Skip to content

update to torch 0.4 #52

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
MorvanZhou merged 1 commit into from
Nov 8, 2018
Merged

update to torch 0.4 #52

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 7 additions & 8 deletions tutorial-contents/203_activation.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
My Youtube Channel: https://www.youtube.com/user/MorvanZhou

Dependencies:
torch: 0.1.11
torch: 0.4
matplotlib
"""
import torch
Expand All @@ -17,12 +17,11 @@
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))
Expand All @@ -46,4 +45,4 @@
plt.ylim((-0.2, 6))
plt.legend(loc='best')

plt.show()
plt.show()