pytorch之 activation funcion

 import torch
 import torch.nn.functional as F
 from torch.autograd import Variable
 import matplotlib.pyplot as plt

 # fake data
 x = torch.linspace(-5, 5, 200)  # x data (tensor), shape=(100, 1)
 x = Variable(x)
 x_np = x.data.numpy()   # numpy array for plotting

 # following are popular activation functions
 y_relu = torch.relu(x).data.numpy()
 y_sigmoid = torch.sigmoid(x).data.numpy()
 y_tanh = torch.tanh(x).data.numpy()
 y_softplus = F.softplus(x).data.numpy() # there's no softplus in torch
 # y_softmax = torch.softmax(x, dim=0).data.numpy() softmax is a special kind of activation function, it is about probability

 # plt to visualize these activation function
 plt.figure(1, figsize=(8, 6))
 plt.subplot(221)
 plt.plot(x_np, y_relu, c='red', label='relu')
 plt.ylim((-1, 5))
 plt.legend(loc='best')

 plt.subplot(222)
 plt.plot(x_np, y_sigmoid, c='red', label='sigmoid')
 plt.ylim((-0.2, 1.2))
 plt.legend(loc='best')

 plt.subplot(223)
 plt.plot(x_np, y_tanh, c='red', label='tanh')
 plt.ylim((-1.2, 1.2))
 plt.legend(loc='best')

 plt.subplot(224)
 plt.plot(x_np, y_softplus, c='red', label='softplus')
 plt.ylim((-0.2, 6))
 plt.legend(loc='best')

 plt.show()