TFboy养成记 多层感知器 MLP

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}))