1. 多曲线
1.1 使用pyplot方式
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import numpy as npimport 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() |
1.2 使用面向对象方式
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import numpy as npimport 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() |
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 tffrom tensorflow.examples.tutorials.mnist import input_dataimport timeimport matplotlib.pyplot as pltimport 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 1filter1 = 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 2filter2 = 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 3filter3 = 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 1W_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 2W_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 + lns2labels = ["Loss", "Accuracy"]# labels = [l.get_label() for l in lns]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
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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))) |
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}) |
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 + lns2labels = ["Loss", "Accuracy"]# labels = [l.get_label() for l in lns]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服务器之家。
原文链接：https://blog.csdn.net/qq_33254870/article/details/81536188
