代码:
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)
运行结果:
Extracting MNIST_data/train-images-idx3-ubyte.gz Extracting MNIST_data/train-labels-idx1-ubyte.gz Extracting MNIST_data/t10k-images-idx3-ubyte.gz Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
代码:
#每个批次100张照片 batch_size = 100 #计算一共有多少个批次 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]) #创建一个简单的神经网络,输入层784个神经元,输出层10个神经元 W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) prediction = tf.nn.softmax(tf.matmul(x,W)+b) #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)) #使用梯度下降法 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)) # 定义一个saver saver = tf.train.Saver() with tf.Session() as sess: sess.run(init) print(sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})) #载入训练好的模型 saver.restore(sess,'net/my_net.ckpt') print(sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}))
运行结果:
0.098 INFO:tensorflow:Restoring parameters from net/my_net.ckpt 0.9168