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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。
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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:
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = 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 accu X = 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}))
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