据集][9 TensorFlow1.x_Mnist]
10. [【北京大学】10 TensorFlow1.x的卷积神经网络(CNN)相关基础知识][10 TensorFlow1.x_CNN]
11. [【北京大学】11 TensorFlow1.x的卷积神经网络模型Lenet5实现][11 TensorFlow1.x_Lenet5]
12. [【北京大学】12 TensorFlow1.x的卷积神经网络模型VGGNet实现][12 TensorFlow1.x_VGGNet]
13. [【北京大学】13 TensorFlow1.x的项目实战之手写英文体识别OCR技术][13 TensorFlow1.x_OCR]
14. [【北京大学】14 TensorFlow1.x的二值神经网络实现MNIST数据集手写数字识别][14 TensorFlow1.x_MNIST]
15. [【北京大学】15 TensorFlow1.x的项目实战之人脸表情识别][15 TensorFlow1.x]
16. [【北京大学】16 TensorFlow1.x的项目实战之图像风格融合与快速迁移][16 TensorFlow1.x]

[TensorFolwNotebook-from-Peking-University]: https://github.com/823316627bandeng/-TensorFolwNotebook-from-Peking-University
[1 TensorFlow1.x_Python]: https://betterbench.blog.csdn.net/article/details/111400935
[2 TensorFlow1.x]: https://betterbench.blog.csdn.net/article/details/111401620
[3 TensorFlow1.x]: https://betterbench.blog.csdn.net/article/details/111407104
[4 TensorFlow1.x]: https://betterbench.blog.csdn.net/article/details/111407700
[5 TensorFlow1.x]: https://betterbench.blog.csdn.net/article/details/111409255
[6 TensorFlow1.x]: https://betterbench.blog.csdn.net/article/details/111410367
[7 TensorFlow1.x]: https://betterbench.blog.csdn.net/article/details/111410935
[8 TensorFlow1.x_Mnist]: https://betterbench.blog.csdn.net/article/details/111414788
[9 TensorFlow1.x_Mnist]: https://betterbench.blog.csdn.net/article/details/111480327
[10 TensorFlow1.x_CNN]: https://betterbench.blog.csdn.net/article/details/111495960
[11 TensorFlow1.x_Lenet5]: https://betterbench.blog.csdn.net/article/details/111499496
[12 TensorFlow1.x_VGGNet]: https://betterbench.blog.csdn.net/article/details/111516848
[13 TensorFlow1.x_OCR]: https://betterbench.blog.csdn.net/article/details/111567501
[14 TensorFlow1.x_MNIST]: https://betterbench.blog.csdn.net/article/details/111590573
[15 TensorFlow1.x]: https://betterbench.blog.csdn.net/article/details/111594356
[16 TensorFlow1.x]: https://betterbench.blog.csdn.net/article/details/111641487