TensorFlow绘制loss/accuracy曲线的实例
1.多曲线
1.1使用pyplot方式
importnumpyasnp importmatplotlib.pyplotasplt x=np.arange(1,11,1) plt.plot(x,x*2,label="First") plt.plot(x,x*3,label="Second") plt.plot(x,x*4,label="Third") plt.legend(loc=0,ncol=1)#参数：loc设置显示的位置，0是自适应；ncol设置显示的列数 plt.show()
1.2使用面向对象方式
importnumpyasnp importmatplotlib.pyplotasplt x=np.arange(1,11,1) fig=plt.figure() ax=fig.add_subplot(111) ax.plot(x,x*2,label="First") ax.plot(x,x*3,label="Second") ax.legend(loc=0) #ax.plot(x,x*2) #ax.legend([”Demo“],loc=0) plt.show()
2.双y轴曲线
双y轴曲线图例合并是一个棘手的操作，现以MNIST案例中loss/accuracy绘制曲线。
importtensorflowastf
fromtensorflow.examples.tutorials.mnistimportinput_data
importtime
importmatplotlib.pyplotasplt
importnumpyasnp
x_data=tf.placeholder(tf.float32,[None,784])
y_data=tf.placeholder(tf.float32,[None,10])
x_image=tf.reshape(x_data,[-1,28,28,1])
#convolvelayer1
filter1=tf.Variable(tf.truncated_normal([5,5,1,6]))
bias1=tf.Variable(tf.truncated_normal([6]))
conv1=tf.nn.conv2d(x_image,filter1,strides=[1,1,1,1],padding='SAME')
h_conv1=tf.nn.sigmoid(conv1+bias1)
maxPool2=tf.nn.max_pool(h_conv1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
#convolvelayer2
filter2=tf.Variable(tf.truncated_normal([5,5,6,16]))
bias2=tf.Variable(tf.truncated_normal([16]))
conv2=tf.nn.conv2d(maxPool2,filter2,strides=[1,1,1,1],padding='SAME')
h_conv2=tf.nn.sigmoid(conv2+bias2)
maxPool3=tf.nn.max_pool(h_conv2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
#convolvelayer3
filter3=tf.Variable(tf.truncated_normal([5,5,16,120]))
bias3=tf.Variable(tf.truncated_normal([120]))
conv3=tf.nn.conv2d(maxPool3,filter3,strides=[1,1,1,1],padding='SAME')
h_conv3=tf.nn.sigmoid(conv3+bias3)
#fullconnectionlayer1
W_fc1=tf.Variable(tf.truncated_normal([7*7*120,80]))
b_fc1=tf.Variable(tf.truncated_normal([80]))
h_pool2_flat=tf.reshape(h_conv3,[-1,7*7*120])
h_fc1=tf.nn.sigmoid(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
#fullconnectionlayer2
W_fc2=tf.Variable(tf.truncated_normal([80,10]))
b_fc2=tf.Variable(tf.truncated_normal([10]))
y_model=tf.nn.softmax(tf.matmul(h_fc1,W_fc2)+b_fc2)
cross_entropy=-tf.reduce_sum(y_data*tf.log(y_model))
train_step=tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy)
sess=tf.InteractiveSession()
correct_prediction=tf.equal(tf.argmax(y_data,1),tf.argmax(y_model,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
sess.run(tf.global_variables_initializer())
mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
fig_loss=np.zeros([1000])
fig_accuracy=np.zeros([1000])
start_time=time.time()
foriinrange(1000):
batch_xs,batch_ys=mnist.train.next_batch(200)
ifi%100==0:
train_accuracy=sess.run(accuracy,feed_dict={x_data:batch_xs,y_data:batch_ys})
print("step%d,trainaccuracy%g"%(i,train_accuracy))
end_time=time.time()
print("time:",(end_time-start_time))
start_time=end_time
print("********************************")
train_step.run(feed_dict={x_data:batch_xs,y_data:batch_ys})
fig_loss[i]=sess.run(cross_entropy,feed_dict={x_data:batch_xs,y_data:batch_ys})
fig_accuracy[i]=sess.run(accuracy,feed_dict={x_data:batch_xs,y_data:batch_ys})
print("testaccuracy%g"%sess.run(accuracy,feed_dict={x_data:mnist.test.images,y_data:mnist.test.labels}))
#绘制曲线
fig,ax1=plt.subplots()
ax2=ax1.twinx()
lns1=ax1.plot(np.arange(1000),fig_loss,label="Loss")
#按一定间隔显示实现方法
#ax2.plot(200*np.arange(len(fig_accuracy)),fig_accuracy,'r')
lns2=ax2.plot(np.arange(1000),fig_accuracy,'r',label="Accuracy")
ax1.set_xlabel('iteration')
ax1.set_ylabel('trainingloss')
ax2.set_ylabel('trainingaccuracy')
#合并图例
lns=lns1+lns2
labels=["Loss","Accuracy"]
#labels=[l.get_label()forlinlns]
plt.legend(lns,labels,loc=7)
plt.show()
注：数据集保存在MNIST_data文件夹下
其实就是三步：
1）分别定义loss/accuracy一维数组
fig_loss=np.zeros([1000]) fig_accuracy=np.zeros([1000]) #按间隔定义方式：fig_accuracy=np.zeros(int(np.ceil(iteration/interval)))
2）填充真实数据
fig_loss[i]=sess.run(cross_entropy,feed_dict={x_data:batch_xs,y_data:batch_ys})
fig_accuracy[i]=sess.run(accuracy,feed_dict={x_data:batch_xs,y_data:batch_ys})
3）绘制曲线
fig,ax1=plt.subplots()
ax2=ax1.twinx()
lns1=ax1.plot(np.arange(1000),fig_loss,label="Loss")
#按一定间隔显示实现方法
#ax2.plot(200*np.arange(len(fig_accuracy)),fig_accuracy,'r')
lns2=ax2.plot(np.arange(1000),fig_accuracy,'r',label="Accuracy")
ax1.set_xlabel('iteration')
ax1.set_ylabel('trainingloss')
ax2.set_ylabel('trainingaccuracy')
#合并图例
lns=lns1+lns2
labels=["Loss","Accuracy"]
#labels=[l.get_label()forlinlns]
plt.legend(lns,labels,loc=7)
以上这篇TensorFlow绘制loss/accuracy曲线的实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持毛票票。
声明：本文内容来源于网络，版权归原作者所有，内容由互联网用户自发贡献自行上传，本网站不拥有所有权，未作人工编辑处理，也不承担相关法律责任。如果您发现有涉嫌版权的内容，欢迎发送邮件至：czq8825#qq.com（发邮件时，请将#更换为@）进行举报，并提供相关证据，一经查实，本站将立刻删除涉嫌侵权内容。