1. 多曲线
1.1 使用pyplot方式
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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 ) # 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()
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1.2 使用面向对象方式
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import numpy as np
import matplotlib.pyplot as plt
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()
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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。
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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, [ None , 10 ])
x_image = tf.reshape(x_data, [ - 1 , 28 , 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 , 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' )
# convolve layer 2
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' )
# convolve layer 3
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)
# 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, [ - 1 , 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, 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 ( "test accuracy %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( 'training loss' )
ax2.set_ylabel( 'training accuracy' )
# 合并图例
lns = lns1 + lns2
labels = [ "Loss" , "Accuracy" ]
# labels = [l.get_label() for l in lns]
plt.legend(lns, labels, loc = 7 )
plt.show()
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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
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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)))
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2)填充真实数据
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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})
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3)绘制曲线
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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( 'training loss' )
ax2.set_ylabel( 'training accuracy' )
# 合并图例
lns = lns1 + lns2
labels = [ "Loss" , "Accuracy" ]
# labels = [l.get_label() for l in lns]
plt.legend(lns, labels, loc = 7 )
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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服务器之家。
原文链接:https://blog.csdn.net/qq_33254870/article/details/81536188