tensorflow 学习笔记7 普通神经网络实现mnist手写识别

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data#number 1 to 10 data自动下载数据集mnist = input_data.read_data_sets('MNIST_data', one_hot=True)def add_layer(inputs, in_size, out_size, activation_function=None,):    # add one more layer and return the output of this layer    Weights = tf.Variable(tf.random_normal([in_size, out_size]))    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,)    Wx_plus_b = tf.matmul(inputs, Weights) + biases    if activation_function is None:        outputs = Wx_plus_b    else:        outputs = activation_function(Wx_plus_b,)    return outputsdef compute_accuracy(v_xs, v_ys):    global prediction    #用v_xs得出y的预测值：y是根据哪个数值概率更高得出来的    y_pre = sess.run(prediction, feed_dict={xs: v_xs})    #比较预测值与真实值是否相等    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))    #计算多少个是对的多少个是错的    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    #run得到一个百分比    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})    return result# define placeholder for inputs to networkxs = tf.placeholder(tf.float32, [None, 784]) # 28x28ys = tf.placeholder(tf.float32, [None, 10]) #10种输出# add output layer，激活函数softmax，用于分类的激活函数prediction = add_layer(xs, 784, 10,  activation_function=tf.nn.softmax)# the error between prediction and real data，分类的loss函数：交叉熵代价函数cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1]))       # loss#梯度下降优化法，最小化loss函数，学习率0.5train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)sess = tf.Session()# important stepinit = tf.global_variables_initializer()sess.run(init)for i in range(1000):    #获取100个数据，100个100个学习    batch_xs, batch_ys = mnist.train.next_batch(100)    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})    if i % 50 == 0:        #输出每一步的准确度        print(compute_accuracy(mnist.test.images, mnist.test.labels))

因为这是一般神经网络实现的手写识别，所以准确度并不是很高: