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Morvan Zhou
2017-05-06 02:05:14 +10:00
committed by Morvan Zhou
parent cd037f256a
commit 42674bdb94
1 changed files with 3 additions and 3 deletions
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tutorial-contents/302_classification.py
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@ -19,8 +19,8 @@ x0 = torch.normal(2*n_data, 1) # class0 x data (tensor), shape=(100, 2)
y0 = torch.zeros(100) # class0 y data (tensor), shape=(100, 1) y0 = torch.zeros(100) # class0 y data (tensor), shape=(100, 1)
x1 = torch.normal(-2*n_data, 1) # class1 x data (tensor), shape=(100, 2) x1 = torch.normal(-2*n_data, 1) # class1 x data (tensor), shape=(100, 2)
y1 = torch.ones(100) # class1 y data (tensor), shape=(100, 1) y1 = torch.ones(100) # class1 y data (tensor), shape=(100, 1)
x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # FloatTensor = 32-bit floating x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # shape (200, 2) FloatTensor = 32-bit floating
y = torch.cat((y0, y1), ).type(torch.LongTensor) # LongTensor = 64-bit integer y = torch.cat((y0, y1), ).type(torch.LongTensor) # shape (200,) LongTensor = 64-bit integer
# torch can only train on Variable, so convert them to Variable # torch can only train on Variable, so convert them to Variable
x, y = Variable(x), Variable(y) x, y = Variable(x), Variable(y)
@ -44,7 +44,7 @@ net = Net(n_feature=2, n_hidden=10, n_output=2) # define the network
print(net) # net architecture print(net) # net architecture
optimizer = torch.optim.SGD(net.parameters(), lr=0.02) optimizer = torch.optim.SGD(net.parameters(), lr=0.02)
loss_func = torch.nn.CrossEntropyLoss() # the target label is not one-hotted loss_func = torch.nn.CrossEntropyLoss() # the target label is NOT an one-hotted
plt.ion() # something about plotting plt.ion() # something about plotting
plt.show() plt.show()