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tutorial-contents/203_activation.py

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+"""
+Know more, visit 莫烦Python: https://morvanzhou.github.io/tutorials/
+My Youtube Channel: https://www.youtube.com/user/MorvanZhou
+
+Dependencies:
+torch: 0.1.11
+matplotlib
+"""
+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 = F.relu(x).data.numpy()
+y_sigmoid = F.sigmoid(x).data.numpy()
+y_tanh = F.tanh(x).data.numpy()
+y_softplus = F.softplus(x).data.numpy()
+# y_softmax = F.softmax(x)  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()