Python学习之路:MINST实战第一版
2024-10-16 17:09:19
1、项目介绍:
搭建浅层神经网络完成MNIST数字图像的识别。
2、详细步骤:
(1)将二维图像转成一维,MNIST图像大小为28*28,转成一维就是784。
(2)定义好神经网络的相关参数:
# MNIST数据集相关的常数
INPUT_NODE = 784;
OUTPUT_NODE = 10; LAYER1_NODE = 500;
BATCH_SIZE = 100; LEARNING_RATE_BASE = 0.8;
LEARNING_RATE_DECAY = 0.99;
REGULARIZATION_RATE = 0.0001;
TRAINING_STEPS = 5000;
MOVING_ACERTAGE_DECAY = 0.99;
(3)定义一个接口来算神网输出结果,之所以设置这个接口是因为为了适应滑动平均的方法:
def interface(input_tensor,avg_class,weights1,biases1,weights2,biases2):
if avg_class == None:
layer1 = tf.nn.relu(tf.matmul(input_tensor,weights1)+biases1);
return tf.matmul(layer1,weights2)+biases2;
else:
layer1 = tf.nn.relu(tf.matmul(input_tensor,avg_class.average(weights1))+avg_class.\
average(biases1));
return tf.matmul(layer1,avg_class.average(weights2))+avg_class.average(biases2);
(4)定义训练主函数:
训练主函数按照:输入输出placeholder,各层网络节点权值与偏移量定义,设置滑动平滑,输出两种结果y和acroos_y,定义y的交叉熵和正则化,定义指数衰减学习,训练。
def train(mnist):
x = tf.placeholder(dtype=tf.float32,shape=[None,INPUT_NODE],name="x_input");
y_ = tf.placeholder(dtype=tf.float32,shape=[None,OUTPUT_NODE],name="y_output"); weights1 = tf.Variable(tf.truncated_normal(shape=[INPUT_NODE,LAYER1_NODE],stddev=0.1));
biases1 = tf.Variable(tf.constant(0.1,dtype=tf.float32,shape=[LAYER1_NODE])); weights2 = tf.Variable(tf.truncated_normal(shape=[LAYER1_NODE,OUTPUT_NODE],stddev=0.1));
biases2 = tf.Variable(tf.constant(0.1,dtype=tf.float32,shape=[OUTPUT_NODE])); y = interface(x,None,weights1,biases1,weights2,biases2); global_step = tf.Variable(0,trainable=False);
variable_averages = tf.train.ExponentialMovingAverage(MOVING_ACERTAGE_DECAY,global_step);
variable_averages_op = variable_averages.apply(tf.trainable_variables());
average_y = interface(x,variable_averages,weights1,biases1,weights2,biases2); # why????????????????????
# 这里的交叉熵是以 y 为标准的
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1));
cross_entropy_mean = tf.reduce_mean(cross_entropy); regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE);
regularization = regularizer(weights1) + regularizer(weights2); loss = cross_entropy_mean + regularization; learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,
global_step,
mnist.train.num_examples / BATCH_SIZE,
LEARNING_RATE_DECAY); train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step = global_step); with tf.control_dependencies([train_step,variable_averages_op]):
train_op = tf.no_op(name="train"); correct_prediction = tf.equal(tf.argmax(average_y,1),tf.argmax(y_,1));
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)); with tf.Session() as sess:
tf.global_variables_initializer().run(); validate_feed = {x:mnist.validation.images, y_:mnist.validation.labels};
test_feed = {x:mnist.test.images, y_:mnist.test.labels}; for i in range(TRAINING_STEPS):
if i % 1000 == 0:
validate_acc = sess.run(accuracy,feed_dict = validate_feed);
print("After %d training step(s), validation accuracy using average model is %g " \
% (i, validate_acc));
xs,ys = mnist.train.next_batch(BATCH_SIZE)
sess.run(train_op,feed_dict={x:xs,y_:ys}); test_acc = sess.run(accuracy,feed_dict = test_feed);
print(("After %d training step(s), test accuracy using average model is %g"
%(TRAINING_STEPS, test_acc)));
(5)主函数代码:
def main(argv = None):
mnist = input_data.read_data_sets("C://Users/hasee/TensorFlow/实战TensorFlow代码/datasets/MNIST_data/",
one_hot=True);
train(mnist);
(6)运行程序:
if __name__ == "__main__":
main();
最新文章
- CSS知识总结(六)
- WEB开发最佳实践
- 神兵利器——Alfred
- geotrellis使用(十九)spray-json框架介绍
- MongoVUE(MongoDB图像管理工具)
- Java中栈结构的自我实现
- 客户视角:Oracle ETL工具ODI
- 1988-B. 有序集合
- 深入理解循环队列----循环数组实现ArrayDeque
- 对于查询调优,你需要的不止STATISTICS IO
- C# XmlDocument操作XML
- java多线程的几种状态
- JVM的垃圾回收机制 总结(垃圾收集、回收算法、垃圾回收器)
- laravel 模型事件 updated 触发条件
- C#实现根据地图上的两点坐标,计算直线距离
- Codeforces Round #541 (Div. 2)
- 数据序列化导读(3)[JSON v.s. YAML]
- centos6.5 64练手安装memcached,PHP调试
- 部署hadoop2.7.2 集群 基于zookeeper配置HDFS HA+Federation
- Unity中SendMessage和Delegate效率比较