資料集：MNIST 啟用函式：Relu 損失函式：交叉熵 Optimizer：AdamOptimizer 視覺化工具：tensorboad

迭代21epoch，accuracy結果如下： Iter 16,Testing Accuracy: 0.9824 ,Training Accuracy: 0.9949273 Iter 17,Testing Accuracy: 0.9822 ,Training Accuracy: 0.99496365 Iter 18,Testing Accuracy: 0.9832 ,Training Accuracy: 0.9950182 Iter 19,Testing Accuracy: 0.983 ,Training Accuracy: 0.99503636 Iter 20,Testing Accuracy: 0.983 ,Training Accuracy: 0.9950727

視覺化展示： Graph：

accuracy變化： loss變化：

第一層部分引數變化圖： bias mean值變化 bias max值變化 weight mean變化

程式碼如下：

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


#讀取資料
mnist = input_data.read_data_sets('MNIST_data',one_hot=True)
batch_size = 100
n_batch = mnist.train.num_examples//batch_size


def
 
 variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean',mean)
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var-mean)))
        tf.summary.scalar('stddev',stddev)
        tf.summary.scalar('max'
 
,tf.reduce_max(var))
        tf.summary.scalar('min',tf.reduce_min(var))
        tf.summary.histogram('histogram',var)


with tf.name_scope('input'):
    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)

with tf.name_scope('layer'):
    with tf.name_scope('layer1'):
        with tf.name_scope('wights_L1'):
            weights_L1 = tf.Variable(tf.truncated_normal([784,500],stddev=0.1))
            variable_summaries(weights_L1)
        with tf.name_scope('biases_L1'):
            biases_L1 = tf.Variable(tf.zeros([500])+0.1)
            variable_summaries(biases_L1)
        wx_plus_b_L1 = tf.matmul(x,weights_L1) + biases_L1
        L1 = tf.nn.relu(wx_plus_b_L1)
        L1_trop = tf.nn.dropout(L1,keep_prob)
    with tf.name_scope('layer2'):
        with tf.name_scope('wights_L2'):
            weights_L2 = tf.Variable(tf.truncated_normal([500,100],stddev=0.1))
            variable_summaries(weights_L2)
        with tf.name_scope('biases_L2'):
            biases_L2 = tf.Variable(tf.zeros([100])+0.1)
            variable_summaries(biases_L2)
        wx_plus_b_L2 = tf.matmul(L1_trop,weights_L2) + biases_L2
        L2 = tf.nn.relu(wx_plus_b_L2)
        L2_drop = tf.nn.dropout(L2,keep_prob)
    with tf.name_scope('layer3'):
        with tf.name_scope('wights_L3'):
            weights_L3 = tf.Variable(tf.truncated_normal([100,10],stddev=0.1))
            variable_summaries(weights_L3)
        with tf.name_scope('biases_L3'):
            biases_L3 = tf.Variable(tf.zeros([10])+0.1)
            variable_summaries(biases_L3)
        wx_plus_b_L3 = tf.matmul(L2_drop,weights_L3) + biases_L3
        predictions = tf.nn.softmax(wx_plus_b_L3)
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=predictions))
    tf.summary.scalar('loss',loss)
with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(lr).minimize(loss)
with tf.name_scope('accuracy'):
    correct_predictions = tf.equal(tf.argmax(y,1),tf.argmax(predictions,1))
    accuracy = tf.reduce_mean(tf.cast(correct_predictions,tf.float32))
    tf.summary.scalar('accuracy',accuracy)

#合併所有的summary
merged = tf.summary.merge_all()

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter('log/',graph=sess.graph)
    for epoch in range(21):
        sess.run(tf.assign(lr,lr*(0.98**epoch)))
        for batch in range(n_batch):
            batch_x,batch_y = mnist.train.next_batch(batch_size)
            summary,_ = sess.run([merged,train_step],feed_dict={x:batch_x,y:batch_y,keep_prob:1.0})
        writer.add_summary(summary,epoch)
        test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1})
        train_acc = sess.run(accuracy,feed_dict={x:mnist.train.images,y:mnist.train.labels,keep_prob:1})
        print('Iter ' + str(epoch) + ',Testing Accuracy: ' + str(test_acc) + '  ,Training Accuracy: ' + str(train_acc))