Tensorflow函数:tf.nn.softmax_cross_entropy_with_logits 讲解
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首先把Tensorflow英文API搬过来:
tf.nn.softmax_cross_entropy_with_logits(_sentinel=None, labels=None, logits=None, dim=-1, name=None)
Computes softmax cross entropy between logits
and labels
.
Measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in exactly one class). For example, each CIFAR-10 image is labeled with one and only one label: an image can be a dog or a truck, but not both.
NOTE: While the classes are mutually exclusive, their probabilities need not be. All that is required is that each row oflabels
is a valid probability distribution. If they are not, the computation of the gradient will be incorrect.
If using exclusive labels
(wherein one and only one class is true at a time), seesparse_softmax_cross_entropy_with_logits
.
WARNING: This op expects unscaled logits, since it performs a softmax
on logits
internally for efficiency. Do not call this op with the output of softmax
, as it will produce incorrect results.
logits
and labels
must have the same shape [batch_size, num_classes]
and the same dtype (either float16
,float32
, or float64
).
Note that to avoid confusion, it is required to pass only named arguments to this function.
Args:
_sentinel
: Used to prevent positional parameters. Internal, do not use.labels
: Each rowlabels[i]
must be a valid probability distribution.logits
: Unscaled log probabilities.dim
: The class dimension. Defaulted to -1 which is the last dimension.name
: A name for the operation (optional).
labels:为神经网络期望的输出
logits:为神经网络最后一层的输出
警告:这个函数内部自动计算softmax,然后再计算交叉熵代价函数,也就是说logits必须是没有经过tf.nn.softmax函数处理的数据,否则导致训练结果有问题。建议编程序时使用这个函数,而不必自己编写交叉熵代价函数。
下面是两层CNN识别mnist的softmax回归实验:
#coding=utf-8import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True)def compute_accuracy(v_xs,v_ys): global prediction y_pre=sess.run(prediction,feed_dict={xs:v_xs,keep_prob:1}) #这里的keep_prob是保留概率,即我们要保留的RELU的结果所占比例 correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1)) accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:1}) return resultdef weight_variable(shape): inital=tf.truncated_normal(shape,stddev=0.1) #stddev爲標準差 return tf.Variable(inital)def bias_variable(shape): inital=tf.constant(0.1,shape=shape) return tf.Variable(inital)def conv2d(x,W): #x爲像素值,W爲權值 #strides[1,x_movement,y_movement,1] #must have strides[0]=strides[3]=1 #padding=???? return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')#def max_pool_2x2(x): # strides[1,x_movement,y_movement,1] return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')#ksize二三维为池化窗口#define placeholder for inputs to networkxs=tf.placeholder(tf.float32,[None,784])/255ys=tf.placeholder(tf.float32,[None,10])keep_prob=tf.placeholder(tf.float32)x_image=tf.reshape(xs, [-1,28,28,1]) #-1为这个维度不确定,变成一个4维的矩阵,最后为最里面的维数#print x_image.shape #最后这个1理解为输入的channel,因为为黑白色所以为1##conv1 layer##W_conv1=weight_variable([5,5,1,32]) #patch 5x5,in size 1 是image的厚度,outsize 32 是提取的特征的维数b_conv1=bias_variable([32])h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)# output size 28x28x32 因为padding='SAME'h_pool1=max_pool_2x2(h_conv1) #output size 14x14x32##conv2 layer##W_conv2=weight_variable([5,5,32,64]) #patch 5x5,in size 32 是conv1的厚度,outsize 64 是提取的特征的维数b_conv2=bias_variable([64])h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)# output size 14x14x64 因为padding='SAME'h_pool2=max_pool_2x2(h_conv2) #output size 7x7x64##func1 layer##W_fc1=weight_variable([7*7*64,1024])b_fc1=bias_variable([1024])#[n_samples,7,7,64]->>[n_samples,7*7*64]h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64])h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob) #防止过拟合##func2 layer##W_fc2=weight_variable([1024,10])b_fc2=bias_variable([10])#prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)prediction=tf.matmul(h_fc1_drop,W_fc2)+b_fc2#h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob) #防止过拟合#the errro between prediction and real data#cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=ys, logits=prediction)train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)sess=tf.Session()sess.run(tf.global_variables_initializer())for i in range(1000): batch_xs,batch_ys=mnist.train.next_batch(100) sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys,keep_prob:0.5}) if i%50 ==0: accuracy = 0 for j in range(10): test_batch = mnist.test.next_batch(1000) acc_forone=compute_accuracy(test_batch[0], test_batch[1]) #print 'once=%f' %(acc_forone) accuracy=acc_forone+accuracy print '测试结果:batch:%g,准确率:%f' %(i,accuracy/10)
实验结果为:测试结果:batch:0,准确率:0.090000测试结果:batch:50,准确率:0.788600测试结果:batch:100,准确率:0.880200测试结果:batch:150,准确率:0.904600测试结果:batch:200,准确率:0.927500测试结果:batch:250,准确率:0.929800测试结果:batch:300,准确率:0.939600测试结果:batch:350,准确率:0.942100测试结果:batch:400,准确率:0.950600测试结果:batch:450,准确率:0.950700测试结果:batch:500,准确率:0.956700测试结果:batch:550,准确率:0.956000测试结果:batch:600,准确率:0.957100测试结果:batch:650,准确率:0.958400测试结果:batch:700,准确率:0.961500测试结果:batch:750,准确率:0.963800测试结果:batch:800,准确率:0.965000测试结果:batch:850,准确率:0.966300测试结果:batch:900,准确率:0.967800测试结果:batch:950,准确率:0.967700
迭代次数没有太多,否则准确率还会提高。
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