text: import tensorflow as tf
  import numpy as np
  import matplotlib.pyplot as plt
  
  # fake data
  x = np.linspace(-5, 5, 200)     # x data, shape=(100, 1)
  # array([-5 ... 5])
  
  # if use np.linspace(2.0, 3.0, num=5, endpoint=False)
  # array([ 2. ,  2.2,  2.4,  2.6,  2.8])
  # if use np.linspace(2.0, 3.0, num=5, retstep=True)
  # array([ 2.  ,  2.25,  2.5 ,  2.75,  3.  ]), 0.25)
  
  
  # following are popular activation functions
  y_relu = tf.nn.relu(x)
  y_sigmoid = tf.nn.sigmoid(x)
  y_tanh = tf.nn.tanh(x)
  y_softplus = tf.nn.softplus(x)
  # y_softmax = tf.nn.softmax(x)  softmax is a special kind of activation function, it is about probability
  
  sess = tf.Session()
  y_relu, y_sigmoid, y_tanh, y_softplus = sess.run([y_relu, y_sigmoid, y_tanh, y_softplus])
  
  # plt to visualize these activation function
  # width * height
  plt.figure(1, figsize=(8, 6))
  
  
  # Relu -------------------------------
  plt.subplot(221)
  plt.plot(x, y_relu, c='red', label='relu')
  plt.ylim((-1, 5))
  plt.legend(loc='best')
  
  
  # Sigmoid -------------------------------
  plt.subplot(222)
  plt.plot(x, y_sigmoid, c='red', label='sigmoid')
  plt.ylim((-0.2, 1.2))
  plt.legend(loc='best')
  
  
  # Tanh -------------------------------
  plt.subplot(223)
  plt.plot(x, y_tanh, c='red', label='tanh')
  plt.ylim((-1.2, 1.2))
  plt.legend(loc='best')
  
  
  # Softplus -------------------------------
  plt.subplot(224)
  plt.plot(x, y_softplus, c='red', label='softplus')
  plt.ylim((-0.2, 6))
  plt.legend(loc='best')
  
  plt.show()