深度學(xué)習(xí)中對于網(wǎng)絡(luò)的訓(xùn)練是參數(shù)更新的過程，需要注意一種情況就是輸入數(shù)據(jù)未做歸一化時，如果前向傳播結(jié)果已經(jīng)是[0,0,0,1,0,0,0,0]這種形式，而真實結(jié)果是[1,0,0,0,0,0,0,0,0]，此時由于得出的結(jié)論不懼有概率性，而是錯誤的估計值，此時反向傳播會使得權(quán)重和偏置值變的無窮大，導(dǎo)致數(shù)據(jù)溢出，也就出現(xiàn)了nan的問題。

解決辦法：

1、對輸入數(shù)據(jù)進行歸一化處理，如將輸入的圖片數(shù)據(jù)除以255將其轉(zhuǎn)化成0-1之間的數(shù)據(jù);

2、對于層數(shù)較多的情況，各層都做batch_nomorlization;

3、對設(shè)置Weights權(quán)重使用tf.truncated_normal(0, 0.01, [3,3,1,64])生成，同時值的均值為0，方差要小一些;

4、激活函數(shù)可以使用tanh;

5、減小學(xué)習(xí)率lr。

實例：

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('data',one_hot = True)def add_layer(input_data,in_size, out_size,activation_function=None):  Weights = tf.Variable(tf.random_normal([in_size,out_size]))  Biases = tf.Variable(tf.zeros([1, out_size])+0.1)  Wx_plus_b = tf.add(tf.matmul(input_data, Weights), Biases)  if activation_function==None:    outputs = Wx_plus_b  else:    outputs = activation_function(Wx_plus_b)  #return outputs#, Weights  return {'outdata':outputs, 'w':Weights}def get_accuracy(t_y):#  global l1#  accu = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(l1['outdata'],1),tf.argmax(t_y,1)), dtype = tf.float32))  global prediction  accu = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(prediction['outdata'],1),tf.argmax(t_y,1)), dtype = tf.float32))  return accuX = tf.placeholder(tf.float32, [None, 784])Y = tf.placeholder(tf.float32, [None, 10])#l1 = add_layer(X, 784, 10, tf.nn.softmax)#cross_entropy = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(l1['outdata']), reduction_indices= [1]))#l1 = add_layer(X, 784, 1024, tf.nn.relu)l1 = add_layer(X, 784, 1024, None)prediction = add_layer(l1['outdata'], 1024, 10, tf.nn.softmax)cross_entropy = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(prediction['outdata']), reduction_indices= [1]))optimizer = tf.train.GradientDescentOptimizer(0.000001)train = optimizer.minimize(cross_entropy)newW = tf.Variable(tf.random_normal([1024,10]))newOut = tf.matmul(l1['outdata'],newW)newSoftMax = tf.nn.softmax(newOut)init = tf.global_variables_initializer()with tf.Session() as sess:  sess.run(init)  #print(sess.run(l1_Weights))  for i in range(2):    X_train, y_train = mnist.train.next_batch(1)    X_train = X_train/255  #需要進行歸一化處理    #print(sess.run(l1['w'],feed_dict={X:X_train}))    #print(sess.run(prediction['w'],feed_dict={X:X_train, Y:y_train}))    #print(sess.run(l1['outdata'],feed_dict={X:X_train, Y:y_train}).shape)    print(sess.run(prediction['outdata'],feed_dict={X:X_train, Y:y_train}))    print(sess.run(newOut, feed_dict={X:X_train}))    print(sess.run(newSoftMax, feed_dict={X:X_train}))    print(y_train)    #print(sess.run(l1['outdata'], feed_dict={X:X_train}))    sess.run(train, feed_dict={X:X_train, Y:y_train})    if i%100 == 0:      #print(sess.run(cross_entropy, feed_dict={X:X_train, Y:y_train}))      accuracy = get_accuracy(mnist.test.labels)      print(sess.run(accuracy,feed_dict={X:mnist.test.images}))        #if i%100==0:    #print(sess.run(prediction, feed_dict={X:X_train}))    #print(sess.run(cross_entropy, feed_dict={X:X_train,Y:y_train}))