手工设计神经MNIST使分类精度达到98%以上

设计了两个隐藏层，激活函数是tanh，使用Adam优化算法，学习率随着epoch的增大而调低

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#载入数据集
mnist = input_data.read_data_sets("MNIST_data",one_hot=True)

#每个批次的大小
batch_size = 32
#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])
keep_prob=tf.placeholder(tf.float32)
lr = tf.Variable(0.001, dtype=tf.float32)

#创建一个简单的神经网络
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)

#交叉熵代价函数
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
#训练
train_step = tf.train.AdamOptimizer(lr).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))

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))

#

Iter 0, Testing Accuracy= 0.954, Learning Rate= 0.001
Iter 1, Testing Accuracy= 0.9624, Learning Rate= 0.00095
Iter 2, Testing Accuracy= 0.9668, Learning Rate= 0.0009025
Iter 3, Testing Accuracy= 0.9665, Learning Rate= 0.000857375
Iter 4, Testing Accuracy= 0.9725, Learning Rate= 0.00081450626
Iter 5, Testing Accuracy= 0.9738, Learning Rate= 0.0007737809
Iter 6, Testing Accuracy= 0.9769, Learning Rate= 0.0007350919
Iter 7, Testing Accuracy= 0.9771, Learning Rate= 0.0006983373
Iter 8, Testing Accuracy= 0.9777, Learning Rate= 0.0006634204
Iter 9, Testing Accuracy= 0.9764, Learning Rate= 0.0006302494
Iter 10, Testing Accuracy= 0.9753, Learning Rate= 0.0005987369
Iter 11, Testing Accuracy= 0.9779, Learning Rate= 0.0005688001
Iter 12, Testing Accuracy= 0.9777, Learning Rate= 0.0005403601
Iter 13, Testing Accuracy= 0.9774, Learning Rate= 0.0005133421
Iter 14, Testing Accuracy= 0.9772, Learning Rate= 0.000487675
Iter 15, Testing Accuracy= 0.9803, Learning Rate= 0.00046329122
Iter 16, Testing Accuracy= 0.9802, Learning Rate= 0.00044012666
Iter 17, Testing Accuracy= 0.9791, Learning Rate= 0.00041812033
Iter 18, Testing Accuracy= 0.9806, Learning Rate= 0.00039721432
Iter 19, Testing Accuracy= 0.9803, Learning Rate= 0.0003773536
Iter 20, Testing Accuracy= 0.9796, Learning Rate= 0.00035848594
Iter 21, Testing Accuracy= 0.9803, Learning Rate= 0.00034056162
Iter 22, Testing Accuracy= 0.9788, Learning Rate= 0.00032353355
Iter 23, Testing Accuracy= 0.9819, Learning Rate= 0.00030735688
Iter 24, Testing Accuracy= 0.975, Learning Rate= 0.000291989
Iter 25, Testing Accuracy= 0.9808, Learning Rate= 0.00027738957
Iter 26, Testing Accuracy= 0.9814, Learning Rate= 0.0002635201
Iter 27, Testing Accuracy= 0.9802, Learning Rate= 0.00025034408
Iter 28, Testing Accuracy= 0.9809, Learning Rate= 0.00023782688
Iter 29, Testing Accuracy= 0.9811, Learning Rate= 0.00022593554
Iter 30, Testing Accuracy= 0.9816, Learning Rate= 0.00021463877
Iter 31, Testing Accuracy= 0.9812, Learning Rate= 0.00020390682
Iter 32, Testing Accuracy= 0.9815, Learning Rate= 0.00019371149
Iter 33, Testing Accuracy= 0.9815, Learning Rate= 0.0001840259
Iter 34, Testing Accuracy= 0.9813, Learning Rate= 0.00017482461
Iter 35, Testing Accuracy= 0.981, Learning Rate= 0.00016608338
Iter 36, Testing Accuracy= 0.9806, Learning Rate= 0.00015777921
Iter 37, Testing Accuracy= 0.9818, Learning Rate= 0.00014989026
Iter 38, Testing Accuracy= 0.982, Learning Rate= 0.00014239574
Iter 39, Testing Accuracy= 0.9813, Learning Rate= 0.00013527596
Iter 40, Testing Accuracy= 0.9818, Learning Rate= 0.00012851215
Iter 41, Testing Accuracy= 0.9827, Learning Rate= 0.00012208655
Iter 42, Testing Accuracy= 0.9826, Learning Rate= 0.00011598222
Iter 43, Testing Accuracy= 0.9814, Learning Rate= 0.00011018311
Iter 44, Testing Accuracy= 0.9823, Learning Rate= 0.000104673956
Iter 45, Testing Accuracy= 0.9828, Learning Rate= 9.944026e-05
Iter 46, Testing Accuracy= 0.9824, Learning Rate= 9.446825e-05
Iter 47, Testing Accuracy= 0.9824, Learning Rate= 8.974483e-05
Iter 48, Testing Accuracy= 0.983, Learning Rate= 8.525759e-05
Iter 49, Testing Accuracy= 0.9827, Learning Rate= 8.099471e-05
Iter 50, Testing Accuracy= 0.9828, Learning Rate= 7.6944976e-05

最终达到了0.9828的准确率