Tensorflow學習教程------普通神經網絡對mnist數據集分類

首先是不含隱層的神經網絡， 輸入層是784個神經元 輸出層是10個神經元

代碼以下

#coding:utf-8
import  tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


#載入數據集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
#每一個批次的大小
batch_size = 100
#計算一共有多少個批次
n_batch =  mnist.train.num_examples // batch_size

#定義兩個placeholder
x = tf.placeholder(tf.float32, [None,784]) #輸入圖像
y = tf.placeholder(tf.float32, [None,10]) #輸入標籤

#建立一個簡單的神經網絡 784個像素點對應784個數  所以輸入層是784個神經元 輸出層是10個神經元 不含隱層 
#最後準確率在92%左右
W = tf.Variable(tf.zeros([784,10])) #生成784行 10列的全0矩陣
b = tf.Variable(tf.zeros([1,10])) 
prediction = tf.nn.softmax(tf.matmul(x,W)+b)

#二次代價函數
loss = tf.reduce_mean(tf.square(y-prediction))
#使用梯度降低法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化變量
init = tf.global_variables_initializer()

#結果存放在布爾型列表中
#argmax能給出某個tensor對象在某一維上的其數據最大值所在的索引值
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(prediction,1))
#求準確率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(21): #21個epoch 把全部的圖片訓練21次
        for batch in range(n_batch): #
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images, y:mnist.test.labels}) 
        print ("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))   

結果以下

Iter 0,Testing Accuracy 0.8304
Iter 1,Testing Accuracy 0.8704
Iter 2,Testing Accuracy 0.8821
Iter 3,Testing Accuracy 0.8876
Iter 4,Testing Accuracy 0.8932
Iter 5,Testing Accuracy 0.8968
Iter 6,Testing Accuracy 0.8995
Iter 7,Testing Accuracy 0.9019
Iter 8,Testing Accuracy 0.9033
Iter 9,Testing Accuracy 0.9048
Iter 10,Testing Accuracy 0.9065
Iter 11,Testing Accuracy 0.9074
Iter 12,Testing Accuracy 0.9084
Iter 13,Testing Accuracy 0.909
Iter 14,Testing Accuracy 0.9094
Iter 15,Testing Accuracy 0.9112
Iter 16,Testing Accuracy 0.9117
Iter 17,Testing Accuracy 0.9128
Iter 18,Testing Accuracy 0.9127
Iter 19,Testing Accuracy 0.9132
Iter 20,Testing Accuracy 0.9144

 接下來是含一個隱層的神經網絡，輸入層是784個神經元，兩個隱層都是100個神經元，輸出層是10個神經元，迭代500次，最後準確率在88%左右，汗。。。。準確率反而下降了，慢慢調參吧

#coding:utf-8
import  tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


#載入數據集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
#每一個批次的大小
batch_size = 50
#計算一共有多少個批次
n_batch =  mnist.train.num_examples // batch_size

#定義兩個placeholder
x = tf.placeholder(tf.float32, [None,784]) #輸入圖像
y = tf.placeholder(tf.float32, [None,10]) #輸入標籤


#定義神經網絡中間層
Weights_L1 = tf.Variable(tf.random_normal([784,100]))
biase_L1 = tf.Variable(tf.zeros([1,100])) 
Wx_plus_b_L1 = tf.matmul(x, Weights_L1)+biase_L1 
L1 = tf.nn.tanh(Wx_plus_b_L1) #使用正切函數做爲激活函數 

Weights_L2 = tf.Variable(tf.random_normal([100,100]))
biase_L2 = tf.Variable(tf.zeros([1,100])) 
Wx_plus_b_L2 = tf.matmul(L1, Weights_L2)+biase_L2 
L2 = tf.nn.tanh(Wx_plus_b_L2) #使用正切函數做爲激活函數 

#定義神經網絡輸出層
Weights_L3 = tf.Variable(tf.random_normal([100,10]))
biase_L3 = tf.Variable(tf.zeros([1,10]))
Wx_plus_b_L3 = tf.matmul(L2,Weights_L3) + biase_L3
prediction = tf.nn.tanh(Wx_plus_b_L3)

#二次代價函數
loss = tf.reduce_mean(tf.square(y-prediction))
#使用梯度降低法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化變量
init = tf.global_variables_initializer()

#結果存放在布爾型列表中
#argmax能給出某個tensor對象在某一維上的其數據最大值所在的索引值
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(prediction,1))
#求準確率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(500): 
        for batch in range(n_batch):             batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images, y:mnist.test.labels}) 
        print ("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))   

Iter 487,Testing Accuracy 0.8847
Iter 488,Testing Accuracy 0.8853
Iter 489,Testing Accuracy 0.878
Iter 490,Testing Accuracy 0.8861
Iter 491,Testing Accuracy 0.8863
Iter 492,Testing Accuracy 0.8784
Iter 493,Testing Accuracy 0.8855
Iter 494,Testing Accuracy 0.8787
Iter 495,Testing Accuracy 0.881
Iter 496,Testing Accuracy 0.8837
Iter 497,Testing Accuracy 0.8817
Iter 498,Testing Accuracy 0.8837
Iter 499,Testing Accuracy 0.8866