mnist数据集下载路径
http://yann.lecun.com/exdb/mnist/
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
#每个批次的大小
batch_size = 100
#计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size
#定义输入输出
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
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([1, 10]))
predictions_1 = tf.nn.softmax(tf.matmul(x, W) + b)
#定义二次代价函数
loss = tf.reduce_mean(tf.square(y - predictions_1))
#使用梯度下降
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bool型列表中
correction = tf.equal(tf.argmax(y, 1), tf.argmax(predictions_1, 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(correction, 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})
a = mnist.test.labels
print("epoch " + str(epoch) + ", accuracy " + str(acc))
思考： 如何优化准确率
