Comments (2)
Looks like an oversight on my part, I've pushed a fix that re-enables them. Thanks for noticing!
from dropout.
Looking at this again we actually get better performance on MNIST without the biases (which is likely why I'd removed them in the first place). Instead of being disabled entirely I've made them optional in 608f276
from dropout.
Related Issues (14)
- About the Resample Issue HOT 1
- Difference between 'dropout' and 'backprop' arguements in script HOT 5
- dropout trainig doesn't work with over 3 hiddent layers
- Dropout rate should be set to 0 if not using dropout HOT 3
- Do all the weights multiply the included probability p during testing? HOT 1
- Why set the W by this formula W=layer.W / (1 - dropout_rates[layer_counter]) in testing? HOT 1
- License HOT 1
- Incorrect weight scaling on inputs
- Momentum bug
- Constrain weight matrix columns instead of rows HOT 1
- Random dropout at each mini-batch? HOT 8
- Momentum again HOT 2
- dropping output units rather than connections HOT 1
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from dropout.