mnist 識別率達98%以上，學習率lr愈來愈小，優化器AdamOptimizer

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data#載入數據集mnist = input_data.read_data_sets("MNIST_data",one_hot=True)#每一個批次的大小batch_size = 100#計算一共有多少個批次n_batch = mnist.train.num_examples // batch_size#定義兩個placeholderx = tf.placeholder(tf.float32,[None,784])y = tf.placeholder(tf.float32,[None,10])keep_prob=tf.placeholder(tf.float32)lr = tf.Variable(0.001, dtype=tf.float32)#建立一個簡單的神經網絡W1 = tf.Variable(tf.truncated_normal([784,500],stddev=0.1))b1 = tf.Variable(tf.zeros([500])+0.1)L1 = tf.nn.tanh(tf.matmul(x,W1)+b1)L1_drop = tf.nn.dropout(L1,keep_prob) W2 = tf.Variable(tf.truncated_normal([500,300],stddev=0.1))b2 = tf.Variable(tf.zeros([300])+0.1)L2 = tf.nn.tanh(tf.matmul(L1_drop,W2)+b2)L2_drop = tf.nn.dropout(L2,keep_prob) W3 = tf.Variable(tf.truncated_normal([300,10],stddev=0.1))b3 = tf.Variable(tf.zeros([10])+0.1)prediction = tf.nn.softmax(tf.matmul(L2_drop,W3)+b3)#交叉熵代價函數loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))#訓練train_step = tf.train.AdamOptimizer(lr).minimize(loss)#初始化變量init = tf.global_variables_initializer()#結果存放在一個布爾型列表中correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一維張量中最大的值所在的位置#求準確率accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))with tf.Session() as sess:    sess.run(init)    for epoch in range(51):        sess.run(tf.assign(lr, 0.001 * (0.95 ** epoch)))        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,keep_prob:1.0})                learning_rate = sess.run(lr)        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})        print ("Iter " + str(epoch) + ", Testing Accuracy= " + str(acc) + ", Learning Rate= " + str(learning_rate))