1 import torch 2 import torch.nn.functional as F 3 from torch.autograd import Variable 4 import matplotlib.pyplot as plt 5 6 # fake data 7 x = torch.linspace(-5, 5, 200) # x data (tensor), shape=(100, 1) 8 x = Variable(x) 9 x_np = x.data.numpy() # numpy array for plotting 10 11 # following are popular activation functions 12 y_relu = torch.relu(x).data.numpy() 13 y_sigmoid = torch.sigmoid(x).data.numpy() 14 y_tanh = torch.tanh(x).data.numpy() 15 y_softplus = F.softplus(x).data.numpy() # there's no softplus in torch 16 # y_softmax = torch.softmax(x, dim=0).data.numpy() softmax is a special kind of activation function, it is about probability 17 18 # plt to visualize these activation function 19 plt.figure(1, figsize=(8, 6)) 20 plt.subplot(221) 21 plt.plot(x_np, y_relu, c='red', label='relu') 22 plt.ylim((-1, 5)) 23 plt.legend(loc='best') 24 25 plt.subplot(222) 26 plt.plot(x_np, y_sigmoid, c='red', label='sigmoid') 27 plt.ylim((-0.2, 1.2)) 28 plt.legend(loc='best') 29 30 plt.subplot(223) 31 plt.plot(x_np, y_tanh, c='red', label='tanh') 32 plt.ylim((-1.2, 1.2)) 33 plt.legend(loc='best') 34 35 plt.subplot(224) 36 plt.plot(x_np, y_softplus, c='red', label='softplus') 37 plt.ylim((-0.2, 6)) 38 plt.legend(loc='best') 39 40 plt.show()