606d0aa188
[Bug fix] redundant layers in ResNet
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In https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/02-intermediate/deep_residual_network/main.py#L115 , it defined a length 4 `layers`.
But in https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/02-intermediate/deep_residual_network/main.py#L84 , it only uses `layers[0]` and `layers[1]`.
So the last entry of [2,2,2,2] should be redundant.
2018-11-06 17:54:07 +08:00
78c6afe681
Update tutorials for pytorch 0.4.0
2018-05-10 17:52:01 +09:00
15488e0db1
delete
2018-05-10 17:47:00 +09:00
b60ac38382
[Fix]invalid URL
2018-04-21 19:56:06 +09:00
f64c7c78c2
Removed unused code
2018-02-22 10:44:07 +01:00
be4c08b0ff
Update main.py
2017-09-16 00:43:12 +09:00
8a824389d9
Update main.py
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I got confused by the use of the binary cross entropy. In particular it wasn't clear to me why the variable real_labels are used in the training of the generator.
I have added some comments. I am not sure if they are correct, so you might want to double check them.
2017-09-15 18:05:51 +08:00
c548e2ae9f
tutorial updated
2017-05-28 20:06:40 +09:00
