1. Multicurve

1.1 use pyplot mode
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) #
parameter :loc Set the position of the display ,0 It's adaptive ;ncol Set the number of columns to display plt.show()

1.2 Using an object-oriented approach
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()

2. double y Axis curve

double y Axis curve legend merging is a difficult operation , Now MNIST In the case loss/accuracy draw a curve .
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})) # draw a curve fig, ax1 = plt.subplots() ax2 = ax1.twinx() lns1 =
ax1.plot(np.arange(1000), fig_loss, label="Loss") # Display the implementation method according to certain interval # 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') # Merge legend lns = lns1 + lns2 labels = ["Loss",
"Accuracy"] # labels = [l.get_label() for l in lns] plt.legend(lns, labels,
loc=7) plt.show()
notes : Data set preservation MNIST_data Under folder

It's actually three steps :

1) Define separately loss/accuracy One dimensional array
fig_loss = np.zeros([1000]) fig_accuracy = np.zeros([1000]) #
Define by interval :fig_accuracy = np.zeros(int(np.ceil(iteration / interval)))
2) Fill in real data
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) draw a curve
fig, ax1 = plt.subplots() ax2 = ax1.twinx() lns1 = ax1.plot(np.arange(1000),
fig_loss, label="Loss") # Display the implementation method according to certain interval # 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') # Merge legend lns = lns1 + lns2 labels = ["Loss",
"Accuracy"] # labels = [l.get_label() for l in lns] plt.legend(lns, labels,
loc=7)

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