????本文與上一篇文章有一點不同,就是中間的隱藏層由一層變成兩層,在神經網絡搭建的過程中,曾出現一點問題,就是正確率圖突然變成0.11,通過調整隱藏節點的數量和W2的初值,正確率達到0.97,不如只有一層神經網絡的結果

神經網絡并不是越深越好

神經網絡的結構應當接近與橄欖型

對于ML而言,不宜多于五層

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Mar 22 22:15:25 2018@author: luogan """ ''' from tensorflow.examples.tutorials.mnist import input_datamnist=input_data.read_data_sets('MNIST_data/',one_hot=True) '''print(mnist.train.images.shape)print(mnist.train.labels.shape)print(mnist.test.images.shape)print(mnist.test.images.shape)a=mnist.train.images[8] #a.reshape(28,28) import pandas as pd #b=pd.DataFrame(a) #b b=pd.DataFrame(a.reshape(28,28)) #b #b=pd.DataFrame(a.reshape(28,28)) b.to_excel('c.xls') d=mnist.train.labels[8]print(mnist.validation.images.shape) print(mnist.validation.labels.shape)import tensorflow as tf sess=tf.InteractiveSession()in_units=784 h1_units=500h2_units=100w1=tf.Variable( tf.truncated_normal([in_units,h1_units],stddev=0.1 ) ) b1=tf.Variable(tf.zeros([h1_units]))w2=tf.Variable(tf.truncated_normal([h1_units,h2_units],stddev=0.1 )) b2=tf.Variable(tf.zeros([h2_units]))print('*'*20)w3=tf.Variable(tf.zeros([h2_units,10])) b3=tf.Variable(tf.zeros([10]))x=tf.placeholder(tf.float32,[None,784]) keep_prob=tf.placeholder(tf.float32)hidden1=tf.nn.relu(tf.matmul(x,w1)+b1) hidden1_drop=tf.nn.dropout(hidden1,keep_prob)hidden2=tf.nn.relu(tf.matmul(hidden1_drop,w2)+b2)hidden2_drop=tf.nn.dropout(hidden2,keep_prob)y2=tf.nn.softmax(tf.matmul(hidden2_drop,w3)+b3)y_=tf.placeholder(tf.float32,[None,10])cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y2),reduction_indices=[1]))train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)tf.global_variables_initializer().run()for i in range(1000):batch_xs,batch_ys=mnist.train.next_batch(1000)train_step.run({x:batch_xs,y_:batch_ys,keep_prob:1.0})correct_prediction=tf.equal(tf.argmax(y2,1),tf.argmax(y_,1))accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels,keep_prob:1})) (55000, 784) (55000, 10) (10000, 784) (10000, 784) (5000, 784) (5000, 10) ******************** 0.9775

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