输出结果
设计代码
#TF之LoR：基于tensorflow实现手写数字图片识别准确率
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
print(mnist)
#设置超参数
lr=0.001 #学习率
training_iters=100 #训练次数
batch_size=100 #每轮训练数据的大小，如果一次训练5000张图片，电脑会卡死，分批次训练会更好
display_step=1
#tf Graph的输入
x=tf.placeholder(tf.float32, [None,784])
y=tf.placeholder(tf.float32, [None, 10])
#设置权重和偏置
w =tf.Variable(tf.zeros([784,10]))
b =tf.Variable(tf.zeros([10]))
#设定运行模式
pred =tf.nn.softmax(tf.matmul(x,w)+b) #
#设置cost function为cross entropy
cost =tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred),reduction_indices=1))
#GD算法
optimizer=tf.train.GradientDescentOptimizer(lr).minimize(cost)
#初始化权重
init=tf.global_variables_initializer()
#开始训练
with tf.Session() as sess:
sess.run(init)
avg_cost_list=[]
for epoch in range(training_iters): #输入所有训练数据
avg_cost=0.
total_batch=int(mnist.train.num_examples/batch_size)
for i in range(total_batch): #遍历每个batch
……
if (epoch+1) % display_step ==0: #显示每次迭代日志
print("迭代次数Epoch:","%04d" % (epoch+1),"下降值cost=","{:.9f}".format(avg_cost))
avg_cost_list.append(avg_cost)
print("Optimizer Finished!")
print(avg_cost_list)
#测试模型
correct_prediction=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
print("Accuracy:",accuracy.eval({x:mnist.test.images[:3000],y:mnist.test.labels[:3000]}))
xdata=np.linspace(0,training_iters,num=len(avg_cost_list))
plt.figure()
plt.plot(xdata,avg_cost_list,'r')
plt.xlabel('训练轮数')
plt.ylabel('损失函数')
plt.title('TF之LiR：基于tensorflow实现手写数字图片识别准确率——Jason Niu')
plt.show()
