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Merge pull request #52 from keineahnung2345/update-203-activation

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update to torch 0.4
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Morvan
2018-11-08 19:42:52 +08:00
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parent bf4cf1e700 59487ef2b4
commit 867aa1d1b2
1 changed files with 7 additions and 8 deletions
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tutorial-contents/203_activation.py
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@ -3,7 +3,7 @@ View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/
My Youtube Channel: https://www.youtube.com/user/MorvanZhou
Dependencies:
torch: 0.1.11
torch: 0.4
matplotlib
"""
import torch
@ -17,12 +17,11 @@ 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
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))
@ -46,4 +45,4 @@ plt.plot(x_np, y_softplus, c='red', label='softplus')
plt.ylim((-0.2, 6))
plt.legend(loc='best')
plt.show()
plt.show()