减小过拟合的几种方法:
我们建一个三层的网络, 并给他加上 droppout 测试一下训练 20 次的准确率
- import tensorflow as tf
- from tensorflow.examples.tutorials.mnist import input_data
- # 载入数据集
- mnist=input_data.read_data_sets("MNIST_data", one_hot=True)
- # 每个批次的大小
- batch_size=100
- # 计算一共有多少批次
- n_batch=mnist.train.num_examples // batch_size
- # 定义两个 placeholder
- x=tf.placeholder(tf.float32,[None,784])
- y=tf.placeholder(tf.float32,[None,10])
- keep_prob=tf.placeholder(tf.float32)
- # 创建一个简单的神经网络
- W1=tf.Variable(tf.truncated_normal([784,2000],stddev=0.1)) #这里我们使用一个截断的正太分布初始化 W
- b1=tf.Variable(tf.zeros([1,2000]))
- L1=tf.nn.tanh(tf.matmul(x,W1)+b1) #激活函数为双曲正切函数
- L1_drop=tf.nn.dropout(L1, keep_prob)
- W2=tf.Variable(tf.truncated_normal([2000,1000],stddev=0.1))
- b2=tf.Variable(tf.zeros([1,1000]))
- L2=tf.nn.tanh(tf.matmul(L1_drop, W2)+b2)
- L2_drop=tf.nn.dropout(L2, keep_prob)
- W3=tf.Variable(tf.truncated_normal([1000,10], stddev=0.1))
- b3=tf.Variable(tf.zeros([1,10]))
- prediction=tf.nn.softmax(tf.matmul(L2_drop,W3)+b3)
- # 二次代价函数
- #loss=tf.reduce_mean(tf.square(y-prediction))
- loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
- # 使用剃度下降法
- train_step=tf.train.GradientDescentOptimizer(0.2).minimize(loss)
- # 初始化变量
- init=tf.global_variables_initializer()
- # 结果存放在一个布尔型列表中
- correct_prediction=tf.equal(tf.argmax(y,1), tf.argmax(prediction,1)) #argmax 返回一维张量中最大的值所在的位置
- # 求准确率
- accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
- with tf.Session() as sess:
- sess.run(init)
- for epoch in range(20):
- 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,keep_prob:0.5})
- test_acc=sess.run(accuracy,feed_dict={x:mnist.test.images, y:mnist.test.labels,keep_prob:1.0})
- train_acc=sess.run(accuracy,feed_dict={x:mnist.train.images, y:mnist.train.labels,keep_prob:1.0})
- print("Iter"+str(epoch)+",Testing Accuracy"+str(test_acc)+"Training Accuracy"+str(train_acc))
来源: http://www.bubuko.com/infodetail-3433905.html