标签:ast 测试 计算 cti oba max lob int nis
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_example // batch_size #定义两个placeholder x = tf.placeholder(tf.float32,[None,784])#输入图像 x = tf.placeholder(tf.float32,[None,10])#输入标签 #创建一个简单的神经网络 W = tf.Variable(tf.zeros([784,10]))#生成784行,10列的全0矩阵 b = tf.Variable(tf.zeros([1,10])) prediction = tf.nn.softmax(tf.matmul(x,W) + b) #二次代价函数 loss = tf.reduce_mean(tf.square(y - prediction)) #使用梯度下降法 train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss) #初始化变量 init = tf.global_variables_initializer() #比较 corrent_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1)) #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(21): 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))
标签:ast 测试 计算 cti oba max lob int nis
原文地址:https://www.cnblogs.com/fzth-gfh/p/10123182.html