tensorflow进行MNIST手写数字识别-简单版

实现一个简单的MNIST手写数字识别，不带隐藏层的前向传播神经网络，使用梯度下降进行训练。

准确率0.92

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('MNIST_data', one_hot=True)#载入数据集batch_size = 100#每个批次的大小#计算一共有多少个批次n_batch = mnist.train.num_examples // batch_sizex = tf.placeholder(tf.float32, [None, 784])y = tf.placeholder(tf.float32, [None, 10])#创建一个简单的神经网络，不带隐藏层，使用梯度下降进行训练，没有用反向传播w = tf.Variable(tf.zeros([784, 10]))#权值矩阵b = tf.Variable(tf.zeros([10]))#偏置值prediction = tf.nn.softmax(tf.matmul(x, w) + b)#使用softmax进行预测loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))#损失函数用交叉熵train_step = tf.train.GradientDescentOptimizer(0.3).minimize(loss)#梯度下降进行训练init = tf.initialize_all_variables()#初始化变量correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))#返回布尔类型的列表accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))#计算准确率with tf.Session() as sess:    sess.run(init)    for epoch in range(30):#训练30个周期        for batch in range(n_batch):            batch_x, batch_y = mnist.train.next_batch(batch_size)            sess.run(train_step, feed_dict={x:batch_x, y:batch_y})        acc = sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels})#使用测试集进行评测准确率        print('Iter',epoch,'Accuracy:',acc)