Comments (2)
You are correct that the learning rule in the code is not exactly the same as what is described in the dropout paper. This also explains why Hinton is able to use such a large learning rate, as you note.
Have you observed better performance by changing the learning rule to match the dropout paper exactly?
from dropout.
Changed in 6f8c362
from dropout.
Related Issues (14)
- no bias in mlp.py HOT 2
- 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
- dropping output units rather than connections HOT 1
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from dropout.