深度学习中对于网络的训练是参数更新的过程，需要注意一种情况就是输入数据未做归一化时，如果前向传播结果已经是[0,0,0,1,0,0,0,0]这种形式，而真实结果是[1,0,0,0,0,0,0,0,0]，此时由于得出的结论不惧有概率性，而是错误的估计值，此时反向传播会使得权重和偏置值变的无穷大，导致数据溢出，也就出现了nan的问题。
解决办法：
1、对输入数据进行归一化处理，如将输入的图片数据除以255将其转化成0-1之间的数据;
2、对于层数较多的情况，各层都做batch_nomorlization;
3、对设置Weights权重使用tf.truncated_normal(0, 0.01, [3,3,1,64])生成，同时值的均值为0，方差要小一些;
4、激活函数可以使用tanh;
5、减小学习率lr。
实例：
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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 #需要进行归一化处理 #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})) |
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：http://blog.csdn.net/fireflychh/article/details/73691373
