1. 多曲線

1.1 使用pyplot方式

import numpy as np
import matplotlib.pyplot as plt
 
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)  # 引數：loc設定顯示的位置，0是自適應；ncol設定顯示的列數
 
plt.show()

1.2 使用面向物件方式

import numpy as np
import matplotlib.pyplot as plt
 
x = np.arange(1,1)
 
fig = plt.figure()
ax = fig.add_subplot(111)
 
 
ax.plot(x,label="First")
ax.plot(x,label="Second")
 
ax.legend(loc=0)
# ax.plot(x,x * 2)
# ax.legend([”Demo“],loc=0)
 
plt.show()

2. 雙y軸曲線

雙y軸曲線圖例合併是一個棘手的操作，現以MNIST案例中loss/accuracy繪製曲線。

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time
import matplotlib.pyplot as plt
import numpy as np
 
x_data = tf.placeholder(tf.float32,[None,784])
y_data = tf.placeholder(tf.float32,10])
x_image = tf.reshape(x_data,[-1,28,1])
 
# convolve layer 1
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],padding='SAME')
h_conv1 = tf.nn.sigmoid(conv1 + bias1)
maxPool2 = tf.nn.max_pool(h_conv1,ksize=[1,2,padding='SAME')
 
# convolve layer 2
filter2 = tf.Variable(tf.truncated_normal([5,6,16]))
bias2 = tf.Variable(tf.truncated_normal([16]))
conv2 = tf.nn.conv2d(maxPool2,filter2,padding='SAME')
h_conv2 = tf.nn.sigmoid(conv2 + bias2)
maxPool3 = tf.nn.max_pool(h_conv2,padding='SAME')
 
# convolve layer 3
filter3 = tf.Variable(tf.truncated_normal([5,16,120]))
bias3 = tf.Variable(tf.truncated_normal([120]))
conv3 = tf.nn.conv2d(maxPool3,filter3,padding='SAME')
h_conv3 = tf.nn.sigmoid(conv3 + bias3)
 
# full connection layer 1
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_conv3,7 * 7 * 120])
h_fc1 = tf.nn.sigmoid(tf.matmul(h_pool2_flat,W_fc1) + b_fc1)
 
# full connection layer 2
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()
for i in range(1000):
  batch_xs,batch_ys = mnist.train.next_batch(200)
  if i % 100 == 0:
    train_accuracy = sess.run(accuracy,feed_dict={x_data: batch_xs,y_data: batch_ys})
    print("step %d,train accuracy %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,y_data: batch_ys})
  fig_accuracy[i] = sess.run(accuracy,y_data: batch_ys})
print("test accuracy %g" % sess.run(accuracy,feed_dict={x_data: mnist.test.images,y_data: mnist.test.labels}))
 
 
# 繪製曲線
fig,ax1 = plt.subplots()
ax2 = ax1.twinx()
lns1 = ax1.plot(np.arange(1000),fig_loss,label="Loss")
# 按一定間隔顯示實現方法
# ax2.plot(200 * np.arange(len(fig_accuracy)),fig_accuracy,'r')
lns2 = ax2.plot(np.arange(1000),'r',label="Accuracy")
ax1.set_xlabel('iteration')
ax1.set_ylabel('training loss')
ax2.set_ylabel('training accuracy')
# 合併圖例
lns = lns1 + lns2
labels = ["Loss","Accuracy"]
# labels = [l.get_label() for l in lns]
plt.legend(lns,labels,loc=7)
plt.show()

注：資料集儲存在MNIST_data資料夾下

其實就是三步：

1）分別定義loss/accuracy一維陣列

fig_loss = np.zeros([1000])
fig_accuracy = np.zeros([1000])
# 按間隔定義方式：fig_accuracy = np.zeros(int(np.ceil(iteration / interval)))

2）填充真實資料

 fig_loss[i] = sess.run(cross_entropy,y_data: batch_ys})
 fig_accuracy[i] = sess.run(accuracy,y_data: batch_ys})

3）繪製曲線

fig,loc=7)

以上這篇TensorFlow繪製loss/accuracy曲線的例項就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。