TFboy养成记 多层感知器 MLP

时间：2023-12-31 10:40:26

内容总结与莫烦的视频。

这里多层感知器代码写的是一个简单的三层神经网络，输入层，隐藏层，输出层。代码的目的是你和一个二次曲线。同时，为了保证数据的自然，添加了mean为0，steddv为0.05的噪声。

添加层代码：

def addLayer(inputs,inSize,outSize,activ_func = None):#insize outsize表示输如输出层的大小，inputs是输入。activ_func是激活函数，输出层没有激活函数。默认激活函数为空

    with tf.name_scope(name = "layer"):

        with tf.name_scope("weigths"):

            Weights = tf.Variable(tf.random_normal([inSize,outSize]),name = "W")

        bias = tf.Variable(tf.zeros([1,outSize]),name = "bias")

        W_plus_b = tf.matmul(inputs,Weights)+bias

        if activ_func == None:

            return W_plus_b

        else:

            return activ_func(W_plus_b)

输入：

 with tf.name_scope(name = "inputs"):#with这个主要是用来在tensorboard上显示用。

     xs = tf.placeholder(tf.float32,[None,1],name = "x_input")#不是-1哦

     ys = tf.placeholder(tf.float32,[None,1],name = "y_input")

 l1 = addLayer(xs,1,10,activ_func= tf.nn.relu)

 y_pre = addLayer(l1,10,1,activ_func=None)

其他部分：

需要注意的是

 with tf.name_scope("loss"):

     loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-y_pre),

                       reduction_indices=[1]))#这里reduction_indices=[1]类似于numpy中的那种用法，是指横向还是竖向，reduce_sum函数貌似主要是用于矩阵的，向量可以不使用

 with tf.name_scope("train"):

     train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

 #在以后的版本中，这里的initialize_all_variable()可能被逐步抛弃使用global_variable_init（大概是这么写的）那个函数。欢迎指正。

 init = tf.initialize_all_variables()#init这一步很重要，在训练前一定要是使用sess.run(init)操作（只要是你用到了Variable）

 writer = tf.summary.FileWriter("logs/",sess.graph)

 with tf.Session() as sess:

     sess.run(init)

     for i in range(1000):

         sess.run(train_step,feed_dict = {xs:x_data,ys:y_data})

         if i % 50 == 0:

             print(sess.run(loss,feed_dict = {xs:x_data,ys:y_data}))#只要是你的操作中有涉及到placeholder一定要记得使用feed_dict

所有代码：

 # -*- coding: utf-8 -*-

 """

 Created on Tue Jun 13 15:41:23 2017

 @author: Jarvis

 """

 import tensorflow as tf

 import numpy as np

 def addLayer(inputs,inSize,outSize,activ_func = None):

     with tf.name_scope(name = "layer"):

         with tf.name_scope("weigths"):

             Weights = tf.Variable(tf.random_normal([inSize,outSize]),name = "W")

         bias = tf.Variable(tf.zeros([1,outSize]),name = "bias")

         W_plus_b = tf.matmul(inputs,Weights)+bias

         if activ_func == None:

             return W_plus_b

         else:

             return activ_func(W_plus_b)

 x_data = np.linspace(-1,1,300)[:,np.newaxis]

 noise = np.random.normal(0,0.05,x_data.shape)

 y_data = np.square(x_data)-0.5+noise

 with tf.name_scope(name = "inputs"):

     xs = tf.placeholder(tf.float32,[None,1],name = "x_input")#不是-1哦

     ys = tf.placeholder(tf.float32,[None,1],name = "y_input")

 l1 = addLayer(xs,1,10,activ_func= tf.nn.relu)

 y_pre = addLayer(l1,10,1,activ_func=None)

 with tf.name_scope("loss"):

     loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-y_pre),

                       reduction_indices=[1]))

 with tf.name_scope("train"):

     train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

 init = tf.initialize_all_variables()

 writer = tf.summary.FileWriter("logs/",sess.graph)

 with tf.Session() as sess:

     sess.run(init)

     for i in range(1000):

         sess.run(train_step,feed_dict = {xs:x_data,ys:y_data})

         if i % 50 == 0:

             print(sess.run(loss,feed_dict = {xs:x_data,ys:y_data}))

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